A Survey of School Psychologists‘ Application of the Problem-Solving Model to

Counseling Services

By

Rebecca Cole

A Dissertation Submitted to the University at Albany,

State University of New York in Partial Fulfillment of

The Requirements for the Degree of

Doctor of Psychology

School of Education

Department of Educational and Counseling Psychology

2012

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ii

Acknowledgements

As I put the finishing touches on my dissertation and my graduate experience at

the University at Albany, I am filled with gratitude for all the people who have helped me

to get to this point in my career. I would like to begin by recognizing Dr. Deborah

Kundert, my dissertation chair. She has worn many hats over the years that we have

known each other, beginning as one of my professors, and then becoming my supervisor,

advisor, and mentor. Deb was a natural choice as a chairperson, as she has always

exuded the qualities of professionalism, attention to detail, and a strong work ethic that I

greatly admire and seek to develop in myself. Thank you, Deb, for helping me to be ―a

woman on a mission,‖ and for devoting so much time, energy, and support to me over the

years. I would not have achieved my dissertation goals without you. Many thanks also

go to Dr. Kevin Quinn and Dr. Stacy Williams, my committee members, for carefully

reading and providing valuable feedback that helped to shape my dissertation. Stacy has

also played multiple roles in my development as a school psychologist, and I am

fortunate to be able to take so many lessons away from our experiences together. Thank

you, Stacy, for ―building me back up‖ and for helping me to stay the course when things

got challenging.

Every endeavor I have pursued has been supported by my parents, Mary and Gary

Cole, who have always taught me that anything worth having is worth working hard for.

Thanks, Mom and Dad, for always being in my corner, for always picking up the phone

(no matter what time it was), for all those signatures, and for all your words and gestures

of support over the years. My sisters, Aleta, Colleen, and Rita, have always reminded me

that, despite the sacrifice, all the hard work would be worth it, and as I take time to

iii

celebrate with them, it is clear that they were right. I have also been blessed with a large

extended family, who always asked, ―How is school going?‖ at regular intervals, while

also wishing me luck and marveling at my achievements, before finally asking if they

would need to call me ―doctor‖ after graduation. When I think of the invaluable support

my family has provided, I am also reminded of my grandmother, the late Ann Malloy,

who, as I was getting ready to return to Albany after visiting (always too soon), would

remind me that she loved me and that ―the problems will still be there on Monday.‖ You

were right, Grandma, and I know you wish you were here to celebrate with me as much

as I wish you were.

Thanks also go out to my friends for always being there for me, and for never

holding a grudge over my inability to keep in touch, or when I cancelled plans at the last

minute to study, complete an assignment, or work on my dissertation. Special thanks go

out to Romann Weber, for playing the role of boyfriend, best friend, lifeline, coach, and

therapist. Your support has proved integral in getting me through my dissertation,

whether this involved well-placed humor, defusing statistics or formatting ―tantrums,‖ or

just reassuring me that I knew what I was doing and that everything would work out. I

am indebted to you for your unwavering support and the time and energy you sacrificed

from your own work to help me get where I am. Thank you, and I love you very much.

Finally, I wish to acknowledge the myriad teachers, administrators, service providers,

paraprofessionals, families and students that I have worked with over the years as a

school psychologist and psychologist in training, and as a teacher. These experiences

have motivated me and taught me more than any class or lecture could have, and for this I

am truly grateful.

iv

A Survey of School Psychologists‘ Application of the Problem-Solving Model to

Counseling Services

Rebecca Cole

University at Albany, State University of New York, 2012

Dissertation Chairperson: Deborah K. Kundert

Abstract

Given the current focus on student outcomes, use of the problem-solving model to plan

interventions is one method by which school psychologists can hold themselves

accountable for implementing counseling interventions that have a positive impact on

student behavioral outcomes and mental health. This study surveyed school

psychologists about their general counseling practices, as well as their application of the

problem-solving model when designing and implementing counseling interventions. A

total of 283 Nationally Certified School Psychologists completed an online survey based

on current research and best practices for behavioral interventions. Results indicated that

counseling is typically provided to general and special education students in group and

individual formats. Infrequently, students are recommended for discontinuation, and in

most cases because counseling goals have reportedly been met. Most often respondents

indicated using many general components of the problem-solving model including

defining and establishing the behavior of concern (e.g., behavioral definition, problem

validation), as well as those involved in determining what should be done about it (e.g.,

goal setting, intervention plan development). Less frequently did they report monitoring

and determining the effectiveness of counseling interventions (e.g., formative and

summative evaluation, decision-making plan), as well as problem-analysis.

v

These results suggest that school psychologists apply many of the steps of the problem-

solving model in accordance with federal special education laws, especially when

defining target behaviors and planning interventions. These results, however, also call

into question the degree to which these school psychologists engage in progress

monitoring and data-based decision making. The quality and frequency of baseline and

progress monitoring data collection may not enable accurate comparison of student

behavior and demonstration that behavioral improvement has been made. Further

research is needed to determine barriers and facilitators to objective data gathering in

practice settings. Implications for school psychology training programs include

knowledge of practices to focus on in order to help new and current practitioners make a

paradigm shift from assessors to researchers and active problem-solvers who consistently

and effectively implement all aspects of the problem-solving model and consult with

other school professionals to gather and evaluate behavioral data and demonstrate

accountability.

vi

TABLE OF CONTENTS

Page

TITLE ................................................................................................................................. i

ACKNOWLEDGEMENTS ............................................................................................... ii

ABSTRACT ...................................................................................................................... iv

TABLE OF CONTENTS .................................................................................................. vi

LIST OF TABLES ............................................................................................................ ix

LIST OF APPENDICES ................................................................................................... xi

CHAPTER 1 – INTRODUCTION .................................................................................... 1

Purpose of Study .................................................................................................... 6

Significance of Study ............................................................................................. 6

CHAPTER 2 – REVIEW OF RELEVANT LITERATURE ............................................. 8

Overview ................................................................................................................ 8

School Psychology: History, Approaches, Training, and Current Status ............ 11

Growth and Development of the Field of School Psychology ................ 11

Refinements in the Provision of School Psychological Services............. 15

Problem-Solving Approaches .................................................................. 16

Training .................................................................................................... 18

Contemporary Roles and Functions ......................................................... 20

Section Summary ..................................................................................... 22

School Psychologists as Counselors .................................................................... 23

Definitions of Counseling and Intervention ............................................. 23

Rationale for School Psychologists and Counseling ............................... 25

Current Counseling Practices of School Psychologists ........................... 29

Section Summary ..................................................................................... 32

Current Best Practices in Counseling .................................................................. 33

Evidence-Based Practices/Evidence-Based Interventions ....................... 34

Factors Supporting the Exclusive Use of Evidence-Based Practices ...... 39

Current Evidence-Based Practices Related to Counseling ...................... 40

Section Summary ..................................................................................... 45

Designing and Evaluating Direct Interventions ................................................... 46

vii

Comparing Academic and Social Emotional Behavioral Interventions .. 46

EBIs for Childhood Behavioral Disorders ............................................... 66

Section Summary ..................................................................................... 77

Legislation Impacting the Field of School Psychology ....................................... 78

Contemporary Legislation ....................................................................... 88

Current Status and Future Directions ....................................................... 93

Section Summary ..................................................................................... 94

Survey Research................................................................................................... 95

Chapter Summary .............................................................................................. 101

Research Questions ............................................................................................ 104

CHAPTER 3 – METHODOLOGY ............................................................................... 106

Overview ............................................................................................................ 106

Participants ......................................................................................................... 106

Instrumentation .................................................................................................. 106

Survey .................................................................................................... 106

Coverletter Email ................................................................................... 107

Follow-up Email .................................................................................... 107

Procedure ........................................................................................................... 108

CHAPTER 4 - RESULTS ............................................................................................ 112

Overview ............................................................................................................ 112

Data Analysis Plan ............................................................................................. 112

Respondent Characteristics/Demographic Data ................................................ 112

General Counseling Practices ............................................................................ 115

Comparison of Demographic Variables and General Counseling Practices ..... 117

Components of the Problem-Solving Model Used ............................................ 123

Behavioral Definition and Baseline Data Collection ............................. 125

Goals, Intervention Planning, Measurement, and Decision-Making ..... 125

Progress Monitoring, Formative Evaluation, Treatment Integrity, and

Summative Assessment ......................................................................... 132

Comparison of Demographic Variables and Use of the Problem-Solving

Model ................................................................................................................ 137

viii

Comments .......................................................................................................... 138

CHAPTER 5 – DISCUSSION ..................................................................................... 139

Overview ............................................................................................................ 139

Respondent Characteristics ................................................................................ 139

Counseling Practices of School Psychologists .................................................. 143

Use of the Problem-Solving Model ................................................................... 145

Implications for School Psychology .................................................................. 153

Limitations and Directions for Future Research ................................................ 155

Summary ............................................................................................................ 160

REFERENCES .............................................................................................................. 162

APPENDICES ............................................................................................................... 213

ix

LIST OF TABLES

Table Page

1. Summary of Important People and Events Contributing to the Development

of the Field of School Psychology ...................................................................... 12

2. Definitions of Evidence-Based Interventions ...................................................... 36

3. Clauses Recommending the Use of Evidence-Based Interventions .................... 41

4. An Outline for Planning Interventions Aligned With the Problem-Solving

Model .................................................................................................................. 43

5. An Outline for Planning Academic and Behavioral Interventions ...................... 50

6. Table of Evidence-Based Interventions ............................................................... 67

7. Criteria for Classifying Evidence-Based Psychosocial Treatments..................... 76

8. Selected Federal Legislation Impacting School Psychology ............................... 80

9. Court Decisions Impacting the Practice of School Psychology........................... 84

10. Expository Reports Impacting the Practice of School Psychology ..................... 86

11. Agencies and Initiatives Impacting the Practice of School Psychology .............. 87

12. Key Provisions of NCLB ..................................................................................... 89

13. Research Findings Guiding IDEA 2004 .............................................................. 92

14. Guidelines and Considerations for Creating Internet Surveys Following the

TDM Framework ............................................................................................... 102

15. Summary of Feedback from Pilot Subjects ....................................................... 109

16. Mailing and Response Data ............................................................................... 111

17. Demographic Characteristics of Respondents ................................................... 113

18. Estimates of Time Allocation ........................................................................... 116

19. Counseling Practices of School Psychologists ................................................. 118

20. Multinomial Regression Predicting Type of Counseling from Graduate Degree,

Years of Experience, and Time Spent Counseling ............................................ 120

21. Multinomial Regression Predicting Students Served in Counseling from Graduate

Degree, Years of Experience, and Time Spent Counseling .............................. 120

22. Multinomial Regression Predicting the Number of Student Discontinued from

Counseling from Graduate Degree, Years of Experience, and Time Spent

Counseling ........................................................................................................ 120

x

23. Multinomial Regression Predicting Reasons for Discontinuation from Counseling

from Graduate Degree, Years of Experience, and Time Spent Counseling ..... 121

24. Chi Square Analysis Comparing Type of Counseling and Years of

Experience ......................................................................................................... 121

25. Chi Square Analysis Comparing Number of Students Discontinued from

Counseling Each Year and Years of Experience ............................................... 122

26. Use of Intervention Components of the General Problem-Solving Model ........ 123

27. Use of Specific Components of Behavioral Definition Composition ................ 126

28. Use of Specific Types of Baseline Data Collection ........................................... 127

29. Average Number of Baseline Data Points Collected to Establish a Stable Pattern

of Student Behavior ........................................................................................... 127

30. Use of Specific Criteria When Writing Behavioral Goals ................................. 128

31. Use of Specific Components of Counseling Intervention Plans ........................ 129

32. Use of Specific Components for Measuring Target Behaviors ......................... 130

33. Use of Specific Decision-Making Components ................................................. 131

34. Use of Specific Progress Monitoring Techniques ............................................. 133

35. Number of Progress Monitoring Data Points Collected to Establish a Stable

Pattern of Student Behavior ............................................................................... 133

36. Use of the Same Method for Baseline Data Point and Progress Monitoring Data

Point Collection ................................................................................................. 134

37. Sources of Data Considered During Formative Assessment ............................. 134

38. Use of Methods to Measure Treatment Integrity ............................................... 135

39. Sources of Data Considered During Summative Assessment ........................... 136

40. Comparison of Demographic Characteristics .................................................... 141

41. Use of General and Specific Intervention Components of the Problem-Solving

Model ................................................................................................................ 146

xi

LIST OF APPENDICES

Appendix Page

A. School Psychologists Survey ............................................................................. 213

B. Coverletter Email ............................................................................................... 225

C. Follow-Up Email ............................................................................................... 226

D. Pilot Survey Feedback Questions ...................................................................... 227

E. Data Analysis Table ........................................................................................... 228

F. Non-Significant Chi-Square Results .................................................................. 243

G. Logistic Regression Models ............................................................................... 248

1

CHAPTER 1: Introduction

As the field of school psychology has grown over time, the duties of the school

psychologist have changed in response to the needs of students, mandates from state and

federal government, and research defining practices proven to be effective for meeting

the academic and behavioral needs of students (Fagan, 2008). The emergence and

growth of the field of school psychology occurred in tandem with the development of the

modern American public school. Efforts at special education in response to the growth

and diversity of the student body in American schools created the need for a variety of

new positions within the school, including attendance officers, guidance counselors,

school nurses, school psychologists, school social workers, and vocational counselors

(Fagan, 1992).

The work of early school psychologists developed as part of several movements,

particularly child study, the beginnings of psychology as a distinct field, and the

emergence of psychological and educational testing (Reisman, 1966; Wallin & Ferguson,

1967). Another sign of development was the mandatory provision of psychological

services to explore deficits in student behavior being written into state laws and

regulations by departments of education (Hollingworth, 1932). Factors beyond the

school setting, such as population increases after World War II, and special education

laws requiring psychoeducational evaluations (Fagan, 2008), also helped promote growth

in the field, as school psychologists began to encounter larger student bodies with diverse

educational and behavioral needs. Current problem-solving approaches used to address

these needs look at the child‘s response to intervention, based on direct assessment of

observable behaviors, with less time devoted to understanding underlying traits or skills

2

(Fagan, 2008; Lichtenstein, 2008). At this time, however, the extent to which the

problem-solving approach informs the current practices of school psychologists is

unknown.

To ensure compliance with federal special education requirements, states passed

credentialing standards requiring that school psychologists complete certain courses and

gain specific experiences before working with children. These laws and credentialing

standards shaped training in school psychology. Over time, training experiences have

expanded to prepare school psychologists to conduct a variety of assessments and design

interventions based on their results, along with providing consultation and prevention

services in schools through a combination of classwork, practica, and internship

experiences (Fagan, 1986, 2008).

In order to meet the needs of students, the activities and responsibilities of the

school psychologist have also evolved. The primary and most enduring role of the school

psychologist has been assessment for the purposes of determining appropriate placement

and educational experiences. The second role entails designing and implementing direct

interventions for academic and behavioral issues, while the third involves applying

interventions on an indirect or systems-level basis as a consultant. Research on time

allotted to these different activities reveals that most school psychologists spend almost

half of their time on assessment, while devoting the remaining half on direct

interventions, and systems-level consultation and research (Bramlett, Murphy, Johnson,

Wallingsford, & Hall, 2002; Fisher, Jenkins, & Crumbley, 1986; Goldwasser, Meyers,

Chistenson, & Graden, 1983; Hartshorne & Johnson, 1985; Lacayo, Sherwood, & Morris,

1981; Meacham & Peckam, 1978; Reschly & Wilson, 1995; Smith, 1984).

3

It is important to recognize that the field of school psychology emerged in direct

response to an expanding population of students in American public schools and their

corresponding needs. Over time, school psychology as a field has evolved in response to

these needs, as seen through refinements in problem-solving practices, credentialing

standards, training programs, and daily activities and responsibilities. Currently, school

psychologists and professional bodies are focusing attention on the behavioral and mental

health needs of students in schools (Agresta, 2004; Hosp & Reschly, 2002; NASP, 2006)

as research reveals that many times these needs go unmet (Ringel & Sturm, 2001),

preventing students from benefitting from instruction and developing academic skills

(Crespi, 2009; Farrell, Guerra, & Tolan, 1996; Haertel, Walberg, & Weinstein, 1983;

Wang, Haertel, & Walberg, 1990).

Counseling is one activity that school psychologists have engaged in to directly

address the social, emotional, and behavioral needs of the students with whom they work

(Doll & Cummings, 2008). Contemporary research on counseling practices reveals that

the most common theoretical orientation guiding school psychologists is cognitive

behavioral, while this service is most often delivered in a group format, in addition to

offering individual and class-wide sessions (Hanchon & Fernald, 2011; Yates, 2003).

Common referral issues addressed using counseling include behavioral issues, emotional

difficulties, academic problems, and social skills deficits (Hanchon & Fernald, 2011).

In an effort to inform the counseling practices of school psychologists,

professional organizations regularly publish research detailing best practices when

working to meet the needs of students. Several current themes within the realm of

counseling and direct interventions are the application of evidence-based interventions,

4

data-based decision making, and accountability. The literature on evidence-based

interventions provides the most support for treatments using cognitive behavior therapy

(CBT) and behavior therapy (BT; e.g., Behavioral classroom management [BCM;

Barkley et al., 2000; Miranda, Prescentacion, & Soriano, 2002; MTA Cooperative Group,

1999]). Furthermore, there is a significant overlap between the activities involved in

CBT and BT and best practices for designing counseling and direct interventions. For

example, best practices recommend designing interventions using a deliberate problem-

solving approach in which the problem behavior is defined in objective and measurable

terms (Miltenberger, 2005), collecting direct measures of observable behavior before and

during the intervention (Upah, 2008), and using regularly gathered assessments of

behavior to make decisions related to continuing or revising the intervention (Kazdin,

1982). When applied to planning and implementing counseling as a direct intervention,

the use of evidence-based interventions and regular measurements of student behaviors

are two practices that will allow school psychologists to determine whether or not their

interventions are effective (Upah, 2008). This provides a measure of accountability for

meeting the behavioral and mental health needs of the students with whom school

psychologists work.

The current focus on accountability extends beyond school psychologists and

their professional organizations, as seen by federal legislation guiding the delivery of

instruction and assessment in American public schools. In a similar fashion to the growth

and evolution of school psychology, federal education legislation has been created and

shaped in response to the needs of students. A variety of court cases (e.g., PARC v.

Pennsylvania, 1972; Mills v. Board of Education, 1972) and expository reports have

5

publicized weaknesses and inequalities in the education provided to specific groups of

students. To address these inequalities, federal laws have been passed and reauthorized

over time (e.g., The Elementary and Secondary Education Act, The Individuals With

Disabilities Education Act) that have defined specific educational practices that must be

followed in schools to ensure access to adequate educational experiences for all students.

Specific examples of some of these practices include evidence-based instruction, positive

behavior supports, and early intervention services (20 U.S.C. § 1400 [5][E]).

A prime example of accountability mandated by federal legislation is the No

Child Left Behind Act (NCLB). This piece of legislation specifies standards for effective

instruction that schools must meet, and mandates regular measurement of student

progress using standardized assessment. In addition, NCLB includes a series of

corrective actions for schools that do not demonstrate adequate gains in student progress

that range from resources and assistance for remediation planning, to staff replacement

and school closure. More contemporary actions by the federal government include grant

programs providing states with federal assistance contingent on their ability to implement

standards-based assessments, progress monitoring and data-based decision making

systems related to student academic achievement, and the use of student achievement

data to assess the performance of teachers and administrators (U.S. Department of

Education, 2009). Drafts of the next reauthorization of NCLB have thus far focused on

improving teacher and administrator effectiveness, the delivery of effective instruction

and interventions, and the implementation of standards and assessments to prepare

students for future success in college and careers (U.S. Department of Education, Office

of Planning, Evaluation and Policy Development, 2010). Judging from these goals and

6

the laws already in place, it would appear that accountability for student outcomes is one

theme that will continue to guide the activities of school professionals, including school

psychologists.

Purpose of Study

The purpose of the current study was to survey school psychologists about their

current counseling practices to determine whether they are implementing research and

best practice guidelines related to the use of evidence-based interventions, progress

monitoring, and data-based decision making. In addition, this study explored whether

specific aspects of counseling practices varied according to demographic characteristics.

Significance of Study

The notion of accountability is a common theme that is repeatedly mentioned not

only in the literature describing best practices in counseling and mental health, but also in

current and proposed federal education legislation (Wright & Wright, 2009). School

psychologists have expressed a desire to spend more time involved in counseling as a

direct intervention to meet the mental health and behavioral needs of their students

(Agresta, 2004). The use of evidence-based interventions, progress monitoring, and data-

based decision making are measures of accountability that can be applied to the design

and implementation of counseling as a direct intervention and may provide assurance to

school psychologists that their efforts are addressing the mental health and behavioral

needs of their students. In addition, although, at this time, accountability standards and

consequences apply to educational practices and outcomes, as well as managed care and

the private sector (Kazdin, 2008), in future, accountability standards and consequences

may also govern school psychologists as they address mental health and behavioral

7

needs. Designing and implementing counseling interventions that allow school

psychologists to determine whether or not these interventions have been effective

provides a measure of accountability for meeting the needs of students, and anticipates a

time when the federal government may mandate such accountability.

Information on the effectiveness of an intervention also promotes the efficient

allocation of resources, in terms of space and materials, time spent by the school

psychologist, and time that the student spends away from classroom instruction and

independent opportunities for socialization with peers. With respect to time as a

resource, data on the effectiveness of an intervention allow school psychologists to make

data-based decisions on continuing, discontinuing, or changing the focus of an

intervention. In addition, these data allow school psychologists to clearly demonstrate to

students, families, teachers, and administrators whether the student has made

improvements, whether counseling is justified as a necessary service for the student, and

whether the mental health needs of the student population are being addressed. On a

larger scale, information on the current counseling practices of school psychologists, with

a specific focus on accountability and data-based decision making, is valuable in terms of

informing the focus of training programs preparing future school psychologists.

8

CHAPTER 2: Review of Relevant Literature

Overview

Public education has always been shaped by legislation. Throughout the history

of public education, the actions of state and local governments have been guided by

evidence documenting the needs of students in American public schools. Early examples

of federal involvement focused on providing access to education beginning with

compulsory attendance laws in the early 1900s. Compulsory attendance laws and the

resulting increase in student needs between 1890 and 1930 were the major forces behind

the emergence and establishment of the field of school psychology (Fagan, 1992). The

Elementary and Secondary Education Act (ESEA; 1965) was designed to improve

educational outcomes and opportunities for children living in poverty by providing

funding for the construction of schools, instructional materials, and teacher salaries.

Around this time, similar bills were passed to improve educational access and outcomes

for students from diverse cultural backgrounds (e.g., Title VIII Bilingual Education Act

(1967); Bilingual Act of 1974 [Stewner-Manzanares, 1988]), as well as students with

educational exceptionalities (e.g., Rehabilitation Act of 1973; Education for All

Handicapped Children Act [1975; Beyer, 1989]).

Over time, after taking into account the results of implementation, pieces of

legislation were either discontinued or amended in an effort to improve the quality of

educational experiences provided to students. The ESEA, for example, has been

amended several times as law makers, educators, and researchers have attempted to

determine the best way to provide quality instruction (e.g., the Stennis Amendment to the

ESEA [1970; Crespino, 2006]), the appropriate amount of funding for specialized

9

education programs (e.g., ESEA Federal Amendments [1974]), and how federal funding

should be distributed (e.g., the Educational Consolidation Improvement Act [1982; Gray,

Caulley, & Smith, 1982]). Issues related to funding and appropriate educational

opportunities are two themes that can also be traced throughout the evolution of

legislation focusing on students with educational exceptionalities. Once access to public

education had been secured, quality educational opportunities were defined initially as

appropriate placements and services (e.g., the Education of the Handicapped Act

Amendments [1986; House Committee on Education and Labor, 1986]), with later

specifications mandating educational experiences with non-disabled peers (e.g.,

Individuals With Disabilities Education Act [1990]; School of Public Health and Health

Professions, University at Buffalo, 2005), and an explicit connection between special

education and the general curriculum (e.g., Individuals With Disabilities Education Act;

National Center for Children and Youths with Disabilities [1998]).

Currently, legislation is focused on accountability for student outcomes. An early

example of accountability can be seen in the 1988 amendments to the ESEA, the

Hawkins-Stafford School Improvement Amendments (House Committee on Education

and Labor, 1988), as it made federal funding contingent on documented gains in student

achievement, with increased regulation for schools unable to demonstrate student

improvement. Since this time, standards defining adequate progress and corrective

actions when gains in achievement have not been made have become more specified, and

have had a significant impact on public education (Nelson & Weinbaum, 2009). A

contemporary example of accountability legislation is the No Child Left Behind Act

(NCLB; 2002), with provisions detailing appropriate training for teachers and

10

paraprofessionals, standards-based instruction and assessment, and corrective actions for

schools in need of improvement. In addition, current legislation focused on students with

educational exceptionalities mandates the provision of evidence-based instruction,

intervention, and behavioral supports, as well as high achievement standards (e.g., the

Individuals With Disabilities Education Improvement Act of 2004; Wright & Wright,

2009).

As one of the school professionals taking on the responsibility of ensuring

positive outcomes for students, the work of school psychologists has been and continues

to be influenced by federal legislation. As such, this chapter will explore the growth and

development of school psychology over time, in order to examine accountability as it

relates to school psychologists working to ensure positive social-emotional-behavioral

student outcomes through the design and implementation of counseling as a direct

intervention. This chapter begins with a brief history of the social factors that have

shaped the training, everyday practices, and responsibilities of school psychologists from

their early days to present times. Following this, the role of the school psychologist as a

counselor will be discussed, including the factors that have established counseling and

mental health services in schools, the current status of these services, and research and

best practices when designing and implementing counseling as an intervention. Because

of its impact on the roles and functions of the school psychologist, federal education

legislation, including actions that have already been taken, as well as research and

legislative mandates intended to guide next steps, is also chronicled. This chapter

concludes with a discussion of the advantages and disadvantages of internet survey

research, as well as the proposed research questions guiding this study.

11

School Psychology: History, Approaches, Training, and Current Status

This section will discuss selected facets of the growth and development of the

practice of school psychology from its origination in the early 1900s to the present.

Selected events and individuals whose actions were vital to the establishment of school

psychology are described briefly to provide necessary context and background. This

context will be followed by a depiction of the changes in the problem-solving approach

guiding school psychologists in their work with children and families. Details on the

evolution of school psychology training programs have also been included. This section

concludes with a chronicle of the changes in the role and function of the school

psychologist over time. The reader will notice that the field of school psychology has

evolved over time in response to the needs of students, as evidenced by federal mandates,

credentialing standards, and training guidelines from professional organizations.

Although the exact form is difficult to predict, it can safely be assumed that the field of

school psychology will continue to change as school psychologists strive to meet the

needs of students in schools today. In Table 1, the reader is provided with a brief

historical context of the foundation and early development of the field of school

psychology.

Growth and development of the field of school psychology. The profession of

school psychology emerged as a response to increased diversification in the academic and

social-emotional needs of students in American schools as a result of the passage of

compulsory education laws. Early growth and solidification of roles in the field occurred

in tandem with improved methods of understanding and addressing educational needs.

Knowledge detailing normative development and learning in children was gained through

12

Table 1

Summary of Important People and Events Contributing to the Development of the Field

of School Psychology

Event/Person Implications

Social Phenomena

Compulsory attendance laws

Immigration

Rapid increase in the number of

students attending American public

schools (Grant & Eiden, 1980)

School staff are presented with

challenges related to truancy, learning

difficulties, and discipline (Irwin, 1915;

Reisner, 1915)

Schools began to offer medical and

psychological examinations (Wallin,

1914), as well as special education

classes (Fagan, 1992; Van Sickle,

Witmer, & Ayers, 1911)

G. Stanley Hall and the child study

movement Data collection on child development

The establishment of a scientific

understanding of children‘s

development and behavior (Benjamin

& Baker, 2003; Hollingworth, 1932)

Parents and school staff were now

armed with more precise knowledge

related to children‘s behavior and

development

Hall advocated for the practice of

child-centered, individualized

instruction, wherein the curriculum of

the school was revised to best meet the

needs of the student (Minton, 1987)

Lightner Witmer The establishment of the psychological

clinic

In-depth diagnosis and treatment for

children who struggled in school

(Benjamin & Baker, 2003)

Creation of an applied field of

psychology focused on the diagnosis

and resolution of education problems

(Witmer, 1897)

Creation and proliferation of

psychoeducational tests Binet-Simon Scales in 1905 (Cutts,

1955)

The Intelligence Quotient (Benjamin &

Baker, 2003)

(table continues)

13

Table 1 continued

Event/Person Implications

Age norms for children (Hollingworth,

1932)

Tests suitable for group administration

(Cutts, 1955)

Developments and refinements in the

use of psychoeducational testing

allowed school psychologists to

classify students in an objective and

standardized fashion

Clauses mandating the provision of

psychological services for students Services provided for students who

came in to contact with their state‘s

juvenile justice system (Hollingworth,

1922) or were being considered for

placement in special education classes

(Hollingworth, 1932; University of the

State of New York, 1931)

Profession and expertise of the school

psychologist were given legitimacy

Thayer Conference in 1954 (Cutts, 1955) Assessed the development and future of

the profession

Defined the roles and function of the

school psychologist

Delineated qualifications and training

standards

Increased number of school psychology

practitioners (Fagan, 2008)

Improved the service ratio of school

psychologists to students (Charvat,

2005; Fagan, 1988; Reschly &

Connolly, 2000)

Establishment, growth, and development

of professional organizations representing

school psychology

Division 16 of the American

Psychological Association (APA) in

1945

The National Association of School

Psychologists in 1969

Promoted growth and development of

the field

(table continues)

14

Table 1 continued

Event/Person Implications

Provided representation for school

psychologists in matters of public

policy

Promoted the use of best practices

through the dissemination of

information

Established nationally recognized

training and certification standards

(Benjamin & Baker, 2003)

15

the child study movement and in early psychological clinics. This knowledge was

applied to the creation of standardized psychoeducational tests, which provided an

objective way of measuring student abilities. Early psychological tests were then piloted

and improved as their use became mandated by state and federal laws. The remainder of

this section will provide greater depth related to changes in service provision, problem-

solving approaches, training, and roles and functions, to describe how the field of school

psychology has developed and evolved.

Refinements in the provision of school psychological services. The discussions

held and consensus reached at the Thayer Conference had a profound impact on the field

of school psychology that helped to solidify the professional identity of the school

psychologist (French, 1984). Fagan (2008) provided an excellent description of the

evolution of the field of school psychology that serves to explain the forces behind the

growth in the number of school psychology practitioners and the refinement in service

ratios. Societal factors impacting the growth in the number of school psychology

practitioners included an increase in the population after World War II, and the passage

of special education laws requiring the provision of psychological services (Fagan, 2008).

Early school psychological services were provided in the schools, through external

agencies. By 1960, however, the school was the primary employment location for school

psychologists. In the 1980s, federal laws regarding evaluations for students with

disabilities, increases in insurance reimbursement for psychologists, and growth in the

number of school psychologists earning doctoral degrees contributed to variety in the

locations where school psychological services were to be provided (Fagan, 2008).

Contemporary practice settings include public and private schools, private practices, state

16

departments of education, colleges or universities, and medical institutions (Curtis,

Lopez, Batsche, & Smith, 2006).

Problem-solving approaches. The evolution in the practice of school

psychology can also be understood by looking at changes in the problem-solving

approach used by practitioners at different points in time. Fagan (2008) described how

this focus initially centered on the child, expanded to consider more ecological variables,

and currently focuses on the child‘s response to intervention. The work of early school

psychologists was largely based on the medical model, as problems children experienced

were thought to be a direct result of their skills, abilities, or personality characteristics

(Irwin, 1915; Reisner, 1915). The responsibility of the school psychologist was to study

the child, diagnose the problem, and design a student-centered intervention (Hutt, 1923).

To understand the problem, school psychologists used questionnaires, interviews,

informal assessments, analysis of work samples, health and developmental histories,

classroom observation, and medical examinations (Fagan, 1992, 2008). The advent of

standardized testing added specificity and objectivity to the diagnosis of problems and

classification of students based on abilities and behaviors. Interventions at this time

included counseling, academic skill remediation, and instructional or alternative

placement special education (Fagan, 2008).

Gradually, the exclusive focus on the child was broadened, such that assessment

and intervention included other variables considered significant to development and

behavior. School psychologists adopted an ecological approach, as they began to

consider how the interactions of factors such as parents or guardians, the home

environment, the child‘s teacher, and the instruction delivered in the classroom were

17

affecting the behavior and educational achievement of the students with whom they

worked. In addition to standardized and unstandardized measures of student intelligence

and academic achievement, practitioners now used formal and informal measures to

gather information on the student‘s peer relationships, feelings about school, relationship

with parents or guardians, instructional environment, and behavior with and without

peers (Fagan, 2008). Child-centered interventions enjoyed continued use, and were

refined by improvements in special education and therapies for groups and individuals,

informed by developments in child-centered, rational-emotive, and behavior modification

theories (Fagan, 2008). Special education placements at this time became less restrictive,

as mainstreaming and inclusion practices became popular. Interventions were now also

provided indirectly, as school psychologists consulted with teachers and parents (Porter

& Holzberg, 1978).

Although the ecological approach had a significant impact on the practice of

school psychology, more contemporary approaches focus on assessment and the design

of corresponding interventions based on directly observable academic skills and

behaviors, with less of a focus on underlying abilities or traits (Lichtenstein, 2008).

School psychologists still consider a range of ecological variables related to peers,

teachers, classroom environment, instruction, and parenting, but only as these relate to

the creation of outcome-based assessments and interventions (Batsche, Castillo, Dixon, &

Forde, 2008). Provisions in special education laws requiring functional behavioral

assessment and treatment accountability reinforced a focus on outcomes, along with

increased priority placed on the use of empirically supported interventions as articulated

by clinical, counseling, and school psychology. Current problem-solving approaches

18

now directly assess academic skills embedded in the school curriculum, with

interventions directly linked to skill development, rather than relying entirely on

normative assessment measures (Hosp, 2008). The movement towards outcome-based

and empirically valid practices is exemplified in the response to intervention (RTI)

model. Procedures used within the RTI model include pre-referral assessment and

screening, assessment, and intervention based directly on classroom curriculum,

criterion-referenced measurement, evidence-based practices, and problem-solving

consultation (Casbarro, 2008). The use of RTI represents a distinct shift from child-

centered approaches to assessment and intervention, but is also one supported by current

federal legislation (Lichtenstein, 2008).

Training. Growth and refinement in the field of school psychology were

accompanied and made possible by increases and changes in preparation programs. At

the time of the Thayer Conference, 28 institutions offered training in school psychology

(Cutts, 1955). As time progressed, the number of institutions offering school psychology

training programs increased from 28 in 1954 to 211 in 1984 (Fagan, 1986). The National

Association of School Psychology (NASP) program directory supplements these data,

indicating that in 1989, there were 231 programs, with more current figures from 2008

listing 238 institutions that provided programs in school psychology (Fagan, 2008).

A look at the development of school psychology training programs provides

another lens for examining the evolution of the field. Early school psychology training

programs provided their students with a foundation in clinical psychology, educational

psychology, or a blend of the two (Fagan, 2008). Up until the 1960s, the content offered

in training programs was primarily determined by the background and interests of faculty

19

members, and their opinions on skills necessary to deliver psychological services in

public schools (Fagan, 1986). State credentialing provisions also influenced training.

National standards did not exist at this time. As such, classes focused on traditional

psychology or education with specialized training in areas such as psychoeducational

assessment (Cutts, 1955). As federal special education laws were passed, states

incorporated compliance provisions into their credentialing regulations, which in turn

impacted coursework provided to prospective school psychologists.

In addition to the effect of state credentials, over time, school psychology training

programs were further impacted by the creation of accreditation standards established by

the American Psychological Association (APA), the National Council for Accreditation

for Teacher Education (NCATE), and NASP. To address these new accreditation

standards, content offered by training programs became more specific, and began to

prepare students to offer different types of assessments, as well as corresponding

interventions, along with refining research methods, and providing consultation and

prevention services in schools (Fagan, 2008). More recent changes in training programs

included the extension of field experience requirements to include practica and internship

experiences, and courses taught by faculty members trained specifically in school

psychology (Fagan, 1986, 2008). Currently, standards specified by state psychology

boards have considerable influence over doctoral-level training programs. Most boards

require students to complete a minimum of two years of supervised preparation.

Contemporary coursework continues to provide training in assessment, intervention, and

consultation, while also focusing on topics such as the use of technology in schools, crisis

intervention, prevention, the use of empirically based practices, and data-based problem-

20

solving.

Contemporary roles and functions. Over time, the role and function of the

school psychologist has also changed. Fagan (2008) described four roles that school

psychologists have played at various points in the history of the field. The first, and most

enduring role, is that of the assessor, whose primary responsibility has been the

administration of psychoeducational assessments for the purpose of special education

placement. This role has evolved, such that the school psychologist is now part of a team

of service providers who use a vast array of assessment tools to determine placement and

services for a variety of students in schools. At times, the role of the assessor has also

included identifying and designing interventions for students considered to be at-risk for

academic and social-emotional difficulties in school. The second role is that of the direct

interventionist, where the school psychologist provides individual and group intervention

in the form of academic remediation or counseling. The range of interventions provided

by school psychologists has expanded over the years as a result of improvements in

training and internships, acceptability of more direct interventions such as psychotherapy

in schools, and a focus on the need for the provision of mental health services to students

in schools (Fagan, 2008; Sandoval, 1993; Talley & Short, 1996). The role of the

consultant is the third function, and while this role was practiced by early school

psychologists, only in recent decades has it become a well-researched and theory-based

process (Fagan, 2008). An extension of the consultant role is the more recent function of

the school psychologist as a systems-level interventionist who designs assessments,

interventions, and prevention efforts at the school level, as opposed to the individual level

(NASP, 2010).

21

Despite a proliferation in roles and functions, assessment activities continue to be

the mainstay of the practice of school psychology (Merrell, Ervin, & Gimpel, 2006b).

National surveys from the past several decades documenting how school psychologists

spend their time reveal some consistencies in time allocation. For example, respondents

rated spending approximately 50% of their time on assessment, 20-25% engaged in direct

intervention, 20-25% on consultation, and the remaining time involved in systems-level

or research activities (Bramlett, Murphy, Johnson, Wallingsford, & Hall, 2002; Fisher,

Jenkins, & Crumbley, 1986; Goldwasser, Meyers, Chistenson, & Graden, 1983;

Hartshorne & Johnson, 1985; Lacayo, Sherwood, & Morris, 1981; Meacham & Peckam,

1978; Reschly & Wilson, 1995; Smith, 1984). Interestingly, studies comparing actual

with desired time allocation reveal that school psychologists would like to spend less

time on assessment, and devote more time and resources to interventions, consultation,

research, and program evaluation (Hosp & Reschly, 2002; Reschly & Wilson, 1995).

Current school psychology practitioners have also expressed a desire to address

the mental health needs of students in school (Agresta, 2004; Hosp & Reschly, 2002).

For example, data from national school psychologist surveys indicated that two-thirds of

their respondents provided the following services to address the mental health needs of

their students: individual counseling, crisis intervention, assessment or evaluation,

behavior-management consultation, case management, referrals to specialized programs,

group counseling, and substance use and/or violence prevention (Brener, Martindale, &

Weist, 2001; Foster et al., 2005). Specifically related to student mental health, Agresta‘s

(2004) research on desired roles revealed that school psychologists would like to spend

more time each week on parent education and consultation, and individual and group

22

counseling activities because they see the need for and value of such services in their

schools.

In addition to the practitioner focus on mental health, professional bodies, such as

NASP, have also responded to concerns and research in this area. Recent NASP position

papers (2006) have recommended that schools provide a range of comprehensive mental

health services where there is found to be a need, in order to promote academic

achievement, school connectedness and community, respectful behavior, student well-

being, and a positive school climate. Current training guidelines (NASP, 2000) have

recommended that school psychologists receive training in designing and implementing

prevention and intervention programs to foster the mental health and physical wellbeing

of the student bodies they serve. Because of their training in mental health and

education, NASP (2006) has advocated for continued role expansion for school

psychologists as effective providers of a wide range of interventions to be delivered in the

school building, such as universal prevention and intervention, specific interventions for

students at risk, and comprehensive interventions as designed or reinforced by

community agencies.

Section Summary. Although the exact direction that the field of school

psychology will take in the future is difficult to predict, a brief look at the history of the

field makes it clear that the role and function of the school psychologist will continue to

evolve. The effect of federal legislation, state credentialing regulations, and training

standards from professional bodies such as APA, NCATE, and NASP will likely continue

to influence training programs and the daily functioning of school psychologists. The

argument can also be made that societal factors, which were largely responsible for the

23

emergence and establishment of the field of school psychology, will also continue to

influence future directions. Concern for the mental health and well-being of children in

schools is beginning to exert considerable influence on school psychologists, training

programs, and professional bodies.

School Psychologists as Counselors

In addition to focusing on educational needs, one of the roles of the school

psychologist has always been addressing the social, emotional, and behavioral needs of

students in school (Doll & Cummings, 2008). Counseling has always been one of the

roles played by the school psychologist, although the time spent on this activity has

periodically been limited by responsibilities related to assessment; comfort, and

willingness by the school psychologist to offer counseling; and, the presence of other

providers of counseling services within a school building (Murphy, 2008). An

understanding of current research and best practices related to evidence-based

interventions, and designing and implementing counseling as a direct intervention is one

way to promote this role in cases where school psychologists report experiencing

discomfort. Given the current focus on mental health needs and student well-being,

knowledge describing effective practice is essential to ensuring that counseling

interventions produce positive social-emotional outcomes for students.

Definitions of counseling and intervention. Many definitions of counseling

have been offered by different authors and professional organizations. For example, the

American Counseling Association (ACA; 2011) defines counseling as ―a professional

relationship that empowers diverse individuals, families, and groups to accomplish

mental health, wellness, education, and career goals‖ (n. pag). According to the

24

Merriam-Webster dictionary (2011), counseling entails providing ―professional guidance

of the individual by utilizing psychological methods especially in collecting case history

data, using various techniques of the personal interview, and testing interests and

aptitudes‖ (n. pag). Of more relevance to the school psychologist are the definition and

descriptions of counseling provided by Velleman and Aris (2010). According to these

authors, ―counseling is primarily about enabling individuals, as far as possible, to

overcome obstacles, to take control of their own lives, [and] to learn how to take

maximum responsibility and decision-making power for themselves and their futures‖

(pp. 19-20). Furthermore, these authors describe how the different approaches to

counseling can be grouped into two different categories. The first approach is a more

―directive or prescriptive, training or skills oriented, external-action oriented style, where

the helper directs, instructs or guides the person in need to an appropriate action‖

(Velleman & Aris, 2010, p. 20), while the second approach involves a more ―facilitative,

internal, insight oriented, personal-exploration oriented style, where the helper is less

directive and seeks to encourage and support the person in need to work with and

discharge emotion and to reach their own realizations of appropriate actions‖ (Velleman

& Aris, 2010, p. 20).

Implied in the definitions provided for counseling is the notion of change, from

one maladaptive behavior or circumstance to another that is considered to be more

adaptive. The idea of change is also found in the definition of an intervention, where

intervening involves coming in or between by way of hindrance or modification

(Merriam-Webster, 2011). Following this line of reasoning, counseling can be thought of

as a specific type of intervention. For the purposes of this discussion, an intervention is

25

being defined as a set of strategies or procedures designed to improve the performance of

one or more students, with the objective of narrowing the divide between current

performance and expectation (Upah, 2008). Furthermore, social-emotional and

behavioral interventions are those that focus on the presence or absence of behaviors that

impede learning and academic achievement, in order to develop attitudes and skills

necessary for effective functioning in society and career success (Forman & Burke,

2008).

Rationale for school psychologists and counseling. Doll and Cummings (2008)

provided important context related to the priority that has been placed on the provision of

counseling and mental health services in schools. In the 1970s, counselors, school

psychologists, and social workers focused their attention on offering guidance to address

minor student issues, while referring more significant cases of behavioral issues to

community mental health providers. The focus of the school was on meeting the

educational needs of the student body, as it was assumed that only a small percentage of

the student body required mental health services, which they were receiving through

outside agencies (President‘s Commission on Mental Health, 1978).

Over time, evidence was revealed that served to challenge the notion that the

mental health needs of children were being adequately addressed outside of the school

building. For example, epidemiological data gathered during the 1980s suggested that at

least 20% of school children had a diagnosable psychiatric disorder, while only one-

fourth of these children were receiving mental health services to address their needs

(Doll, 1996; U. S. Department of Health and Human Services, 1999).

Research from the past two decades is ripe with examples to support the

26

evaluation by Tolan and Dodge (2005) that students in America are currently

experiencing a mental health crisis. For instance, Huang et al. (2005) reported that 1 in 5

children have a diagnosable mental disorder. Furthermore, 3-7% of children have been

diagnosed with Attention-Deficit/Hyperactivity Disorder (Root & Resnick, 2003).

According to the American Psychiatric Association (2000), some of the contemporary

issues currently found to have a negative impact on children include family disputes,

child abuse, attention disorders, and violence. One in seven children are reported to have

been punched, kicked, or choked by a parent (Moore, 1994). In addition, Crespi and

Howe (2002) estimate that approximately 80% of children have been exposed to some

form of spousal abuse. One in six families have had to cope with the effects of parental

alcoholism, resulting in 28-34 million people who have directly experienced life in an

alcoholic family (Newcomb, Galaif, & Locke, 2001).

It is reported that more than 8 million children are in need of psychological

services (Carnegie Council on Adolescent Development, 1996), but that most youth with

a psychological disorder never receive mental health care (Farmer, Burns, Philip, Angold,

& Costello, 2003; Ringel & Sturm, 2001). For example, a 2002 study by Kataoka,

Zhang, and Wells described the percentages of children who accessed mental health

services across three cross-sectional, nationally representative samples comprised of

more than 11,500 households. In their sample, it was found that 15-21% of youth ages 6-

17 had a mental health problem, while only 6-7.5% of those same youth (or 29-49% of

the entire sample) were receiving some form of mental health treatment. One reason for

this may be an inability to pay for such care, as it is estimated that 1 in 7 adolescents lack

health insurance or third-party reimbursement for mental health services in the private

27

sector (Crespi & Howe, 2002).

On average, children spend six hours of each day in school (Crespi, 2009).

Observations have revealed that children who exhibit problematic behaviors in school

come from families who have experienced difficulties in one or more aspects of

functioning (Fergusson, Horwood, & Lynskey, 1994), such that children who learn

aggressive behaviors at home tend to bring them to school (Farrell, Guerra, & Tolan,

1996). Furthermore, academic performance and difficulties with behavioral adjustment

have been correlated with conflict children experience at home (Crespi, 2009). When

developing an understanding of a child‘s behavior, however, one perspective to take

comes from Bandura‘s Social Learning Theory (Bandura, 1977). According to the

reciprocal determinism tenet of this theory, a student‘s behaviors, cognitions and

personality variables, and environment can be understood as interconnected factors that

have a bi-directional effect on each other (Bandura, 2004). The strength of the influence

of one factor relative to the others will vary depending on the child, his or her

environment, and the specific circumstances of that environment (Bandura, 2004).

Therefore, the factors causing and maintaining negative behaviors come from a variety of

differences sources, and form a complex constellation of potential cause and effect

relationships.

Of note to school psychologists and mental health professionals is that children

have tended to access more mental health services at school than in any other venue

(Burns et al., 1995; Hoagwood & Erwin, 1997; Leaf et al., 1996). To illustrate this,

Farmer et al. (2003) pointed to epidemiological research on mental health prevalence

rates and service delivery, stating that 11-12% of youth in any given year sought services

28

through the education sector, while only 7% pursued these services through specialty

mental health providers, or in a medical facility (4%). These results suggest that schools

may be seen as the primary source for mental health services for children and youth. In

addition, school-based early intervention programs have been found to be effective in

reducing delinquent behavior in adolescents (Crespi & Rigazio-DiGilio, 1996),

suggesting that schools may be an ideal location for the provision of psychological

services (Crespi & Fischetti, 1997). Developmental research highlights the fact that

student mental health and psychological well-being are necessary conditions for

educational success at school (Haertel, Walberg, & Weinstein, 1983; Wang, Haertel, &

Walberg, 1990). As these findings have become more well-known, schools have

responded by becoming the default provider of mental health services for most children

and adolescents (Hoagwood & Johnson, 2003).

The provision of counseling and mental health services in schools is also

mandated by federal legislation. For example, according to IDEA, counseling and

psychological services are two related services that schools must provide to students, if it

is found that these services are necessary for students with disabilities to benefit from

special education (Wright & Wright, 2009). These authors have explained specific legal

requirements related to the provision of counseling in schools. Counseling services can

only be provided by social workers, psychologists, guidance counselors, or other

qualified professionals. The legal definition of psychological services covers a variety of

responsibilities carried out by the school psychologist. Responsibilities specifically

related to counseling include administering and interpreting assessments, obtaining and

sharing information about a child‘s behavior and conditions necessary for learning,

29

collaborating on the development of positive behavioral intervention strategies, and

planning and implementing psychological services such as psychological counseling for

children and parents.

In addition to legal mandates, professional organizations representing school

psychology, such as NASP and APA, specify that school psychologists provide

counseling to the students with whom they work. For example, according to the NASP

(2010) publication listing the professional services of school psychologists, some of the

direct services that school psychologists deliver to children, families, and schools include

interventions and mental health services designed specifically to develop social and life

skills. According to these standards, mental health services may also be provided

indirectly at the systems level through the implementation of prevention and responsive

services designed by school psychologists (NASP, 2010). Furthermore, the APA (2011)

defines one of the roles of the school psychologist as one who is able to ―conceptualize

children‘s development from multiple theoretical perspectives and translate current

scientific findings to alleviate cognitive, behavioral, social, and emotional problems

encountered in schooling‖ (n. pag).

Current counseling practices of school psychologists. Several researchers have

gathered data describing the counseling practices of school psychologists using surveys

(Curtis et al., 2008; Hanchon & Fernald, 2011; Yates, 2003) and focus groups (Suldo,

Friedrich, & Michalowski, 2010). The most common theoretical orientation guiding the

counseling process of the school psychologists sampled was cognitive behavioral,

followed by behavioral, brief solution-focused, and reality-based (Hanchon & Fernald,

2011; Yates, 2003). Focus group responses indicated that group counseling was the most

30

commonly utilized format (Suldo et al., 2010), with a range of 53-74% of school

psychologists indicating that they offered this service (Hanchon & Fernald, 2011; Yates,

2003). The average number of student groups counseled each year was 8.8 (Curtis et al.,

2008), with group sizes ranging from 2-4 students (Yates, 2003). Respondents reported

seeing 1-5 groups each week, while offering each group between 5-16 sessions (Yates,

2003). Specific issues addressed during group counseling sessions included social skills

development, anger management, study skills, anxiety, grief, and organizational skills

(Suldo et al., 2010).

The second most commonly used counseling format was individual counseling

(Suldo et al., 2010), with 61-88% of school psychologists providing this service

(Hanchon & Fernald, 2011; Yates, 2003). School psychologists met with an average of

9.9 students each year for individual counseling (Curtis et al., 2008). Students received 5

or more sessions each year, with session lengths ranging from 30-45 minutes each (Yates,

2003). Specific behaviors addressed using individual counseling included crisis

intervention, suicide assessment and intervention, threat assessment, de-escalation, and

other various intervention components (Suldo et al., 2010). A range of 32-52% of those

sampled administered classroom counseling sessions (Hanchon & Fernald, 2011; Yates,

2003), making this the third most common counseling format. Issues addressed during

classroom sessions included teaching social skills, family issues, girls‘ issues, violence

prevention, study skills, art and music therapies, and self-esteem issues (Yates, 2003).

Respondents from two of the surveys also provided family counseling (8-31%; Hanchon

& Fernald, 2011; Yates, 2003). In addition, respondents to the Hanchon and Fernald

survey (2011) also provided crisis response counseling (51%), individual counseling with

31

adults (7%), and brief solution-focused counseling sessions (45%).

In addition to the provision of direct counseling services, school psychologists

interviewed by Suldo and colleagues (2010) during their focus group described offering a

variety of indirect mental health services to students, teachers, and parents. For example,

after individual counseling, consultation with school staff and teams, and parents was the

next most frequently delivered service, after which school psychologists also indicated

that they commonly designed behavioral interventions. These school psychologists also

provided case management services in collaboration with psychiatrists and other

outpatient agencies, in addition to engaging in their own social-emotional behavioral

assessments. These school psychologists, moreover, offered counseling services to other

school employees to address various personal needs affecting their ability to fulfill their

educational responsibilities. Furthermore, school psychologists delivered in-service

professional development and training to school staff and parents, and implemented

prevention measures such as school- and class-wide screening, and drug education. The

final service they administered involved support groups for parents.

Several researchers have surveyed school psychologists about the types of referral

issues with which they are confronted. A discrepancy in responses was noted in this area,

depending on when data on referral issues had been collected. For example, according to

the responses provided by Bramlett, Murphy, Johnson, and Wallingsford (2002) and

Yates (2003), the most common referral issue that school psychologists in their samples

received involved academic problems, followed by externalizing behaviors (e.g., conduct,

anger, ADHD), peer relationship problems, and self-esteem issues. Data collected

recently, however, indicated that behavioral issues were the most common referral

32

received by school psychologists, followed by emotional difficulties, academic problems,

and social skills deficits (Hanchon & Fernald, 2011). This change suggests that the focus

on meeting the mental health needs of students may be warranted, as behavioral issues

may be having a more significant impact on the ability of students to benefit from

instruction and participate in school. Although not as frequently, internalizing behaviors,

such as depression, anxiety, suicidal thoughts and thought disorders, as well as

difficulties related to trauma and abuse, were also referred to school psychologists (Yates,

2003). Referrals also seemed to vary by age. Depression, motivation, school refusal,

substance abuse, and truancy referrals were more frequently received by school

psychologists working with students in grades 6-12 than those who worked in elementary

schools (Yates, 2003).

Section summary. Although school psychologists continue to report allocating a

majority of their time on assessment activities (Bramlett, Murphy, Johnson, Wallingsford,

& Hall, 2002; Fisher, Jenkins, & Crumbley, 1986; Goldwasser, Meyers, Chistenson, &

Graden, 1983; Hartshorne & Johnson, 1985; Lacayo, Sherwood, & Morris, 1981;

Meacham & Peckam, 1978; Reschly & Wilson, 1995; Smith, 1984), several factors have

converged to focus attention on student mental health and emotional well-being.

Research from different fields and specialties has documented examples of student

exposure to a variety of stressors (American Psychiatric Association, 2000) and the

difficulty some students experience functioning efficiently in school as a result (Crespi,

2009). Furthermore, student mental health needs have not consistently been addressed

outside of school (Kataoka, Zhang, & Wells, 2002). At the same time, there is also

evidence citing the success of prevention and early intervention programs delivered in the

33

school setting (Crespi & Fischetti, 1997; Crespi & Rigazio-DiGilio, 1996).

These factors have prompted school psychologists to offer counseling and mental

health services on a direct and indirect basis (Curtis et al., 2008; Hanchon & Fernald,

2011; Suldo, Friedrich, & Michalowski, 2010; Yates, 2003). Professional organizations

have also responded by delineating training standards and guidelines (NASP, 2010), as

well as federal legislators who have passed legal mandates regulating these services

(Wright & Wright, 2009) in an attempt to address student mental health needs. Given the

needs of students, knowledge detailing the current counseling practices of school

psychologists is valuable. Determining whether school psychologists‘ counseling

practices have adequately addressed student needs, however, may require establishing a

solid foundation of research documenting effective practices in counseling, and ensuring

that those practices and strategies proven to be effective are those being used.

Current Best Practices in Counseling

A variety of different authors and professional organizations provide guidance for

school psychologists as they implement direct and indirect mental health services. As an

example, NASP regularly publishes information detailing best practices which help to

translate research findings into steps that can be taken within the school setting. At this

time, data-based decision making and accountability are two practices that form the basis

for service delivery within the field of school psychology. These practices are also

heavily researched and a popular focus of discussion in relation to educational and mental

health interventions. Doll and Cummings (2008) discussed the importance of data-based

decision making and accountability in relation to the provision of population-based

mental health services, stressing that, based on an assessment of the needs of the school

34

population, school mental health teams should identify indicators of student emotional

well-being early in the process of planning services. In addition, once these indicators

have been specified, methods for regularly evaluating whether these objectives are being

met must also be determined. These authors recommended continuous and formative

assessment to inform the actions of mental health providers.

In their publication for the NASP School Psychology Forum, Coffee and Ray-

Subramanian (2009) described the use of Goal Attainment Scaling (GAS) as one method

for regular progress monitoring of behavioral interventions that can be completed by a

variety of school professionals familiar with the student, or even by the student him- or

herself. According to these authors, additional benefits of using GAS include its utility as

a repeated measure to monitor student behavior on a daily or weekly basis due to its

sensitivity to small changes in behavior, as well as being a tool to evaluate the overall

effectiveness of a given intervention.

Furthermore, Doll and Cummings (2008) supported the exclusive selection and

use of evidence-based treatments as school psychologists provide direct and indirect

mental health services to students. In relation to this, as part of best practices related to

brief individual counseling, Murphy (2008) recommended developing clear and

meaningful goals with the student, while evaluating progress towards goals regularly

throughout the counseling process using feedback from the client, and by comparing

precounseling and postcounseling data gathered using observations, behavior rating

scales, grades, and discipline records.

Evidence-based practices/evidence-based interventions. Over the past 10

years, practitioners in the fields of mental health and education have expressed significant

35

interest in psychosocial treatments that have been empirically proven to successfully

address a variety of child and adolescent behavior problems (Silverman & Hinshaw,

2008). Throughout the literature, these types of psychosocial treatments are referred to as

evidence-based practices (APA, 2005), evidence-based treatments (Doll & Cummings,

2008), evidence-based interventions (Kratochwill & Shernoff, 2004), and empirically-

supported treatments (Association for Behavioral and Cognitive Therapies [ABCT] &

Society of Clinical Child and Adolescent Psychology [SCCAP], 2010b). Presented in

Table 2 are examples of definitions for the different terminology used to describe

evidence-based interventions. Despite the differences noted among these definitions, a

common theme in the focus on evidence-based interventions is the use of strategies,

therapies, or practices that have been empirically proven to achieve a specific result, or

produce a specific behavior. To maintain clarity, I will reference the definition provided

by Kratochwill and Shernoff (2004), who maintain that evidence-based interventions

(EBIs) are those whose contextual applications have been demonstrated, and which have

been proven to be efficacious when implemented and evaluated in practice settings.

Several issues have been noted in the research literature surrounding EBIs. The

first relates directly to terminology. Throughout the last several decades of the evidence-

based practice movement, one key stakeholder, the American Psychological Association

Task Force on Psychological Intervention Guidelines, decided to replace the phrase

empirically validated with empirically supported, as it was their opinion that a particular

treatment could never be completely validated (Chambless & Hollon, 1998; Lonigan,

Elbert, & Johnson, 1998). Currently, this group advocates for the use of the term

evidence based, as it is their view that having the word evidence in their definition

36

Table 2

Definitions of Evidence-Based Interventions

Terminology from the Literature Definition

Evidence-based practice in psychology

(APA, 2005)

―the integration of the best available

research with clinical expertise in the

context of patient characteristics, culture,

and preferences‖ (n. pag)

Evidence-based practice (Association for

Behavioral and Cognitive Therapies

[ABCT] & Society of Clinical Child and

Adolescent Psychology [SCCAP], 2010a)

―treatments that are based directly on

scientific evidence that has revealed the

strongest contributors and risk factors for

psychological symptoms. Most EBPs have

been studied in several large-scale clinical

trials, involving thousands of children

and/or adolescents and careful comparison

of the effects of EBPs vs. other types of

psychological treatments. Dozens of multi-

year studies have shown that EBPs can

reduce symptoms significantly for many

years following the end of psychological

treatment-similar evidence for other types

of therapies is not currently available‖ (n.

pag)

Evidence-based interventions (Forman &

Burke, 2008)

Evidence-based interventions (Kratochwill

& Shernoff, 2004)

Empirically-supported treatments

(Ollendick & King, 2004)

Empirically-supported treatments

(Association for Behavioral and Cognitive

―interventions [that] are empirically

supported… substantiated with findings in

the research literatures that demonstrate

that they are likely to produce predictable,

beneficial, and effective results‖ (p. 799)

―an intervention should carry the evidence-

based designation when information about

its contextual application in actual practice

is specified and when it has demonstrated

efficacy under the conditions of

implementation and evaluation in practice‖

(p. 35)

―treatments of scientific value‖ (p. 5)

―interventions that have been found to be

efficacious for one or more psychological

(table continues)

37

Table 2 continued

Terminology from the Literature Definition

Therapies [ABCT] & Society of Clinical

Child and Adolescent Psychology

[SCCAP], 2010b)

conditions, like major depression, panic

disorder, or obsessive-compulsive disorder,

within a given population‖ (n. pag)

38

facilitates the use and understanding of evidence-based interventions by professionals in

other fields (Silverman & Hinshaw, 2008). At this time, the term evidence based is also

used by the APA Presidential Task Force on Evidence-Based Practice (2006), as well as

researchers and advocates in the field of medicine (Sackett, Rosenberg, Gray, Haynes, &

Richardson, 1996).

Another issue is related to distinctions made by researchers between efficacy and

effectiveness studies (Hoagwood, Hibbs, Brent, & Jensen, 1995; Weisz, Donenberg, Han,

& Weiss, 1995). Efficacy studies provide information on whether a specific treatment

has been found to reduce symptoms or impairment when the researchers have used

experimental methods, such as random assignment, control groups, and manualized

treatment protocols (Silverman & Hinshaw, 2008). On the otherhand, effectiveness

studies describe interventions or treatments that have been found to decrease symptoms

or impairment in environments where such interventions are most likely to be delivered,

such as classrooms or mental health centers (Silverman & Hinshaw, 2008).

One additional distinction that has recently gained importance in the literature on

EBIs involves defining mediators and moderators related to treatment outcomes

(Hinshaw, 2002; Hombeck, 1997; Kraemer, Wilson, Fairburn, & Agras, 2002).

Mediation describes how therapeutic change is produced when a specific intervention is

used (Silverman & Hinshaw, 2008). Moderators of treatment outcome are factors that

are independent of treatment condition but exert a significant influence on the differential

effects of a treatment condition (Kraemer, Wilson, Fairburn, & Agras, 2002).

Moderation details for whom or under what conditions therapeutic change takes place

(Silverman & Hinshaw, 2008). Mediators of treatment outcome are not factors

39

independent of the treatment condition, but instead, are a consequence of the treatment

that explain why one treatment produces improved outcomes compared to another

(Kraemer, Wilson, Fairburn, & Agras, 2002).

These distinctions and terminology are important contextual factors within the

evidence-based practice movement. Their significance stems from a continued gap

between research and practice, or the difference between what clinical scientists know

about which treatments successfully reduce symptoms, and what clinicians and

practitioners are actually using when working with children and adolescents (Herschell,

McNeil, & McNeil, 2004; Kazdin, Kratochwill, & VandenBos, 1986; Weisz, Weiss, &

Donenberg, 1992). Researchers have cautioned against relying on the assumption that

treatments or interventions found to be successful using efficacy studies will also be

effective when used in routine practice settings (Hoagwood, Burns, Kiser, Ringeisen, &

Schoenwald, 2001). Although there are a variety of factors maintaining the gap between

research and practice, the increased dissemination of research detailing effectiveness

studies, the efficacy of interventions along with consideration of their generalizibility or

transportability to non-research settings, and practice guidelines related to mediation and

moderation of treatment outcomes are recommendations for narrowing this gap to ensure

that children and adolescents are receiving the highest quality treatment available

(Hoagwood, Burns, Kiser, Ringeisen, & Schoenwald, 2001; Silverman & Hinshaw,

2008).

Factors supporting the exclusive use of evidence-based practices. The

movement supporting the use of EBIs has been seen in several different fields, such as

medicine, education, social work, nursing, and dentistry (Kazdin, 2008). Nemade, Reiss,

40

and Dombeck (2007) described the role that insurance companies have played in

promoting the use of EBIs in mental health care because these treatments provide a

measure of accountability as they have scientific evidence supporting their use.

Furthermore, when compared to other treatments such as psychotherapy, EBIs tend to be

more short-term, while allowing a scientifically-based method for clinicians to justify the

number of sessions they will need to address a specific behavior or disorder. Within the

field of education, the federal No Child Left Behind Act (2002) specified that all school

practices must be based on scientifically-based research, the definition of which was

clarified by the U.S. Department of Education (2003) in the following way:

―scientifically based research means there is reliable evidence that the program or

practice works‖ (n. pag). In addition, professional organizations representing the fields

of school psychology and social work have affirmed their commitment to the use of EBIs

through various ethical standards and practice models described in Table 3.

Current evidence-based practices related to counseling. Research findings by

Kazdin (2003) indicated that counseling has been used as a feature of interventions for

many issues addressed in schools today, including externalizing and internalizing

problems, learning and mental disabilities, and with profound forms of psychopathology,

such as autism. In line with the movement promoting the use of EBIs, and in response to

research findings indicating that one of the factors preventing some school psychologists

from delivering counseling and mental health services is a lack of adequate training and

experience with these tasks (Suldo, Friedrich, & Michalowski, 2010), this section will

discuss best practices related to designing, implementing, and evaluating evidence-based

interventions.

41

Table 3

Clauses Recommending the Use of Evidence-Based Interventions

Professional Organization Recommendation

National Association of School

Psychologist‘s (NASP) Model for

Comprehensive and

Integrated School Psychological

Services (2010)

―NASP‘s mission is accomplished through

identification of appropriate evidence-

based education and mental health services

for all children; implementation of

professional practices that are empirically

supported, data driven, and culturally

competent;…‖ (p. 1)

―School psychologists collect and use

assessment data to understand students‘

problems and to select and implement

evidence-based instructional and mental

health services‖ (p. 4)

American Psychological Association

(APA) Ethical Principles of Psychologists

and Code of Conduct (2010)

2.04 Bases for Scientific and Professional

Judgments: Psychologists' work is based

upon established scientific and professional

knowledge of the discipline.

Code of Ethics of the National Association

of Social Workers (NASW; 2008)

4.01 Competence

(c) Social workers should base practice on

recognized knowledge, including

empirically based knowledge, relevant to

social work and social work ethics.

42

In relation to indirect, population-based mental health services, Doll and

Cummings (2008) recommended first conducting an assessment of the mental health

needs of the student body, and then identifying mental health resources in the

community. Furthermore, these authors suggested offering a continuum of mental health

services within the school building following from the three-tiered service model

proposed by Walker et al. (1996) and Osher, Dwyer, and Jackson (2004). Depending on

identified needs, universal services would be provided to the entire school body, with

more individualized and direct services provided to approximately 15-20% of the student

body considered to be at a high functional or demographic risk (Doll & Cummings,

2008). In addition, highly intensive, direct educational and social emotional services

along with referral to a private mental health provider may be necessary for the remaining

1-5% of the school students whose needs have not been met through interventions

delivered in the first two tiers.

Although a number of suggestions have been offered to help school psychologists

plan and implement interventions, these suggestions seem to rely on a deliberate

problem-solving approach. As an example of this, Upah (2008) provided steps to take

when implementing an intervention that are aligned with the problem-solving model

(Bergan & Kratochwill, 1990), which may be helpful to school psychologists as they plan

counseling interventions. These steps can be found in Table 4.

As part of her explanation of the different intervention components listed in Table

4, Upah (2008) recommended that any behavior that is the focus of intervention first be

operationally defined, or described using specific, observable, and measureable terms.

Over the past few decades, several researchers have acknowledged the link between

43

Table 4

An Outline for Planning Interventions Aligned with the Problem-Solving Model

Problem-solving logic Intervention components

What is the problem? Behavioral definition

Baseline data

Problem validation

Why is it happening? Problem analysis steps

What should be done about it? Goal setting

Intervention plan development

Measurement strategy

Decision-making plan

Did it work? Progress monitoring

Formative evaluation

Treatment integrity

Summative evaluation

44

grounding interventions in a behavioral definition of the target behavior and the success

of the intervention designed to change that behavior (Baer, Wolf, & Risley, 1987; Deno,

1995; Flugum & Reschly, 1994; Reynolds, Gutkin, Elliot, & Witt, 1984; Steege &

Wacker, 1995). Behavioral definitions allow for a common understanding among those

involved in the intervention of when the target behavior does and does not occur, and as

such, are necessary for reliable measurement of the target behavior during

implementation (Kazdin, 1982; Steege & Wacker, 1995). Behavioral definitions must

satisfy the following three conditions, in that they must be: objective, or descriptive of

observable actions that can be seen or heard; clear and unambiguous enough so that

someone unfamiliar with the student or the intervention could repeat or accurately

summarize the behavioral definition; and, complete in describing examples and

nonexamples of the behavior so that anyone observing the child‘s behavior is able to tell

when the target behavior is and is not occurring (Hawkins & Dobes, 1977; Howell &

Nolett, 2000; Kazdin, 1982; Reschly, Tilly, & Grimes, 2000).

In addition to the intervention components listed in Table 4, Forman and Burke

(2008) provided additional recommendations designed to help improve the effectiveness

of counseling interventions. Once goals have been formulated, during the intervention

plan development phase, these authors propose that school psychologists conduct a

review of the literature pertaining to EBIs proven to be effective in remediating the

identified problem, and select an intervention from these sources. Forman and Burke

(2008) also suggested that school psychologists identify intervention implementers and

stakeholders, assess their perceptions, attitudes, and beliefs related to the intervention,

and develop administrative and stakeholder support for the intervention. In addition, they

45

proposed that intervention implementers be provided with training, technical assistance,

and resources necessary for effective implementation. Along with providing training,

school psychologists should also consider the manner in which the normal functioning of

the school (e.g., the school‘s mission, policies and procedures, other programs already in

place) will impact stakeholders and intervention implementers, and acknowledge that

implementation should be rewarded, supported, and expected.

Section summary. Two themes noted in the literature on best practices in

counseling involve accountability and data-based decision making (e.g., Doll &

Cummings, 2009; Forman & Burke, 2008). The exclusive use of evidence-based

interventions (EBIs), or those interventions that have been found to be effective when

used in practice settings, has been advocated in a variety of different fields (Kazdin,

2008), and in particular, by professional organizations representing school psychologists

(APA, 2010; NASP, 2010) and social workers (NASW, 2008). One way for school

psychologists to hold themselves accountable for their work with students is through the

use of EBIs as they implement counseling as a direct intervention. Furthermore, the

problem-solving model (Bergan & Kratochwill, 1990), as applied to designing and

implementing interventions (Upah, 2008), includes components such as establishing a

behavioral definition for the problem behavior, collecting baseline data before

implementation, and regular progress monitoring of the student‘s behavior during the

intervention. These behavioral data are then used for formative and summative

assessment, providing information on whether the intervention is working, or needs

revision, and when and if the intervention should be continued (Upah, 2008).

In addition to considering the broad guidelines provided by Upah (2008) and

46

other researchers, it may be helpful to look at the research literature for more specific

guidelines related to designing direct behavioral interventions and EBIs. With these

more specific guidelines in mind, researchers are better able to evaluate whether the gap

between research and practice (Herschell & McNeill, 2004; Kazdin, Kratochwill, &

VandenBos, 1986; Weisz, Weiss, & Donenberg, 1992) extends to school psychologists in

terms of their application of current research related to accountability and data-based

decision making in the provision of counseling services.

Designing and Evaluating Direct Interventions

This section will discuss literature on designing and implementing direct

interventions. The current status of research related to academic and behavioral

interventions is a necessary starting point for school psychologists looking to align their

work with students with best practices. Although, at this time, research describing

effective practices in terms of social-emotional-behavioral interventions is in its early

stages, available research guidelines reinforce the importance of repeated measures of

student behavior to inform interventions, as well as the use of evidence-based techniques

and strategies. As such, this section concludes with a review of evidence-based

interventions for behavioral issues commonly seen in children.

Comparing academic and social-emotional-behavioral interventions. Under

traditional assessment models, teachers would identify students displaying behavioral

problems and school psychologists would conduct evaluations for special education

eligibility (Kamphaus, DiStefano, Dowdy, Eklund, & Dunn, 2010) with the result that

only students demonstrating high levels of need would be provided with services (Cash &

Nealis, 2004). Large numbers of students with behavioral and emotional problems (Mills

47

et al., 2006), however, called into question the utility and effectiveness of the teacher

referral system for several reasons: teachers may not be adequately trained to recognize

developing problem behaviors; teachers vary in their ability to address problem

behaviors, leading to different rates of referral; some students are not identified in an

effective and efficient manner (Tilly, 2008); and, some teachers consider behavior

problems and difficulties with social-emotional adjustment to be beyond their area of

responsibility (Severson, Walker, Hope-Doolittle, Kratochwill, & Gresham, 2007).

A problem-solving approach to identifying and supporting students with

behavioral problems has been recommended as an alternative to the teacher referral

system (Tilly, 2008). Although there are a variety of problem-solving approaches in use

in different contexts, some common features have been noted across different models,

such as the importance of universal screening and periodic assessment (Schwanz &

Barbour, 2005). Despite the existence of multiple problem-solving approaches,

traditional school-based assessment practices have not always meshed well with the

problem-solving approach to assessment and data-based decision making (Gresham et al.,

2010). An example of this is the use of standardized ability and achievement tests and

behavioral measures that have proved useful for making eligibility decisions, but do not

possess the treatment validity needed to inform instruction, have not been found to be

feasible or designed to progress-monitor a student‘s response to intervention (Fuchs &

Fuchs, 1998; Gresham, 2002; Gresham & Witt, 1997), and have not been designed to

measure response to intervention in order to make special education eligibility or exit

decisions (Gresham, 2007; Shinn, 2008).

Furthermore, traditional school-based assessment practices do not always fit in

48

with the RTI paradigm (Briesch, Chafouleas, & Riley-Tilman, 2010). According to the

RTI framework, problem behaviors must be systematically and proactively defined

through the screening process and then regularly measured as part of progress monitoring

to determine whether the intervention was successful or other remediation strategies are

necessary. Some potential problems arise, however, when an RTI paradigm is applied to

behavioral assessment, as many available psychometrically sound assessment measures

are not feasible for repeated administration with large groups of students because of their

length and the frame of reference raters must consider when providing responses

(Briesch, Chafouleas, & Riley-Tilman, 2010).

The current link between social-emotional-behavior (SEB) assessment and

intervention is tenuous (Merrell, 2010). Following the problem-solving model, the

strength of current SEB assessments lies in the practitioner‘s ability to use these measures

to determine problem behaviors and the factors maintaining them, without providing

guidance on how to address these problems and determine whether what has been done

was effective (Merrell, 2010). Many available rating scales for common DSM-IV

disorders are in existence (Merrell, 2008; Pelham, Fabiano, & Massetti, 2005), but are not

sensitive enough to change to be useful as a repeated measure of the target behavior of an

intervention (Volpe & Gadow, 2010). This is especially true in cases when the target

behavior is not the sole focus of the rating scale, or when a given scale is not best suited

to the referral concern (Volpe & Gadow, 2010). In addition, limited time and resources

may prevent practitioners from collecting enough data to make informed decisions

related to student behavior, underscoring the need for continued research and

development of feasible and psychometrically sound behavioral assessment measures

49

(Briesch, Chafouleas, & Riley-Tilman, 2010).

School-based problem-solving teams are able to make decisions effectively when

their actions are guided by clearly defined and measurable academic and SEB outcomes

(Newton, Horner, Algozzine, Todd, & Algozzine, 2009). There is a current impetus in

the field of education for the development of psychometrically sound, data-based

decision-making tools to be utilized within problem-solving frameworks (National Center

on Response to Intervention, 2011), but the result has been a focus on academic skills,

while research, knowledge, and resources related to SEB assessment are lacking

(Briesch, Chafouleas, & Riley-Tilman, 2010). Although it may appear logical to

compare problem-solving tools for academic difficulties with those used for SEB

difficulties, doing so is problematic for a number of reasons (Chafouleas, Volpe,

Gresham, & Cook, 2010). While there exists a variety of objective and measurable

indicators of academic growth, consensus has not been reached regarding general

outcome measures for behavior (Chafouleas, Volpe, Gresham, & Cook, 2010). The fact

that a general outcome measure of behavioral health does not exist the way that general

outcome measures exist for assessment of academic abilities has limited and complicated

efforts to develop feasible methods for monitoring SEB problems (Shinn, 2007; Volpe &

Gadow, 2010). The literature on designing academic and behavioral interventions

following the problem-solving model is summarized in Table 5.

A review of the literature related to designing academic and behavioral

interventions reveals that, although comparison of these types of interventions is

problematic, because they are grounded in the problem-solving model, they share some

common elements. For example, both models stress the importance of grounding

50

Tab

le 5

An O

utl

ine

for

Pla

nnin

g A

cadem

ic a

nd

Beh

avi

ora

l In

terv

enti

ons

Pro

ble

m-

solv

ing l

ogic

Inte

rven

tion

com

ponen

t

RT

I L

iter

ature

on D

esig

nin

g A

cadem

ic

Inte

rven

tions

Lit

erat

ure

on B

ehav

iora

l In

terv

enti

ons

What

is

the

pro

ble

m?

Beh

avio

ral

def

init

ion

Oper

atio

nal

ly d

efin

e th

e beh

avio

r in

cle

ar

langu

age

that

incl

udes

to

pogra

ph

y (

how

the

beh

avio

r lo

oks)

, fr

equen

cy (

how

oft

en t

he

beh

avio

r occ

urs

), d

ura

tion (

how

long t

he

beh

avio

r la

sts)

, an

d i

nte

nsi

ty

(the

deg

ree

to w

hic

h t

he

beh

avio

r is

pro

ble

mat

ic)

(Bro

wn

-Chid

sey &

Ste

ege,

2010)

C

reat

e co

des

in o

rder

to q

uic

kly

and

accu

rate

ly i

den

tify

and r

ecord

beh

avio

rs

duri

ng d

ata

coll

ecti

on a

nd d

ocu

men

tati

on

(Bro

wn

-Chid

sey &

Ste

ege,

2010)

D

eter

min

e an

d d

escr

ibe

the

loca

tions(

s)

wher

e th

e b

ehav

ior(

s) o

f co

nce

rn w

ill

be

obse

rved

and r

eco

rded

(B

row

n-C

hid

sey

& S

teeg

e, 2

010

)

D

efin

e th

e re

pla

cem

ent

beh

avio

r, o

r

targ

et s

kil

l th

at n

eeds

impro

vem

ent,

and

des

crib

es w

hat

the

studen

t nee

ds

to b

e

able

to d

o i

n c

oncr

ete,

mea

sura

ble

ter

ms

(Bat

sch

e, C

asti

llo, D

ixon, &

Fo

rde,

2008)

D

efin

e ta

rget

beh

avio

rs b

y i

den

tify

ing

exac

tly w

hat

the

studen

t sa

ys

and d

oes

that

mak

e up t

he

pro

ble

m a

nd

the

des

irab

le

beh

avio

r (M

ilte

nber

ger

, 2005)

U

se a

ctiv

e v

erbs

des

crib

ing t

he

obse

rvab

le

acti

ons

of

the

studen

t, u

sing a

thoro

ugh

def

init

ion i

ncl

udin

g a

ll t

he

dif

fere

nt

resp

onse

s en

com

pas

sed w

ithin

the

beh

avio

r bei

ng d

efin

ed (

Mil

tenber

ger

,

2005)

Id

enti

fy t

he

dim

ensi

ons

of

the

beh

avio

r b

y

des

crib

ing t

he

freq

uen

cy (

num

ber

of

tim

es

the

beh

avio

r occ

urs

), l

aten

cy (

tim

e th

at

pas

ses

from

the

pre

senta

tion o

f a

stim

ulu

s

and t

he

studen

t‘s

resp

onse

or

beh

avio

r),

inte

nsi

ty (

stre

ngth

or

forc

e w

ith w

hic

h t

he

beh

avio

r is

dis

pla

yed

), t

opogra

ph

y

(confi

gura

tion, fo

rm, or

shap

e of

the

beh

avio

r),

accu

racy (

mea

sure

of

how

wel

l

the

beh

avio

r fi

ts a

sta

nd

ard o

r is

corr

ect)

,

and d

ura

tion (

the

amount

of

tim

e th

at

pas

ses

bet

wee

n t

he

onse

t an

d e

ndin

g o

f a

beh

avio

r) (

Sulz

er-A

zaro

ff &

Mayer

, 1991)

(tab

le c

onti

nues

)

50

51

Table

5 c

onti

nued

Pro

ble

m-

solv

ing L

ogic

Inte

rven

tion

Com

ponen

t

RT

I L

iter

ature

on D

esig

nin

g A

cadem

ic

Inte

rven

tions

Lit

erat

ure

on B

ehav

iora

l In

terv

enti

ons

B

asel

ine

dat

a

At

leas

t 3 d

ata

poin

ts a

re n

eeded

to

esta

bli

sh a

bas

elin

e le

vel

of

beh

avio

r or

per

form

ance

(H

ayes

, B

arlo

w, &

Nel

son

-

Gra

y, 1999)

B

asel

ine

dat

a sh

ould

be

stab

le o

ver

tim

e;

bas

elin

e dat

a th

at d

emonst

rate

s

var

iabil

ity/u

nst

able

tre

nd

s pro

vid

es a

n

unre

liab

le b

asis

for

det

erm

inin

g w

het

her

or

not

the

inte

rven

tion w

as e

ffec

tive

(Hayes

, B

arlo

w, &

Nel

son

-Gra

y, 1999

)

A

void

over

lap o

f b

asel

ine

and

inte

rven

tion d

ata

as t

his

wea

ken

s

con

fiden

ce i

n t

he

effe

ctiv

enes

s of

the

inte

rven

tion (

Hayes

, B

arlo

w, &

Nel

son

-

Gra

y, 1999)

L

evel

of

bas

elin

e dat

a sh

ould

be

seri

ous

eno

ugh t

o w

arra

nt

an i

nte

rven

tion a

nd

mak

e cl

ear

any b

ehav

ior

chan

ges

cau

sed

by a

n i

nte

rven

tion (

Hayes

, B

arlo

w,

&

Nel

son

-Gra

y, 1999)

T

rends

in b

asel

ine

dat

a sh

ould

not

be

in

the

sam

e dir

ecti

on a

s w

ould

be

tren

ds

cause

d b

y a

n e

ffec

tive

inte

rven

tion

(Hayes

, B

arlo

w, &

Nel

son

-Gra

y, 1999

)

P

roce

dure

s fo

r re

cord

ing b

ehav

ior

duri

ng

bas

elin

e ar

e th

e sa

me

as t

hose

use

d t

o

D

irec

t, a

ccura

te, obje

ctiv

e an

d s

yst

emat

ic

mea

sure

men

t of

the

targ

et b

ehav

ior

as i

t

occ

urs

in a

nat

ura

l se

ttin

g t

o e

stab

lish

the

studen

t‘s

level

of

funct

ionin

g b

efo

re

imple

men

ting a

n i

nte

rven

tion (

Upah

, 2008)

C

oll

ecti

on o

f bas

elin

e dat

a co

nsi

sts

of

repea

ted m

easu

res

of

beh

avio

r over

mult

iple

ses

sions/

day

s/w

eeks

to e

stab

lish

a

stab

le p

atte

rn o

f b

ehav

ior

(no n

ew h

ighs

or

low

s fo

r th

ree

conse

cuti

ve

dat

a poin

ts)

(Sulz

er-A

zaro

ff &

Mayer

, 1991)

S

oci

al-e

moti

onal

-beh

avio

ral

asse

ssm

ent

stra

tegie

s fa

ll i

nto

1 o

f 6 d

iffe

rent

cate

gori

es:

dir

ect

beh

avio

ral

obse

rvat

ion,

3rd

par

ty b

ehav

ior

rati

ng s

cale

s,

soci

om

etri

c te

chniq

ues

, in

terv

iew

s,

obje

ctiv

e se

lf-r

eport

mea

sure

s, o

r

pro

ject

ive-

expre

ssiv

e te

chniq

ues

(M

erre

ll,

2010)

(tab

le c

onti

nues

)

51

52

Table

5 c

onti

nued

Pro

ble

m-

solv

ing L

ogic

Inte

rven

tion

Com

ponen

t

RT

I L

iter

ature

on D

esig

nin

g A

cadem

ic

Inte

rven

tions

Lit

erat

ure

on B

ehav

iora

l In

terv

enti

ons

reco

rd t

he

sam

e beh

avio

r duri

ng

inte

rven

tion (

Bro

wn-C

hid

sey &

Ste

ege,

2010)

P

has

e ch

ange

line

is u

sed

to s

epar

ate

bas

elin

e an

d i

nte

rven

tion p

has

es d

uri

ng

gra

phic

rep

rese

nta

tion o

f dat

a (B

row

n-

Chid

sey &

Ste

ege,

2010)

P

roble

m

Val

idat

ion

Det

erm

ine

if t

he

dif

ficu

lty i

s sp

ecif

ic t

o

the

chil

d o

r th

e cl

assr

oo

m w

ithin

he/

she

resi

des

, an

d t

hen

det

erm

ine

if t

he

pro

ble

m i

s ca

use

d b

y a

def

icit

in s

kil

l or

per

form

ance

by p

rovid

ing t

he

opport

unit

y f

or

addit

ional

rei

nfo

rcem

ent

con

tingen

t upon t

he

per

form

ance

of

a

suit

able

rep

lace

men

t beh

avio

r (B

atsc

he,

Cas

till

o, D

ixon, &

Ford

e, 2

008)

C

om

par

e th

e ch

ild‘s

rat

e of

pro

gre

ss t

o a

pro

ject

ed r

ate

of

pro

gre

ss n

eeded

to

obta

in p

rofi

cien

cy w

ithin

a s

et p

erio

d o

f

tim

e (S

hin

n, 1989)

D

eter

min

e th

e m

agnit

ude

of

the

pro

ble

m

by e

stab

lish

ing t

he

dif

fere

nce

bet

wee

n t

he

studen

t‘s

curr

ent

level

of

per

form

ance

and

the

envir

onm

enta

l ex

pec

tati

ons

(How

ell

&

Nole

t, 2

000)

C

om

par

e th

e st

uden

t‘s

bas

elin

e

per

form

ance

lev

el t

o a

sta

ndar

d f

or

appro

pri

ate

and a

ccep

tab

le p

erfo

rman

ce

(Upah

, 20

08)

D

eter

min

e th

e st

and

ard o

f ap

pro

pri

ate

and

acce

pta

ble

per

form

ance

usi

ng t

he

beh

avio

r

of

typic

al p

eers

, buil

din

g o

r dis

tric

t norm

s,

teac

her

/cla

ssro

om

ex

pec

tati

ons,

cri

teri

a fo

r

a fu

ture

envir

onm

ent,

or

school

poli

cy

(Upah

, 20

08)

Wh

y i

s it

hap

pen

ing?

Pro

ble

m

Anal

ysi

s

step

s

Id

enti

fy a

ny c

han

gea

ble

var

iable

s

con

trib

uti

ng t

o t

he

pro

ble

m b

ehav

ior

(Hea

rtla

nd A

rea

Educa

tion A

gen

cy,

2007

)

D

evel

op a

hypoth

esis

sta

tem

ent

by

des

crib

ing t

he

even

ts t

hat

occ

ur

just

bef

ore

and i

mm

edia

tely

aft

er t

he

pro

ble

m

beh

avio

r (s

uch

as

sett

ing e

ven

ts o

r

(tab

le c

onti

nues

)

52

53

Table

5 c

onti

nued

Pro

ble

m-

solv

ing L

ogic

Inte

rven

tion

Com

ponen

t

RT

I L

iter

ature

on D

esig

nin

g A

cadem

ic a

nd

Beh

avio

ral

Inte

rven

tions

Lit

erat

ure

on B

ehav

iora

l In

terv

enti

ons

C

oll

ect

rele

van

t in

form

atio

n d

etai

ling

studen

t in

stru

ctio

n, cu

rric

ulu

m,

envir

onm

ent

and l

earn

er t

hro

ugh r

evie

ws,

Inte

rvie

ws,

obse

rvat

ions

and t

ests

(Hea

rtla

nd A

rea

Educa

tion A

gen

cy,

200

7)

U

se t

his

info

rmat

ion t

o a

sses

s th

e

stud

ent‘

s sk

ills

, ev

aluat

ing t

he

rate

(e.

g.,

the

spee

d w

ith w

hic

h t

he

skil

ls c

an b

e

succ

essf

ull

y d

emonst

rate

d)

as w

ell

as t

he

accu

racy w

ith w

hic

h t

he

skil

l is

com

ple

ted (

Hea

rtla

nd A

rea

Educa

tion

Agen

cy, 2007)

imm

edia

te a

nte

ced

ents

); d

escr

ibe

the

targ

et

beh

avio

r, a

nd f

inal

ly, id

enti

fy t

he

pre

sum

ed f

un

ctio

n o

f th

e beh

avio

r (K

ern,

2005)

G

round t

he

hypoth

esis

in d

ata

coll

ecte

d

about

the

studen

t, m

anip

ula

ting d

iffe

rent

var

iable

s sy

stem

atic

ally

to t

est

dif

fere

nt

hypoth

eses

(K

ern, 2005)

U

se h

ypo

thes

es t

o d

evel

op i

nte

rven

tions

(Ker

n, 2005

)

What

shou

ld

be

done

about

it?

Goal

Set

ting

Usi

ng a

no

rm-r

efer

ence

d a

ppro

ach t

o

eval

uat

e le

vel

and a

cri

teri

on a

ppro

ach t

o

add

ress

slo

pe

of

gro

wth

, ta

rget

rat

es f

or

gro

wth

can

be

com

pute

d b

y d

eter

min

ing

score

s th

e pre

dic

t pas

sin

g o

n s

tate

tes

ts a

t

thre

e bas

elin

e as

sess

men

ts, w

her

e ta

rget

ben

chm

ark s

core

s ar

e ca

lcula

ted t

o

pre

dic

t a

pro

fici

ent

score

on a

sta

te t

est

and

a r

ate

of

gro

wth

nec

essa

ry t

o a

chie

ve

thes

e sc

ore

s is

cal

cula

ted

(Van

Der

Heyd

en &

Bu

rns,

2010)

G

oal

s fo

r th

e ra

te o

f st

ud

ent

per

form

ance

can b

e d

eriv

ed u

sin

g i

nst

ruct

ional

-lev

el

U

ses

clea

r an

d m

easu

rable

cri

teri

a to

def

ine

what

the

stud

ent

wil

l be

able

to d

o i

f

the

inte

rven

tion i

f ef

fect

ive

(Upah

, 2008

)

In

cludes

the

tim

efra

me

(when

the

expec

ted

pro

gre

ss w

ill

be

acco

mpli

shed

), c

ondit

ion

(spec

ific

cir

cum

stan

ces

in w

hic

h t

he

beh

avio

r w

ill

occ

ur

as e

stab

lish

ed d

uri

ng

pro

ble

m i

den

tifi

cati

on a

nd a

nal

ysi

s st

ages

),

the

beh

avio

r (w

ritt

en i

n o

bje

ctiv

e,

obse

rvab

le,

and m

easu

rable

ter

ms)

, an

d

crit

eria

(th

e st

andar

d o

f h

ow

wel

l th

e

beh

avio

r is

to b

e per

form

ed)

(Up

ah, 2008)

(tab

le c

onti

nues

)

53

54

Table

5 c

onti

nued

Pro

ble

m-

solv

ing L

ogic

Inte

rven

tion

Com

po

nen

t

RT

I L

iter

ature

on D

esig

nin

g A

cadem

ic

Inte

rven

tions

Lit

erat

ure

on B

ehav

iora

l In

terv

enti

ons

crit

eria

or

wit

h n

atio

nal

norm

s (e

.g.,

score

s at

or

above

the

25

th p

erce

nti

le)

(Van

Der

Heyd

en &

Bu

rns,

2010)

A

ccura

cy g

oal

s ca

n b

e ca

lcula

ted w

ith

inst

ruct

ional

lev

el c

rite

ria

usi

ng t

he

per

centa

ge

of

item

s co

rrec

tly c

om

ple

ted

(Gic

kli

ng &

Thom

pso

n,

1985);

the

use

of

93-9

7%

of

know

n m

ater

ial

can b

e se

t as

a

crit

erio

n f

or

inst

ruct

ional

lev

el a

ccura

cy

(Bu

rns,

2007;

Gic

kli

ng &

Arm

stro

ng,

197

8;

Tre

pto

w, B

urn

s, &

McC

om

as,

200

7)

G

oal

s ca

n b

e se

t fo

r m

ath

and o

ther

skil

ls

asid

e fr

om

rea

din

g c

om

pre

hen

sion

and

eval

uat

ed u

sin

g d

rill

tas

ks

wher

e 90

% i

s

con

sider

ed a

n a

deq

uat

e cr

iter

ion f

or

accu

racy f

or

skil

ls s

uch

as

mult

ipli

cati

on

fact

s, l

ette

r so

unds,

com

pre

hen

sion

ques

tions,

and o

ther

skil

ls w

ith

the

exce

pti

on o

f re

adin

g f

luen

cy

(Van

Der

Heyd

en &

Bu

rns,

2010)

G

oal

lin

es c

an b

e ca

lcula

ted b

y

esti

mat

ing s

tuden

t pro

gre

ss a

t a

rate

of

25%

im

pro

vem

ent

in a

giv

en s

kil

l ea

ch

wee

k;

afte

r co

llec

ting a

t le

ast

3 d

ata

poin

ts a

nd e

stab

lish

ing a

bas

elin

e, s

elec

t

the

med

ian s

core

and m

ult

iply

that

by

F

or

vis

ual

rep

rese

nta

tion, ch

oose

the

centr

al b

asel

ine

dat

a poin

t, d

raw

a v

erti

cal

line

afte

r th

e la

st b

asel

ine

dat

a poin

t, d

raw

a se

cond e

ndp

oin

t at

the

hig

hes

t

per

form

ance

lev

el o

n t

he

dat

e w

hen

it

is

expec

ted t

hat

the

studen

t w

ill

hav

e m

et t

he

goal

, an

d d

raw

a s

trai

ght

line

connec

tin

g

both

endpoin

ts (

Upah

, 20

08)

(tab

le c

onti

nues

)

54

55

Table

5 c

onti

nued

Pro

ble

m-

solv

ing L

ogic

Inte

rven

tion

Com

ponen

t

RT

I L

iter

ature

on D

esig

nin

g A

cadem

ic

Inte

rven

tions

Lit

erat

ure

on B

ehav

iora

l In

terv

enti

ons

1.2

5 (

25%

) to

set

a t

arget

for

the

foll

ow

ing w

eek;

use

this

pro

cedure

to s

et

goal

s fo

r ea

ch w

eek o

f th

e in

terv

enti

on

(Wil

liam

s, 2

010

)

In

terv

enti

on

Pla

n

U

sing h

ypoth

eses

confi

rmed

by d

ata

duri

ng p

roble

m a

nal

ysi

s, d

evel

op

inte

rven

tions

for

as m

any c

onfi

rmed

hypoth

esis

as

poss

ible

giv

en a

vai

lable

reso

urc

es in

ord

er t

o r

emove

bar

rier

s to

lear

nin

g (

Bat

sch

e, C

asti

llo, D

ixon,

&

Ford

e, 2

008

)

In

terv

enti

ons

sele

cted

must

be

evid

ence

-

bas

ed, an

d s

hould

be

sele

cted

bas

ed o

n

evid

ence

sho

win

g t

hei

r ef

fect

iven

ess

when

pre

vio

usl

y i

mple

men

ted f

or

sim

ilar

pro

ble

ms

(Bat

sche,

Cas

till

o, D

ixon, &

Ford

e, 2

008

)

T

he

inte

rven

tion i

mple

men

tati

on p

lan

sho

uld

det

ail

per

sonnel

res

ponsi

ble

for

sup

port

ing t

each

ers

as t

hey

im

ple

men

t an

inte

rven

tion, th

e re

sponsi

bil

itie

s of

the

sup

port

per

sonnel

, an

d w

hen

and w

her

e

thes

e ac

tivit

ies

wil

l occ

ur

(Bat

sch

e,

Cas

till

o, D

ixon, &

Ford

e, 2

008)

T

ier

II i

nte

rven

tions

should

be

del

iver

ed

to a

sm

all

gro

up o

f st

ud

ents

(2

-8 i

n

C

lear

des

crip

tio

n o

f th

e pro

cedure

s to

be

use

d d

uri

ng a

n i

nte

rven

tion (

spec

ific

stra

tegie

s bas

ed o

n t

he

pro

ble

m a

nd t

he

lite

ratu

re o

n E

BIs

, w

hat

wil

l be

done,

how

each

ste

p w

ill

be

com

ple

ted, m

ater

ials

nee

ded

fo

r ea

ch s

tep, w

ho w

ill

do w

hat

and

when

, w

her

e pro

cedure

s w

ill

take

pla

ce)

(Upah

, 20

08)

(ta

ble

conti

nues

)

55

56

Table

5 c

on

tinued

Pro

ble

m-

solv

ing L

ogic

Inte

rven

tion

Com

ponen

t

RT

I L

iter

ature

on D

esig

nin

g A

cadem

ic

Inte

rven

tions

Lit

erat

ure

on B

ehav

iora

l In

terv

enti

ons

elem

enta

ry s

chool,

8-1

0 f

or

mid

dle

school,

or

10

-12 f

or

hig

h s

chool)

,

del

iver

ed 3

-5 t

imes

a w

eek f

or

20

-30

min

ute

ses

sions,

des

igned

to l

ast

at l

east

8 w

eeks

(Van

Der

Heyd

en &

Burn

s, 2

010)

T

ier

III

inte

rven

tions

sho

uld

be

del

iver

ed

on a

n i

ndiv

idual

bas

is, w

ith i

nte

rven

tion

sess

ions

occ

urr

ing d

aily

(V

anD

erH

eyd

en

& B

urn

s, 2

010);

oth

er r

esea

rch

ers

reco

mm

end t

hat

Tie

r II

I in

terv

enti

ons

can b

e d

eliv

ered

in g

roup

s of

1-3

, 5 t

imes

per

wee

k, in

30

-90 m

inute

ses

sions

(Bro

wn

-Chid

sey &

Ste

ege,

2010)

M

easu

rem

e

nt

Str

ateg

y

T

o f

acil

itat

e ac

cura

te d

ecis

ion m

akin

g,

duri

ng a

n i

nte

rven

tion, dir

ect

evid

ence

of

inte

rven

tion i

mple

men

tati

on (

e.g.,

obse

rvat

ion o

f im

ple

men

tati

on,

com

ple

ted w

ork

shee

ts, lo

g-i

n r

ecord

s at

a

com

pute

r, a

sel

f-m

onit

ori

ng c

hec

kli

st

com

ple

ted b

y t

he

teac

her

indic

atin

g t

hat

the

inte

rven

tion s

teps

hav

e bee

n

com

ple

ted

for

the

day, or

a st

uden

t sc

ore

on a

n a

sses

smen

t ac

tivit

y t

rack

ing t

he

inte

rven

tion e

ffec

ts)

and s

tuden

t le

arnin

g

dat

a (e

.g., d

irec

t as

sess

men

ts o

f th

e sk

ills

targ

eted

fo

r in

terv

enti

on,

and/o

r a

crit

erio

n-l

evel

skil

l re

flec

ting w

hat

is

D

efin

e th

e ta

rget

beh

avio

rs, dec

ide

wh

en

and w

her

e re

cord

ing w

ill

occ

ur,

dec

ide

who w

ill

reco

rd t

he

targ

et b

ehav

iors

,

choose

the

most

appro

pri

ate

reco

rdin

g

met

hod, ch

oose

the

most

appro

pri

ate

reco

rdin

g i

nst

rum

ent

(Mil

tenber

ger

, 2004

)

(

table

conti

nues

)

56

57

Table

5 c

onti

nued

Pro

ble

m-

solv

ing L

ogic

Inte

rven

tion

Com

ponen

t

RT

I L

iter

ature

on D

esig

nin

g A

cadem

ic

Inte

rven

tions

Lit

erat

ure

on B

ehav

iora

l In

terv

enti

ons

nec

essa

ry f

or

succ

ess

in t

he

clas

sroom

)

sho

uld

be

coll

ecte

d (

Van

Der

Heyden

&

Burn

s, 2

010)

D

ecis

ion

-

mak

ing P

lan

Once

a p

re-d

eter

min

ed a

mount

of

dat

a

hav

e bee

n c

oll

ecte

d, th

e dat

a w

ill

be

revie

wed

to d

eter

min

e w

het

her

the

inte

rven

tion p

roduce

d d

esir

ed o

utc

om

es

by c

om

par

ing t

he

studen

t‘s

beh

avio

rs/p

erfo

rman

ce t

o g

oal

s se

t fo

r

the

studen

t (B

row

n-C

hid

sey &

Ste

ege,

2010)

D

ata

can b

e an

alyze

d w

ith r

espec

t to

level

(a

sco

re v

alu

e obta

ined

on a

giv

en

mea

sure

or

at a

giv

en p

oin

t in

tim

e, t

o b

e

com

par

ed t

o t

he

per

form

ance

of

oth

ers)

and

slo

pe

(the

rate

of

pro

gre

ss a

stu

den

t

is m

akin

g, to

det

erm

ine

when

and

whet

her

or

not

astu

den

t w

ill

mee

t a

go

al

in a

pre

-det

erm

ined

per

iod o

f ti

me)

(Bro

wn

-Chid

sey &

Ste

ege,

2010)

D

ecis

ions

about

how

to p

roce

ed w

ith

inst

ruct

ion a

nd i

nte

rven

tions

are

mad

e

bas

ed o

n w

het

her

slo

pe

and l

evel

dat

a

sho

w p

rogre

ss i

n m

eeti

ng a

sp

ecif

ic g

oal

(Bro

wn

-Chid

sey &

Ste

ege,

2010)

T

ask d

iffi

cult

y i

s in

crea

sed

D

eter

min

e how

dec

isio

ns

wil

l be

mad

e b

y

dec

idin

g t

he

freq

uen

cy o

f dat

a co

llec

tion,

how

the

dat

a w

ill

be

sum

mar

ized

for

eval

uat

ion p

urp

ose

s, h

ow

man

y d

ata

poin

ts

or

how

much

tim

e sh

ould

occ

ur

bef

ore

dat

a

wil

l be

anal

yze

d,

and d

ecis

ion r

ule

s fo

r

resp

ondin

g t

o s

pec

ific

dat

a pat

tern

s (T

illy

& F

lugum

, 1995)

T

wo a

spec

ts o

f th

e dat

a ev

aluat

ed t

o j

udge

the

effe

cts

of

an i

nte

rven

tion a

re t

he

lev

el

(how

much

the

beh

avio

r is

occ

urr

ing

duri

ng b

asel

ine

and i

nte

rven

tion p

has

es a

s

judged

by i

ts f

requen

cy,

dura

tion/i

nte

nsi

ty,

or

per

centa

ge

of

inte

rval

s of

its

occ

urr

ence

)

and t

rend

(w

het

her

the

level

of

the

beh

avio

r is

incr

easi

ng o

r dec

reas

ing w

ithin

the

bas

elin

e an

d i

nte

rven

tion p

has

e)

(Mil

tenber

ger

, 2005

)

(

table

conti

nues

)

57

58

Table

5 c

onti

nued

Pro

ble

m-

solv

ing L

ogic

Inte

rven

tion

Com

ponen

t

RT

I L

iter

ature

on D

esig

nin

g A

cadem

ic

Inte

rven

tions

Lit

erat

ure

on B

ehav

iora

l In

terv

enti

ons

syst

emat

ical

ly a

s st

uden

t le

arnin

g

impro

ves

; gen

eral

izat

ion s

hould

be

asse

ssed

at

routi

ne

inte

rval

s

(Van

Der

Heyd

en &

Bu

rns,

2010)

A

n a

imli

ne

can b

e use

d t

o d

ocu

men

t th

e

expec

ted r

ate

of

pro

gre

ss a

nd t

hus

eval

uat

e th

e st

uden

t‘s

resp

onse

to

inte

rven

tion;

a li

ne

is d

raw

n c

onnec

tin

g

the

init

ial

level

of

per

form

ance

and t

he

des

ired

lev

el a

t th

e goal

dat

e, s

tuden

t dat

a

are

then

plo

tted

in a

tim

e-se

ries

gra

ph,

wit

h p

rogre

ss m

easu

re b

y c

om

par

ing n

ew

dat

a poin

ts t

o t

he

aim

line;

dat

a poin

ts t

hat

coin

cide

wit

h t

he

aim

line

indic

ate

that

the

studen

t is

mak

ing a

deq

uat

e pro

gre

ss;

3

conse

cuti

ve

dat

a poin

ts a

bove

the

aim

line

sugges

t th

at t

he

go

al s

hou

ld b

e re

vis

ed t

o

be

more

ch

alle

ngin

g,

whil

e th

ree

conse

cuti

ve

dat

a poin

ts b

elow

the

aim

line

sugges

t th

at t

he

inte

rven

tion i

s not

work

ing (

Fu

chs,

Fu

chs,

Hin

tze,

&

Lem

bk

e, 2

006;

Mir

kin

, D

eno,

Tin

dal

, &

Kueh

nle

, 1982;

Shin

n, 1989)

or

that

the

inte

rven

tion i

s not

at t

he

right

inte

nsi

ty

and t

he

studen

t m

ay r

equ

ire

pla

cem

ent

in

a dif

fere

nt

tier

(F

uch

s et

al.

, 2006)

Chan

ges

to i

nte

rven

tion t

o i

ncr

ease

(

table

conti

nues

)

58

59

Table

5 c

onti

nued

Pro

ble

m-

solv

ing L

ogic

Inte

rven

tion

Com

pon

ent

RT

I L

iter

ature

on D

esig

nin

g A

cadem

ic

Inte

rven

tions

Lit

erat

ure

on B

ehav

iora

l In

terv

enti

ons

succ

ess

incl

ude

incr

easi

ng t

he

amount

of

tim

e fo

r in

terv

enti

on s

essi

ons,

red

uci

ng

the

num

ber

of

studen

ts i

n t

he

inte

rven

tion

gro

up, o

r in

crea

sin

g t

he

num

ber

of

tim

es

the

studen

t pra

ctic

es a

sp

ecif

ic s

kil

l

duri

ng t

he

inte

rven

tion (

Bro

wn

-Chid

sey

& S

teeg

e, 2

010

)

W

hen

a s

tuden

t has

ach

ieves

six

or

more

dat

a poin

ts i

ndic

atin

g g

rade-

level

per

form

ance

of

a sp

ecif

ic s

kil

l th

e

inte

rven

tion c

an b

e fa

ded

or

dis

conti

nued

(Bro

wn

-Chid

sey &

Ste

ege,

2010)

Did

it

work

? P

rogre

ss

Monit

ori

ng

Pro

gre

ss m

onit

ori

ng a

sses

smen

t in

Tie

r II

sho

uld

typic

ally

occ

ur

on

a w

eekly

bas

is

(Fuch

s, K

ov

ales

ki,

& C

arru

th, 2009)

, or

no l

ess

than

once

ev

ery o

ther

wee

k

(Van

Der

Heyd

en &

Bu

rns,

2010);

pro

gre

ss m

onit

ori

ng d

ata

in T

ier

III

are

coll

ecte

d a

t le

ast

once

eac

h w

eek

(Van

Der

Heyd

en &

Bu

rns,

2010)

A

min

imum

of

3-4

wee

ks

of

inte

rven

tion

and

8 d

ata

poin

ts s

hould

be

coll

ecte

d

bef

ore

you e

xam

ine

studen

t pro

gre

ss t

o

det

erm

ine

if c

han

ges

nee

d t

o b

e m

ade

to

the

inte

rven

tion (

Fu

chs,

Koval

eski,

&

Car

ruth

, 2009)

T

hro

ughout

inte

rven

tion i

mple

men

tati

on,

studen

t per

form

ance

sho

uld

be

asse

ssed

fo

r

conti

nuous

eval

uat

ion a

nd t

o m

ake

nee

ded

modif

icat

ions

(Upah

, 20

08)

D

ata

gat

her

ing f

or

pro

gre

ss m

onit

ori

ng

should

en

tail

the

sam

e pro

cedure

s use

d t

o

gat

her

bas

elin

e dat

a duri

ng p

roble

m

iden

tifi

cati

on (

Upah

, 200

8)

M

ethods

of

pro

gre

ss m

onit

ori

ng i

ncl

ude

chec

kli

sts,

fre

qu

ency c

ou

nts

, o

bse

rvat

ions,

per

centa

ge

calc

ula

tions,

per

man

ent

pro

duct

s, p

ort

foli

os,

rat

ing s

cale

s, r

ubri

cs,

and t

ime

(e.g

., m

easu

res

of

dura

tion o

r

late

ncy)

(Upah

, 2008)

(tab

le c

onti

nues

)

59

60

Table

5 c

onti

nued

Pro

ble

m-

solv

ing L

ogic

Inte

rven

tion

Com

ponen

t

RT

I L

iter

ature

on D

esig

nin

g A

cadem

ic

Inte

rven

tions

Lit

erat

ure

on B

ehav

iora

l In

terv

enti

ons

W

hen

anal

ysi

s sh

ow

s 4 c

onse

cuti

ve

dat

a

poin

ts b

elow

the

goal

lin

e, t

eam

s ca

n

concl

ude

that

its

not

likel

y t

hat

the

studen

t w

ill

achie

ve

his

/her

yea

r en

d g

oal

and s

hould

consi

der

mak

ing i

nst

ruct

ional

chan

ges

(F

uch

s, K

oval

eski,

& C

arru

th,

200

9)

U

sing f

acto

r-d

eriv

ed o

r in

div

idual

ized

met

hods

to a

bbre

via

te l

onger

beh

avio

r

rati

ng s

cale

s m

ay a

llow

for

the

crea

tion o

f

more

fea

sible

pro

gre

ss-m

onit

ori

ng

beh

avio

r ra

ting s

cale

s th

at r

etai

n t

he

tech

nic

al q

ual

itie

s of

thei

r ori

gin

al f

orm

ats

(Volp

e &

Gad

ow

, 2010

)

A

t le

ast

3-5

str

uct

ure

d d

irec

t obse

rvat

ions

wit

hin

or

acro

ss d

ays

may

be

nee

ded

to

obta

in a

dep

end

able

est

imat

e of

acad

emic

engag

emen

t (B

ries

ch, C

haf

oule

as,

&

Ril

ey-T

ilm

an, 2010)

F

orm

ativ

e

Eval

uat

ion

Ass

essm

ents

conduct

ed a

t re

gula

r

inte

rval

s al

on

g t

he

way t

o a

chie

vin

g a

spec

ific

lea

rnin

g g

oal

to i

ndic

ate

whet

her

or

not

a st

ud

ent

is m

akin

g p

rogre

ss i

n

mee

ting a

n i

nst

ruct

ional

goal

(B

row

n-

Chid

sey &

Ste

ege,

2010)

O

n-g

oin

g e

val

uat

ion t

o d

eter

min

e w

het

her

or

not

an i

nte

rven

tion w

ill

be

succ

essf

ul,

and w

hen

nec

essa

ry t

o m

ake

modif

icat

ions

to i

ncr

ease

th

e ch

ance

s th

at g

oal

s fo

r th

e

studen

t w

ill

be

met

(U

pah

, 2008)

V

isual

ly a

nal

yze

inte

rven

tion p

erfo

rman

ce

dat

a co

mpar

ed t

o b

asel

ine

dat

a (U

pah

,

2008)

wit

h r

espec

t to

chan

ge

in m

ean (

Is

the

aver

age

rate

of

per

form

ance

hig

her

or

low

er d

uri

ng t

he

inte

rven

tion v

s. duri

ng

bas

elin

e), ch

ange

in l

evel

(does

the

studen

t‘s

per

form

ance

rep

rese

nt

a ch

ange

in t

he

des

ired

dir

ecti

on f

rom

the

end o

f

bas

elin

e dat

a to

the

star

t of

the

(tab

le c

onti

nues

)

60

61

Table

5 c

onti

nued

Pro

ble

m-

solv

ing L

ogic

Inte

rven

tion

Com

ponen

t

RT

I L

iter

ature

on D

esig

nin

g A

cadem

ic

Inte

rven

tions

Lit

erat

ure

on B

ehav

iora

l In

terv

enti

ons

in

terv

enti

on?),

chan

ge

in t

rend (

has

the

studen

t‘s

per

form

ance

tre

nd i

ncr

ease

d o

r

dec

reas

ed o

ver

tim

e?),

and l

aten

cy o

f

chan

ge

(is

ther

e a

chan

ge

in p

erfo

rman

ce

afte

r th

e fi

rst

wee

k o

f in

terv

enti

on

imple

men

tati

on?)

(Kaz

din

, 1982)

E

val

uat

e st

uden

t pro

gre

ss r

elat

ive

to t

he

goal

lin

e fo

r in

terv

enti

on

; if

3-4

dat

a poin

ts

are

at o

r ab

ove

the

goal

lin

e, c

onsi

der

rais

ing t

he

go

al, pro

gra

m f

or

mai

nte

nan

ce

and g

ener

aliz

atio

n o

f a

skil

l, a

nd/o

r dis

cuss

dis

conti

nuat

ion;

if 3

-4 d

ata

poin

ts a

re

bel

ow

the

goal

lin

e, c

onsi

der

rev

isin

g t

he

inte

rven

tion w

ith r

espec

t to

chan

gin

g t

he

pac

e of

the

inte

rven

tion,

modif

yin

g t

he

mat

eria

ls b

ein

g u

sed, pro

vid

ing m

ore

resp

onse

opport

unit

ies,

or

imple

men

ting o

r

adju

stin

g r

einfo

rcem

ent

of

des

ired

beh

avio

rs;

if d

ata

are

var

iable

, co

nsi

der

ways

of

moti

vat

ing t

he

studen

t to

more

consi

sten

tly d

ispla

y t

he

des

ired

beh

avio

r

(Upah

, 2008)

T

reat

men

t

Inte

gri

ty

T

echnic

ally

ad

equat

e R

tI i

mple

men

tati

on

occ

urs

wh

en i

nte

rven

tion

inte

gri

ty i

s

man

aged

in s

uch

a w

ay t

hat

dec

isio

ns

are

mad

e in

a t

imel

y m

ann

er,

and s

yst

ems

are

in p

lace

to v

erif

y p

roper

inte

rven

tion

T

reat

men

t in

tegri

ty a

s th

e deg

ree

to w

hic

h

the

inte

rven

tion w

as i

mple

men

ted a

s

pla

nned

(G

resh

am, 1989;

Tel

zrow

, 1995;

Yea

ton &

Sch

rest

, 19

81)

(table

conti

nues

)

61

62

Table

5 c

onti

nued

Pro

ble

m-

solv

ing L

ogic

Inte

rven

tion

Com

ponen

t

RT

I L

iter

ature

on D

esig

nin

g A

cadem

ic

Inte

rven

tions

Lit

erat

ure

on B

ehav

iora

l In

terv

enti

ons

man

agem

ent,

to d

etec

t an

d c

orr

ect

pro

ble

ms

in i

mple

men

tati

on, an

d t

o

pote

nti

ally

tak

e ov

er i

mp

lem

enta

tion t

o

pre

ven

t del

ays

in i

den

tifi

cati

on a

nd

dec

isio

n-m

akin

g (

Van

Der

Heyden

&

Burn

s, 2

010)

D

irec

t ev

iden

ce o

f co

rrec

t in

terv

enti

on

imple

men

tati

on c

ould

incl

ude

obse

rvat

ion o

f im

ple

men

tati

on,

com

ple

ted w

ork

shee

ts, lo

g-i

n r

ecord

s at

a

com

pute

r, a

sel

f-m

onit

ori

ng c

hec

kli

st

com

ple

ted b

y t

he

teac

her

indic

atin

g t

hat

the

inte

rven

tion s

teps

hav

e bee

n

com

ple

ted f

or

the

day, or

a st

uden

t sc

ore

on a

n a

sses

smen

t ac

tivit

y t

rack

ing t

he

inte

rven

tion e

ffec

ts (

Van

Der

Heyd

en &

Burn

s, 2

010)

Dif

fere

nt

appro

aches

to m

easu

rin

g

trea

tmen

t in

tegri

ty i

ncl

ude

self

-rep

ort

s,

logs,

chec

kli

sts,

per

man

ent

pro

duct

s, a

nd

dir

ect

obse

rvat

ions

of

imple

men

tati

on b

y

those

not

adm

inis

teri

ng t

he

inte

rven

tion

(Upah

, 20

08)

S

um

mat

ive

Eval

uat

ion

Ass

essm

ent

that

all

ow

s pra

ctit

ioner

s to

det

erm

ine

whet

her

or

not

an i

nst

ruct

ional

goal

has

bee

n m

et (

Bro

wn

-Chid

sey &

Ste

ege,

2010)

U

sing c

urr

iculu

m-b

ased

mea

sure

s, p

ost

-

inte

rven

tion l

evel

can

be

eval

uat

ed w

ith

crit

erio

n-r

efer

ence

d c

om

par

ison (

e.g., a

score

abov

e th

e 25

th p

erce

nti

le o

n a

nat

ional

norm

, or

scori

ng w

ithin

the

low

-

E

val

uat

ion t

hat

occ

urs

aft

er t

he

inte

rven

tion h

as b

een c

om

ple

ted t

o

det

erm

ine

whet

her

or

not

it w

as e

ffec

tive

in

pro

duci

ng p

osi

tive

studen

t outc

om

es

(Upah

, 20

08)

T

o c

onduct

sum

mat

ive

eval

uat

ions,

tea

ms

can r

efer

ence

the

dec

isio

n r

ule

s es

tabli

shed

duri

ng p

lan i

mple

men

tati

on, an

d/o

r

com

par

e th

e st

ud

ent‘

s per

form

ance

at

(

table

conti

nues

)

62

63

Table

5 c

onti

nued

Pro

ble

m-

solv

ing L

ogic

Inte

rven

tion

Com

ponen

t

RT

I L

iter

ature

on D

esig

nin

g A

cadem

ic

Inte

rven

tions

Lit

erat

ure

on B

ehav

iora

l In

terv

enti

ons

risk

cat

egory

fro

m t

he

DIB

EL

S s

tandar

d)

(Van

Der

Heyd

en &

Bu

rns,

2010)

R

ate

of

gro

wth

can

be

nu

mer

ical

ly

det

erm

ined

usi

ng l

oca

l n

orm

s (e

.g.,

pla

cing a

t or

abov

e th

e 2

5th

per

centi

le f

or

a sp

ecif

ic g

rade

or

scori

ng w

ithin

1

stan

dar

d d

evia

tion o

f th

e av

erag

e ra

te o

f

gro

wth

for

a sp

ecif

ic g

rade)

(Van

Der

Heyd

en &

Bu

rns,

2010)

A

stu

den

t w

ho s

core

s b

elow

a c

rite

rion

for

post

-inte

rven

tion l

evel

and w

hose

slope

of

gro

wth

was

more

than

1 s

tandar

d

dev

iati

on b

elow

the

mea

n w

ould

pro

vid

e

evid

ence

of

an i

nef

fect

ive

inte

rven

tion;

thes

e sc

ore

s co

uld

just

ify i

nte

rven

tion

del

iver

ed i

n a

more

inte

nsi

ve

tier

(Van

Der

Heyd

en &

Bu

rns,

2010)

bas

elin

e an

d p

ost

inte

rven

tion (

Upah

, 2008)

S

oci

al v

alid

ity:

coll

ecti

ng s

ubje

ctiv

e

rati

ngs

of

impro

vem

ent

in t

he

targ

et

beh

avio

r or

impro

vem

ent

in t

he

studen

t‘s

life

(K

azdin

, 1977;

Wolf

, 1978)

63

64

intervention design in a clear and objective behavioral definition of the problem.

Collection of data measuring the student‘s behavior before and during intervention

implementation is essential. Practitioners are advised to use only those interventions,

techniques, and strategies that have a strong evidence base in the literature. Intervention

development also involves coming up with a clear plan by describing the different

intervention components, materials, and responsibilities of those involved with

implementation. The importance of measuring treatment integrity throughout

intervention implementation is also stressed in both intervention types. Despite having a

common foundation, more specific guidelines for academic assessment related to goal

setting, measurement of behavior, and data-based decision making have been empirically

validated when compared to what is known regarding SEB assessment as a direct result

of the lack of a general outcome measure and limited research.

It is recommended that the behavioral targets to be measured should fit with the

purpose and objectives of the intervention by including short-term performance

objectives and long-term, broad general objectives (Kratochwill & Bergan, 1990).

Potential long-term objectives of SEB interventions could include reductions in the

severity of symptoms associated with an SEB disorder and the level of functional

impairment (Pelham, Fabiano, & Massett, 2005). Despite these recommendations,

researchers have not yet identified a task, behavior, or skill that can be manipulated and is

a sensitive indicator of SEB problems in children, making it difficult to accurately define

appropriate methods of measurement, define target behaviors, and design comprehensive

models to integrate screening and progress monitoring (Chafouleas, Volpe, Gresham, &

Cook, 2010). Without accurately defining and measuring target behaviors, practitioners

65

are also unable to design formative assessment measures or determine decision rules

specifying what an appropriate response to the intervention would look like (Chafouleas,

Volpe, Gresham, & Cook, 2010).

A variety of different methods of behavioral assessment are in existence, each

with its own set of strengths and weaknesses with respect to psychometric defensibility,

flexibility, feasibility, and repeatability (Chafouleas, Volpe, Gresham, & Cook, 2010).

As such, there is no single best assessment method, and while a combination of different

methods may be the best approach, at this point in time, researchers have not yet defined

a clear set of guidelines based on empirical evidence (Chafouleas, Volpe, Gresham, &

Cook, 2010). Some researchers have proposed academic engagement as a target behavior

serving as the foundation of general outcome measures (Briesch, Chafouleas, & Riley-

Tilman, 2010). Consensus has not been reached, however, on the definition of a general

outcome measure for behavior in school-based assessment, or whether it is possible to

establish such a construct in behavioral assessment, and as such, discussion of the most

appropriate behavioral targets to measure using the problem-solving model is on-going

(Chafouleas, Volpe, Gresham, & Cook, 2010).

Another deviation from academic interventions involves the difficulty inherent in

measuring the growth or development of a specific skill related to a target behavior

(Chafouleas, Volpe, Gresham, & Cook, 2010). Unlike academic domains, benchmarks

for desirable or appropriate behaviors have not been established or agreed upon

universally. For some students, no change in behavior is desirable, while for others,

desirable behavior is explicitly tied to the target behavior and the context in which it

occurs (Chafouleas, Volpe, Gresham, & Cook, 2010). Furthermore, visual analysis of

66

data is commonly recommended when evaluating interventions; however, quantitative

methods of analysis may provide a more defensible method of evaluation (Chafouleas,

Volpe, Gresham, & Cook, 2010). An assessment of social validation is another

potentially useful metric of intervention effectiveness (Gresham, 2005), while other

researchers recommend evaluating the level of impairment and quality of life pre- and

post-intervention (Kazdin, 2005).

EBIs for childhood behavioral disorders. As stated previously, promotion of

the use of EBIs has occurred in a variety of different fields. As such, school

psychologists have many treatment options available to them. This section describes

EBIs that have been identified to address common childhood behavior disorders. Listed

in Table 6 are the EBIs as well as the level of empirical support they have received. The

criteria used to evaluate each intervention before assigning it a level of empirical support

are also listed in Table 7.

Several conclusions can be drawn from the EBIs listed in Table 6. The well-

established category listed the fewest number of treatments, while the possibly

efficacious category contained the largest number of interventions. This suggests that a

large number of treatment strategies are in need of further research. Across all three

categories, cognitive behavior therapy (CBT) and behavior therapy (BT) have received

the most empirical support as treatments for common childhood disorders. Given these

results, it is interesting to note that the most common theoretical orientations guiding the

current counseling practices of school psychologists were cognitive behavioral and

behavioral, suggesting that there may be some agreement between evidence-based

practices and the common counseling practices of school psychologists. In addition to

67

Tab

le 6

Table

of

Evi

den

ce-B

ase

d I

nte

rven

tions

Beh

avio

r/In

terv

enti

on

C

lass

ific

atio

n

Anx

iety

– G

ener

al S

ym

pto

ms

CB

T

In

div

idual

CB

T (

Bar

rett

, D

adds,

& R

apee

, 1996

; F

lanner

y-S

chro

eder

& K

endal

l, 2

000

;

Ken

dal

l, 1

994;

Ken

dal

l,F

lanner

y-S

chro

eder

, P

anic

hel

li-M

indel

, S

outh

am-G

erow

, H

enin

,

& W

arm

an,

1997)

G

roup C

BT

(w

ithout

par

ents

) (B

arre

tt, 1998;

Fla

nner

y-S

chro

eder

& K

endal

l, 2

000;

Men

dlo

wit

z, M

anas

sis,

Bra

dle

y, S

capil

lato

, M

iezi

tis,

& S

haw

, 1999;

Rap

ee,

Abbott

, &

Lyn

eham

, 2006)

G

roup C

BT

wit

h p

aren

ts (

Bar

rett

, 1998;

Men

dlo

wit

z, M

anas

sis,

Bra

dle

y, S

capil

lato

,

Mie

ziti

s, &

Shaw

, 1999

; S

ilver

man

, K

urt

ines

, G

insb

urg

, W

eem

s, L

um

pkin

, &

Car

mic

hae

l, 1

999;

Spen

ce, H

olm

es, M

arch

, &

Lip

p,

2006)

Pro

bab

ly E

ffic

acio

us

(Sil

ver

man

, P

ina,

&

Vis

wes

var

an, 2008)

CB

T

In

div

idual

CB

T w

ith p

aren

ts (

Corn

wal

l, S

pen

ce,

& S

chott

e, 1

996)

In

div

idual

CB

T w

ith c

ognit

ive

par

ent

trai

nin

g (

Nau

ta, S

chooli

ng, E

mm

elkam

p, &

Min

der

aa,

2003

)

G

roup C

BT

wit

h p

aren

tal

anx

iety

man

agem

ent

for

anx

ious

par

ents

(C

obham

, D

adds,

&

Spen

ce,

1998)

F

amil

y C

BT

(B

ogel

s &

Siq

uel

and, 2006;

Wood, P

iace

nti

ni,

South

-Ger

ow

, C

hu, &

Sig

man

, 2006)

P

aren

t gro

up C

BT

(w

ithout

youth

involv

emen

t) (

Men

dlo

wit

z, M

anas

sis,

Bra

dle

y,

Sca

pil

lato

, M

iezi

tis,

& S

haw

, 1999;

Thie

nem

ann,

Moore

, &

Tom

pkin

s, 2

006

)

G

roup C

BT

wit

h p

aren

ts p

lus

inte

rnet

(S

pen

ce, H

olm

es, M

arch

, &

Lip

p,

20

06

)

Poss

ibly

Eff

icac

ious

(Sil

ver

man

, P

ina,

&

Vis

wes

var

an, 2008)

(table

conti

nues

)

67

68

Table

6 c

onti

nued

Beh

avio

r/In

terv

enti

on

C

lass

ific

atio

n

Sch

ool

Ref

usa

l

CB

T

In

div

idual

CB

T f

or

scho

ol

refu

sal

(Heyne,

Kin

g,

Tonge,

Roll

ings,

Youn

g,

Pri

tchar

d e

t al

.,

2002;

Las

t, H

anse

n,

& F

ranco

, 1998)

In

div

idual

CB

T f

or

scho

ol

refu

sal

wit

h p

aren

t/te

acher

tra

inin

g (

Heyne,

Kin

g, T

on

ge,

Roll

ings,

Young, P

ritc

har

d e

t al

., 2

002;

Kin

g,

Ton

ge,

Heyne,

Pri

tch

ard, R

oll

ings,

Youn

g,

et a

l., 1998)

P

aren

t/te

ach

er t

rain

ing f

or

school

Ref

usa

l (H

eyne,

Kin

g, T

on

ge,

Roll

ings,

Young,

Pri

tchar

d e

t al

., 2

002)

Poss

ibly

Eff

icac

ious

(Sil

ver

man

, P

ina,

&

Vis

wes

var

an, 2008)

Chil

d a

nd A

dole

scen

t O

CD

CB

T

In

div

idual

CB

T (

Ped

iatr

ic O

CD

Tre

atm

ent

Stu

dy T

eam

, 2004)

In

div

idual

CB

T, plu

s S

ertr

alin

e (P

edia

tric

OC

D T

reat

men

t S

tud

y T

eam

, 20

04

)

Pro

bab

ly E

ffic

acio

us

(Bar

rett

, F

arre

ll, P

ina,

Per

is, &

Pia

centi

ni,

200

8)

CB

T

F

amil

y-f

ocu

sed i

ndiv

idual

CB

T (

Bar

rett

, H

ealy

-Far

rell

, &

Mar

ch,

2004)

F

amil

y-f

ocu

sed

gro

up C

BT

(B

arre

tt, H

ealy

-Far

rell

, &

Mar

ch,

2004

)

Poss

ibly

Eff

icac

ious

(Bar

rett

, F

arre

ll, P

ina,

Per

is, &

Pia

centi

ni,

200

8)

Chil

d a

nd A

dole

scen

t P

TS

D

CB

T

T

raum

a fo

cuse

d C

BT

(C

ohen

, D

ebli

nger

, M

annar

ino, &

Ste

er, 200

4;

Cohen

&

Man

nar

ino, 1996, 1997

; C

ohen

, M

annar

ino,

& K

nudse

n, 2005;

Deb

linger

, L

ippm

an,

&

Ste

er, 1996;

Jaber

gh

ader

i, G

reen

wal

d, R

ubin

, Z

and, &

Dola

tab

adi,

2004;

Kin

g e

t al

.,

2000;

Kolk

o, 1996)

Wel

l-E

stab

lish

ed

(Sil

ver

man

, P

ina,

&

Vis

wes

var

an, 2008)

CB

T

S

chool-

bas

ed g

roup C

BT

(K

atao

ka

et a

l., 2003;

Ste

in e

t al

., 2

003)

Pro

bab

ly E

ffic

acio

us

(Sil

ver

man

, P

ina,

&

Vis

wes

var

an, 2008)

(table

conti

nues

)

68

69

Table

6 c

onti

nued

Beh

avio

r/In

terv

enti

on

C

lass

ific

atio

n

CB

T

R

esil

ient

Pee

r T

reat

men

t (F

antu

zzo e

t al

., 1

996;

Fan

tuzz

o, M

anz,

Atk

ins,

& M

eyer

s,

2005)

G

roup C

GT

(D

ebli

nger

, S

tauff

er,

& S

teer

, 2001

)

C

ognit

ive

Pro

cess

ing T

her

apy (

Ahre

ns

& R

exfo

rd, 2002)

E

ye

Mov

emen

t D

esen

siti

zati

on a

nd R

epro

cess

ing (

Chem

tob, N

akas

him

a, &

Car

lson,

2002;

Jaber

ghad

eri,

Gre

enw

ald, R

ubin

, Z

and,

& D

ola

tabad

i, 2

004

)

C

lien

t C

ente

red T

her

apy (

Cohen

, D

ebli

nger

, M

annar

ino, &

Ste

er,

2004)

F

amil

y T

her

apy (

Kolk

o,

1996)

C

hil

d P

aren

t P

sych

oth

erap

y (

Lie

ber

man

, V

an H

orn

, &

Ippen

, 2005)

Poss

ibly

Eff

icac

ious

(Sil

ver

man

, P

ina,

&

Vis

wes

var

an, 2008)

Soci

al P

hobia

CB

T

G

roup C

BT

fo

r S

OP

(S

oci

al P

hobia

; G

alla

gher

, R

abia

n, &

McC

losk

ey, 20

03;

Hay

war

d,

Var

ady,

Alb

ano, T

hie

nem

ann, H

ender

son,

& S

chat

zber

g,

2000;

Spen

ce, D

on

ovan

, &

Bre

chm

an-T

ouss

aint,

2000)

S

oci

al E

ffec

tiven

ess

Tra

inin

g f

or

Chil

dre

n f

or

SO

P (

Bei

del

, &

Morr

is,

200

0)

Pro

bab

ly E

ffic

acio

us

(Sil

ver

man

, P

ina

&

Vis

wes

var

an, 2008)

Spec

ific

Phobia

CB

T

E

moti

ve

imag

ery f

or

SP

of

dar

knes

s (C

orn

wal

l, S

pen

ce,

& S

chott

e, 1

996)

In

-viv

o b

ehav

iora

l ex

posu

res

wit

h E

MD

R f

or

SP

of

spid

ers

(Muri

s, M

erck

elb

ach,

Hold

rinet

, &

Sij

senaa

r, 1

998)

E

xposu

res

plu

s co

nti

ngen

cy m

anag

emen

t fo

r S

P (

Sil

ver

man

, K

urt

ines

, G

insb

urg

, W

eem

s,

Rab

ian, &

Ser

afin

i, 1

99

9)

E

xposu

res

plu

s se

lf-c

ontr

ol

for

SP

(S

ilver

man

, K

urt

ines

, G

insb

urg

, W

eem

s, R

abia

n, &

Ser

afin

i, 1

999)

Poss

ibly

Eff

icac

ious

(Sil

ver

man

, P

ina,

&

Vis

wes

var

an, 2008)

(table

conti

nues

)

69

70

Table

6 c

onti

nued

Beh

avio

r/In

terv

enti

on

C

lass

ific

atio

n

O

ne-

sess

ion e

xposu

re t

reat

men

t fo

r S

P (

Ost

, S

ven

sson, H

ells

trom

, &

Lin

dw

all,

2001)

O

ne-

sess

ion e

xposu

re t

reat

men

t w

ith p

aren

ts f

or

SP

(O

st,

Sven

sson, H

ells

tro

m, &

Lin

dw

all,

2001)

Dep

ress

ion –

Chil

dre

n

CB

T

In

div

idual

CB

T (

Asa

rno

w, S

cott

, &

Min

tz, 2002;

Gil

lham

, R

eivic

h, Ja

yco

x, &

Sel

igm

an,

1995;

Jayco

x, R

eivic

h, G

illh

am, &

Sel

igm

an, 199

4;

Kah

n, K

ehle

, Je

nso

n, &

Cla

rk, 1990;

Nel

son,

Bar

nar

d,

& C

ain,

2003;

Rober

ts, K

ane,

Th

om

son, B

ishop

, &

Har

t, 2

003;

Sta

rk,

Rey

nold

s, &

Kas

low

, 19

87;

Sta

rk, R

ouse

, &

Liv

ingst

on,

1991;

Wei

sz, T

hurb

er, S

wee

ney,

Pro

ffit

t, &

LeG

agnoux

, 1997;

Yu &

Sel

igm

an, 20

02

)

C

BT

gro

up, ch

ild o

nly

(G

illh

am, R

eivic

h, Ja

yco

x, &

Sel

igm

an, 1995;

Jayco

x, R

eivic

h,

Gil

lham

, &

Sel

igm

an, 19

94;

Kah

n, K

ehle

, Je

nso

n, &

Cla

rk, 1990;

Rober

ts,

Kan

e,

Thom

son, B

ishop

, &

Har

t, 2

003;

Sta

rk, R

eynold

s, &

Kas

low

, 1987;

Wei

sz, T

hurb

er,

Sw

eeney, P

roff

itt,

& L

eGag

noux

, 1997;

Yu &

Sel

igm

an, 2002)

C

BT

chil

d g

roup, plu

s par

ent

com

ponen

t (A

sarn

ow

, S

cott

, &

Min

tz, 2002;

Sta

rk, R

ouse

,

& L

ivin

gst

on,

1991)

CB

T

P

enn P

reven

tion P

rogra

m (

PP

P)

- in

cludin

g c

ult

ura

lly r

elev

ant

modif

icat

ions

as s

een i

n

the

Pen

n O

pti

mis

m P

rogra

m (

PO

P;

Gil

lham

, R

eivic

h, Ja

yco

x, &

Sel

igm

an,

1995;

Jayco

x,

Rei

vic

h, G

illh

am, &

Sel

igm

an, 1994;

Rober

ts, K

ane,

Thom

son, B

ishop

, &

Har

t, 2

003;

Yu &

Sel

igm

an, 2002)

S

elf-

Contr

ol

Ther

apy (

Sta

rk, R

eynold

s, &

Kas

low

, 1987;

Sta

rk, R

ouse

, &

Liv

ingst

on,

1991)

B

ehav

ior

Ther

apy (

Kah

n, K

ehle

, Je

nso

n, &

Cla

rk,

1990;

Kin

g &

Kir

sch

enb

aum

, 1990)

Wel

l-E

stab

lish

ed (

Dav

id-

Fer

ndon,&

Kas

low

, 2008

)

Pro

bab

ly E

ffic

acio

us

(Dav

id-F

erndon,

&

Kas

low

, 2008)

(table

conti

nues

)

70

71

Table

6 c

onti

nued

Beh

avio

r/In

terv

enti

on

C

lass

ific

atio

n

Dep

ress

ion –

Adole

scen

ts

CB

T

G

roup C

ognit

ive

Beh

avio

r T

her

apy,

adole

scen

t o

nly

(C

lark

e, H

awkin

s, M

urp

hy, S

hee

ber

,

Lew

inso

hn, &

See

ley,

19

95;

Cla

rke,

Roh

de,

Lew

inso

hn, H

ops,

& S

eele

y, 1

999;

Kow

lenko e

t al

., 2

005;

Lew

inso

hn, C

lark

e, H

ops,

& A

ndre

ws,

1990;

Lew

inso

hn, C

lark

e,

Rohde,

Hops,

& S

eele

y,

1996;

Rey

nold

s &

Co

ats,

19

86)

Inte

rper

son

al P

sych

oth

erap

y (

IPT

)

In

div

idual

IP

T (

Mu

fson

, D

ort

a, W

ickra

mar

atne,

Nom

ura

, O

lfso

n,

& W

iess

man

, 2004;

Mufs

on, W

eiss

man

, M

ore

au,

& G

arfi

nkel

, 1999;

Ross

ello

& B

ernal

, 1999)

Wel

l-E

stab

lish

ed (

Dav

id-

Fer

ndon,

& K

aslo

w, 200

8)

CB

T

A

dole

scen

t gro

up C

BT

, plu

s p

aren

t co

mponen

t (C

lark

e, H

awkin

s, M

urp

hy,

Shee

ber

,

Lew

inso

hn, &

See

ley,

19

95

; C

lark

e, R

oh

de,

Lew

inso

hn, H

ops,

& S

eele

y, 1

999;

Kow

elen

ko e

t al

., 2

005;

Lew

inso

hn, C

lark

e, H

ops,

& A

ndre

ws,

1990;

Lew

inso

hn,

Cla

rke,

Roh

de,

Hops,

& S

eele

y, 1996)

In

div

idual

CB

T (

Ross

ello

& B

ern

al, 1999;

Wood, H

arri

ngto

n,

& M

oore

, 1

99

6)

In

div

idual

CB

T, plu

s par

ent/

fam

ily c

om

pon

ent

(Bre

nt

et a

l., 1997;

Mel

vin

, T

onge,

Kin

g,

Heyne,

Go

rdon, &

Kli

mkei

t, 2

006;

Tre

atm

ent

for

Adole

scen

ts w

ith D

epre

ssio

n S

tud

y

(TA

DS

) T

eam

, 2004)

A

dole

scen

ts C

opin

g w

ith D

epre

ssio

n (

CW

D-A

; C

lark

e, H

awkin

s, M

urp

hy,

Shee

ber

,

Lew

inso

hn, &

See

ley,

19

95

; C

lark

e et

al.

, 2001;

Cla

rke,

Roh

de,

Lew

inso

hn, H

ops,

&

See

ley, 1999;

Lew

inso

hn

, C

lark

e, H

ops,

& A

ndre

ws,

1990;

Lew

inso

hn, C

lark

e, R

oh

de,

Hops,

& S

eele

y, 1996;

Rohde,

Cla

rke,

Mac

e, J

org

ense

n, &

See

ley, 2004)

Inte

rper

son

al P

sych

oth

erap

y (

IPT

)

IP

T f

or

Dep

ress

ed A

dole

scen

ts (

IPT

-A;

Mufs

on,

Dort

a, W

ickra

mar

atne,

Nom

ura

, O

lfso

n,

& W

iess

man

, 2004;

Mufs

on, W

eiss

man

, M

ore

au,

& G

arfi

nkel

, 1999

Pro

bab

ly E

ffic

acio

us

(Dav

id-F

erndon,

&

Kas

low

, 2008)

(table

conti

nues

)

71

72

Table

6 c

onti

nued

Beh

avio

r/In

terv

enti

on

C

lass

ific

atio

n

Chil

d a

nd A

dole

scen

t A

DH

D

B

ehav

iora

l par

ent

trai

nin

g (

BP

T;

Bar

kle

y e

t al

., 2

000;

Bor,

San

der

s, &

Mar

kie

-Dad

ds,

2002;

Hoat

h &

San

der

s, 2

002;

MT

A C

ooper

ativ

e G

roup, 1999;

Sonuga-B

arke,

Dal

ey,

Thom

pso

n,

Lav

er-B

radb

ury

, &

Wee

ks,

2001

)

B

ehav

iora

l cl

assr

oom

man

agem

ent

(BC

M;

Bar

kle

y e

t al

., 2

000;

Mir

anda,

Pre

scen

taci

on,

& S

ori

ano, 2002;

MT

A C

ooper

ativ

e G

roup

, 1999

)

B

ehav

iora

l pee

r in

terv

enti

ons

(BP

I; P

elh

am e

t al

., 2

000)

Opposi

tional

Def

iant

Dis

ord

er a

nd C

ondu

ct D

iso

rder

Beh

avio

r T

her

apy

P

aren

t M

anag

emen

t T

rain

ing (

Ber

nal

, K

linner

t, &

Sch

ult

z, 1

980;

Chri

sten

sen

, Jo

hnso

n,

Phil

lips,

& G

lasg

ow

, 19

80;

Pat

ters

on, R

eid, Jo

nes

, &

Con

ger

, 1975

)

CB

T

A

nger

Contr

ol

Tra

inin

g (

Loch

man

, C

oie

, U

nder

wood, &

Ter

ry, 1993;

Robin

son, S

mit

h,

& M

ille

r, 2

002)

R

atio

nal

-em

oti

ve

men

tal

hea

lth p

rogra

m (

Blo

ck, 1

978)

Beh

avio

r T

her

apy

H

elpin

g t

he

Nonco

mpli

ant

Chil

d (

Pee

d, R

ober

ts, &

Fo

rehan

d, 1977;

Wel

ls &

Egan

,

1988)

T

riple

P (

Posi

tive

Par

enti

ng P

rogra

m)

- S

tandar

d (

Bor,

San

der

s, &

Mar

kie

-Dad

ds,

2002;

San

der

s, M

arkie

-Dad

ds,

Tull

y, &

Bor,

2000

); E

nh

ance

d (

Bor,

San

der

s, &

Mar

kie

-Dad

ds,

2002;

San

der

s, M

arkie

-Dad

ds,

Tull

y,

& B

or,

2000

)

In

cred

ible

Yea

rs -

Par

ent

trai

nin

g (

Web

ster

-Str

atto

n &

Ham

mon

d, 1997;

Web

ster

-

Str

atto

n, R

eid

& H

amm

ond, 2004);

Chil

d t

rain

ing (

Web

ster

-Str

atto

n &

Ham

mond, 1997;

Web

ster

- S

trat

ton, R

eid

& H

amm

ond, 2001;

Web

ster

-Str

atto

n, R

eid, &

Ham

mond, 2004)

Wel

l-E

stab

lish

ed (

Pel

ham

& F

abia

no, 2008)

Wel

l-E

stab

lish

ed (

Eyb

erg,

Nel

son,

& B

oggs,

2008)

Pro

bab

ly E

ffic

acio

us

(Eyb

erg, N

elso

n,

& B

og

gs,

2008)

(table

conti

nues

)

72

73

Table

6 c

onti

nued

Beh

avio

r/In

terv

enti

on

C

lass

ific

atio

n

P

aren

t-C

hil

d I

nte

ract

ion T

her

apy (

Nix

on, S

wee

ney

, E

rick

son, &

Tou

yz,

20

03

;

Sch

uhm

ann, F

oote

, E

yber

g,

Bo

ggs,

& A

lgin

a, 1

99

8)

P

roble

m-S

olv

ing S

kil

ls T

rain

ing –

Sta

ndar

d (

Kaz

din

, B

ass,

Sie

gel

, &

Thom

as, 1989;

Kaz

din

, E

svel

dt-

Daw

son

, F

ren

ch,

& U

nis

, 1987b;

Kaz

din

, S

iegel

, &

Bas

s, 1

992);

Pro

ble

m-S

olv

ing S

kil

ls T

rain

ing a

nd P

ract

ice

(Kaz

din

, B

ass,

Sie

gel

, &

Tho

mas

, 1989);

Pro

ble

m-S

olv

ing S

kil

ls T

rain

ing a

nd P

aren

t M

anag

emen

t T

rain

ing (

Kaz

din

, E

svel

dt-

Daw

son, F

rench

, &

Unis

, 1987a)

G

roup A

sser

tiven

ess

Tra

inin

g -

Counse

lor-

led (

Hu

ey &

Ran

k, 1984

); P

eer-

led (

Huey &

Ran

k, 1984)

M

ult

idim

ensi

on

al T

reat

men

t fo

ster

car

e (C

ham

ber

lain

& R

eid, 1998;

Lev

e, C

ham

ber

lain

,

& R

eid, 2005)

Mult

isyst

emic

Th

erap

y

M

ult

isyst

emic

Th

erap

y (

Bord

uin

et

al., 1

995;

Hen

ggel

er, M

elto

n, B

rondin

o, S

cher

er,

&

Han

ley, 1997;

Hen

ggel

er, M

elto

n, &

Sm

ith, 1992;

Hen

ggel

er, P

ick

rel,

& B

rondin

o, 1999)

CB

T

G

roup A

nger

Contr

ol

Tra

inin

g (

Fei

ndle

r, M

arri

ot,

& I

wat

a, 1

984)

Rea

chin

g E

duca

tors

, C

hil

dre

n, an

d P

aren

ts (

RE

CA

P;

Wei

ss, H

arri

s, C

atro

n, &

Han

,

2003)

Beh

avio

r T

her

apy

T

riple

P (

Posi

tive

Par

enti

ng P

rogra

m)

- st

and

ard g

roup t

reat

men

t (L

eun

g, S

ander

s,

Leu

ng, M

ak,

& L

au,

200

3)

F

irst

Ste

p t

o S

ucc

ess

Pro

gra

m (

Wal

ker

, K

avan

agh

, S

till

er, G

oll

y, S

ever

son,

& F

eil,

1998)

S

elf-

adm

inis

tere

d T

reat

men

t, p

lus

Sig

nal

Sea

t (H

amil

ton &

Mac

Quid

dy,

1984)

In

cred

ible

Yea

rs P

aren

t T

rain

ing a

nd C

hil

d T

rain

ing (

Web

ster

-Str

atto

n &

Ham

mond,

1997);

Par

ent

Tra

inin

g a

nd T

each

er T

rain

ing (

Web

ster

-Str

atto

n, R

eid, &

Ham

mond,

2004);

In

cred

ible

Yea

rs P

aren

t T

rain

ing, T

each

er T

rain

ing,

and C

hil

d T

rain

ing

Poss

ibly

Eff

icac

ious

(Eyb

erg, N

elso

n,

& B

oggs,

2008)

(table

conti

nues

)

73

74

Table

6 c

onti

nued

Beh

avio

r/In

terv

enti

on

C

lass

ific

atio

n

(Web

ster

-Str

atto

n, R

eid

& H

amm

ond, 2004);

Tea

cher

Tra

inin

g a

nd C

hil

d T

rain

ing

(Web

ster

-Str

atto

n, R

eid

& H

amm

ond, 2004)

Adole

scen

t S

ubst

ance

Abuse

CB

T

G

roup C

BT

(B

attj

es,

Go

rdon, O

‘Gra

dy,

Kin

lock

, K

atz

& S

ears

, 2004;

Kam

iner

&

Burl

eson, 1999, K

amin

er, B

url

eson,

Bli

tz, S

uss

man

& R

ounsa

vil

le, 1998;

Kam

iner

,

Burl

eson &

Gold

ber

ger

, 2002;

Lid

dle

, R

ow

e, D

akof,

Un

gar

o &

Hen

der

son,

2004;

Wal

dro

n, O

zech

ow

ski,

Turn

er &

Bro

dy, 2005;

Wal

dro

n, S

lesn

ick, B

rod

y,

Turn

er,

&

Pet

erso

n, 2001)

M

ult

idim

ensi

on

al F

amil

y T

her

apy (

Den

nis

et

al., 2

004;

Lid

dle

, D

ako

f, D

iam

ond, P

arker

,

Bar

rett

& T

ejed

a, 2

001;

Lid

dle

, R

ow

e, D

akof,

Un

gar

o &

Hen

der

son, 2004)

F

unct

ional

Fam

ily T

her

apy (

Wal

dro

n, O

zech

ow

ski,

Turn

er &

Bro

dy, 200

5;

Wal

dro

n,

Sle

snic

k, B

rod

y, T

urn

er &

Pet

erso

n, 2001

)

Fam

ily T

her

apy

B

rief

Str

ateg

ic F

amil

y T

her

apy (

San

tist

eban

et

al.,

2003)

In

tegra

ted B

ehav

iora

l F

amil

y T

her

apy (

Hops

et a

l., 2007;

Wal

dro

n e

t al

., 2

007;

Wal

dro

n, O

zech

ow

ski,

Turn

er &

Bro

dy, 2005;

Wal

dro

n, S

lesn

ick, B

rod

y,

Tu

rner

&

Pet

erso

n, 2001)

M

ult

isyst

emic

Th

erap

y )

Hen

ggel

er, P

ick

rel

& B

rondin

o, 1999;

Hen

ggel

er,

Cli

ngem

pee

l,

Bro

ndin

o &

Pic

kre

l, 2

00

2)

Wel

l-E

stab

lish

ed (

Wal

dro

n

& T

urn

er, 2008)

Pro

bab

ly E

ffic

acio

us

(Wal

dro

n &

Turn

er, 200

8)

Adole

scen

t A

nore

xia

Ner

vosa

F

amil

y T

her

apy f

or

AN

(R

uss

el, S

zmukle

r, D

are

& E

isle

r, 1

987)

P

sych

oan

alyti

c T

her

apy (

Sel

f P

sych

olo

gy)

for

AN

(B

achar

, L

atze

r, K

reit

ler

& B

erry

,

1999)

C

ash‘s

Bod

y I

mag

e T

her

apy, plu

s V

irtu

al R

eali

ty (

Per

pin

a et

al.

, 1999)

Pro

bab

ly E

ffic

acio

us

(Kee

l

& H

aedt,

2008)

Poss

ibly

Eff

icac

ious

(Kee

l

& H

aedt,

2008)

(table

conti

nues

)

74

75

Table

6 c

onti

nued

Beh

avio

r/In

terv

enti

on

C

lass

ific

atio

n

Adole

scen

t B

uli

mia

Ner

vosa

CB

T

G

uid

ed s

elf-

care

for

bin

ge-

eati

ng i

n B

N (

Sch

mid

t et

al.

, 2007)

F

amil

y T

her

apy f

or

BN

(L

e G

ran

ge,

Cro

sby, R

ath

ouz,

& L

even

thal

, 2007)

Poss

ibly

Eff

icac

ious

(Kee

l

& H

aedt,

2008)

Chil

d a

nd A

dole

scen

t B

ipola

r D

isord

er

Fam

ily T

her

apy

F

amil

y-F

ocu

sed T

reat

men

t fo

r A

dole

scen

ts (

Mik

low

itz

et a

l., 2008)

Psy

choth

erap

y

M

ult

i-F

amil

y P

sych

oed

uca

tional

Psy

choth

erap

y (

Fri

stad

, V

erducc

i, W

alte

rs, &

Youn

g,

2009;

Young &

Fri

stad

, 2007)

Pro

bab

ly E

ffic

acio

us

(Ass

oci

atio

n f

or

Beh

avio

ral

and C

ognit

ive

Ther

apie

s [A

BC

T]

&

Soci

ety o

f C

linic

al C

hil

d

and A

dole

scen

t

Psy

cholo

gy [

SC

CA

P],

2010b)

CB

T

C

hil

d a

nd F

amil

y-F

ocu

sed C

BT

(W

est

et a

l., 2009

)

D

iale

ctic

al B

ehav

ior

Th

erap

y (

Gold

stei

n, A

xel

son,

Bir

mah

er &

Bre

nt,

200

7)

Psy

choth

erap

y

In

div

idual

Fam

ily P

sych

oed

uca

tion (

Youn

g &

Fri

stad

, 2007)

Poss

ibly

Eff

icac

ious

(Ass

oci

atio

n f

or

Beh

avio

ral

and C

ognit

ive

Ther

apie

s [A

BC

T]

&

Soci

ety o

f C

linic

al C

hil

d

and A

dole

scen

t

Psy

cholo

gy [

SC

CA

P],

2010b)

Auti

sm

Beh

avio

r T

her

apy

L

ovaa

s' M

ethod (

Cohen

, A

mer

ine-

Dic

ken

s, &

Sm

ith,

2006;

Eik

eset

h,

Sm

ith, Ja

hr,

&

Eld

evik

, 2002;

Lovaa

s, 1

987;

Sm

ith, L

ovaa

s, &

Lovaa

s, 2

002)

P

aren

t T

rain

ing (

Ald

red,

Gre

en,

& A

dam

s, 2

004;

Dre

w e

t al

., 2

002;

Joce

lyn, C

asir

o,

Bea

ttie

, B

ow

& K

nei

sz, 1998)

Wel

l-E

stab

lish

ed (

Roger

s

& V

ism

ara,

2008)

Poss

ibly

Eff

icac

ious

(Roger

s &

Vis

mar

a, 2

00

8)

75

76

Table 7

Criteria for Classifying Evidence-Based Psychosocial Treatments

Well-Established Treatments

There must be at least two good group-design experiments, conducted in at least two

independent research settings and by independent investigatory teams, demonstrating

efficacy by showing the treatment to be:

a) statistically significantly superior to pill or psychological placebo or to another

treatment

OR

b) equivalent (or not significantly different) to an already established treatment in

experiments with statistical power being sufficient to detect moderate differences

AND

Treatment manuals or logical equivalent were used for the treatment

Conducted with a population, treated for specified problems, for whom inclusion

criteria have been delineated in a reliable, valid manner

Reliable and valid outcome assessment measures, at minimum tapping the problems

targeted for change were used, and

Appropriate data analyses

Probably Efficacious Treatments

There must be at least two good experiments showing the treatment is superior

(statistically significantly so) to a wait-list control group

OR

One or more good experiments meeting the Well-Established Treatment Criteria with

the one exception of having been conducted in at least two independent research

settings and by independent investigatory teams

Possibly Efficacious Treatments

At least one ‗‗good‘‘ study showing the treatment to be efficacious in the absence of

conflicting evidence

Experimental Treatments

Treatment not yet tested in trials meeting task force criteria for methodology

Adapted from the Division 12 Task Force on Psychological Interventions‘ reports

(Chambless et al., 1996, 1998), from Chambless and Hollon (1998), and from Chambless

and Ollendick (2001).

77

CBT and BT, other therapies that have been determined to be well-established include

Family Therapy, Multidimensional Family Therapy, Functional Family Therapy,

Interpersonal Therapy, and Behavioral Parent Training, Behavioral Classroom

Management, and Behavioral Peer Interventions.

One reason for the high level of empirical support for CBT and BT techniques

could be related to the overlap between best practices in counseling and central tenets of

these treatments. For example, cognitive-behavioral practices are grounded in

empirically validated psychological theories, such as those related to learning and

cognition. The selection of specific treatment strategies is based on specific

characteristics of the child, and EBIs that have been shown to effectively address the

behaviors being displayed. Empiricism is the foundation for cognitive-behavioral

practices. The description and treatment of problematic behaviors is done using objective

terms and definitions, measurable goals, and a quantitative analysis of behavior. A major

component of treatments using CBT involves gathering objective data before, during, and

after an intervention. On-going evaluation of treatment goals is essential, as decisions

governing further intervention are made by examining data demonstrating the efficacy of

strategies already in use.

Section summary. Information detailing the current mental health needs of

students and the connection between mental health and academic success has been

acknowledged by school psychologists (Haertel et al., 1983; Wang et al., 1990). Data

collected describing the current counseling practices of school psychologists detail their

efforts to address the needs of the students with whom they work. Evidence of the

commitment to meeting the behavioral needs of students can be seen through the research

78

focus on the development of best practices related to indirect and direct intervention,

particularly in the area of counseling, the commitment to the use of evidence-based

interventions and research in this area, and the application of the problem-solving model

to the design and implementation of behavioral interventions. If research describing best

practices, evidence-based interventions, and the application of the problem-solving model

are applied to the design and implementation of counseling interventions, school

psychologists would employ strategies proven to be effective in addressing behavioral

problems in a manner that allows them to continuously monitor whether their efforts are

impacting student behavior as intended. Although research in these areas is on-going, the

use of evidence-based interventions, and the tenets of regular progress-monitoring of

student behavior and data-based decision making inherent in the problem-solving model

allow school psychologists to hold themselves accountable for meeting the mental health

needs of students when implementing counseling interventions with students who display

problem behavior.

Legislation Impacting the Field of School Psychology

In a similar fashion to the way the field of school psychology has evolved, federal

education policy has developed and changed in response to the needs of students.

Pertinent legislation influencing the practice of school psychology has been shaped by

federal involvement regarding students with disabilities, and students who come from

socially disenfranchised or economically disadvantaged backgrounds. The federal

government has responded to reports indicating that these groups of students were not

being provided with appropriate educational opportunities by passing laws and

regulations to address these inequalities and ensure that certain standards of practice are

79

followed. Over time these laws have been amended, and the role of the school

psychologist has evolved in response to these changes. This section provides an

overview of how federal policy and agencies, cases, and expository reports have

influenced education in general and the practice of school psychology more specifically.

A summary of selected influential federal policies and agencies, cases, and expository

reports can be found in tables 8, 9, 10, and 11.

This selected review of federal legislation, court decisions, expository reports, and

agencies provides an important context for examining the current climate of education in

American schools. From the period of the 1960s to the 1980s, a series of expository

reports (e.g., A Nation at Risk, Time for Results) publicized weaknesses in the education

of students in American schools, while at the same time proposing standardized testing

and increased autonomy for schools, contingent on improved outcomes. Rulings in

several court cases have specified standards for assessing special education eligibility and

determining how best to educate students with disabilities (e.g., PARC v. Pennsylvania,

Mills v. Board of Education). In response to these decisions, the federal government has

passed a series of laws codifying these decisions, and as such, shaped the role of the

school psychologist (e.g., IDEA). Several agencies have been created in order to

disseminate information on effective instruction, monitor the allocation and use of federal

funding, and collect and organize information detailing student achievement (e.g., NAEP,

OERI, National Assessment Governing Board). Federal legislation has evolved from

providing funding for programs to benefit students from minority or economically

disadvantaged backgrounds, to making such funding contingent on improvements in

student outcomes. Increased government regulation and accountability for student

80

Tab

le 8

Sel

ecte

d F

eder

al

Leg

isla

tion I

mpact

ing S

chool

Psy

cholo

gy

Tit

le a

nd Y

ear

Des

crip

tio

n

Ele

men

tary

and S

econdar

y E

du

cati

on

Act

(E

SE

A;

1965)

pro

vid

ed s

chools

fin

anci

al a

id t

o b

e use

d t

o b

enef

it e

conom

ical

ly d

isad

van

taged

chil

dre

n

Tit

le V

II B

ilin

gual

Educa

tion A

ct

(1967)

T

itle

added

to t

he

ES

EA

to a

ddre

ss e

conom

ic d

isad

van

tage

cause

d b

y l

imit

ed

Engli

sh p

rofi

cien

cy b

y f

undin

g b

ilin

gu

al c

lass

es

Ste

nnis

Am

endm

ent

(1970)

Cre

spin

o (

2006):

A

ppli

ed d

eseg

regat

ion r

equir

emen

ts t

o a

ll s

chools

in t

he

U.S

.

P

reven

ted p

aren

ts f

rom

choosi

ng w

her

e to

sen

d t

hei

r ch

ildre

n i

f doin

g s

o m

eant

that

sch

ools

would

be

raci

ally

im

bal

ance

d

Equal

Educa

tion O

pport

unit

y A

ct

(1974)

A

ffir

med

the

enti

tlem

ent

of

all

studen

ts i

n p

ubli

c sc

hools

to e

qual

educa

tional

opport

unit

y, as

det

erm

ined

by t

he

nei

ghborh

ood i

n w

hic

h t

hey l

ived

S

pec

ifie

d p

roce

du

res

for

dis

man

tlin

g t

he

dual

sch

ool

syst

em

ES

EA

Am

endm

ents

(197

4)

In

crea

sed f

eder

al a

id f

or

com

pen

sato

ry p

rogra

ms

for

studen

ts i

n p

over

ty b

y 2

3%

Bil

ingual

Act

of

1974

S

tew

ner

-Man

zanar

es (

19

88):

T

arget

ed a

id t

o s

chools

wit

h l

arge

num

ber

s of

no

n-E

ngli

sh-s

pea

kin

g s

tuden

ts

Reh

abil

itat

ion A

ct o

f 1973

Ex

tended

the

civil

rig

hts

of

studen

ts w

ith d

isab

ilit

ies

by p

rohib

itin

g t

hei

r

excl

usi

on f

rom

pro

gra

ms

rece

ivin

g f

eder

al f

undin

g

(table

conti

nues

)

80

81

Table

8 c

onti

nued

Tit

le a

nd Y

ear

Des

crip

tio

n

Educa

tion f

or

All

Han

dic

apped

Chil

dre

n

Act

(1975)

Beyer

(1989):

M

andat

ed t

hat

all

chil

dre

n w

ith d

isab

ilit

ies

bet

wee

n t

he

ages

of

5-2

1 b

e

pro

vid

ed w

ith

a f

ree

appro

pri

ate

educa

tion t

hro

ugh t

he

use

of

spec

ial

edu

cati

on a

nd r

elat

ed

serv

ices

tai

lore

d t

o m

eet

thei

r in

div

idual

nee

ds

S

pec

ifie

d d

ue

pro

cess

pro

vis

ions

to p

rote

ct t

he

rights

of

par

ents

and g

uar

dia

ns

and i

ncl

ude

them

in e

duca

tional

dec

isio

n-m

akin

g

P

rovid

ed i

ncr

ease

d f

inan

cial

ass

ista

nce

to S

tate

s an

d l

oca

liti

es t

o f

und

cate

gori

cal

pro

gra

ms

for

chil

dre

n w

ith

dis

abil

itie

s

Dep

artm

ent

of

Edu

cati

on

Org

aniz

atio

n

Act

(1979)

U.S

. D

epar

tmen

t of

Edu

cati

on (

2010):

C

reat

ed a

cab

inet

-lev

el p

osi

tion t

o r

egula

te f

eder

al e

duca

tio

n p

rogra

ms

Educa

tional

Conso

lidat

ion I

mp

rov

emen

t

Act

(1982)

Gra

y, C

aull

ey &

Sm

ith (

1982):

R

educe

d f

eder

al e

duca

tion s

pen

din

g b

y 1

5%

P

rovid

ed e

ach s

tate

wit

h a

fix

ed a

mount

of

money

dep

endin

g o

n t

he

num

ber

of

studen

ts i

n n

eed o

f sp

ecia

l se

rvic

es,

allo

win

g t

hem

to s

pen

d t

hes

e fu

nds

as t

hey

dee

med

app

ropri

ate

Educa

tion o

f th

e H

andic

apped

Act

Am

endm

ents

(1986)

House

Com

mit

tee

on E

duca

tion a

nd L

abo

r (1

986):

R

equir

ed t

hat

sta

tes

pro

vid

e pla

cem

ents

and s

ervic

es f

or

studen

ts w

ith

dis

abil

itie

s ac

cord

ing t

o f

eder

al m

and

ates

usi

ng e

ither

fed

eral

or

stat

e fu

ndin

g

M

andat

ed s

pec

ial

educa

tion s

ervic

es f

or

studen

ts a

ges

3-5

P

rovid

ed f

inan

cial

ince

nti

ves

to s

tate

s pro

vid

ing s

ervic

es f

or

chil

dre

n f

rom

bir

th

to t

hre

e yea

rs o

ld

(table

conti

nues

)

81

82

Tab

le 8

conti

nued

Tit

le a

nd Y

ear

Des

crip

tio

n

Haw

kin

s-S

taff

ord

Sch

ool

Impro

vem

ent

Am

endm

ents

(1988)

House

Com

mit

tee

on E

duca

tion a

nd L

abo

r (1

990

):

In

crea

sed t

he

amount

of

feder

al a

id t

o s

chools

docu

men

ting i

ncr

ease

s in

stu

den

t

achie

vem

ent

usi

ng t

est

score

s or

oth

er a

chie

vem

ent

mea

sure

s

M

andat

ed i

ncr

ease

d r

egu

lati

on f

rom

loca

l dis

tric

ts a

nd s

tate

dep

artm

ents

of

educa

tion f

or

schools

unab

le t

o d

ocu

men

t gai

ns

In

crea

sed f

undin

g f

or

sch

ool-

wid

e re

form

s

Indiv

idual

s w

ith D

isab

ilit

ies

Ed

uca

tion

Act

(1990)

Mer

rell

, E

rvin

, &

Gim

pel

(2006):

R

eauth

ori

zed t

he

ori

gin

al p

rovis

ions

of

EH

A

R

equir

ed t

ransi

tion s

ervic

es f

or

studen

ts w

ith d

isab

ilit

ies

A

dded

auti

sm a

nd t

raum

atic

bra

in i

nju

ry t

o t

he

list

of

feder

al d

isab

ilit

y

condit

ions

for

whic

h s

pec

ial

educa

tion s

ervic

es a

re p

rovid

ed

Sch

ool

of

Publi

c H

ealt

h a

nd H

ealt

h P

rofe

ssio

ns,

Univ

ersi

ty a

t B

uff

alo, (2

005):

D

efin

ed A

ssis

tive

Tec

hn

olo

gy D

evis

es a

nd S

ervic

es t

o b

e in

cluded

in s

tuden

t

IEP

s

E

xte

nded

the

Lea

st R

estr

icti

ve

Envir

onm

ent

clau

se r

equir

ing t

hat

, to

the

max

imum

ex

tent,

stu

den

ts w

ith

dis

abil

itie

s be

educa

ted w

ith t

hei

r non

-dis

able

d

pee

rs

Goal

s 200

0:

The

Educa

te A

mer

ica

Act

(1994)

O

ffer

ed g

rants

to s

chools

for

the

dev

elopm

ent

of

stan

dar

ds

and a

sses

smen

ts

P

rovid

ed f

undin

g t

o s

tate

s in

the

pla

nn

ing o

r im

ple

men

tati

on p

has

es o

f sy

stem

ic

refo

rm b

ased

on a

set

of

nat

ional

educa

tion g

oal

s re

late

d t

o s

tuden

t outc

om

es

and e

duca

tional

ach

iev

emen

t

Impro

vin

g A

mer

ica‘

s S

chools

Act

(1994)

P

rovid

ed f

eder

al f

undin

g c

onti

ngen

t of

a st

ate‘

s ab

ilit

y t

o a

lign c

onte

nt

and

per

form

ance

sta

nd

ards,

inst

ruct

ion, te

stin

g, te

acher

tra

inin

g,

curr

iculu

m, an

d

acco

unta

bil

ity

(table

conti

nues

)

82

83

Table

8 c

onti

nued

Tit

le a

nd Y

ear

Des

crip

tio

n

Req

uir

ed t

hat

eco

nom

ical

ly d

isad

van

taged

stu

den

ts b

e hel

d t

o t

he

sam

e

stan

dar

ds

as t

hei

r p

eers

Indiv

idual

s W

ith D

isab

ilit

ies

Educa

tion

Act

(1997)

Nat

ional

Cen

ter

for

Chil

dre

n a

nd Y

outh

s w

ith D

isab

ilit

ies

(1998):

R

equir

ed t

hat

stu

den

ts w

ith d

isab

ilit

ies

par

tici

pat

e in

sta

te a

nd l

oca

l

asse

ssm

ents

, w

ith a

ccom

modat

ions

if n

eces

sary

S

pec

ifie

d t

hat

sch

ools

use

a v

arie

ty o

f as

sess

men

t to

ols

to d

eter

min

e ed

uca

tional

nee

ds,

whil

e only

gat

her

ing n

ew a

sses

smen

t dat

a w

hen

nec

essa

ry

P

reven

ted s

chools

fro

m c

lass

ifie

d s

tuden

ts a

s hav

ing a

dis

abil

ity b

ased

on

inad

equat

e in

stru

ctio

n o

r li

mit

ed E

ngli

sh p

rofi

cien

cy

S

tres

sed t

he

role

of

par

ents

as

mem

ber

s of

the

IEP

tea

m (

e.g., r

equir

ing t

hei

r

conse

nt

for

eval

uat

ions

and i

nput

and p

arti

cipat

ion i

n e

duca

tional

dec

isio

ns)

M

andat

ed t

hat

IE

Ps

mak

e m

ore

ex

pli

cit

connec

tio

n b

etw

een s

pec

ial

edu

cati

on

and t

he

gen

eral

cu

rric

ulu

m

D

etai

led s

pec

ial

fact

ors

(e.

g., b

ehav

iora

l su

pp

ort

s, l

angu

age

and c

om

munic

atio

n

nee

ds,

ass

isti

ve

tech

nolo

gy)

to b

e co

nsi

der

ed b

y t

eam

s w

riti

ng I

EP

S

R

equir

ed a

unif

orm

num

ber

of

pro

gre

ss r

eport

s fo

r st

uden

ts w

ith a

nd w

ithout

dis

abil

itie

s

In

cluded

new

pro

vis

ions

rela

ted t

o p

lannin

g t

ransi

tion s

ervic

es a

nd t

he

tran

sfer

of

legal

rig

hts

for

studen

ts r

each

ing t

he

age

of

maj

ori

ty

83

84

Tab

le 9

Cou

rt D

ecis

ions

Impact

ing t

he

Pra

ctic

e of

Sch

ool

Psy

cholo

gy

Cas

e an

d Y

ear

Des

crip

tio

n

San

Anto

nio

Ind

epen

den

t S

chool

Dis

tric

t v

. R

odri

guez

(1

973)

Nel

son &

Wei

nbau

m (

20

09):

R

ule

d t

hat

fed

eral

court

s w

ould

uphold

sta

tes‘

sch

ool

fundin

g s

yst

ems

pro

vid

ed

that

sta

te e

duca

tion s

yst

ems

dev

eloped

the

bas

ic s

kil

ls n

eces

sary

fo

r st

uden

ts t

o

par

tici

pat

e in

a d

emocr

atic

soci

ety

M

ade

stat

es r

esponsi

ble

for

cover

ing t

he

cost

of

educa

tional

ser

vic

es m

andat

ed

by f

eder

al l

aws

or

feder

al c

ourt

dec

isio

ns

S

pec

ifie

d t

hat

sch

ool

fun

din

g d

ecis

ions

would

no l

onger

be

han

dle

d b

y f

eder

al

court

s, a

s ed

uca

tional

qu

alit

y a

nd r

esourc

es c

ould

no l

onger

be

consi

der

ed

feder

al r

ights

Pen

nsy

lvan

ia A

ssoci

atio

n f

or

Ret

ard

ed

Chil

dre

n (

PA

RC

) v. P

ennsy

lvan

ia

(1972)

Nel

son &

Wei

nbau

m (

20

09):

R

ule

d t

hat

stu

den

ts w

ith d

isab

ilit

ies

in p

ubli

c sc

hools

must

be

educa

ted i

n t

hei

r

leas

t re

stri

ctiv

e en

vir

onm

ent

E

stab

lish

ed t

he

pre

ceden

t th

at s

tuden

ts w

ith d

isab

ilit

ies

had

to b

e m

ainst

ream

ed

into

reg

ula

r cl

asse

s w

hen

ever

poss

ible

as

a nec

essa

ry c

ondit

ion

for

pro

vid

ing

equal

educa

tional

opport

unit

ies

Mil

ls v

. B

oar

d o

f E

duca

tion (

1972)

Nel

son &

Wei

nbau

m (

20

09):

R

ule

d t

hat

spec

ial

clas

ses

for

studen

ts w

ith d

isab

ilit

ies

wer

e al

low

ed o

nly

if

such

envir

onm

ents

wer

e fo

und

to i

mpro

ve

the

qual

ity o

f ed

uca

tion f

or

studen

t

M

andat

ed t

hat

sch

ools

pro

vid

e ad

equat

e se

rvic

es t

o s

tuden

ts w

ith d

isab

ilit

ies,

in

inte

gra

ted o

r se

par

ate

clas

ses,

acc

ord

ing t

o r

ecom

men

dat

ions

reac

hed

at

regula

r

pla

cem

ent

mee

tin

gs,

reg

ardle

ss o

f th

e fi

nan

cial

cost

to t

he

school

(table

conti

nues

)

84

85

Table

9 c

onti

nued

Cas

e an

d Y

ear

Des

crip

tio

n

Lar

ry P

. v

. R

iles

(1979,

1986)

Jaco

b &

Har

tshorn

e (2

00

7):

E

stab

lish

ed t

he

legal

pre

ceden

t th

at s

tandar

diz

ed t

ests

adm

inis

tere

d t

o c

hil

dre

n

from

div

erse

cult

ura

l bac

kgro

unds

must

hav

e bee

n v

alid

ated

for

this

purp

ose

85

86

Tab

le 1

0

Exp

osi

tory

Rep

ort

s Im

pa

ctin

g t

he

Pra

ctic

e of

Sch

ool

Psy

cholo

gy

Rep

ort

D

escr

ipti

on

Tit

le I

of

the

ES

EA

: Is

it

hel

pin

g p

oor

chil

dre

n?

Was

hin

gto

n R

esea

rch P

roje

ct &

NA

AC

P L

egal

Def

ense

and E

duca

tional

Fund

(1969):

R

eport

ed a

lac

k o

f ev

iden

ce c

onn

ecti

ng T

itle

I f

undin

g a

nd i

ncr

ease

s in

acad

emic

ach

iev

emen

t am

ong s

tuden

ts i

n p

over

ty

H

ighli

ghte

d t

he

mis

use

of

Tit

le I

funds

in s

ever

al s

tate

s w

her

e it

had

bee

n f

ound

that

money

had

bee

n d

isp

roport

ionat

ely a

lloca

ted t

o s

uburb

an d

istr

icts

Am

eric

an I

nst

itute

for

Res

earc

h S

tud

y

(1977)

Nel

son &

Wei

nbau

m (

20

09

):

T

hre

e-yea

r st

ud

y i

ndic

atin

g t

hat

innovat

ive

pro

gra

ms

in s

chools

had

not

pro

duce

d i

mpro

vem

ents

in r

eadin

g,

and w

ere

sho

wn t

o h

ave

had

a n

egat

ive

impac

t on g

row

th i

n a

rith

met

ic s

kil

ls

A N

atio

n a

t R

isk:

The

Imper

ativ

e fo

r

Educa

tional

Ref

orm

Nat

ional

Com

mis

sion o

n E

duca

tion (

1983):

U

sed s

tandar

diz

ed t

est

score

dat

a to

publi

cize

the

wea

k a

cad

emic

per

form

ance

of

Am

eric

an s

tuden

ts

Rec

om

men

ded

a n

atio

nw

ide

syst

em o

f st

and

ardiz

ed t

ests

to m

easu

re e

du

cati

onal

achie

vem

ent

and o

pport

unit

y a

s an

alt

ernat

ive

to f

inan

cial

aid

, sp

ecia

l pro

gra

ms,

or

raci

al d

eseg

regat

ion

M

andat

ed t

hat

sch

ools

dem

onst

rate

incr

ease

s in

tes

t sc

ore

s in

ord

er t

o r

emai

n

elig

ible

fo

r fe

der

al a

id

Tim

e fo

r R

esult

s (1

986)

Ale

xan

der

(1986):

P

ropose

d t

hat

sch

ools

be

rele

ased

fro

m g

over

nm

ent

regula

tions

if t

hey

pro

duce

d

mea

sura

ble

gai

ns

in s

tuden

t ac

hie

vem

ent

L

imit

ed t

he

auto

nom

y o

f sc

hools

fai

ling t

o d

emon

stra

te i

mpro

vem

ent

86

87

Tab

le 1

1

Agen

cies

and I

nit

iati

ves

Impact

ing t

he

Pra

ctic

e of

Sch

ool

Psy

cholo

gy

Agen

cy

Des

crip

tio

n

Nat

ional

Inst

itute

of

Edu

cati

on

U

.S. N

atio

nal

Arc

hiv

es a

nd R

ecord

s A

dm

inis

trat

ion (

2011):

C

reat

ed i

n 1

972

S

erves

as

an a

cco

unta

bil

ity m

ech

anis

m f

or

feder

ally

funded

educa

tion p

rogra

ms

thro

ugh s

tud

y o

f th

e co

nnec

tion b

etw

een f

eder

al d

oll

ars

and a

cadem

ic

achie

vem

ent

Nat

ional

Ass

essm

ent

of

Educa

tional

Pro

gre

ss (

NA

EP

)

U.S

. D

epar

tmen

t of

Edu

cati

on I

nst

itute

of

Educa

tion S

cien

ces

(2011):

E

stab

lish

ed i

n 1

964

N

atio

nal

tes

ting s

yst

em p

rovid

ing d

ata

about

stre

ngth

s, w

eaknes

ses

and c

han

ges

in s

tuden

t ac

hie

vem

ent

Off

ice

of

Educa

tional

Res

earc

h a

nd

Impro

vem

ent

(OE

RI)

Nel

son &

Wei

nbau

m (

20

09):

C

reat

ed i

n 1

979

C

oll

ects

and d

isse

min

ates

res

earc

h o

n e

ffec

tive

teac

hin

g s

trat

egie

s, c

urr

icu

lum

,

and a

dm

inis

trat

ion t

hro

ugh t

he

Edu

cati

onal

Res

ourc

es I

nfo

rmat

ion C

ente

r

(ER

IC)

E

xam

ines

dif

fere

nt

cause

and e

ffec

t re

lati

onsh

ips

among s

chool

var

iable

s

Nat

ional

Ass

essm

ent

Go

ver

nin

g B

oar

d

Nel

son &

Wei

nbau

m (

20

09)

F

orm

ed i

n 1

988

A

dm

inis

ters

and r

egula

tes

stat

e-b

y-s

tate

rep

ort

ing o

f N

AE

P r

esult

s

S

hif

ted t

he

emphas

is o

f fe

der

al e

duca

tion p

oli

cy f

rom

input

var

iable

s to

stu

den

t

outc

om

es b

y c

han

gin

g t

he

inte

rpre

tati

on o

f te

st s

core

s fr

om

sh

ow

ing w

hat

studen

ts w

ere

capab

le o

f to

est

abli

shin

g a

chie

vem

ent

level

s on N

AE

P s

cale

s

show

ing w

hat

stu

den

ts s

hould

be

able

to d

o

87

88

improvement are two factors that continue to impact the field of education in present

times.

Contemporary legislation. Shortly after taking office in 2001, one of George W.

Bush‘s first legislative actions was to propose the No Child Left Behind Act (NCLB;

2002), which would serve as a reauthorization of the ESEA and IASA. The NCLB

represented a continuation of several provisions from its previous versions, including

Title I, the 21st Century Schools Act, bilingual education, Title II grants funding

innovation, and a sizeable reading program, among others. Provided in Table 12 is a

summary of the key provisions of NCLB.

The provisions of NCLB signified the intent and commitment of the federal

government to measure and monitor the educational achievement of all students in

schools. At the same time, teachers and administrators were being held increasingly

responsible for demonstrating student progress and proficiency across a variety of subject

areas. In addition, parents were empowered with information and alternatives for

remediation to ensure that their children were being provided with quality instruction.

After a lengthy series of negotiations, the NCLB Act was passed in 2001. From

the beginning, various education interest groups, such as the National School Boards

Association, the American Association of School Administrators, the National Education

Association, and the National Conference of State Legislatures, have voiced concern over

this bill, citing the claim that school districts would not be able to meet the demands of

this piece of legislation given the limited federal funding it provided. In response to this

concern, the Bush administration maintained that, without federally mandated

accountability and assessment practices outlined in NCLB, states would continue the

89

Table 12

Key Provisions of NCLB

Accountability for Student Outcomes:

provided federal financing to bolster achievement using standards, assessments, and

accountability regulations

mandated that standards and assessments were to be applied to all students

specified accountability measures in the form of corrective actions for schools in need

of improvement

detailed a formula to determine how and when to take corrective action for schools

that failed to meet progress targets, such that:

a) by the year 2014, all students were expected to be performing at a ―proficient‖

level in reading, mathematics, and science

b) each school year, gains must have been made in student ―adequate yearly

progress‖ such that 100% proficiency would be reached by 2014

c) the annual rate of progress would be calculated for aggregated as well as

disaggregated student groups based on income, race, gender, English language

proficiency, and special education classification, with the entire school considered

in need of improvement if any one of these groups were not meeting goals for

expected progress

Adequate Yearly Progress

A school receiving Title I funding that had not met AYP for two consecutive years

was to be referred to as a school in need of improvement:

the school was given the responsibility of writing a plan for improving students‘

educational progress

the local education agency provided the school with technical resources for plan

implementation

students were given the choice of transferring to another school within the district

that was not in need of improvement

If during the following year, the school was still not able to make AYP, it retained the

status of a school in need of improvement:

Students retained the option to transfer

Students from low socioeconomic backgrounds could receive supplemental

educational services, such as tutoring or remedial classes, from either a public or

private state-approved agency

When a school did not make AYP for four consecutive years:

The district enforced corrective actions, such as replacing staff or making

curricular changes

Parents were given the opportunity to send their children to a different school

In the event that AYP was not met for a fifth consecutive year, a restructuring plan

was implemented by the school district:

(table continues)

90

Table 12 continued

The school could reopen as a charter school

Staff members could be replaced

The leadership of the school could be turned over to the state or a private agency

Effective Instruction

stressed the use of effective instructional methods, particularly in reading, by

offering grants to fund research-based instructional programs

required that teachers meet certain training standards, including the completion of

a bachelor‘s degree, demonstration of competency in specific areas of instruction,

and documentation that they had met their state‘s requirements for licensure or

certification

required that paraprofessionals meet certain training standards, including the

completion of two years of college, or demonstration of their ability to support

student learning in reading, writing, and math

mandated that schools make public the certification status and educational

attainment of the teachers and paraprofessionals employed in their buildings

required that schools begin conducting yearly testing in reading, math, and

science for students in grades 3-8 to determine whether or not students were

meeting goals for AYP, with the overall goal that all students would be proficient,

or demonstrating grade-level competency, by the 2013-2014 school year

91

legacy of leaving behind students with disabilities and those from minority or

economically disadvantaged backgrounds. To provide extra funding, NCLB permitted

states to reallocate funding from non-Title I federal programs into their Title I budgets.

Additionally, the State and Local Flexibility Demonstration Act allowed states to redirect

administrative and activity funds from other ESEA programs into supplemental learning

programs that were specifically designed to help students make AYP. Overall, NCLB

increased Title I funding by 20% for schools in urban areas or areas with a high

concentration of students from economically disadvantaged backgrounds.

In order to meet yearly testing requirements, many states needed only to revise the

testing programs they had previously created using Goals 2000 funding to account for the

provision that all students in grades 3-8, rather than certain samples or benchmark grades,

were tested. By 2001, 49 states had written content standards and mandatory tests for

graduation and grade-level promotion. Despite this fact, the quality of proficiency

standards varied from one state to another (Nelson & Weinbaum, 2009). As a result,

variability was also found when determining AYP, resulting in a non-uniform distribution

of schools in need of improvement between and within states.

The most current revision to IDEA occurred in 2004, when the Individuals with

Disabilities Education Improvement Act of 2004 (IDEA 2004; Wright & Wright, 2009)

was signed into law (Merrell, Ervin, & Gimpel, 2006). Jacob and Hartshorne (2007)

provided information related to the passage of this latest revision. To guide their

amendments, Congress cited several notable research findings, which are listed in Table

13. This most current authorization of IDEA placed the onus on schools for providing

students with disabilities with effective early intervention services, evidence-based

92

Table 13

Research Findings Guiding IDEA 2004

The effective education of children with disabilities is achieved with a foundation

based on high achievement standards [20 U.S.C. § 1400 (5)(A)]

Special education is a service and not a place [20 U.S.C. § 1400 (5)(C)], and

therefore students with disabilities should be provided with access to the general

education curriculum in the regular education classroom[20 U.S.C. § 1400 (5)(D)]

Funding should be provided for school-wide practices, evidence-based instruction in

reading, positive behavioral supports, and early intervention services [20 U.S.C. §

1400 (5)(E)]

Data on the increasing diversity of the school-aged population highlights the need for

more effective instruction for students with limited proficiency in English [20 U.S.C.

§ 1400 (10)(A)]

93

instruction, and behavioral supports, driven by high achievement standards. These

provisions have had direct relevance to school psychologists as they work directly and

indirectly not only with students with disabilities, but also at a systems level, to address

the academic and behavioral needs of all of the students in the buildings they serve.

Current status and future directions. To help states continue to meet the

challenge of instruction and assessment under NCLB and improve the educational

outcomes of students in American schools, $40 billion from the American Recovery and

Reinvestment Act was appropriated to state governments to cover the cost of budget

deficits and to fund educational requirements spelled out in Title I and IDEA legislation.

States could also apply for grant money to be awarded contingent on educational reform

efforts. An example of this was the Race to the Top Fund, which is a competitive grant

program awarding financial resources to states that submitted plans to address education

reform goals related to the use of internationally-benchmarked standards and

assessments, the recruitment and retention of quality teachers and principals, the

implementation of data systems to monitor student progress, and the development of

under-performing schools (U.S. Department of Education, 2009). All states have also

been required to facilitate the use of information detailing student achievement to assess

teacher and administrator performance, in addition to doing away with limits to the

number of charter schools allowed in each state (U.S. Department of Education, 2009).

Currently, the Obama administration, along with Arne Duncan, the Secretary of

Education, is drafting legislation for another revision of the ESEA, though no legislation

has yet been passed. Reauthorization efforts are being guided, however, by the following

goals: to improve the effectiveness of teachers and administrators; to give parents the

94

information they need to evaluate school choice and contribute to school effectiveness; to

provide teachers and administrators with information on delivering effective instruction;

to determine and implement standards and assessments that will ensure that American

students are college- and career-ready; and, to provide students in under-performing

schools with support and interventions that will boost their educational achievement

(U.S. Department of Education, Office of Planning, Evaluation and Policy Development,

2010). Despite these goals, at this time, it is unknown whether evidence-based practices

and interventions are being consistently implemented for non-academic purposes.

Section summary. This section has reviewed past and current efforts by various

groups to address educational inequalities experienced by students with disabilities, and

students who come from socially disenfranchised or economically disadvantaged

backgrounds. An important theme that can be drawn from this review is that, over time,

weaknesses in the educational programming provided to different student groups have

been revealed, resulting in the federal government passing laws and regulations holding

school professionals accountable for demonstrating that these specific weaknesses have

been addressed. In contemporary times, more proactive measures have been

implemented, as school professionals are being held accountable for providing students

with quality educational experiences. Consequences are enforced when schools are

unable to demonstrate regular growth in student achievement. As an education

professional, school psychologists are now responsible for meeting the needs of a range

of students, as they address the educational and behavioral needs of the entire student

body through direct and indirect prevention and intervention efforts. Although current

legislation is still being drafted, it appears as though mandates for accountability, through

95

the implementation of standards, and regular monitoring and assessment of student

abilities and behaviors, will continue to impact the way schools are run, and the specific

practices of school psychologists.

Survey Research

Surveys are one traditional method that researchers have used to gather data about

specific phenomena (Dillman, 2007). Over time, improvements have been made to the

survey approach, including purposeful sampling methods, improvements in questionnaire

design, and computerized analysis of data (Evans & Mathur, 2005). Recent

improvements to survey methodology have been spurred on by advances in technology,

such that e-mail, web-based, and internet surveys have become a popular method of

survey implementation (Dillman, 2007; Heun, 2001; Jackson, 2003). This section will

discuss the assets and limitations associated with internet surveys. The conclusion of this

section includes guidelines for the creation of internet surveys provided by Dillman

(2007) in line with the Tailored Design Method.

Researchers have enumerated on a number of benefits associated with internet

survey research. When compared to paper surveys, internet surveys offer flexibility,

convenience related to design and administration, and allow for administrative savings

associated with reduced need for paper, postage, mail-out, and data entry resources

(Couper, Kapteyn, Schonlau, & Winter, 2007; Dillman, 2007; Evans & Mathur, 2005;

Granello & Wheaton, 2004; Sax, Gilmartin, & Bryant, 2003). Researchers using internet

survey methods have potential access to an international sampling pool that might not

have been possible or as accessible using mail surveys (Dillman, 2007), as the number of

people using the internet continues to increase (Evans & Mathur, 2005; Pealer & Weiler,

96

2003). Internet surveys may also provide researchers with access to participants who

oppose providing identifying or personal information, and the perceived anonymity

associated with internet surveys may yield more accurate responses (Granello &

Wheaton, 2004) as some participants consider this survey method to be more secure

compared to mail or telephone surveys (Granello & Wheaton, 2004; Van Selm &

Jankowski, 2006).

In many cases, a reduction in survey implementation and response time for

internet surveys has been noted (Dillman, 2007; Evans & Mathur, 2005; Granello &

Wheaton, 2004), as researchers are not encumbered by the time it takes for participants to

mail in their responses (Vaux & Briggs, 2006). Less time is also required when sending

follow-up requests for participation, which may result in higher response rates within a

specified timeframe for conducting research (Evans & Mathur, 2005). Online surveys

can also be designed to require participants to provide responses for all items before

submitting their surveys (Evans & Mathur, 2005). The survey designer can program a

skip pattern, preventing respondents from having to respond to irrelevant items (Dillman,

2007). In addition, the designer can program pop-up instructions for immediate

assistance rather than referring the respondent to a separate set of instructions removed

from the actual item (Dillman, 2007). Drop-down boxes containing lengthy lists of

possible responses can be used to make coding of answers easier for the researcher

(Dillman, 2007). Responses can be used to screen respondents (Alreck & Settle, 2004;

Dillman 2007) and automatically direct them to the next most relevant set of items

(Dillman, 2007).

Some respondents perceive internet surveys to be more interesting when they are

97

designed to be interactive, as a variety of pictures, animation, and audio and video clips

can be incorporated into an electronic survey (Dillman, 2007; Sax et al., 2003). Internet

survey questions can also be tailored to change dependent on participant response (Evans

& Mathur, 2005). Furthermore, internet surveys can also be completed during the

respondent‘s leisure time, whereas this is not always the case when researchers utilize

telephone survey methods (Sax et al., 2003).

Before deciding to use surveys as a method of data collection, there are several

limitations that researchers should consider. Once the survey has been designed and

administered, it cannot be changed or altered throughout the data collection process. In

addition, at the conclusion of data collection, researchers may discover that the sample

they created and surveyed did not match the population of interest (Mangione, 1995).

Survey questions must be general enough to facilitate comprehension by a large number

of respondents, and as a result, may omit questions of interest to the researcher and

certain respondents (Barribeau et al., 2005). Self-administered surveys also do not take

into account idiosyncrasies in context associated with each respondent, and cannot

always account for how this will impact the accuracy of their responses in reference to

the intentions of the researcher (Barribeau et al., 2005). Furthermore, respondents may

have difficulty aligning their views and experiences with the dichotomies or scales

presented to them as answer choices on surveys, which is a potential threat to validity

(Barribeau et al., 2005). Additional concerns related to self-administered surveys include

bias in the responding sample (i.e., purposefully falsifying responses, picking the

response that immediately comes to mind as opposed to the most accurate response, or

selecting the same response without considering the question), nonresponse to certain

98

items, and misinterpretation of survey content or questions by survey respondents

(Mangione, 1995).

Despite the many benefits that are associated with internet surveys, this

methodology also has some weaknesses that researchers need to be aware of when

considering using them. Although researchers may have access to a larger population,

current response rates to internet surveys are poor (Dillman, 2007; Evans & Mathur,

2005), and are generally no better than postal mail survey response rates (Sax et al.,

2003). Researchers cannot assume that people have had previous experiences with

electronic surveys the way they may have had with mail and telephone surveys (Dillman,

2007). Furthermore, it is important to note that computer skills are not uniformly

developed throughout the population (Dillman, 2007; Evans & Mathur, 2005), and that

even within households, skill and comfort with the internet vary (Dillman, 2007).

Respondents may be reluctant to put their computers at risk for some form of computer

virus by clicking on a survey invitation link, and may also hesitate to respond to a survey

due to concerns that doing so will expose them to an individual or group who will

persistently send them junk email (Dillman, 2007; Evans & Mathur, 2005; Sax et al.,

2003). The level of technical sophistication that can be employed when designing an

electronic survey can also increase the chances of survey error or incompletion (Dillman,

2007), and can potentially make it difficult for respondents to view and respond to

surveys due to issues with screen configuration and differences in computer operating

systems (Dillman, 2007; Evans & Mathur, 2005; Granello & Wheaton, 2004). Currently,

internet service provision is a private industry, and as such, access to respondents is not

assumed the way it previously was with telephone surveys (Dillman, 2007). Many

99

professional organizations will not allow researchers to survey their membership without

obtaining some form of permission or initiating some form of relationship (Dillman,

2007).

Recent data gathered by the Nielsen Company (2008) estimated that 80% of

homes across the United States contain a computer (desktop or laptop), and among those,

91.6% have some form of internet access. Despite these findings, however, the

distribution of internet access is not uniform (Nielsen Company, 2008), may be limited

within various areas, cultures, and countries (Van Selm & Jankowski, 2006), and appears

to vary depending on a variety of factors. For example, internet access is correlated with

education level and the combined annual income of a given household, such that

increases in these factors also increased the likelihood of internet access (Chesley, 2006;

Nielsen Company, 2008; Redpath et al., 2006). Across the country, internet access is

lowest among Hispanic and African-American households (Sax et al., 2003), as well as

those in which the head of the household has not completed high school (Chesley, 2006;

Nielsen Company, 2008). Within the Southeast Central region of the United States,

encompassing Alabama, Mississippi, Tennessee, and Kentucky, the highest number of

households without internet access can be found (Nielsen Company, 2008). In contrast,

larger cities, such as Washington, DC, Norfolk, Salt Lake City, Boston, and Portland,

contain the highest percentage of homes with internet access (Nielsen Company, 2008).

Given this information, researchers are cautioned that, although electronic survey

methods provide less expensive access to a larger portion of the population, and more

households report computer and internet access, it is inaccurate to assume that a

nationally-representative section of the population has been sampled without collecting

100

and analyzing demographic data (Andrews et al., 2003; Vaux & Briggs, 2006) to avoid

sampling bias and error (Alreck & Settle, 2004).

When considering the use of survey research to gather information, researchers

are advised to study the population of interest in order to determine whether this

population has uniform internet access and has a history of using internet surveys

(Dillman, 2007). It is more likely that researchers will obtain acceptable response rates,

if their survey population has regular access to the internet and e-mail (Granello &

Wheaton, 2004; Pealer & Weiler, 2003). The potential for sample bias may be reduced

when, in their sampling practices, researchers account for differences in internet access

and use among different groups in the general population (Andrews et al., 2003; Vaux &

Briggs, 2006), and decide whether an internet based survey is the most appropriate

format for gathering information (Pealer & Weiler, 2003). If the use of an internet survey

is considered appropriate, researchers are advised to design internet surveys in such a

manner that they can be clearly read and interpreted (Dillman, 2007). In addition, it is

recommended that, before formal survey implementation, researchers pilot their surveys

with a small subset of the sample population to examine brevity, reliability, and validity

(Andrews et al., 2003; Granello & Wheaton, 2004).

Application of the Tailored Design Method (TDM; Dillman, 2007) when creating

internet surveys is one way that researchers can ensure that the questionnaires used in

their research can be clearly read and interpreted by respondents. The development of

TDM has occurred over time, guided by social exchange theory, and revised by research

results exploring specific aspects of the survey process and their effect on the quality and

quantity of responses (Dillman, 2007). Contemporary research on TDM has focused on

101

ways that the design and layout of internet surveys impact respondents. As a summary of

this research, Table 14 provides a list of guidelines from the TDM that researchers can

use to design surveys with minimal error to achieve accuracy and high response rates.

Although internet surveys are a popular and efficient method of collecting

information on a population of interest (Dillman, 2007; Heun, 2001; Jackson, 2003),

researchers are advised to consider several limitations and factors associated with this

survey method before implementing it (Dillman, 2007). For example, because internet

familiarity, use, and access are not uniform across the country (Dillman, 2007; Evans &

Mathur, 2005), researchers are cautioned to examine demographic characteristics related

to the population of interest to determine whether an internet survey is the most efficient

way to collect data (Andrews et al., 2003; Vaux & Briggs, 2006). When designing

internet surveys, attention should be paid to the use of words and symbols in order to

facilitate ease and accuracy of responses by participants (Dillman, 2007). Piloting the

survey with a small group of the target population is one way that researchers can obtain

valuable feedback related to the effectiveness of their surveys before engaging in large-

scale implementation (Andrews et al., 2003; Granello & Wheaton, 2004).

Chapter Summary

One common theme throughout the topics discussed in this chapter is the

evolution of educational practices in response to the needs of students in American public

schools. The position of the school psychologist emerged in response to the rapid

expansion of the school population in the early 1900s, and continued to evolve over time

as methods of problem-solving, training, and the daily responsibilities of the school

psychologist developed and changed to provide an optimal learning environment.

102

Table 14

Guidelines and Considerations for Creating Internet Surveys Following the TDM

Framework

1) Respondents were more likely to endorse items presented in a forced-choice as

opposed to a check-all format

2) For scalar questions with answer choices presented in a drop down box, display

all answer choices in a drop down box without requiring scrolling.

3) The use symbols can increase response rate by cueing respondents to attend to

specific elements of questions and response choices without adding words

(Christian & Dillman, 2004).

4) When designing items with specific instructions that deviate from the general

directions, it is recommended that those instructions be placed after the question

but before the answer choices.

5) Numbering items can prevent respondents from making response errors. Some

respondents consider unnumbered items at the beginning of a survey to be

practice items, and as such, do not always answer them (Dillman & Redline,

2004). Skip patterns can be programmed so respondents answer only relevant

items, and in these cases, numbers can cause confusion. In place of numbers,

some survey designers will signify items using symbols, such as a question mark

or asterisk. This may be confusing to respondents, as the omission of a culturally

understood guideline may require extra attention to figuring out the use and

meaning of the symbol. Survey designers are cautioned to evaluate the use of

numbers in survey construction and navigation, as they are a culturally understood

and reliable method of survey navigation.

6) Cultural expectations prompt respondents to assume that the most positive

categories will be placed at the top of vertical scales and to the left on horizontal

scales, while the most negative categories will be at the bottom or to the right

(Tourangeau, Couper, & Conrad, 2004). Respondents also tend to assume that the

middle option will represent the average or typical value (Tourangeau, Couper, &

Conrad, 2004).

7) Responding to scalar questions is more difficult when graphics or verbal

descriptions of the scale are taken out of the respondent‘s visual display, requiring

the respondent to refer back to the question stem, and then to the response area.

8) Provide instructions that facilitate accurate responding the first time the

respondent attempts an item, as an error message containing corrective feedback

can cause the respondent to experience frustration, and discontinue responding.

9) To enable respondents to provide answers in the correct format without receiving

and error message, designers may need to provide multiple visual cues (e.g.,

appropriate answer space size, use of symbols instead of words, numbers clearly

associated with symbols).

103

Research and best practices in counseling now focus on helping school psychologists

deliver counseling interventions that are evidence-based (Silverman & Hinshaw, 2008),

using the problem-solving model (Upah, 2008). The literature on student mental health

cites not only the connection between emotional well-being and academic success

(Haertel et al., 1983; Wang et al., 1990), but also the importance of the problem-solving

model and data-based decision making when designing and implementing counseling

interventions (Miltenberger, 2005; Tilly & Flugum, 1995; Upah, 2008).

Federal education legislation has also evolved to meet the needs of students over

time, such as disparities in the quality of education as a result of economic disadvantage

(e.g., ESEA) or special needs (e.g., IDEA). Currently, however, it is no longer enough

for those working with children to strive to meet the needs of students, as those working

in both the public and private sector are being held increasingly more responsible for

using evidence-based practices and documenting accountability for their actions (Kazdin,

2008). Although, at this time, federal legislation holds school professionals accountable

for students‘ educational outcomes (e.g., NCLB), in time such standards of accountability

may also explicitly apply to school psychologists as they provide counseling

interventions to improve students‘ social-emotional and behavioral outcomes. Research

exists documenting evidence-based interventions, the problem-solving model, and data-

based decision making practices for counseling and behavioral interventions that may

also provide measures of accountability for these interventions. At this time, however,

the extent to which school psychologists use evidence-based interventions, the problem-

solving model, and data-based decision making in their counseling interventions is

unknown.

104

Research Questions

This study set out to survey practicing school psychologists in an attempt to

identify (a) their general counseling practices, (b) their use of best practices related to the

problem-solving model, and (c) demographic variables that might impact their counseling

practices (e.g., training, years of experience, school size, other roles and responsibilities).

General counseling practices of school psychologists. The following questions

were designed to gather information related to the current counseling practices of school

psychologists.

1) What percentage of school psychologists provide group and/or individual

counseling? Are school psychologists counseling general or special education

students, or both?

2) Approximately how many students do school psychologists recommend

declassifying from counseling each year, and what reasons are most commonly

cited when making this recommendation?

3) Are there any demographic differences (e.g., training, years of experience, other

roles and responsibilities) related to school psychologists‘ group and individual

counseling practices?

4) What type of training and professional development have school psychologists

received related to planning and implementing counseling as a direct social

emotional behavioral intervention?

School psychologists’ use of best practices related to the problem-solving

model. The following questions were developed to determine which aspects of the

problem-solving model are most often employed in the design and implementation of

105

counseling interventions. These questions also explored whether demographic variables

were related to the use of these practices.

5) Which aspects of the general problem-solving model (i.e., behavioral definition,

baseline data, problem validation, problem analysis, goal-setting, intervention

plan development, measurement strategy, decision-making plan, progress

monitoring, formative evaluation, treatment integrity, and summative evaluation)

are most commonly used when designing and implementing counseling as a direct

intervention?

6) How frequently are specific components of selected steps of the problem-solving

model (i.e., behavioral definition, baseline data, behavioral goal, intervention plan

development, measurement strategy, decision-making plan, progress monitoring,

formative evaluation, treatment integrity, and summative evaluation)

implemented by school psychologists as they design and implement counseling as

a direct intervention?

7) Are there any demographic differences (e.g., training, years of experience, other

roles and responsibilities) between school psychologists in terms of their use of

the general steps of the problem-solving model when designing and implementing

counseling as a direct intervention?

106

CHAPTER 3: Methodology

Overview

The purpose of this chapter is to describe the methods and procedures that were

used in the current study. A description of the participants and instrumentation that were

used to gather information regarding the counseling practices of school psychologists is

provided.

Participants

Participants for the current study were selected from a random sample of school

psychologists who were listed in the registry of Nationally Certified School Psychologists

(NCSPs) on the National Association of School Psychologists (NASP) website

(www.nasponline.org). The original survey population was composed of 1,000 school

psychologists currently employed in public schools. A second sample of 500 school

psychologists was utilized, and gathered using the same method as the first sample.

Instrumentation

Several instruments were used in this study to explain to potential participants the

purpose and importance of this study and to gather information related to specific

counseling practices employed by school psychologists currently practicing in school

settings. Specific information describing each instrument is described in this section.

Survey. The survey employed in this study was modeled on a previous survey of

school psychology counseling practices (Yates, 2003), and was written using Psychdata

(www.psychdata.com), incorporating guidelines provided Dillman (2007) according to

the Tailored Design Method (TDM; see Appendix A). The use of TDM was meant to

maximize response rates while at the same time minimizing survey error.

107

Survey items were phrased as specific statements about the provision of

counseling services. The questionnaire consisted of a variety of closed and partially

close-ended questions. The responses were designed to gather information about: a) the

specific practices used by school psychologists in their school counseling practice,

specifically in reference to the use of the problem-solving model and data-based decision

making; b) demographic variables that may potentially impact the percentage of time that

school psychologists spend on counseling; and, c) training that school psychologists have

received that would impact their school counseling practice.

Coverletter email. Prospective respondents received an email which identified

the researcher and affiliated institution (Appendix B). In addition, this email explained

the purpose and significance of the study, and concluded with a link that respondents

could click in order to access the survey. The coverletter informed respondents that their

participation was voluntary, and that they were free to withdraw from the study at any

time without penalty. Furthermore, respondents were made aware that specific

demographic information would not be collected, and that their responses would be

anonymous. This email also identified individuals who could be contacted in the event

that the respondent had any questions about the survey. Respondents were also informed

that, at the end of the survey, they would be invited to click another link where they could

provide contact information for the purposes of entering a raffle for one of two $50 gift

certificates.

Follow-up email. As planned, after two weeks of data collection, the survey

response rate was evaluated and found to be low, and a follow-up email was sent out to

prospective respondents. This email thanked those who had already responded, and

108

solicited the participation of those who had not (Appendix C). This email contained the

same information as the original coverletter email, in terms of identifying the researcher

and affiliated institution, and informing participants of the purpose, significance,

anonymity, and procedural safeguards associated with the study.

Procedure

Prior to sending coverletter emails inviting potential respondents to complete the

survey, five school psychologists from the Albany, New York area previewed the

instruments. These school psychologists were asked to complete the survey and provide

feedback related to the instruments‘ readability, structure, and clarity (Appendix D).

Based on this feedback, survey items were evaluated for content, design, and length.

All five school psychologists who were contacted participated in the pilot study.

Analysis of the feedback that was provided revealed that survey completion time ranged

from 10 to 20 minutes. The majority of the pilot subjects indicated that the questions and

terms used were clear and easy to understand. None of the questions was deemed

unnecessary or irrelevant. Provided in Table 15 is a summary of respondent comments

and suggestions that were considered in revisions to the survey before its final

implementation. Overall, the results of the pilot study were positive and supported the

decision to move forward with the study.

This study was then conducted via an online survey. The coverletter email was

sent to a random sample of school psychologists selected from the NASP NCSP

directory. This coverletter included a link to the survey created using PsychData, which

is a secure website that allows for the collection of research data while preserving

respondent anonymity. In order to maintain anonymity, specific identifying information,

109

Table 15

Summary of Feedback From Pilot Subjects

Comment/Suggestion

Changed

Yes/No

Rationale/Outcome

Differentiate between school

psychologists who provide crisis

based or more long-term counseling

No

Subjects could specify their

counseling practices on Item 4, and

at the end of the survey

Provide option for reporting the

number of students discontinued

from counseling in relation to the

number of students seen for

counseling each year

No

The relationship between students

counseled and students seen each

year was not considered pertinent to

the research questions guiding this

study

Change presentation of

psychologist:student (Item 9) as this

might be confusing

No

The design of this question is

patterned off of similar questions in

the research literature (e.g., Bramlett

et al., 2002; Fagan, 1988, 2008)

Items requesting the frequency of

certain counseling practices were

written such that respondents could

select more than one response

Yes

Questions revised to restrict

responses to a single frequency for

each practice

Add an option for collecting

―0‖progress monitoring data points

to Item 68

No

Survey is designed so that

respondents who report not

collecting any progress monitoring

data points on Item 61 are not

directed to Item 68

Adjust length of survey as

respondents may not complete all

items

No

Survey is tailored to individual

counseling practices of each

respondent so that respondents only

see specific items they deemed

relevant to their experience; length is

necessary to answer research

questions; no items rated as

unnecessary; completion time was

not indicated to be unreasonable

110

such as respondents‘ names, was not requested. At the end of the survey, all respondents

were invited to follow a second link to enter a raffle for one of two $50 gift certificates as

compensation for their participation. Illustrated in Table 16 are the mailing and response

data.

Initially, survey invitations were sent to 1,000 NCSPs. Due to a low response

rate, a second sample of 500 NCSPs was gathered and sent an email invitation to

complete the survey. Both the original sample and the second sample received initial and

reminder email invitations. In an attempt to increase the response rate, the initial sample

received a second reminder email. Each mailing had a certain number of emails that

were returned to the sender as undeliverable. Additionally, some of those contacted

indicated that they (a) were retired, (b) did not provide counseling, and/or (c) did not

provide psychological services in a school and therefore did not complete the survey.

The total number of emails sent (n=1,500) was adjusted for the emails that were returned

(n=171) and the replies declining participation in the survey for one of the reasons listed

earlier in this paragraph (n=65). Dividing the total number of responses (n=283) by the

adjusted number of valid emails (n=1264) yielded a total response rate of 22.39%.

111

Tab

le 1

6

Mail

ing a

nd R

esponse

Data

Mai

ling (

Dat

e

Sen

t)

n S

ent

Ret

urn

ed

Undel

iver

able

Ret

ired

Do N

ot

Pro

vid

e

Counse

ling

Not

Em

plo

yed

in a

Sch

ool

Com

ple

ted

Surv

eys

Ret

urn

Rat

e

Init

ial

Sam

ple

(8/2

911)

1,0

00

135

6

10

1

97

11.4

3%

Init

ial

Sam

ple

Rem

inder

Em

ail

(9/1

2/1

1)

1,0

00

8

8

5

7

64

6.5

8%

Sec

ond S

ample

(9/1

9/1

1)

500

15

2

0

5

50

10.4

6%

Sec

ond S

ample

Rem

inder

Em

ail

(10/3

/11)

500

2

4

1

3

18

3.6

7%

Init

ial

Sam

ple

Rem

inder

Em

ail

(10/1

6/1

1)

1,0

00

11

5

7

1

54

5.5

3%

Tota

l 1,5

00

171

25

23

17

283

22.3

9%

111

112

CHAPTER 4: Results

Overview

This study was conducted to gain information about the counseling practices of

school psychologists. This chapter summarizes the results of the survey data gathered in

response to the research questions presented in Chapter 2. A discussion of the respondent

characteristics and demographic data follows, along with the quantitative and qualitative

results of the survey.

Data Analysis Plan

Several methods of data analysis were used to address the research questions

posed in this study. Descriptive statistics (e.g., frequencies, percentages, and averages)

were calculated to report on the general counseling practices of school psychologists, and

their use of the problem-solving model and accountability. A series of chi-square

analyses were conducted to determine the presence of relationships between counseling

practices and use of the problem-solving model and accountability and the demographic

information describing the school psychologists in this sample. A table displaying the

specific data analysis procedure used for each survey item can be found in Appendix E.

Respondent Characteristics/Demographic Data

As discussed in Chapter 3, an online survey was created using Psychdata and

emailed to a national sample of 1,500 randomly selected Nationally Certified School

Psychologists (NCSPs). A total of 283 survey responses were analyzed, representing a

response rate of 22.39%. Demographic characteristics of the respondents are

summarized in Table 17. It should be noted that not all participants completed all of the

demographic information items, and that the rate of non-response is listed next to each

113

Table 17

Demographic Characteristics of Respondents

Variable (% of Non-Response) n %

Graduate Degrees Received

MA/MS 43 15.4

Certificate/Specialist 172 61.6

PhD/PsyD/EdD 64 22.9

Graduate Program Accreditation

NASP 247 87.3

APA 78 27.6

NCATE 50 17.7

Your State 75 61.8

Not Accredited 1 00.4

Course Topics Included in Graduate Coursework

Academic Interventions 247 87.3

Behavioral Interventions 256 90.5

Counseling and Psychotherapy With Children 244 86.2

Counseling Children With Developmental

Disabilities

127

44.9

Group Counseling 212 74.9

Multicultural Counseling 169 59.7

Years Since Last Degree Earned

0-5 125 45.0

6-10 62 22.3

>10 91 32.7

Years of Employment in a School Setting (1.1%)

0-5 116 41.4

6-10 54 19.3

>10 110 39.3

Grade Levels Served By School Psychologists

Elementary School Students 220 77.7

Middle/Junior High School Students 136 48.1

High School Students 123 43.5

Types of Schools Served (2.5%)

Rural 67 24.3

Suburban 106 38.4

Urban 50 18.1

Mixed 43 15.6

Other (e.g., reservation, department of defense

school, small city)

10

03.6

(table continues)

114

Table 17 continued

Variable (% of Non-Response) n %

Psychologist to Student Ratio (3.2%)

1:<500 33 12.0

1:500-999 77 28.1

1:1000-1499 81 29.6

1:1500-2000 47 17.2

1:>2000 36 13.1

Region of Employment (1.8%)

Northeast 70 25.2

Midwest 60 21.6

South 75 27.0

West 73 26.3

Themes of Professional Development Attended Over the

Past Five Years

No Child Left Behind 61 21.6

Academic and Behavioral Accountability 162 57.2

Provision of Counseling 104 36.7

Evidence-Based Behavioral Interventions 226 79.9

Data-Based Decision Making 195 68.9

Response to Intervention 245 86.6

Other (e.g., Autism, Crisis Response, Positive

Behavior Support, Ethics, Legal Mandates)

49

17.3

115

variable displayed. Percentages were calculated from the total number of participants

that provided demographic data on each item, and not from the overall number of

participants. The majority of respondents held certificate or specialist degrees (61.6%)

from NASP-approved (87.3%) and state-accredited (61.8%) graduate programs. Their

coursework included training related to academic (87.3%) and behavioral (90.5%)

interventions, counseling and psychotherapy with children (86.2%), and group counseling

(74.9%). Over half of the respondents (59.7%) also studied multicultural counseling as

part of their training. Almost half of the respondents (45%) completed their graduate

training within the last 5 years. Nearly equal numbers of respondents indicated that they

had been employed in a school setting for 5 years or less (41.4%) or had more than 10

years of experience (32.7%). Typical school settings were suburban (38.4%) elementary

(77.7%) schools, at a psychologist-to-student ratio of 1:500-999 (28.1%) or 1:1,000-1499

(29.6%). Respondents were employed in similar numbers across the different regions of

the United States (responses range from 21.6% to 27% depending on region). Within the

past five years, many had attended professional development related to Response to

Intervention (86.6%), Evidence-Based Behavioral Interventions (79.9%), Data-Based

Decision Making (68.9%), and Academic and Behavioral Accountability (57.2%). As

displayed in Table 18, in terms of time allocation, 42.2% of respondents spent between

25-50% of their time on assessment, and devoted 25% of their time or less to direct

interventions (80%), consultation and indirect services (70.9%), research (100%),

administration (96.4%), or systems-level activities (97.1%).

General Counseling Practices

In addition to demographic data, respondents were asked to provide general

116

Tab

le 1

8

Est

imate

s of

Tim

e A

lloca

tion

% T

ime

All

oca

tion (

n)

Act

ivit

y

M

(SD

) 0-2

5

26-5

0

5

1-7

5

76-1

00

Ass

essm

ent

42.6

7 (

23.0

0)

28.4

(7

8)

42.2

(116)

20.7

(57

)

8.7

(24)

Dir

ect

Inte

rven

tions

16.4

(1

4.6

7)

80.0

(220)

18.2

(5

0)

1.8

(5

)

0.0

(0

)

Con

sult

atio

n a

nd

Indir

ect

Ser

vic

es

22.2

1 (

14.0

7)

70.9

(1

95)

26.5

(7

3)

1.8

(5

)

0.7

(2

)

Res

earc

h

2.1

9

(9.7

2)

100.0

(275)

0

.0

(0)

0.0

(0

)

0.0

(0

)

Adm

inis

trat

ion

5.9

3

(9.7

2)

6.4

(2

65)

3.3

(9

)

0.0

(0

)

0.4

(1

)

Syst

ems-

level

Act

ivit

ies

7.4

1 (

10.1

4)

97.1

(267)

2.2

(6

)

0.4

(1

)

0.4

(1

)

Oth

er (

e.g.,

pap

erw

ork

)

3.5

5

(8.0

0)

98.2

(270)

1.1

(3

)

0.7

(2

)

0.0

(0

)

Note

: 2.8

% n

on-r

esponse

rat

e fo

r ea

ch a

ctiv

ity w

ithin

this

ite

m

116

117

information related to their counseling practices. As shown in Table 19, the majority of

respondents (54.8%) provide group and individual counseling to both general and

special education students (73.5%). Some variation was noted in the average number of

students recommended for discontinuation from counseling each year, with respondents

recommending discontinuation at low (e.g., 0 [15.1%], 1 and 3 [13.8%], 2 [24.6%]

students) and high frequencies (e.g., >7 [12.5%] students). The most common reason for

recommending discontinuation from counseling was that counseling goals were met

(55.9%). Nearly half of the sample (44.4%) indicated using print or online resources

when writing behavioral goals, clarifying the problem, or when determining behavioral

expectations.

Comparison of Demographic Variables and General Counseling Practices

In addition to exploring specific counseling and discontinuation practices of

current school psychologists, one of the goals of this survey was to determine whether

group and individual counseling practices varied by demographic characteristics (e.g.,

training, years of experience, other roles and responsibilities). To determine this, four

multinomial logistic regression models were created to ascertain whether respondents‘

graduate degree (training), years of experience, and time spent counseling could predict

the type of counseling they engaged in, the students they served, the number of students

they discontinued each year, and their reasons for discontinuation. For the purposes of

analysis, the variable ―graduate degree‖ was re-categorized from three to two categories,

such that the first category included respondents with a MA/MS or Certificate/Specialist

degree, and the second category included respondents with doctoral-level training (PhD,

PsyD, EdD). The number of students discontinued from counseling each year was also

118

Table 19

Counseling Practices of School Psychologists

Variable (% Non-Response) n %

Type of Counseling Provided (14.5%)

Group Counseling Only 19 07.9

Individual Counseling Only 68 28.1

Group and Individual Counseling 155 54.8

Children Served in Counseling (14.5%)

Special Education Students 62 25.6

General Education Students 3 01.2

Special and General Education Students 177 73.5

Number of Students Recommended for Discontinuing

Counseling Each Year (18%)

0 35 15.1

1 32 13.8

2 57 24.6

3 32 13.8

4 21 09.1

5 22 09.5

6 2 00.9

7 2 00.9

>7 29 12.5

Reasons for Discontinuing Counseling Services

Counseling Goals Have Been Met 127 55.9

No Positive Effect on Behavior 24 10.6

Student Leaves School/District 26 11.5

Parent Preference 5 02.2

Other (e.g., school policy, outside referral, student

need)

45

19.8

Use of Print/Online Resources for Writing Goals, Problem

Clarification or Determining Behavioral Expectations (12.4%)

Use Print or Online Resources 110 44.4

Do Not Use Print or Online Resources 138 55.6

119

collapsed from 8 to 4 groups (e.g., group 1: 0 students; group 2: 1, 2, or 3 students; group

3: 4, 5, or 6 students; group 4: 7, or >7 students) to ensure adequate cell counts. The

variable measuring time spent on direct interventions was also renamed ―Time Spent

Counseling,‖ and recoded into three groups (low counseling = 0-9% of time spent

counseling; medium counseling = 10-24% of time spent counseling; high counseling =

25-100% of time spent counseling).

Results of these regression analyses can be found in Tables 20, 21, 22, and 23.

Although the overall predictive utility of these demographic variables was unimpressive

(as indicated by R2 values close to 0), they offered some predictive improvement over the

null model when comparing demographic variables with the type of counseling

administered, and the number of students discontinued from counseling each year (as

shown by significance levels for the Final model <0.05). Furthermore, in four instances,

predictor variables met or approached a level of significance in the final model, and as

such, chi-square analysis was used to ascertain the presence of any relationships between

each predictor variable and counseling outcomes.

Over the course of data analysis, a total of 10 chi-square analyses were conducted.

To reduce the likelihood of Type I error caused by repeated analyses, the Sidak (1967,

1968) correction was applied, adjusting the level of significance from 0.05 to 0.005.

Over the four chi-square analyses that explored relationships between demographic

variables and counseling practices, two reached the adjusted level of significance, and are

displayed in Tables 24 and 25 (results of the two non-significant chi-squares can be found

in Appendix F). A relationship was found between counseling type and years of

experience, such that respondents with more than 10 years of experience were more

120

Table 20

Multinomial Regression Predicting Type of Counseling from Graduate Degree, Years of

Experience, and Time Spent Counseling

Model/Effect

-2 Log

Likelihood

χ2

df

Sig

Intercept 122.991

Final 96.701 26.29 10 .003

Graduate Degree 101.099 4.40 2 .111

Years of Experience 110.014 13.31 4 .010

Time Spent Counseling 104.223 7.52 4 .111

Note: R2=.105 (Cox and Snell), .129 (Nagelkerke), .066 (McFadden)

Table 21

Multinomial Regression Predicting Students Served in Counseling from Graduate

Degree, Years of Experience, and Time Spent Counseling

Model/Effect

-2 Log

Likelihood

χ2

df

Sig

Intercept 76.18

Final 62.98 13.20 10 .213

Graduate Degree 63.07 0.09 2 .956

Years of Experience 73.82 10.84 4 .028

Time Spent Counseling 65.57 2.59 4 .629

Note: R2= .054 (Cox and Snell), .075 (Nagelkerke), .044 (McFadden)

Table 22

Multinomial Regression Predicting the Number of Students Discontinued from

Counseling from Graduate Degree, Years of Experience, and Time Spent Counseling

Model/Effect

-2 Log

Likelihood

χ2

df

Sig

Intercept 180.84

Final 131.42 49.42 15 .000

Graduate Degree 135.87 4.46 3 .216

Years of Experience 158.64 27.22 6 .000

Time Spent Counseling 143.85 12.43 6 .053

Note: R2= .195 (Cox and Snell), .214 (Nagelkerke), .090 (McFadden)

121

Table 23

Multinomial Regression Predicting Reasons for Discontinuation from Counseling from

Graduate Degree, Years of Experience, and Time Spent Counseling

Model/Effect

-2 Log

Likelihood

χ2

df

Sig

Intercept 161.58

Final 135.95 25.62 20 .178

Graduate Degree 136.77 .82 4 .925

Years of Experience 150.30 14.35 8 .073

Time Spent Counseling 147.64 11.69 8 .166

Note: R2= .108 (Cox and Snell), .119 (Nagelkerke), .047 (McFadden)

Table 24

Chi Square Analysis Comparing Type of Counseling and Years of Experience

Years of Experience

Variable 0-5 6-10 >10 Total

Group Counseling Only

Observed 10.0 1.0 8.0 19

Expected 8.1 3.7 7.2 19

Std. Residual 0.7 -1.4 -0.3

Individual Counseling Only

Observed 19.0 11.0 37.0 67

Expected 28.6 13.1 25.3 67

Std. Residual -1.8 -0.6 2.3

Group and Individual Counseling

Observed 74.0 35.0 46.0 155

Expected 66.2 30.2 58.5 155

Std. Residual 1.0 0.9 -1.6

Total 103.0 47.0 91.0 241

Note: χ2= 15.83, df=4, Sig.=0.003

122

Table 25

Chi Square Analysis Comparing Number of Students Discontinued From Counseling

Each Year and Years of Experience

Years of Experience

Number of Students 0-5 6-10 >10 Total

0

Observed 17.0 2.0 16.0 35

Expected 14.9 6.6 13.4 35

Std. Residual 0.5 -1.8 0.7

1-3

Observed 60.0 29.0 32.0 121

Expected 51.6 22.9 46.4 121

Std. Residual 1.2 1.3 -2.1

4-6

Observed 18.0 10.0 17.0 45

Expected 19.2 8.5 17.3 45

Std. Residual -0.3 0.5 -0.1

≥7

Observed 4.0 3.0 24.0 31

Expected 13.2 5.9 11.9 31

Std. Residual -2.5 -1.2 3.5

Total 99.0 44.0 89.0 232

Note: χ2= 31.96, df=6, Sig.=0.000

123

likely than expected to engage in individual counseling exclusively; it was found to be

less likely that those with up to 5 years of experience would see students only on an

individual basis. A second relationship was noted between the number of students

discontinued from counseling each year and years of experience. Respondents with the

least amount of experience were less likely than expected to discontinue seven or more

students from counseling, while those with more than 10 years of experience were more

likely to discontinue students at this frequency.

Components of the Problem-Solving Model Used

This survey was designed to gather data on the use of common components of the

problem-solving model, as well as the application of selected steps involved in each

component. All subjects were asked to report on their use of common components, and

then asked to provide responses on selected steps for only those common components

that they reported using. As listed in Table 26, the most common components of the

problem-solving model used by respondents when designing and implementing

counseling as a direct intervention were constructing a behavioral definition for the

problem (87.5%), intervention plan development (85.1%), problem validation (83.8%),

progress monitoring (82.8%), and goal-setting (81.3%). Components that were least

often used were formative evaluation (55.3%), problem analysis (69.2%), summative

evaluation (69.3%), and a decision-making plan (70%). The format for the item

exploring treatment integrity included an option for sometimes implementing this during

counseling interventions. This intervention component received the lowest endorsement

for consistent use (15.2%), with 77.9% of respondents indicating that they measure

treatment integrity at least some of the time. The following paragraphs describe the

124

Table 26

Use of Intervention Components of the General Problem-Solving Model

Intervention component

Always Employ This

Component % (n)

Do Not Employ

This Component

% (n)

Did Not

Respond to this

Item % (n)

Behavioral definition

87.5 (217) 12.5 (31) 12.4 (35)

Baseline data

77.7 (188) 22.3 (51) 14.5 (41)

Problem validation

83.8 (196) 16.2 (38) 17.3 (49)

Problem analysis

69.2 (162) 30.8 (72) 17.3 (49)

Goal setting

81.3 (191) 18.7 (44) 17.0 (48)

Intervention plan

development

85.1 (194) 14.9 (34) 19.4 (55)

Measurement strategy

78.9 (176) 21.1 (47) 21.2 (60)

Decision-making plan

70.0 (156) 30.0 (67) 21.2 (60)

Progress monitoring

82.8 (173) 17.2 (36) 26.1 (74)

Formative evaluation

55.3 (114) 44.7 (92) 27.2 (77)

Treatment integrity

77.9 (159)* 22.1 (45) 27.9 (79)

Summative evaluation 69.3 (142) 30.7 (63) 27.6 (78)

Note: Number of responses and percentages vary by item as some respondents elected

not to respond; * This item contained options for Sometimes (62.7% [128]) and Always

(15.2% [31]) measuring Treatment Integrity. For the purposes of analysis and reporting,

these categories have been combined.

125

frequency with which respondents reportedly apply specific aspects of the most common

components of the problem-solving model as they design and implement counseling as a

direct intervention. For data analysis purposes, the frequency categories of ―sometimes‖

and ―always‖ used were combined. Frequency percentages for selected components were

calculated from the total number of participants that indicated using each corresponding

common component, and not from the overall number of participants.

Behavioral definition and baseline data collection. When writing behavioral

definitions, highest response rates were noted for action verbs describing student

behavior in observable terms (99.1%), and descriptions of the frequency (94.9%),

intensity (86%), and duration (80.2%) of the behavior. Lowest rates of endorsement were

provided for describing latency (52.1%) and accuracy (59.4%) of student behavior. The

most commonly used methods of baseline data collection were direct behavioral

observations (98.4%), third-party behavior ratings (99%), objective self-reports (87.1%),

and third-party interviews (90.3%). To determine a stable pattern of student behavior,

respondents reported collecting 3 to 5 baseline data points. Displayed in Tables 27, 28,

and 29 are the results for the use of these specific components.

Goals, intervention planning, measurement, and decision-making.

Determining what should be done to address the problem behavior involves goal setting,

developing an intervention plan, devising a measurement strategy, and coming up with a

plan for decision-making. Results showing the use of these specific components can be

found in Tables 30, 31, 32, and 33. Respondents indicated that they include timeframe

(92%), condition (97.8%), behavior (98.4%), and criteria (97.7%) some or all of the time

when writing a behavioral goal. When writing counseling intervention plans,

126

Table 27

Use of Specific Components of Behavioral Definition Composition

Behavioral Definition Component

Employ This

Component

% (n)

Did Not

Respond

% (n)

Action verbs describing what the student does in

observable terms

99.1 (214)

23.7 (67)

Frequency (the number of times the behavior occurs

during an observation period)

94.9 (203)

24.4 (69)

Latency (how much time passes between the presentation

of a stimulus and the student‘s response or behavior)

52.1 (101)

31.4 (89)

Intensity (the strength or force with which the behavior is

displayed)

86.0 (178)

26.9 (76)

Topography (the configuration, shape, or form of the

behavior)

61.0 (119)

31.1 (88)

Accuracy (a measure of how the student‘s behavior is

correct or fits a standard)

59.4 (117)

30.4 (86)

Duration (how much time passes between the onset and

the ending of a behavior)

80.2 (166)

26.9 (76)

Note: Number of responses and percentages vary by item as some respondents elected

not to respond

127

Table 28

Use of Specific Types of Baseline Data Collection

Baseline Data Collection Method

Sometimes

Use This

Method

% (n)

Always Use

This

Method

% (n)

Did Not

Respond

% (n)

Direct behavioral observation

31.9 (61) 66.5 (127) 32.5 (92)

3rd

party behavior rating (from parent,

teacher, or related service provider)

52.4 (100)

46.6 (89)

32.5 (92)

Sociometric techniques

60.7 (108) 5.1 (9) 37.1 (105)

3rd

party interview

60.2 (112) 30.1 (56) 34.2 (97)

Objective self-report

68.1 (124) 22.0 (40) 35.7 (101)

Projective-expressive technique 34.6 (63) 02.2 (4) 35.7 (101)

Note: Number of responses and percentages vary by item as some respondents elected

not to respond

Table 29

Average Number of Baseline Data Points Collected To Establish a Stable Pattern of

Student Behavior

Number of Points

Collected

% of Responses (n)

1 03.7 (07)

2 12.0 (23)

3 37.2 (71)

4 14.1 (27)

5 18.8 (36)

6 03.7 (07)

7 or Greater 10.5 (20)

Note: 32.5% (92) Non-Response rate

128

Table 30

Use of Specific Criteria When Writing Behavioral Goals

Behavioral Goal Criterion

Sometimes

Use This

Criteria

% (n)

Always Use

This

Criteron

% (n)

Did Not

Respond

% (n)

Timeframe (when the expected progress

will be made in terms of days, weeks, and

months)

36.3 (66)

56.6 (103)

35.7 (101)

Condition (the specific circumstances in

which the behavior will occur)

37.2 (67)

60.6 (109)

36.4 (103)

Behavior (written in objective, observable,

and measurable terms describing what the

student will be able to do)

19.8 (36)

78.6 (143)

35.7 (101)

Criteria (a standard for how well the

behavior is to be performed)

39.5 (70)

58.2 (103)

37.5 (106)

Note: Number of responses and percentages vary by item as some respondents elected

not to respond

129

Table 31

Use of Specific Components of Counseling Intervention Plans

Intervention Plan Component

Sometimes

Include This

Component

% (n)

Always Use

This

Component

% (n)

Did Not

Respond

% (n)

A clear description of the procedures to be

used

41.6 (77)

53.5 (99)

34.6 (98)

Documentation that the strategies to be

used have been empirically validated in

the literature on evidence-based

interventions

58.2 (107)

21.2 (39)

35.0 (99)

A description of the specific steps and

activities that will be engaged in during

counseling sessions

52.2 (95)

39.6 (72)

35.7 (101)

A description of how each step or activity

will be completed

46.2 (85)

32.1 (59)

35.0 (99)

The materials needed for each step or

activity

52.7 (96)

26.9 (49)

35.7 (101)

A description of what each person

engaged in the activity will do

47.3 (86)

35.7 (65)

35.7 (101)

The location where the intervention is to

take place

41.4 (75)

44.8 (81)

36.0 (102)

Note: Number of responses and percentages vary by item as some respondents elected

not to respond

130

Table 32

Use of Specific Components for Measuring Target Behaviors

Measurement Component

Sometimes

Use This

Component

% (n)

Always Use

This

Component

% (n)

Did Not

Respond

% (n)

A behavioral definition of the target

behavior

19.3 (34)

79.0 (139)

37.8 (107)

A clear description of where the behavior

will be measured

37.5 (66)

57.4 (101)

37.8 (107)

A clear description of when the behavior

will be measured

39.1 (68)

56.9 (99)

38.5 (109)

A clear delineation of who will measure

the behavior

37.1 (65)

58.3 (102)

38.2 (108)

A description of the recording method

most appropriate for the behavior

38.5 (67)

55.7 (97)

38.5 (109)

A description of the most appropriate

recording measure

44.2 (76)

45.3 (78)

39.2 (111)

Note: Number of responses and percentages vary by item as some respondents elected

not to respond

131

Table 33

Use of Specific Decision-Making Components

Decision-Making Component

Sometimes

Include This

Component

% (n)

Always

Include This

Component

% (n)

Did Not

Respond

% (n)

A determination of the frequency of

behavioral measurements and data to be

collected

37.8 (56)

60.8 (90)

47.7 (135)

A decision on how the data will be

summarized for the purposes of

intervention evaluation (e.g., visual

presentation, written report or summary)

53.1 (78)

36.1 (53)

48.1 (136)

A determination of how many behavioral

data points will be collected before the

intervention data will be analyzed

47.6 (70)

42.2 (62)

48.1 (136)

A determination of how much time will

pass before the intervention data will be

analyzed

42.6 (63)

52.7 (78)

47.7 (135)

A set of decision rules for responding to

specific data points

62.1 (90)

24.1 (35)

48.8 (138)

Note: Number of responses and percentages vary by item as some respondents elected

not to respond

132

respondents describe the procedures to be used (95.1%), the steps and activities to be

completed during sessions (91.8%), the location where the intervention is to take place

(86.2%), and what each person‘s role in the session is to be (83%). Respondents

indicated that, when writing measurement plans, they include a behavioral definition of

the target behavior (98.3%), a description of where (94.3%), when (96%) and who

(95.4%) will measure the behavior, a description of the recording method (89.5%), and a

rationale for why the method is appropriate for the target behavior (94.2%). Decision-

making plans reportedly specify the frequency with which behavioral data would be

collected (98.6%), how the data would be summarized and reported (89.2%), how many

data points would be collected (89.8%) and how much time would pass before

intervention data analysis (95.3%), and decision rules for responding to specific data

points (86.2%).

Progress monitoring, formative evaluation, treatment integrity, and

summative assessment. The final intervention components of the problem-solving

model are progress monitoring, formative evaluation, treatment integrity, and summative

evaluation. Data regarding the use of these specific components can be found in Tables

34, 35, 36, 37, 38, and 39. Commonly used methods of collecting progress monitoring

data include direct behavioral observation (98.9%), third-party behavior rating scales

(97.2%), interviews (96%), and objective self-report measures (88%). Variability was

noted in the number of progress monitoring data points respondents considered necessary

to establish a stable pattern of student behavior, with some respondents indicating that

they collect a range of 3 to 8 data points. Only one-third of respondents who engage in

baseline and progress monitoring data collection consistently use the same procedure

133

Table 34

Use of Specific Progress Monitoring Techniques

Data Collection Technique

Sometimes

Use This

Technique

% (n)

Always Use

This

Technique

% (n)

Did Not

Respond To

This Item

% (n)

Direct behavioral observation

43.0 (77) 55.9 (100) 36.7 (104)

3rd

party behavior rating scales (from

parent, teacher, or related service provider)

62.0 (111)

35.2 (63)

36.7 (104)

Sociometric techniques

59.5 (100) 1.8 (3) 40.6 (115)

Interviews

57.6 (102) 38.4 (68) 37.5 (106)

Objective self-report measures

71.3 (124) 16.7 (29) 38.5 (109)

Projective-expressive techniques 32.7 (56) 2.9 (5) 39.6 (112)

Note: Number of responses and percentages vary by item as some respondents elected

not to respond

Table 35

Number of Progress Monitoring Data Points Collected to Establish a Stable Pattern of

Student Behavior

Number of Data

Points Collected

% of Item Responses (n)

1 01.7 (03)

2 06.7 (12)

3 28.7 (51)

4 13.5 (24)

5 13.5 (24)

6 10.1 (18)

7 05.1 (09)

8 01.1 (02)

>8 19.7 (35)

Note: 32.5% (105) non-response rate

134

Table 36

Use of the Same Method for Baseline Data Point and Progress Monitoring Data Point

Collection

Response Type

% of Item

Responses (n)

Yes 33.3 (60)

Sometimes, depending on the situation 65.6 (118)

No 01.1 (02)

Note: 36.4% (103) non-response rate

Table 37

Sources of Data Considered During Formative Assessment

Data Source

Consider

This Data

Source

% (n)

Did Not

Respond

% (n)

The level of the behavior (how much the behavior is

occurring during baseline and intervention phases as

judged by repeated, objective measurements of its

frequency, duration, intensity, or the percentage of

intervals in which it occurs)

90.0 (108)

57.6 (163)

The trend of the behavior (whether the level of the

behavior is increasing or decreasing within the baseline

and intervention phases)

94.1 (111)

58.3 (165)

Anecdotal information from the student, his/her family,

teachers, or related service providers

95.0 (114)

57.6 (163)

Your own subjective assessment of the student‘s

behavior

90.0 (108)

58.0 (164)

Data documenting the student‘s performance in school

(e.g., work samples, grades, attendance records,

behavioral referrals)

95.8 (114)

58.0 (164)

Note: Number of responses and percentages vary by item as some respondents elected

not to respond

135

Table 38

Use of Methods to Measure Treatment Integrity

Measurement Method

Use This

Measurement

Method

% (n)

Did Not

Respond

% (n)

Self-Report

86.5 (135) 44.9 (127)

Logs documenting sessions

88.0 (139) 44.2 (125)

Checklists for intervention components

62.9 (95) 46.6 (132)

Permanent products of student work

77.6 (121) 44.9 (127)

Direct observation by a 3rd

party not directly

involved in implementation

53.8 (84)

44.9 (127)

Note: Number of responses and percentages vary by item as some respondents elected

not to respond

136

Table 39

Sources of Data Considered During Summative Assessment

Data Source

Consider

This Data

Source

% (n)

Did Not

Respond

% (n)

The level of the behavior (how much the behavior is

occurring during baseline and intervention phases as

judged by repeated, objective measurements of its

frequency, duration, intensity, or the percentage of

intervals in which it occurs)

89.7 (130)

48.8 (138)

The trend of the behavior (whether the level of the

behavior is increasing or decreasing within the baseline

and intervention phases)

97.3 (143)

48.1 (136)

Anecdotal information from the student, his/her family,

teachers, or related service providers

98.6 (144)

48.4 (137)

Your own subjective assessment of the student‘s

behavior

90.7 (127)

50.5 (143)

Data documenting the student‘s performance in school

(e.g., work samples, grades, attendance records,

behavioral referrals)

97.9 (138)

50.2 (142)

Note: Number of responses and percentages vary by item as some respondents elected

not to respond

137

when collecting each type of data (33.3%), while a majority use the same procedure only

sometimes (65.6%). About half of respondents did not respond to questions regarding

formative assessment, treatment integrity, or summative assessment. Respondents that

collect formative assessment data do so by considering the level (90%) and trend (94.1%)

of the behavior, anecdotal descriptions (95%), their own subjective assessment (90%),

and data documenting student performance (95.8%). Treatment integrity was most

commonly measured using self-reports (86.5%), session logs (88%), and permanent

products of student work (77.6%). Similarities in the collection of formative (discussed

previously) and summative data were found, with anecdotal data (98.6%), data

documenting student performance (97.9%), and the trend of student behavior (97.3%)

being the most popular methods of summative assessment, followed by subjective

assessment (90.7%) and evaluation of the level of the behavior (89.7%).

Comparison of Demographic Variables and Use of the Problem-Solving Model

Respondent data on frequency of use of the major components of the problem-

solving model were also compared with demographic variables (training, years of

experience, school type, other roles and responsibilities) to determine whether these

variables impacted design and implementation of counseling as a direct intervention. The

same re-coded variables (with the addition of school type and psychologist:student ratio)

as described previously were compared against frequency data on use of the major

components of the problem-solving model in 12 separate binary logistic regression

models.

Overall, these demographic variables were not found to offer predictive value for

determining use of the major components of the problem-solving model, as indicated by

138

insignificant overall statistics and low R2

values. Summary tables for these models can be

found in Appendix G. Despite these results, six instances were noted in which one or

more predictive factors made a significant contribution to the model. Therefore, 6 chi-

square analyses were conducted to explore any possible relationships; no significant

relationships were found between these demographic variables and use of the problem-

solving model (see Appendix F for these chi square tables).

Comments

At the end of the survey a section was created for participants to provide any

additional comments they might have had regarding the survey or research topic being

explored. A total of 58 respondents (20.49%) provided comments. Further analysis of

the comments revealed two themes. Some respondents chose to comment in order to

specify their counseling practices (e.g., do not counsel, offer less or limited counseling

than in previous years, employed in a specialized school with specific population of

students). Furthermore, of those who do not currently offer counseling, many indicated

that they based their responses on previous counseling experiences, or behavioral and

academic interventions they administer by themselves or as a member of a school-based

team. Other respondents critiqued the length and focus of the survey (e.g., focus on a

more narrow set of behaviors, offer more options for elaboration and context on specific

use of techniques).

139

CHAPTER 5: Discussion

Overview

The emphasis placed on accountability for educational and behavioral outcomes

in schools has increased over time as research continues to document student needs and

achievement. Current best practices in instruction and the design of behavioral and

counseling interventions specify how school psychologists can make data-based

decisions in their counseling practice (Upah, 2008), while federal education legislation

holds them accountable for their work with students (Wright & Wright, 2009). The

problem-solving model integrates research from response to intervention and single-

subject design paradigms with a focus on repeated, objective, and observable

measurements of student behavior over time to demonstrate the effectiveness of an

intervention. School psychologists were surveyed regarding their counseling practices,

with a specific focus on their implementation of research and best practice guidelines

related to the use of evidence-based interventions, progress monitoring, and data-based

decision making.

This chapter focuses on the discussion of the results of this study. The discussion

begins with consideration of respondent characteristics and how these demographics

compare with those of other research studies. The chapter continues with a review of the

findings within the major areas (i.e., counseling practices, use of general and specific

components of the problem-solving model) examined in the current study. In addition,

the implications of the results of this study for the field of school psychology, limitations

of the study, and potential directions for future research are discussed.

Respondent Characteristics

140

When considering the results of a research study, it is important to determine

whether the results can be generalized to the larger population of school psychologists.

In terms of sampling method, Curtis, Hunley, and Grier (2004) cited the NASP registry

as a comprehensive source of current contact information for school psychologists

interested in doing research with this population. As described in Chapter 3, NCSPs were

sampled in this study to ensure uniformity in training, and to select respondents who were

currently practicing in the field. For the current study, demographic data reported in

Chapter 4 were compared with current data describing the demographics of practicing

school psychologists reported by Curtis, Lopez, Castillo, Batsche, Minch, and Smith

(2008). It is important to note differences in populations and purpose, and therefore

variables of interest between Curtis et al. (2008) and this study (i.e., to describe

demographic characteristics versus the counseling practices of current practitioners).

Direct comparisons were made between graduate degrees received, psychologist-to-

student ratio, school setting served, and themes of professional development attended in

the last five years. Data on these comparisons can be found in Table 40.

Despite the fact that Curtis et al. (2008) achieved a higher response rate (59.3%)

than the current study (22.39%), both samples appear to have had similar graduate

training and comparable representation along the continuum of psychologist-to-student

ratio. Suburban schools were the most common employment settings for both samples.

Although not reported in this table, the Curtis et al. (2008) sample appears to have more

experience (M = 14 years), while a more bimodal distribution between more (32.7%, >10

years of experience) and less (45%, 0-5 years of experience) experienced practitioners

was found in the current study. Several demographic studies (e.g., Curtis, Grier, &

141

Table 40

Comparison of Demographic Characteristics

Variable

Curtis et al.

(2008)

Current

Study

Graduate Degrees Received

Masters/Specialist 71.6% 77%

Doctorate 24.4% 22.9%

Psychologist to Student Ratio

1:<1,000 40% 40.1%

1:<1,500 65% 69.7%

1:1,500-2,000 17% 17.2%

1:>2,000 18% 13%

Type of School Served

Urban 28.4% 18.1%

Suburban 50.2% 38.4%

Rural 28.8% 24.3%

Themes of Professional Development Attended

Over the Past Five Years

Behavioral Interventions 47.1% 79.9%

Social-Emotional Intervention/Provision of

Counseling

28.7%

36.7%

Response to Intervention 26.3% 86.6%

142

Hunley, 2004; Curtis, Hunley, & Grier, 2004) document the ―graying‖ of the field of

school psychology, and warn of projected shortages due to retiring practitioners

beginning in 2010. The split in years of experience in the current sample could be

explained by an influx of recent graduates entering the field to replace those who have

already retired.

Without over-interpretation of data provided by Curtis et al. (2008), several

commonalities were noted between topics explored through professional development.

Given the fact that opportunities for professional development face a variety of

constraints (e.g., financial resources, time availability, location), it may be more valuable

to note similarities in topics, rather than agreement in percentages of attendance.

Furthermore, data from the Curtis et al. (2008) study were gathered during the 2004-05

school year, around the same time that the Individuals With Disabilities Education Act

(2004; Wright & Wright, 2009) specified that schools could use a student‘s response to

intervention to determine eligibility for special education and related services. Current

legislation, such as the No Child Left Behind Act (NCLB, 2002), specifies accountability

for student outcomes and achievement as well as data-based decision-making.

Respondents in the current study continued to pursue professional development on

Response to Intervention (86.6%), along with evidence-based behavioral interventions

(79.9%), data-based decision-making (68.9%), and academic and behavioral

accountability (57.2%).

Consistency in time allocation was also found when respondent data from the

current study were compared to previous research. For example, in the current study,

42.2% of respondents reported spending between 25-50% of their time on assessment,

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while allocating 25% of their time or less on direct interventions (80%), consultation and

indirect services (70.9%), research (100%), administration (96.4%), or systems-level

activities (97.1%). Demographic studies conducted over the past few decades mirror

these findings, with respondents indicating that they spend approximately 50% of their

time on assessment, 20-25% engaged in direct intervention, 20-25% on consultation, and

their remaining time involved in systems-level or research activities (Bramlett, Murphy,

Johnson, Wallingsford, & Hall, 2002; Fisher, Jenkins, & Crumbley, 1986; Goldwasser,

Meyers, Chistenson, & Graden, 1983; Hartshorne & Johnson, 1985; Lacayo, Sherwood,

& Morris, 1981; Meacham & Peckam, 1978; Reschly & Wilson, 1995; Smith, 1984).

Overall, the statistics describing graduate preparation, experience, and

employment conditions (school setting, psychologist-to-student ratio, time allocation) for

this study are comparable to current and historical demographic data. One difference that

should be noted was years of experience. This should be considered when interpreting

results and generalizing to the larger population. The results of this study may apply to

the general population given that few differences were found between this sample and

current demographic research. At this time, however, the small sample size of this study

limits the ability to generalize these results to any population beyond those school

psychologists who responded to the survey.

Counseling Practices of School Psychologists

The opening questions of this study were designed to gather data on the general

counseling practices of school psychologists. Specific areas of interest included

counseling format, students served, discontinuation from counseling, and the use of print

and/or online resources. The majority of respondents (54.8%) see special and general

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education students (73.5%) for counseling in group and individual formats. Respondents

with the most experience were more likely than new practitioners to provide individual

counseling exclusively. It is important to note that 24 respondents indicated in the

comments section that they do not currently counsel students. Additionally, of the 65

respondents who declined to participate, 23 did so because they do not counsel within

their school buildings. Due to the narrow focus of this study on the application of the

problem-solving model to counseling as a behavioral intervention, limited comparisons

could be made between this study and the literature on counseling practices. As stated in

Chapter 2, previous research findings indicate that a range of 53-88% of school

psychologists provide group and/or individual counseling (Hanchon & Fernald, 2011;

Yates, 2003), and surveys of time allotment indicate that 20-25% of schools

psychologists‘ time is spent on direct interventions (Bramlett et al., 2002).

To develop an understanding of data-based decision-making, several survey items

explored the discontinuation practices of school psychologists and their use of print or

online resources. Most respondents recommended discontinuing 2 students from

counseling each year, with the most common reason being that counseling goals have

been met. Experience was found to have an impact on discontinuation practices.

Practitioners with 10 or more years of experience were more likely than expected to

discontinue 7 or more students from counseling, while those with the least amount of

experience were less likely to discontinue students at this frequency. Similar numbers of

respondents reported that they did or did not use print or online resources to assist in

writing goals, clarifying problem behavior, and when setting behavioral expectations.

Data regarding discontinuation of counseling services have not been reported in the

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available literature; however, due to the small sample size in this study, these results

should be viewed as preliminary.

Use of the Problem-Solving Model

The major focus of this investigation was to determine the frequency with which

school psychologists use components of the problem-solving model. To this end,

respondents were asked to provide data on their use of general and specific components

of the problem-solving model. Provided in Table 41 is a summary of the frequency of

use of these components. Overall, general components of the problem-solving model

used to define and establish the behavior of concern (e.g., behavioral definition [87.5%],

and problem validation [83.8%]), as well as those involved in determining what should

be done about it (goal setting [81.3%], and intervention plan development [85.1%]) were

found to be used most often. In addition, the collection of progress monitoring data

(82.8%) was another practice in which a majority of respondents reported engaging.

Social-emotional behavioral (SEB) research conducted by Merrell (2010) pointed

out that while there are a variety of SEB assessment tools available, their utility lies in

determining problem behavior and the factors maintaining these behaviors, and provide

only limited guidance on intervention and determining whether remediation efforts have

been successful. A majority of respondents in this study have also attended professional

development in recent years focused on evidence-based behavioral interventions, data-

based decision-making and academic and behavioral accountability, during which time

they may have received strategies and resources for monitoring student behavior.

Agreement was also found between general and specific components used by

these respondents and legal requirements. For example, as reiterated in IDEA 2004,

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Table 41

Use of General and Specific Intervention Components of the Problem-Solving Model

General and Specific PSM Component

% Usage (n)

% Non-

Response (n)

Behavioral definition 87.5 (217) 12.4 (35)

Action verbs describing behavior in observable terms 99.1 (214) 23.7 (67)

Describe frequency 94.9 (203) 24.4 (69)

Describe intensity 86.0 (178) 26.9 (76)

Describe duration 80.2 (166) 26.9 (76)

Describe topography 61.0 (119) 31.1 (88)

Describe accuracy 59.4 (117) 30.4 (86)

Describe latency 52.1 (101) 31.4 (89)

Intervention plan development 85.1 (194) 19.4 (55)

Describe the procedures to be used 95.1 (176) 35.0 (99)

Describe activities/steps to be completed in each

session

91.8 (167)

35.7 (101)

Specify location where intervention takes place 86.2 (156) 36.0 (102)

Describe what each person will do 83.0 (151) 35.7 (101)

Specify materials needed for each activity/step 79.6 (145) 35.7 (101)

Document that intervention is empirically valid 79.4 (146) 35.0 (99)

Describe how each activity/step will be completed 78.3 (141) 35.0 (99)

Problem validation 83.8 (196) 17.3 (49)

Progress monitoring 82.8 (173) 26.1 (74)

Direct behavioral observation 98.9 (177) 36.7 (104)

Third-party behavioral rating scales 97.2 (174) 36.7 (104)

Interviews 96.0 (170) 37.5 (106)

Objective self-report measures 88.0 (153) 38.5 (109)

Sociometric techniques 61.3 (103) 40.6 (115)

Projective-expressive techniques 35.6 (61) 39.6 (112)

Goal setting 81.3 (191) 17.0 (48)

Specify behavior 98.4 (179) 35.7 (101)

Specify condition (circumstance in which behavior

occurs)

97.8 (176)

36.4 (103)

Specify criteria (standard for behavioral performance) 97.7 (173) 35.7 (106)

Specify timeframe when expected progress will be

made

92.9 (169)

35.7 (101)

Measurement strategy 78.9 (176) 21.2 (60)

Behavioral definition of the target behavior 98.3 (173) 37.8 (107)

Describe when the target behavior will be measured 96.0 (167) 38.5 (109)

Describe who will measure the target behavior 95.4 (167) 38.2 (108)

Describe where the target behavior will be measured

Describe appropriate recording measure

94.9 (167)

89.5 (154)

37.8 (107)

39.2 (111)

(table continues)

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Table 41 continued

General and Specific PSM Component

% Usage (n)

% Non-

Response (n)

Treatment Integrity 77.9 (159) 27.9 (79)

Logs documenting sessions 88.0 (139) 44.2 (125)

Self-reports 86.5 (135) 44.9 (127)

Permanent products of student work 77.6 (121) 44.9 (127)

Checklists for intervention components

Direct observation by non-involved 3rd

party

62.9 (95)

53.8 (84)

46.6 (132)

44.9 (127)

Baseline data 77.7 (188) 14.5 (41)

Third-party behavior ratings 99.0 (189) 32.5 (92)

Direct behavioral observation 98.4 (188) 32.5 (92)

Third-party interviews 90.3 (168) 34.2 (97)

Objective self-report 90.1 (164) 35.7 (101)

Sociometric techniques 65.8 (117) 37.1 (105)

Projective-expressive techniques 36.8 (67) 35.7 (101)

Decision-making plan 70.0 (156) 21.2 (60)

Frequency with which data will be collected 98.6 (146) 47.7 (135)

How much time will pass before intervention data

analysis

95.3 (141)

47.7 (135)

How many data points will be collected before data

analysis

89.8 (132)

48.1 (136)

How data will be summarized for intervention

evaluation

89.2 (131) 48.1 (136)

Decision rules for responding to specific data points 86.2 (125) 48.8 (138)

Summative evaluation 69.3 (142) 27.6 (78)

Anecdotal information 98.6 (144) 48.4 (137)

Data documenting student performance in school 97.9 (138) 50.2 (142)

Trend (change in level from baselines to intervention

phases)

97.3 (143)

48.1 (136)

Practitioner‘s subjective assessment of student behavior 90.7 (127) 50.5 (143)

Level (comparison of behavioral occurrence in baseline

and intervention phases)

89.7 (130)

48.8 (138)

Problem analysis 69.2 (162) 17.3 (49)

Formative evaluation 55.3 (114) 27.2 (77)

Data documenting student performance in school 95.8 (114) 58.0 (164)

Anecdotal information 95.0 (114) 57.6 (163)

Trend (change in level from baselines to intervention

phases)

94.1 (111)

58.3 (165)

Practitioner‘s subjective assessment of student behavior 90.0 (108) 58.0 (164)

Level (comparison of behavioral occurrence in baseline

and intervention phases)

90.0 (108)

57.6 (163)

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individualized education plans (IEPs) must include a description of the student‘s present

levels of academic and functional abilities, including justification for why his or her

disability affects participation and progress in the general curriculum (Wright & Wright,

2009). This tenet of special education law fits with the literature defining problem

validation as the comparison of student behavior with a standard of appropriate behavior

based on the performance of peers, building or district norms, and/or teacher expectations

(Upah, 2008). Because the majority of respondents indicated that they work with general

and special education students, problem validation may be an activity that is

systematically engaged in when working with all students.

Furthermore, special education law requires that IEPs include measurable

academic and functional goals, a description of how those goals will be measured, and

when progress towards those goals will be evaluated (Wright & Wright, 2009). IEPs

must be evaluated ―periodically‖ (Wright & Wright, 2009, p. 104) to determine whether

the student is making progress towards meeting goals and to revise goals when this is

considered necessary. In addition, IEPs in some states also specify the location where

related services, such as counseling, will take place. These clauses may explain the

reported frequency of goal setting, the use of action verbs, and the specific problem-

solving model components that respondents may incorporate into service delivery plans.

Before discussing least often used components of the problem-solving model, it

should be noted that, as the survey continued, the percentage of non-responses per item

increased. Several items that were used infrequently also had higher rates of non-

response. Components used most infrequently were also those that would be used to

monitor and determine the effectiveness of counseling interventions (e.g., formative

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[55.3%] and summative [69.3%] evaluation, and decision-making plan [70%]).

Problem-analysis (69.2%) was indicated as another infrequently used component.

According to recommendations in the literature, problem analysis requires developing

and testing hypotheses about the function of the student‘s behavior (Kern, 2005). Data

are gathered on student behavior under different conditions, and interventions are

designed based on the fit between data and hypotheses (Kern, 2005). Although

informative, the amount of time and possible teacher cooperation needed to adequately

complete problem analysis might be more than some school psychologists are able to

manage, especially given that the majority of respondents in this sample reportedly spend

at least half of their time on assessment.

Current research on behavioral interventions may help to explain some of the

results found in this study. As mentioned in Chapter 2, research on SEB disorders has

not identified a general outcome measure for behavioral interventions (Chafouleas,

Volpe, Gresham, & Cooke, 2010). Therefore, the identification of measureable tasks,

behaviors, and skills that represent sensitive indicators of SEB problems and form the

foundation for measurement tools, definitions of target behaviors, and screening and

progress monitoring plans is on-going (Chafouleas et al., 2010). At this time, designing

counseling interventions based on the problem-solving model along with response to

intervention or single-case design paradigms (in which the student‘s behavior is

measured over time to determine whether behavioral improvements have been made)

promotes examining effectiveness and demonstrating accountability for student

outcomes.

Research guidelines on response to intervention (Briesch, Chafouleas, & Riley-

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Tilman, 2010), and single subject design (Horner et al., 2005) share some common

themes with the steps of the problem-solving model. The student is the unit of analysis,

and student behavior prior to intervention is compared to behavior during and after the

intervention is completed. One or more observable behaviors are operationally defined

and measured repeatedly, with some assessment of the consistency of measurement. The

target behavior should also have some social significance and relevance to the student.

The intervention must also be operationally defined, with specific and detailed

descriptions of what will take place and where the intervention will occur. Fidelity of

implementation is also important to maintain as the intervention is applied over time.

Student behavior is compared during baseline and intervention phases through regular,

on-going documentation, and then by visual analysis of all phases of behavior (Kazdin,

1982; Upah, 2008). The focus on the objective measurement of observable behaviors

over time, along with demonstrations of behavioral growth and intervention fidelity,

allow for data-based decision making and accountability for student outcomes.

While school psychologists are applying many aspects of the problem-solving

model with some consistency, the results of this study suggest that they may not be doing

so in such a way that would allow for easy demonstration of accountability. Although

respondents have indicated that they are behaviorally defining target behaviors, the

infrequent use of formative and summative evaluation could be an indication that school

psychologists may not be measuring these behaviors adequately enough to use these data

to determine whether the student has successfully responded to the intervention. The

frequent use of interviews and self-reports during baseline data collection calls into

question whether school psychologists are able to establish an observable, objective

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baseline of student behavior before beginning an intervention. Interviews and self-

reports are more global, non-standardized, and non-empirically validated measures of

data collection, and as such, may not provide reliable and valid indications of student

behavior. Furthermore, high rates of non-response for specific methods of baseline data

collection invites speculation on the adequacy of these data for comparison of student

behavior across different phases (baseline to intervention). Steps that would allow for the

comparison of baseline and intervention data (e.g., decision-making plans [70%] and

measurement strategy [78.9%]) were not consistently used by respondents in this sample.

Although respondents reported using direct behavioral observation to progress

monitor their interventions, it is unclear whether these observations are being conducted

during sessions, or in an environment where the problem behavior occurs. Third-party

behavior rating scales have support in SEB literature, however many are not sensitive

enough to be used repeatedly to measure change over the course of an intervention

(Volpe & Gadow, 2010). In addition, less empirically valid methods of progress

monitoring, such as interviews and self-reports, were used almost as often as behavioral

observations. Limited time and resources may prevent practitioners from collecting

sufficient data to make informed decisions about student behavior (Briesch, Chafouleas,

& Riley-Tilman, 2010), which may explain the infrequent use of formative and

summative evaluation.

While data-gathering methods such as anecdotal information, self-reports,

interviews, and practitioner‘s subjective assessment of student behavior may provide

valuable information, research recommendations call for observable, objective, and

empirical assessment of student behavior gathered over time and presented for visual

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analysis (Kazdin, 1982; Upah, 2008). The use of these non-observable and subjective

methods to establish baseline and progress monitor could compromise the value of using

an analysis of level and trend as formative and summative assessment tools. Similarly,

the majority of respondents in this study reported discontinuing students because

counseling goals had been met; however, the low frequency with which students are

discontinued each year might also be related to the adequacy of the data gathering

methods used by this sample.

The high rates of non-response for treatment integrity are another deviation from

recommendations in the literature. Nearly half the sample in this study did not respond to

items involving specific methods for measuring treatment integrity. This suggests that,

for this sample, treatment integrity is not something that they regularly engage in during

their counseling practices. While useful for planning, session logs, self-reports, and

permanent products of student work may not provide direct evidence that intervention

components have been implemented consistently and accurately over time the way that

session checklists and direct observations might.

In summary, it would appear that the respondents in this sample are using many

available tools to define and determine target behaviors. Many of their reported

counseling practices appear to conform to legislative guidelines for working with special

education students. These school psychologists indicated that they apply many of the

steps of the problem-solving model in their counseling practice, especially when defining

target behaviors and planning interventions. These results, however, also call into

question the degree to which these practitioners engage in progress monitoring and data-

based decision making, as the quality and frequency of baseline and progress monitoring

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data collection may not enable documentation and comparison of student behavior to

determine whether behavioral improvement has been made (formative and summative

evaluation).

Implications for School Psychology

Although the conclusions of the current study are tentative at this point in time,

the results suggest that school psychologists might improve their ability to make data-

based decisions and demonstrate accountability for student outcomes. Specific areas for

growth for current practitioners and training programs include gathering more observable

and objective measurements of behavior over time to clearly demonstrate behavioral

change across baseline and intervention phases, corresponding improvements in

formative and summative assessment, and more consistent demonstration and evaluation

of treatment integrity.

Current research on the roles and responsibilities of school psychologists should

be considered in the discussion on possible barriers to improvements in data gathering.

As mentioned in Chapter 2, the four roles of the school psychologist include assessment,

direct intervention, consultation, and systems-level intervention (Fagan, 2008; NASP,

2010). School psychologists have expressed a preference for spending less time on

assessment, and more time and resources on interventions and consultation (Hosp &

Reschly, 2002; Reschly & Wilson, 1995), with a specific focus on student mental health

needs (Agresta, 2004). Briesch, Chafouleas, and Riley-Tilman (2010) cited limited time

and resources as factors that prevent practitioners from collecting enough data to make

informed decisions about student behavior. Given the connection between emotional

well-being and successful learning experiences (Haertel, Walberg, & Weinstein, 1983;

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Wang, Haertel, & Walberg, 1990) and the focus on accountability for student outcomes

(Wright & Wright, 2009), researchers, practitioners, and professional organizations

should continue to advocate for role expansion and re-allocation of the responsibilities of

school psychologists. A paradigm shift, whereby school psychologists translate

behavioral research into practice and become active problem-solvers within their school

buildings, is necessary to demonstrate accountability for student outcomes and

achievement.

To increase accountability, school psychologists may need to re-allocate the

amount of time they spend engaged in research, consultation, and systems level activities,

in addition to spending more time on direct interventions. Behavioral research and best

practice guidelines exist specifying how to most effectively gather and evaluate objective,

observable, and repeated measures of student behavior (Briesch, Chafouleas, & Riley-

Tilman, 2010; Upah, 2008; Volpe & Gadow, 2010). Current and historical time

allocation data suggest that consuming and applying this information is not possible

unless school psychologists are able to prioritize research as a professional function. At

the systems level, school psychologists may need to advocate for gathering more precise

behavioral data at a higher frequency than what is currently done in many school

buildings. Although questions remain regarding who collects behavioral data and in what

environment, it would appear as though consensus needs to be reached on how data will

be collected, by whom, and at what frequency. This may involve school psychologists

collecting data themselves, but may also entail other school professionals who are in a

better position to see students display problematic behaviors collecting data. If other

school professionals collect data, then school psychologists may need to have more time

155

available for consultation in order to provide their colleagues with necessary tools and

training, to measure fidelity to established protocols, and to increase buy-in, if this

becomes a factor.

Issues related to data gathering, decision-making, and accountability should be

addressed by school psychology training programs, as well as organizations providing

continuing education and professional development. These issues highlight a necessary

paradigm shift where research is integrated into practice, with new and experienced

practitioners receiving training to become active problem-solvers. The results of this

study would suggest that the focus should be on developing and disseminating knowledge

and supervised experience gathering repeated, objective measurements of student

behavior to establish baseline, document progress during intervention, and to design and

evaluate formative and summative assessment. Building in measurement of treatment

integrity should also be an essential component of training and continuing education.

Experiences with these skills should be connected to coursework, practice, and

professional development with direct and indirect interventions, to ensure that

practitioners know how to implement standards of data-based decision making and

accountability for outcomes within the settings in which they are employed.

Limitations and Directions for Future Research

The results of this survey provide a broad overview of the implementation of

counseling interventions based on the problem-solving model, a topic that had not

previously been addressed in the literature. In the process of evaluating the results of this

study, it is necessary to address the limitations of this study, particularly with respect to

the reliability and validity of this survey in accurately capturing information on

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counseling practices. Consideration of these limitations allows for speculation on what

could be done differently in future research.

Although some commonalities in training and work conditions were noted

between this sample and other current demographic surveys, the low response rate

achieved in this study allows for only a tentative review of its results. Following from

this, the low response rate for many items that would provide insight into the

respondents‘ ability to engage in data-based decision making and accountability (e.g.,

treatment integrity, formative and summative evaluation, decision-making plan) is

problematic, as these were key themes of the problem-solving model that this study was

designed to explore. In addition, several respondents indicated that they do not currently

offer counseling, or declined to participate in this study for that reason.

At this time, it can only be speculated that, although legal requirements for

designing and implementing counseling appear to be met by this sample, the observable

and objective data gathering and assessment of student behavior over time to demonstrate

change and make decisions is likely not being done in accordance with research and best

practice guidelines. In order to have more confidence in the results obtained at this time,

a higher response rate for all items on this survey from practitioners who consistently

engage in counseling would be needed. To increase response rates, future research could

apply recommendations from Dillman (2007), by sampling using a variety of different

formats, including mail and telephone invitations for participation.

Regardless of the sampling method used, survey questions must be general

enough to facilitate comprehension by a large number of respondents, and as a result,

may omit questions of interest to the researcher and certain respondents (Barribeau et al.,

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2005). The length of this survey was a concern from the beginning, and therefore,

question selection was limited to those items considered most likely to answer the

research questions. As such, items pertaining to specific components of problem analysis

and validation were omitted. When analyzing the results of this study, however, it would

be beneficial to have information regarding the data used to establish and learn about

behaviors of concern, how these data are gathered, and to what these data are compared.

Future research exploring data-based decision making and accountability should focus on

fewer components of the problem-solving model (e.g., baseline and progress monitoring

data collection, and/or formative and summative assessment) in greater depth, now that

preliminary research has examined these topics with a broad lens. Shorter measures may

also increase the response rate and provide a more valid picture of counseling practices

implemented in schools today.

Additional limitations for this survey match several of those listed in Chapter 2

for self-administered surveys. For example, once respondents begin completing the

survey, it cannot be altered, and some respondents may struggle to align their own

experiences with those choices presented to them on a survey (Barribeau et al., 2005). In

the case of this survey, after implementation, it was discovered that the treatment

integrity item allowed respondents to select whether they ―sometimes‖ or ―always‖ used

this component. All other items pertaining to the use of general components of the

problem-solving model forced respondents to state whether they did or did not employ

each component. When comparing the usage of general and specific components of the

problem-solving model with the rates of non-response, it would appear as though, in

some cases, respondents indicated that they engaged in a general component without

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being able to describe their practice by selecting any of the corresponding specific

components. Large increases in the rate of non-response between general and specific

components for steps such as treatment integrity, and summative and formative

evaluation would suggest that, on some items, there might have been a discrepancy

between the intent of the researcher and the understanding of the respondent.

To address these limitations, future surveys should include uniform response

choices, and data on counseling might be obtained using a mixed method research design.

Focus groups comprised of school psychologists who spend varying amounts of time on

assessment and direct interventions could provide insight on their counseling practices to

determine the level of agreement between what is being done and what is specified in

research and best practices. An important question to ask relates to barriers and

facilitators practitioners face, especially when gathering empirical and objective

behavioral data. Researchers could also examine de-identified examples of

documentation related to counseling practices, such as intervention, decision-making, and

measurement plans, evidence of treatment integrity, and aggregated student data used for

visual analysis as part of formative and summative evaluation. Information gathered

from these data would allow school psychologists as a field to evaluate their level of

accountability, while providing guidance for researchers, professional organizations,

practitioners, and training programs.

Analysis of the results of this study also produced additional questions related to

where baseline and progress monitoring data gathering occurs. Respondents reported

using direct behavioral observation to gather baseline and progress monitoring data.

These items, however, did not specify where such behavioral observations occur.

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Specifically, it is unknown whether behavioral observations are gathered during

counseling sessions, where practitioners have control over the environment and can

provide the student with support and correction, or whether observations are conducted in

an environment beyond the practitioner‘s control, where the problem behavior may be at

its most severe. The wording of these items also presumes that the school psychologist is

the one conducting the observations; however, it is possible that some practitioners

misinterpreted this, and considered observations by others when responding, or when

evaluating these data to make decisions in their practice. Problems with validity and

reliability of behavioral observations arise, depending on the level of training,

standardization of methods, and agreement among behavioral raters.

Future research must examine the connection between what school psychologists

do with students in counseling and student behavior in the classroom. The question of

who gathers behavioral data for baseline and progress monitoring should be posed to

practitioners who offer counseling. It is important to specify who gathers behavioral data

and when and where these data are gathered. The question of barriers and facilitators to

data collection should also be addressed. Focus group research and/or brief survey

measures can be used to answer these questions.

In the event that school psychologists are relying on behavioral data gathered by

others who have more exposure to the student, such as teachers or related service

providers, then research on consultation becomes more relevant, in terms of creating

effective and unobtrusive measurement tools, and encouraging buy-in and cooperation

with others. The quality of behavioral data depends on who collects it and what tools and

resources they have available to them. The questions on this survey and the best practice

160

literature recommend stringent observations of student behavior conducted over the

course of the intervention. Whether these data gathering measures are used directly or

indirectly, as active problem solvers, current practitioners should be invited to provide

their input and help to create these tools and resources.

Summary

The field of school psychology emerged and has evolved over time to best meet

the needs of students in American public schools. Recent documentation of student

behavioral and mental health needs and the connection between emotional well-being and

academic achievement have placed accountability for outcomes at the forefront of

educational legislation, research, and practice. Counseling is a direct intervention that

school psychologists use to address the behavioral and social-emotional difficulties

students face. Current research supports the use of the problem-solving model (PSM) to

plan interventions as one method by which school psychologists can demonstrate

accountability for implementing counseling interventions that have a positive impact on

student behavioral outcomes and mental health. Similar to response to intervention and

single-case design paradigms, the PSM focuses on repeated, objective measures of

observable behavior at baseline and intervention phases to demonstrate whether

behavioral change has occurred as a result of counseling.

This study involved a survey of school psychologists‘ counseling practices, with a

specific focus on their application of the PSM. The results of this survey suggest that

school psychologists are using available behavioral measurement tools in accordance

with special education law to engage in activities such as behaviorally defining the target

behavior, developing an intervention plan, validating the problem behavior, and

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monitoring the progress of their interventions. Areas for improvement suggested by

these results include more deliberative formative and summative assessment, problem

analysis, and devising a decision-making plan. Based on these results, it is hypothesized

that, at this time, school psychologists may struggle to gather the type of objective,

empirical behavioral observations that would allow for accurate baseline and progress

monitoring data collection. Although behavioral data are being gathered, it appears as

though these data do not enable data-based decision making and the demonstration of

accountability using formative and summative assessment. Despite the fact that

respondents in this sample reported discontinuing students in the majority of cases

because counseling goals have been met, the low number of students discontinued each

year suggests that data-based decision making and accountability are areas for growth.

Noteworthy limitations of this study include several threats to validity, including a small

sample size, and increasing rates of non-response as respondents completed survey items.

Future research is needed to explore single facets of the PSM in greater depth using a

mixed methods approach to sampling and research design given that future legislative

initiatives continue to focus on accountability for student achievement. Implications for

school psychology practitioners and trainers focus on how best to train current and future

practitioners to become consumers of behavioral research and active problem solvers

who are able to collect and evaluate empirical behavioral measurements in accordance

with best practices.

162

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213

APPENDIX A: SCHOOL PSYCHOLOGIST SURVEY

Survey of School Psychologists’ Counseling Training and Practice

(Reformatted to Align With APA Style)

Instructions: Please complete the following questions to the best of your ability.

1) Do you provide group and/or individual counseling services?

o I provide only group counseling.

o I provide only individual counseling

o I provide both group and individual counseling.

2) What group(s) of children do you serve in your counseling practice?

o Special education students

o General education students

o Both special and general education students

3) On average, how many students do you recommend discontinuing counseling

services for each year?

o 0

o 1

o 2

o 3

o 4

o 5

o 6

o 7

o >7

4) What is the most common reason for you to recommend discontinuing counseling

services for a student?

o Individual counseling goals have been met

o Counseling does not appear to have a positive effect on the student‘s

behavior

o Student leaves the school or district

o Parents prefer that counseling be discontinued

o Other (please specify)

_____________________________

5) How many years have you been a school psychologist employed in a school

setting?

o 0-5

o 6-10

o >10

6) What are the grade levels of the students you predominantly work with (please

check all that apply)?

o Elementary school students

o Middle/Junior high school students

o High school students

7) Please estimate the percentage of time you spend each year on the following

activities.

214

__________ Assessment

__________ Direct Interventions

__________ Consultation and Indirect services

__________ Research

__________ Administration

__________ Systems-level activities

__________ Other

8) In what type of school do you primarily work?

o Rural

o Suburban

o Urban

o Mixed

o Other (please specify)

______________________________

9) What is the psychologist:student ratio at your school district?

o 1:<500

o 1:500-999

o 1:1000-1499

o 1:1500-2000

o 1:>2000

10) What region do you work in?

o Northeast (Connecticut, Maine, Massachusetts, New Hampshire, Rhode

Island, Vermont, New Jersey, New York, Pennsylvania)

o Midwest (Indiana, Illinois, Michigan, Ohio, Wisconsin, Iowa, Nebraska,

Kansas, North Dakota, Minnesota, South Dakota, Missouri)

o South (Delaware, District of Columbia, Florida, Georgia, Maryland, North

Carolina, South Carolina, Virginia, West Virginia, Alabama, Kentucky,

Mississippi, Tennessee, Arkansas, Louisiana, Oklahoma, Texas)

o West (Arizona, Colorado, Idaho, New Mexico, Montana, Utah, Nevada,

Wyoming, Alaska, California, Hawaii, Oregon, Washington)

11) Have you attended any continuing education programs over the past 5 years that

were specifically focused on (check all that apply):

o The No Child Left Behind Act

o Accountability for student academic and behavioral outcomes

o The provision of counseling services

o Evidence-based behavioral interventions

o Data-based decision making

o Response to Intervention

o Other (please specify)

______________________________

12) How many years has it been since you received your last degree?

o 0-5

o 6-10

o >10

13) Was your graduate program accredited by (check all that apply):

o NASP

215

o APA

o NCATE

o Your state

o Not accredited

14) What is the highest degree that you have earned?

o MA/MS

o Certificate/Specialist

o PhD/PsyD/EdD

o Other (please specify)

______________________________

15) Did your graduate academic training include specific coursework in the following

areas (please check all that apply):

o Academic interventions

o Behavioral interventions

o Counseling and Psychotherapy with children

o Counseling children with developmental disabilities

o Group counseling

o Multicultural counseling

16) When planning, implementing, or up-dating a counseling intervention, do you use

any print or online resources (e.g., IEP Pro, IEP Direct) to help you write goals,

clarify the problem, or determine expectations for the student?

o Yes

o No

17) When planning for a counseling intervention, do you come up with a behavioral

definition of the problem?

o Yes

o No

When coming up with a definition of the behavior to be addressed in counseling,

what factors do you include?

Yes No

18) Action verbs describing

what the student does in

observable terms

o o

19) Frequency (the number of

times the behavior occurs

during an observation

period

o o

20) Latency (how much time

passes between the

presentation of a stimulus

and the student‘s response

or behavior)

o o

21) Intensity (the strength or

force with which the

behavior is displayed)

o o

22) Topography (the o o

216

configuration, shape, or

form of the behavior)

23) Accuracy (a measure of

how the student‘s

behavior is correct or fits

a standard)

o o

24) Duration (how much time

passes between the onset

and the ending of a

behavior)

o o

25) When planning for a counseling intervention, do you collect baseline data before

beginning the intervention?

o Yes

o No

When you collect baseline data, how often do you use each of the following

techniques?

Never Sometimes Always

26) Direct behavioral

observation

o o o

27) 3rd party behavior

rating (from parent,

teacher, or related

service provider)

o o o

28) Sociometric

techniques

o o o

29) 3rd party interview o o o

30) Objective self-report o o o

31) Projective-

expressive technique

o o o

32) On average, how many baseline data points do you collect in order to establish a

stable pattern of the student‘s behavior?

o 1

o 2

o 3

o 4

o 5

o 6

o 7 or greater

33) When planning for a counseling intervention, do you validate the problem

behavior by comparing the identified student with a peer or a standard of

performance?

o Yes

o No

34) When planning for a counseling intervention, do you analyze the problem

217

behavior by developing and testing hypotheses related to its function?

o Yes

o No

35) When planning for a counseling intervention, do you set one or more behavioral

goals using clear and measurable criteria, defining what the student will be able to

do if the intervention is effective?

o Yes

o No

When setting a behavioral goal for a counseling intervention, how often do you

use each of the following components?

Never Sometimes Always

36) Timeframe (when the

expected progress

will be made in terms

of days, weeks, and

months)

o o o

37) Condition (the

specific

circumstances in

which the behavior

will occur)

o o o

38) Behavior (written in

objective, observable,

and measurable terms

describing what the

student will be able to

do)

o o o

39) Criteria (a standard

for how well the

behavior is to be

performed)

o o o

40) When planning for a counseling intervention, do you come up with an

intervention plan?

o Yes

o No

When writing a counseling intervention plan, how often to you use each of the

following components?

Never Sometimes Always

41) A clear description

of the procedures

to be used

o o o

42) Documentation

that the strategies

to be used have

been empirically

o o o

218

validated in the

literature on

evidence-based

interventions

43) A description of

the specific steps

and activities that

will be engaged in

during counseling

sessions

o o o

44) A description of

how each step or

activity will be

completed

o o o

45) The materials

needed for each

step or activity

o o o

46) A description of

what each person

engaged in the

activity will do

o o o

47) The location

where the

intervention is to

take place

o o o

48) When planning for a counseling intervention, do you come up with a plan to

measure the problem behavior?

o Yes

o No

When coming up with a plan for measuring the target behavior, how often do you

include each of the following components?

Never Sometimes Always

49) A behavioral

definition of

the target

behavior

o o o

50) A clear

description

of where the

behavior will

be measured

o o o

51) A clear

description

of when the

behavior will

o o o

219

be measured

52) A clear

delineation

of who will

measure the

behavior

o o o

53) A

description

of the

recording

method most

appropriate

for the

behavior

o o o

54) A

description

of the most

appropriate

recording

measure

o o o

55) When planning for an intervention, do you come up with a decision-making plan

for determining how behavioral data on the student will be collected and

interpreted?

o Yes

o No

When developing a decision-making plan, how often do you use each of the

following components?

Never Sometimes Always

56) A

determination

of the

frequency of

behavioral

measurements

and data to be

collected

o o o

57) A decision on

how the data

will be

summarized

for the

purposes of

intervention

evaluation

(e.g., visual

o o o

220

presentation,

written report

or summary)

58) A

determination

of how many

behavioral

data points

will be

collected

before the

intervention

data will be

analyzed

o o o

59) A

determination

of how much

time will pass

before the

intervention

data will be

analyzed

o o o

60) A set of

decision rules

for responding

to specific

data points

o o o

61) During implementation of a counseling intervention, do you collect progress

monitoring data on the student‘s behavior?

o Yes

o No

When you collect progress monitoring data, how often do you use each of the

following techniques?

Never Sometimes Always

62) Direct

behavioral

observation

o o o

63) 3rd party

behavior rating

scales (from

parent, teacher,

or related

service

provider)

o o o

64) Sociometric o o o

221

techniques

65) Interviews o o o

66) Objective self-

report

measures

o o o

67) Projective-

expressive

techniques

o o o

68) On average, how many progress monitoring data points do you collected to

established a stable pattern of the student‘s behavior?

o 1

o 2

o 3

o 4

o 5

o 6

o 7

o 8

o >8

69) Do you use the same method for collecting baseline data points as you do for

collecting progress monitoring data points?

o Yes

o Sometimes, depending on the situation

o No

70) During the implementation of a counseling intervention, do you engage in any

formative assessment of the student‘s behavior?

o Yes

o No

When you engage in formative assessment of the student‘s behavior, what sources

of data do you consider?

Yes No

71) The level of the

behavior (how much

the behavior is

occurring during

baseline and

intervention phases

as judged by

repeated, objective

measurements of its

frequency, duration,

intensity, or the

percentage of

intervals in which it

occurs)

o o

222

72) The trend of the

behavior (whether

the level of the

behavior is

increasing or

decreasing within

the baseline and

intervention phases)

o o

73) Anecdotal

information from the

student, his/her

family, teachers, or

related service

providers

o o

74) Your own

subjective

assessment of the

student‘s behavior

o o

75) Data documenting

the student‘s

performance in

school (e.g., work

samples, grades,

attendance records,

behavioral referrals)

o o

76) Do you measure the treatment integrity with which you implement counseling

intervention?

o Yes

o Sometimes, depending on the situation

o No

What methods do you use to measure the treatment integrity of the counseling

interventions you implement?

Yes No

77) Self-Report o o

78) Logs documenting

sessions

o o

79) Checklists for

intervention

components

o o

80) Permanent products

of student work

o o

81) Direct observation

by a 3rd

party not

directly involved in

implementation

o o

223

82) At the end of a counseling intervention, do you engage in any summative

assessment activities of the student‘s behavior and the effectiveness of the

intervention?

o Yes

o No

When you engage in summative assessment of the student‘s behavior, what

sources of data do you consider?

Yes No

83) The level of the

behavior (how much

the behavior is

occurring during

baseline and

intervention phases

as judged by

repeated, objective

measurements of its

frequency, duration,

intensity, or the

percentage of

intervals in which it

occurs)

o o

84) The trend of the

behavior (whether

the level of the

behavior is

increasing or

decreasing within

the baseline and

intervention phases)

o o

85) Anecdotal

information from the

student, his/her

family, teachers, or

related service

providers

o o

86) Your own

subjective

assessment of the

student‘s behavior

o o

87) Data documenting

the student‘s

performance in

school (e.g., work

samples, grades,

attendance records,

o o

224

behavioral referrals)

88) Thank you for taking the time to complete this survey! If you have any feedback

or comments related to this experience that you would like to share with the

researchers, please feel free to enter it here.

Thank you!

If you would like to have your name entered in to a raffle for one of two $50.00 gift

certificates, please click this link: https://www.psychdata.com/s.asp?SID=143237. You

will then be prompted to provide your name and email address. The information you

provided on the survey will in no way be connected with your contact information.

Winners will be chosen at random and notified once all data have been collected.

If you have any further questions, please feel free to contact me or my research advisor.

Rebecca Cole, M.S.

Doctoral Candidate

School Psychology

rmcole@albany.edu

Deborah Kundert

Dissertation Chair

School Psychology

dkundert@albany.edu

225

APPENDIX B: COVERLETTER EMAIL

Dear School Psychologist Colleagues:

Meeting the academic and behavioral needs of students in schools are two traditional

roles that school psychologists of the past and present have addressed in their practice.

Counseling as a direct intervention is one method that school psychologists have used to

address emotional and behavioral challenges faced by students. Current school

professionals find themselves in a climate where accountability for positive student

outcomes has been made a priority. Many school psychologists are familiar with the

problem-solving model, the Response to Intervention (RTI) movement, and with more

specific aspects of these paradigms related to repeated measures of student performance,

and data-based decision making.

At the end of this email, you will find a link to a survey that has been designed to gather

information on the counseling practices of school psychologists. In addition to

describing current counseling practices, it will also provide information on the

availability of counseling services based on current research and best practices.

This survey has been emailed to a random sample of school psychologists in the US who

are listed in the directory of Nationally Certified School Psychologists. Participation in

this study is strictly voluntary, and entails no known risks or discomforts. You are free to

skip any questions you do not wish to answer, and you may withdraw your responses at

any time. Completing this survey indicates your consent to participate in this study. All

responses are anonymous, and specific identifying information will not be collected.

Only group responses will be reported (e.g., type of school, rural, suburban, urban). As a

token of our appreciation, at the end of the survey, you will be directed to a separate link,

where you can provide identifying information for the purpose of entering a raffle for one

of two $50.00 gift certificates. If you have any questions concerning your rights as a

subject, you may contact the Office of Regulatory Research Compliance at (518) 442-

9050, or at orrc@uamail.albany.edu.

It is estimated that completion of this survey will require approximately 20 minutes of

your time. You can access the survey by clicking here. Thank you in advance for your

time and cooperation.

Sincerely,

Rebecca Cole

Doctoral Student

School Psychology

rc2398@albany.edu

Deborah Kundert, PhD

Dissertation Chairperson

School Psychology

Dkundert@albany.edu

226

APPENDIX C: FOLLOW-UP EMAIL

Date

Dear School Psychologist Colleague,

You recently received an email requesting your participation in a survey related to the

current counseling practices of school psychologists. If you have already responded to

this survey, we appreciate your time and input.

If you have not yet completed this survey, please do so as soon as possible. As

researchers, we are interested in obtaining a complete picture of the counseling practices

of school psychologists, and this is not possible without your responses!

You can access the survey by clicking here.

Thank you again for your time and cooperation!

Sincerely,

Rebecca Cole

Doctoral Student

School Psychology

rc2398@albany.edu

Deborah Kundert, PhD

Dissertation Chairperson

School Psychology

Dkundert@albany.edu

227

APPENDIX D: PILOT SURVEY FEEDBACK QUESTIONS

Dear School Psychologist,

Thank you for taking the time to complete a pilot version of my dissertation survey. Here

are several questions related to this survey. Your responses will help me to make any

necessary revisions and improvements. If you could provide your responses to me in an

email, I would appreciate it.

1) How long did it take you to complete this survey?

2) Did you find any questions to be confusing, or difficult to understand? If so,

which ones?

3) Did you understand all of the terms used?

4) Were there any questions that you thought were unnecessary or irrelevant? If so,

which ones?

5) Do you have any recommendations for making completing this survey a more

positive experience?

Thank you again for your time and assistance.

Sincerely,

Rebecca Cole

Doctoral Candidate

School Psychology

228

AP

PE

ND

IX E

: D

AT

A A

NA

LY

SIS

Gen

eral

Counse

ling P

ract

ices

of

Sch

ool

Psy

cholo

gis

ts

Res

earc

h Q

ues

tion

C

orr

espondin

g S

urv

ey Q

ues

tion(s

) D

ata

Anal

yse

s

1)

What

per

centa

ge

of

school

psy

cholo

gis

ts

pro

vid

e gro

up a

nd/o

r

indiv

idual

counse

ling? A

re

school

psy

cholo

gis

ts

counse

ling g

ener

al o

r

spec

ial

educa

tion

studen

ts, or

both

?

8)

1)

Do y

ou p

rovid

e gro

up a

nd/o

r in

div

idual

counse

ling s

ervic

es?

o

I p

rovid

e only

gro

up c

ou

nse

ling.

o

I p

rovid

e only

indiv

idual

counse

ling

o

I p

rovid

e both

gro

up a

nd i

ndiv

idual

counse

lin

g.

2)

What

gro

up(s

) o

f ch

ildre

n d

o y

ou s

erve

in y

our

coun

seli

ng p

ract

ice?

o

Spec

ial

educa

tion s

tuden

ts

o

Gen

eral

educa

tion s

tuden

ts

o

Both

sp

ecia

l an

d g

ener

al e

duca

tion s

tuden

ts

Per

centa

ges

2)

Appro

xim

atel

y h

ow

man

y s

tuden

ts d

o

school

psy

cholo

gis

ts

reco

mm

end

dec

lass

ifyin

g f

rom

counse

ling e

ach y

ear,

and w

hat

rea

sons

are

most

com

monly

cit

ed

when

mak

ing t

his

reco

mm

endat

ion?

3)

On a

ver

age,

how

man

y s

tuden

ts d

o y

ou r

ecom

men

d d

isco

nti

nuin

g

counse

ling s

ervic

es f

or

each

yea

r?

o

0

o

1

o

2

o

3

o

4

o

5

o

6

o

7

o

>7

4)

What

is

the

most

com

mon r

easo

n f

or

you t

o r

eco

mm

end d

isco

nti

nuin

g

counse

ling s

ervic

es f

or

a st

uden

t?

o

Indiv

idual

counse

ling g

oal

s hav

e bee

n m

et

o

Counse

ling d

oes

not

app

ear

to h

ave

a posi

tive

effe

ct o

n t

he

studen

t‘s

beh

avio

r

o

Stu

den

t le

aves

the

school

or

dis

tric

t

Fre

qu

enci

es

Per

centa

ges

228

229

Res

earc

h Q

ues

tion

C

orr

espondin

g S

urv

ey Q

ues

tion(s

) D

ata

Anal

yse

s

o

Par

ents

pre

fer

that

counse

ling b

e dis

conti

nued

o

Oth

er (

ple

ase

spec

ify)

3)

Are

ther

e an

y

dem

ogra

phic

dif

fere

nce

s (e

.g.,

trai

nin

g, pro

fess

ional

dev

elopm

ent,

yea

rs o

f

exper

ience

, oth

er

role

s an

d

resp

onsi

bil

itie

s)

rela

ted t

o s

chool

psy

cholo

gis

ts‘

gro

up

and i

ndiv

idual

counse

ling p

ract

ices

?

5)

How

man

y y

ears

hav

e yo

u b

een a

sch

ool

psy

cholo

gis

t em

plo

yed

in a

school

sett

ing?

o

0-5

o

6-1

0

o

>10

6)

What

are

the

gra

de

level

s of

the

studen

ts y

ou s

erve

(ple

ase

chec

k a

ll

that

apply

)?

o

Ele

men

tary

sch

ool

studen

ts

o

Mid

dle

/Junio

r hig

h s

cho

ol

studen

ts

o

Hig

h s

chool

studen

ts

7)

Ple

ase

esti

mat

e th

e per

centa

ge

of

tim

e you s

pen

d e

ach y

ear

on t

he

foll

ow

ing a

ctiv

itie

s.

__________ A

sses

smen

t

__________ D

irec

t In

terv

enti

ons

__________ C

onsu

ltat

ion a

nd I

ndir

ect

serv

ices

__________ R

esea

rch

__________ A

dm

inis

trat

ion

__________ S

yst

ems-

lev

el a

ctiv

itie

s

__________ O

ther

8)

In w

hat

type

of

school

do y

ou p

rim

aril

y w

ork

?

o

Rura

l

o

Suburb

an

o

Urb

an

o

Mix

ed

o

Oth

er (

ple

ase

spec

ify)

9)

What

is

the

psy

cholo

gis

t:st

uden

t ra

tio a

t you

r sc

ho

ol

dis

tric

t?

o

1:<

500

o

1:5

00

-999

Per

centa

ges

,

Chi

Squar

e

Anal

yse

s

229

230

Res

earc

h Q

ues

tion

C

orr

espondin

g S

urv

ey Q

ues

tion(s

) D

ata

Anal

yse

s

o

1:1

000

-1499

o

1:1

500

-2000

o

1:>

2000

10)

What

reg

ion d

o y

ou w

ork

in?

o

Nort

hea

st (

Co

nnec

ticu

t, M

aine,

Mas

sach

use

tts,

New

Ham

psh

ire,

Rho

de

Isla

nd

, V

erm

ont,

New

Jer

sey,

New

York

,

Pen

nsy

lvan

ia)

o

Mid

wes

t (I

ndia

na,

Ill

inois

, M

ichig

an, O

hio

, W

isco

nsi

n,

Iow

a,

Neb

rask

a, K

ansa

s, N

ort

h D

akota

, M

innes

ota

, S

outh

Dak

ota

,

Mis

souri

)

o

South

(D

elaw

are,

Dis

tric

t of

Colu

mbia

, F

lori

da,

Geo

rgia

,

Mar

yla

nd, N

ort

h C

aroli

na,

South

Car

oli

na,

Vir

gin

ia, W

est

Vir

gin

ia,

Ala

bam

a, K

entu

cky, M

issi

ssip

pi,

Ten

nes

see,

Ark

ansa

s, L

ouis

ian

a, O

kla

hom

a, T

exas

)

o

Wes

t (A

rizo

na,

Colo

rado,

Idah

o, N

ew M

exic

o, M

onta

na,

Uta

h,

Nev

ada,

Wyom

ing,

Ala

ska,

Cal

iforn

ia, H

awai

i, O

regon,

Was

hin

gto

n)

11)

Hav

e you a

tten

ded

an

y c

onti

nuin

g e

duca

tion p

rogra

ms

ov

er t

he

pas

t 5

yea

rs t

hat

wer

e sp

ecif

ical

ly f

ocu

sed o

n (

chec

k a

ll t

hat

apply

):

o

The

No C

hil

d L

eft

Beh

ind A

ct

o

Acc

ounta

bil

ity f

or

stud

ent

acad

emic

and b

ehav

iora

l outc

om

es

o

The

pro

vis

ion o

f co

unse

ling s

ervic

es

o

Evid

ence

-bas

ed b

ehav

iora

l in

terv

enti

ons

o

Dat

a-bas

ed d

ecis

ion m

akin

g

o

Res

ponse

to I

nte

rven

tion

o

Oth

er (

ple

ase

spec

ify)

12)

How

man

y y

ears

has

it

bee

n s

ince

you r

ecei

ved

your

last

deg

ree?

o

0-5

o

6-1

0

o

>10

230

231

Res

earc

h Q

ues

tion

C

orr

espondin

g S

urv

ey Q

ues

tion(s

) D

ata

Anal

yse

s

13)

Was

your

gra

duat

e p

rogra

m a

ccre

dit

ed b

y (

chec

k a

ll t

hat

apply

):

o

NA

SP

o

AP

A

o

NC

AT

E

o

Your

stat

e

o

Not

accr

edit

ed

14)

What

is

the

hig

hes

t deg

ree

that

you h

ave

earn

ed?

o

MA

/MS

o

Cer

tifi

cate

/Spec

iali

st

o

PhD

/Psy

D/E

dD

o

Oth

er (

ple

ase

spec

ify)

15)

Did

your

gra

duat

e ac

adem

ic t

rain

ing i

ncl

ude

spec

ific

cours

ewo

rk i

n

the

foll

ow

ing a

reas

(ple

ase

chec

k a

ll t

hat

apply

):

o

Aca

dem

ic i

nte

rven

tions

o

Beh

avio

ral

inte

rven

tions

o

Counse

ling a

nd P

sych

oth

erap

y w

ith c

hil

dre

n

o

Counse

ling c

hil

dre

n w

ith

dev

elopm

enta

l dis

abil

itie

s

o

Gro

up c

ounse

lin

g

o

Mult

icult

ura

l co

unse

ling

4)

What

type

of

trai

nin

g

and p

rofe

ssio

nal

dev

elopm

ent

hav

e sc

hool

psy

cholo

gis

ts r

ecei

ved

rela

ted t

o p

lannin

g a

nd

imple

men

ting c

ounse

lin

g

as a

dir

ect

soci

al

emoti

onal

beh

avio

ral

inte

rven

tion?

11)

Hav

e you a

tten

ded

an

y c

onti

nuin

g e

duca

tion p

rogra

ms

ov

er t

he

pas

t 5

yea

rs t

hat

wer

e sp

ecif

ical

ly f

ocu

sed o

n (

chec

k a

ll t

hat

apply

):

o

The

No C

hil

d L

eft

Beh

ind A

ct

o

Acc

ounta

bil

ity f

or

studen

t ac

adem

ic a

nd b

ehav

iora

l outc

om

es

o

The

pro

vis

ion o

f co

unse

ling s

ervic

es

o

Evid

ence

-bas

ed b

ehav

iora

l in

terv

enti

ons

o

Dat

a-bas

ed d

ecis

ion m

akin

g

o

Res

ponse

to I

nte

rven

tion

o

Oth

er (

ple

ase

spec

ify)

14)

What

is

the

hig

hes

t deg

ree

that

you h

ave

earn

ed?

o

MA

/MS

Per

centa

ges

,

Chi

Squar

e

Anal

yse

s

231

232

Res

earc

h Q

ues

tion

C

orr

espondin

g S

urv

ey Q

ues

tion(s

) D

ata

Anal

yse

s

o

Cer

tifi

cate

/Spec

iali

st

o

PhD

/Psy

D/E

dD

o

Oth

er (

ple

ase

spec

ify)

15)

Did

your

gra

duat

e ac

adem

ic t

rain

ing i

ncl

ude

spec

ific

cours

ewo

rk i

n

the

foll

ow

ing a

reas

(ple

ase

chec

k a

ll t

hat

apply

):

o

Aca

dem

ic i

nte

rven

tions

o

Beh

avio

ral

inte

rven

tions

o

Counse

ling a

nd P

sych

oth

erap

y w

ith c

hil

dre

n

o

Counse

ling c

hil

dre

n w

ith

dev

elopm

enta

l dis

abil

itie

s

o

Gro

up c

ounse

lin

g

232

233

Sch

ool

Psy

cholo

gis

ts‘

Use

of

Bes

t P

ract

ices

Rel

ated

to t

he

Pro

ble

m-S

olv

ing M

odel

Res

earc

h Q

ues

tion

C

orr

espondin

g S

urv

ey Q

ues

tion(s

) D

ata

Anal

yse

s

5)

Whic

h a

spec

ts o

f th

e

gen

eral

pro

ble

m-s

olv

ing

model

(i.

e., beh

avio

ral

def

init

ion, bas

elin

e dat

a,

pro

ble

m v

alid

atio

n,

pro

ble

m a

nal

ysi

s, g

oal

-

sett

ing, in

terv

enti

on p

lan

dev

elopm

ent,

mea

sure

men

t st

rate

gy,

dec

isio

n-m

akin

g p

lan,

pro

gre

ss m

onit

ori

ng,

form

ativ

e ev

aluat

ion,

trea

tmen

t in

tegri

ty,

and

sum

mat

ive

eval

uat

ion)

are

most

com

monly

use

d

when

des

ignin

g a

nd

imple

men

ting c

ounse

lin

g

as a

dir

ect

inte

rven

tion?

16)

When

pla

nnin

g, im

ple

men

ting, or

up

-dat

ing a

cou

nse

ling i

nte

rven

tion,

do y

ou u

se a

ny p

rint

or

onli

ne

reso

urc

es (

e.g., I

EP

Pro

, IE

P D

irec

t) t

o

hel

p y

ou w

rite

goal

s, c

lari

fy t

he

pro

ble

m b

ehav

ior,

or

det

erm

ine

expec

tati

ons

for

the

studen

t?

o

Yes

o

No

17)

When

pla

nnin

g f

or

a co

unse

ling i

nte

rven

tion, do y

ou c

om

e up w

ith a

beh

avio

ral

def

init

ion o

f th

e pro

ble

m?

o

Yes

o

No

25)

When

pla

nnin

g f

or

a co

unse

ling i

nte

rven

tion, do y

ou c

oll

ect

bas

elin

e

dat

a bef

ore

beg

innin

g t

he

inte

rven

tion?

o

Yes

o

No

33)

When

pla

nnin

g f

or

a co

unse

ling i

nte

rven

tion, do y

ou v

alid

ate

the

pro

ble

m b

ehav

ior

by c

om

par

ing t

he

iden

tifi

ed s

tuden

t w

ith a

pee

r o

r a

stan

dar

d o

f per

form

ance

?

o

Yes

o

No

34)

When

pla

nnin

g f

or

a co

unse

ling i

nte

rven

tion, do y

ou a

nal

yze

th

e

pro

ble

m b

ehav

ior

by d

evel

opin

g a

nd t

esti

ng h

ypoth

eses

rel

ated

to i

ts

funct

ion?

o

Yes

o

No

35)

When

pla

nnin

g f

or

a co

unse

ling i

nte

rven

tion, do y

ou s

et o

ne

or

more

beh

avio

ral

goal

s usi

ng c

lear

and m

easu

rable

cri

teri

a, d

efin

ing w

hat

the

studen

t w

ill

be

able

to d

o i

f th

e in

terv

enti

on i

s ef

fect

ive?

o

Yes

o

No

Per

centa

ges

233

234

Res

earc

h Q

ues

tion

C

orr

espondin

g S

urv

ey Q

ues

tion(s

) D

ata

Anal

yse

s

40)

When

pla

nnin

g f

or

a co

unse

ling i

nte

rven

tion, do y

ou c

om

e up w

ith a

n

inte

rven

tion p

lan?

o

Yes

o

No

48)

When

pla

nnin

g f

or

a co

unse

ling i

nte

rven

tion, do y

ou c

om

e up w

ith a

pla

n t

o m

easu

re t

he

pro

ble

m b

ehav

ior?

o

Yes

o

No

55)

When

pla

nnin

g f

or

an i

nte

rven

tion, do y

ou c

om

e u

p w

ith a

dec

isio

n-

mak

ing p

lan f

or

det

erm

inin

g h

ow

beh

avio

ral

dat

a on

the

studen

t w

ill

be

coll

ecte

d a

nd i

nte

rpre

ted?

o

Yes

o

No

61)

Duri

ng i

mple

men

tati

on o

f a

counse

lin

g i

nte

rven

tion, do y

ou c

oll

ect

pro

gre

ss m

onit

ori

ng d

ata

on t

he

studen

t‘s

beh

avio

r?

o

Yes

o

No

70)

Duri

ng t

he

imple

men

tati

on o

f a

counse

lin

g i

nte

rven

tion, do y

ou

engag

e in

an

y f

orm

ativ

e as

sess

men

t of

the

studen

t‘s

beh

avio

r?

o

Yes

o

No

76)

Do y

ou m

easu

re t

he

trea

tmen

t in

tegri

ty w

ith w

hic

h y

ou i

mple

men

t

counse

ling i

nte

rven

tion?

o

Yes

o

Som

etim

es, dep

endin

g o

n t

he

situ

atio

n

o

No

82)

At

the

end o

f a

counse

lin

g i

nte

rven

tion, do y

ou e

ngag

e in

an

y

sum

mat

ive

asse

ssm

ent

acti

vit

ies

of

the

studen

t‘s

beh

avio

r an

d t

he

effe

ctiv

enes

s of

the

inte

rven

tion?

o

Yes

234

235

Res

earc

h Q

ues

tion

C

orr

espondin

g S

urv

ey Q

ues

tion(s

) D

ata

Anal

yse

s

o

No

6)

How

fre

quen

tly a

re

spec

ific

com

pon

ents

of

sele

cted

ste

ps

of

the

pro

ble

m-s

olv

ing m

odel

(i.e

., b

ehav

iora

l

def

init

ion, bas

elin

e dat

a,

beh

avio

ral

goal

,

inte

rven

tion p

lan

dev

elopm

ent,

mea

sure

men

t st

rate

gy,

dec

isio

n-m

akin

g p

lan,

pro

gre

ss m

onit

ori

ng,

form

ativ

e ev

aluat

ion,

trea

tmen

t in

tegri

ty,

and

sum

mat

ive

eval

uat

ion)

imple

men

ted b

y s

chool

psy

cholo

gis

ts a

s th

ey

des

ign a

nd i

mple

men

t

counse

ling a

s a

dir

ect

inte

rven

tion?

When

com

ing u

p w

ith a

def

init

ion o

f th

e beh

avio

r to

be

addre

ssed

in

cou

nse

ling,

what

fac

tors

do y

ou i

ncl

ude?

(Y

es, N

o)

18)

Act

ion v

erbs

des

crib

ing w

hat

the

studen

t does

in o

bse

rvab

le t

erm

s

19)

Fre

qu

ency (

the

num

ber

of

tim

es t

he

beh

avio

r occ

urs

duri

ng a

n o

bse

rvat

ion

per

iod

20)

Lat

ency (

ho

w m

uch

tim

e pas

ses

bet

wee

n t

he

pre

senta

tion o

f a

stim

ulu

s an

d

the

studen

t‘s

resp

onse

or

beh

avio

r)

21)

Inte

nsi

ty (

the

stre

ngth

or

forc

e w

ith w

hic

h t

he

beh

avio

r is

dis

pla

yed

)

22)

Topogra

ph

y (

the

confi

gu

rati

on, sh

ape,

or

form

of

the

beh

avio

r)

23)

Acc

ura

cy (

a m

easu

re o

f how

the

studen

t‘s

beh

avio

r is

corr

ect

or

fits

a

stan

dar

d)

24)

Dura

tion (

how

much

tim

e pas

ses

bet

wee

n t

he

onse

t an

d t

he

endin

g o

f a

beh

avio

r)

When

you c

oll

ect

bas

elin

e dat

a, h

ow

oft

en d

o y

ou u

se e

ach o

f th

e

foll

ow

ing t

echniq

ues

? (

Nev

er, S

om

eti

mes

, A

lways)

26)

Dir

ect

beh

avio

ral

obse

rvat

ion

27)

3rd

par

ty b

ehav

ior

rati

ng (

from

par

ent,

tea

cher

, or

rela

ted s

ervic

e pro

vid

er)

28)

Soci

om

etri

c te

chniq

ues

29)

3rd

par

ty i

nte

rvie

w

30)

Obje

ctiv

e se

lf-r

eport

31)

Pro

ject

ive-

expre

ssiv

e te

chniq

ue

32)

On a

ver

age,

how

man

y b

asel

ine

dat

a poin

ts d

o y

ou c

oll

ect

in o

rder

to

esta

bli

sh a

sta

ble

pat

tern

of

the

studen

t‘s

beh

avio

r?

o

1

o

2

o

3

o

4

Per

centa

ges

235

236

Res

earc

h Q

ues

tion

C

orr

espondin

g S

urv

ey Q

ues

tion(s

) D

ata

Anal

yse

s

o

5

o

6

o

7 o

r gre

ater

When

set

ting a

beh

avio

ral

goal

fo

r a

counse

ling i

nte

rven

tion, how

oft

en

do y

ou u

se e

ach o

f th

e fo

llow

ing c

om

ponen

ts?

(Nev

er,

So

met

imes

, A

lways)

36

) T

imef

ram

e (w

hen

th

e ex

pec

ted p

rogre

ss w

ill

be

mad

e in

ter

ms

of

days,

wee

ks,

and m

onth

s)

37

) C

ondit

ion (

the

spec

ific

cir

cum

stan

ces

in w

hic

h t

he

beh

avio

r w

ill

occ

ur)

38

) B

ehav

ior

(wri

tten

in o

bje

ctiv

e, o

bse

rvab

le, an

d m

easu

rable

ter

ms

des

crib

ing w

hat

the

studen

t w

ill

be

able

to d

o)

39

) C

rite

ria

(a s

tandar

d f

or

how

wel

l th

e beh

avio

r is

to b

e per

form

ed)

When

wri

ting a

counse

ling i

nte

rven

tion p

lan, how

oft

en t

o y

ou u

se e

ach

of

the

foll

ow

ing c

om

pon

ents

? (N

ever

, S

om

etim

es,

Alw

ays)

41)

A c

lear

des

crip

tio

n o

f th

e pro

cedure

s to

be

use

d

42)

Docu

men

tati

on t

hat

the

stra

tegie

s to

be

use

d h

ave

bee

n e

mpir

ical

ly v

alid

ated

in

the

lite

ratu

re o

n e

vid

ence

-bas

ed i

nte

rven

tions

43)

A d

escr

ipti

on o

f th

e sp

ecif

ic s

teps

and a

ctiv

itie

s th

at w

ill

be

engag

ed i

n d

uri

ng

counse

ling s

essi

ons

44)

A d

escr

ipti

on o

f how

eac

h s

tep o

r ac

tivit

y w

ill

be

com

ple

ted

45)

The

mat

eria

ls n

eed

ed f

or

each

ste

p o

r ac

tivit

y

46)

A d

escr

ipti

on o

f w

hat

eac

h p

erso

n e

ngag

ed i

n t

he

acti

vit

y w

ill

do

47)

The

loca

tion w

her

e th

e in

terv

enti

on i

s to

tak

e pla

ce

When

com

ing u

p w

ith a

pla

n f

or

mea

suri

ng t

he

targ

et b

ehav

ior,

ho

w o

ften

do y

ou i

ncl

ude

each

of

the

foll

ow

ing c

om

ponen

ts?

(Nev

er,

So

met

imes

,

Alw

ays)

49

) A

beh

avio

ral

def

init

ion o

f th

e ta

rget

beh

avio

r

236

237

Res

earc

h Q

ues

tion

C

orr

espondin

g S

urv

ey Q

ues

tion(s

) D

ata

Anal

yse

s

50

) A

cle

ar d

escr

ipti

on o

f w

her

e th

e b

ehav

ior

wil

l be

mea

sure

d

51

) A

cle

ar d

escr

ipti

on o

f w

hen

the

beh

avio

r w

ill

be

mea

sure

d

52

) A

cle

ar d

elin

eati

on o

f w

ho w

ill

mea

sure

the

beh

avio

r

53

) A

des

crip

tio

n o

f th

e re

cord

ing m

ethod m

ost

appro

pri

ate

for

the

beh

avio

r

54

) A

des

crip

tio

n o

f th

e m

ost

appro

pri

ate

reco

rdin

g m

easu

re

When

dev

elopin

g a

dec

isio

n-m

akin

g p

lan, how

oft

en d

o y

ou u

se e

ach o

f

the

foll

ow

ing c

om

pon

ents

? (

Nev

er, S

om

eti

mes

, A

lway

s)

56

) A

det

erm

inat

ion o

f th

e fr

equen

cy o

f b

ehav

iora

l m

easu

rem

ents

and

dat

a to

be

coll

ecte

d

57

) A

dec

isio

n o

n h

ow

the

dat

a w

ill

be

sum

mar

ized

for

the

purp

ose

s of

inte

rven

tion e

val

uat

ion (

e.g., v

isu

al p

rese

nta

tion,

wri

tten

rep

ort

or

sum

mar

y)

58

) A

det

erm

inat

ion o

f how

man

y b

ehav

iora

l d

ata

poin

ts w

ill

be

coll

ecte

d

bef

ore

th

e in

terv

enti

on d

ata

wil

l be

anal

yzed

59

) A

det

erm

inat

ion o

f how

much

tim

e w

ill

pas

s bef

ore

the

inte

rven

tion

dat

a w

ill

be

anal

yze

d

60

) A

set

of

dec

isio

n r

ule

s fo

r re

spondin

g t

o s

pec

ific

dat

a poin

ts

When

you c

oll

ect

pro

gre

ss m

onit

ori

ng d

ata,

how

oft

en d

o y

ou u

se e

ach o

f

the

foll

ow

ing t

echniq

ues

? (N

ever

, S

om

eti

mes

, A

lways)

62

) D

irec

t beh

avio

ral

obse

rvat

ion

63

) 3

rd p

arty

beh

avio

r ra

ting s

cale

s (f

rom

par

ent,

tea

cher

, or

rela

ted s

ervic

e

pro

vid

er)

64

) S

oci

om

etri

c te

chniq

ues

65

) In

terv

iew

s

66

) O

bje

ctiv

e se

lf-r

eport

mea

sure

s

67

) P

roje

ctiv

e-ex

pre

ssiv

e te

chniq

ues

68

) O

n a

ver

age,

how

man

y p

rogre

ss m

onit

ori

ng d

ata

po

ints

do y

ou

237

238

Res

earc

h Q

ues

tion

C

orr

espondin

g S

urv

ey Q

ues

tion(s

) D

ata

Anal

yse

s

coll

ecte

d t

o e

stab

lish

ed a

sta

ble

pat

tern

of

the

stud

ent‘

s beh

avio

r?

o

1

o

2

o

3

o

4

o

5

o

6

o

7

o

8

o

>8

69)

Do y

ou u

se t

he

sam

e m

ethod f

or

coll

ecti

ng b

asel

ine

dat

a poin

ts a

s you

do f

or

coll

ecti

ng p

rogre

ss m

on

itori

ng d

ata

poin

ts?

o

Yes

o

Som

etim

es, dep

endin

g o

n t

he

situ

atio

n

o

No

When

you e

ngag

e in

form

ativ

e as

sess

men

t of

the

studen

t‘s

beh

avio

r, w

hat

sou

rces

of

dat

a do y

ou c

onsi

der

? (

Yes

, N

o)

71

) T

he

level

of

the

beh

avio

r (h

ow

much

the

beh

avio

r is

occ

urr

ing d

uri

ng

bas

elin

e an

d i

nte

rven

tion p

has

es a

s ju

dged

by r

epea

ted, obje

ctiv

e

mea

sure

men

ts o

f it

s fr

equen

cy, dura

tion, in

tensi

ty, or

the

per

centa

ge

of

inte

rval

s in

whic

h i

t occ

urs

)

72

) T

he

tren

d o

f th

e b

ehav

ior

(whet

her

th

e le

vel

of

the

beh

avio

r is

incr

easi

ng o

r

dec

reas

ing w

ithin

the

bas

elin

e an

d i

nte

rven

tion p

has

es)

73

) A

nec

dota

l in

form

atio

n f

rom

the

studen

t, h

is/h

er f

amil

y, te

ach

ers,

or

rela

ted

serv

ice

pro

vid

ers

74

) Y

our

ow

n s

ubje

ctiv

e as

sess

men

t of

the

studen

t‘s

beh

avio

r

75

) D

ata

docu

men

tin

g t

he

studen

t‘s

per

form

ance

in s

chool

(e.g

., w

ork

sam

ple

s,

gra

des

, at

tend

ance

rec

ord

s, b

ehav

iora

l re

ferr

als)

238

239

Res

earc

h Q

ues

tion

C

orr

espondin

g S

urv

ey Q

ues

tion(s

) D

ata

Anal

yse

s

What

met

hods

do y

ou u

se t

o m

easu

re t

he

trea

tmen

t in

tegri

ty o

f th

e

cou

nse

ling i

nte

rven

tions

yo

u i

mple

men

t? (

Yes

, N

o)

77

) S

elf-

Rep

ort

78

) L

ogs

docu

men

ting s

essi

ons

79

) C

hec

kli

sts

for

inte

rven

tio

n c

om

ponen

ts

80

) P

erm

anen

t pro

duct

s of

studen

t w

ork

81

) D

irec

t obse

rvat

ion b

y a

3rd

par

ty n

ot

dir

ectl

y i

nvolv

ed i

n i

mple

men

tati

on

When

you e

ngag

e in

su

mm

ativ

e as

sess

men

t of

the

studen

t‘s

beh

avio

r,

what

sourc

es o

f dat

a do y

ou c

onsi

der

? (

Yes

, N

o)

83

) T

he

level

of

the

beh

avio

r (h

ow

much

the

beh

avio

r is

occ

urr

ing d

uri

ng

bas

elin

e an

d i

nte

rven

tion p

has

es a

s ju

dged

by r

epea

ted, obje

ctiv

e

mea

sure

men

ts o

f it

s fr

equen

cy, dura

tion, in

tensi

ty, or

the

per

centa

ge

of

inte

rval

s in

whic

h i

t occ

urs

)

84

) T

he

tren

d o

f th

e b

ehav

ior

(whet

her

th

e le

vel

of

the

beh

avio

r is

incr

easi

ng o

r

dec

reas

ing w

ithin

the

bas

elin

e an

d i

nte

rven

tion p

has

es)

85

) A

nec

dota

l in

form

atio

n f

rom

the

studen

t, h

is/h

er f

amil

y, te

ach

ers,

or

rela

ted

serv

ice

pro

vid

ers

86

) Y

our

ow

n s

ubje

ctiv

e as

sess

men

t of

the

studen

t‘s

beh

avio

r

87

) D

ata

docu

men

tin

g t

he

studen

t‘s

per

form

ance

in s

chool

(e.g

., w

ork

sam

ple

s,

gra

des

, at

tend

ance

rec

ord

s, b

ehav

iora

l re

ferr

als)

7)

Are

ther

e an

y

dem

ogra

phic

dif

fere

nce

s (e

.g.,

trai

nin

g, pro

fess

ional

dev

elopm

ent,

yea

rs o

f

exper

ience

, oth

er

role

s an

d

resp

onsi

bil

itie

s)

5)

How

man

y y

ears

hav

e yo

u b

een a

sch

ool

psy

cholo

gis

t em

plo

yed

in a

school

sett

ing?

o

0-5

o

6-1

0

o

>10

6)

What

are

the

gra

de

level

s of

the

studen

ts y

ou s

erve

(ple

ase

chec

k a

ll

that

apply

)?

o

Ele

men

tary

sch

ool

studen

ts

Per

centa

ges

,

Chi

Squar

e

Anal

yse

s

239

240

Res

earc

h Q

ues

tion

C

orr

espondin

g S

urv

ey Q

ues

tion(s

) D

ata

Anal

yse

s

bet

wee

n s

cho

ol

psy

cholo

gis

ts i

n

term

s of

thei

r use

of

the

gen

eral

ste

ps

of

the

pro

ble

m-s

olv

ing

model

when

des

ignin

g a

nd

imple

men

ting

counse

ling a

s a

dir

ect

inte

rven

tion?

o

Mid

dle

/Junio

r hig

h s

cho

ol

studen

ts

o

Hig

h s

chool

studen

ts

7)

Ple

ase

esti

mat

e th

e per

centa

ge

of

tim

e you s

pen

d e

ach y

ear

on t

he

foll

ow

ing a

ctiv

itie

s.

__________ A

sses

smen

t

__________ D

irec

t In

terv

enti

ons

__________ C

onsu

ltat

ion a

nd I

ndir

ect

serv

ices

__________ R

esea

rch

__________ A

dm

inis

trat

ion

__________ S

yst

ems-

lev

el a

ctiv

itie

s

__________ O

ther

8)

In w

hat

type

of

school

do y

ou p

rim

aril

y w

ork

?

o

Rura

l

o

Suburb

an

o

Urb

an

o

Mix

ed

o

Oth

er (

ple

ase

spec

ify)

9)

What

is

the

psy

cholo

gis

t:st

uden

t ra

tio a

t you

r sc

ho

ol

dis

tric

t?

o

1:<

500

o

1:5

00

-999

o

1:1

000

-1499

o

1:1

500

-2000

o

1:>

2000

10)

What

reg

ion d

o y

ou w

ork

in?

o

Nort

hea

st (

Co

nnec

ticu

t, M

aine,

Mas

sach

use

tts,

New

Ham

psh

ire,

Rho

de

Isla

nd

, V

erm

ont,

New

Jer

sey,

New

York

,

Pen

nsy

lvan

ia)

o

Mid

wes

t (I

ndia

na,

Ill

inois

, M

ichig

an, O

hio

, W

isco

nsi

n,

Iow

a,

Neb

rask

a, K

ansa

s, N

ort

h D

akota

, M

innes

ota

, S

outh

Dak

ota

,

Mis

souri

)

240

241

Res

earc

h Q

ues

tion

C

orr

espondin

g S

urv

ey Q

ues

tion(s

) D

ata

Anal

yse

s

o

South

(D

elaw

are,

Dis

tric

t of

Colu

mbia

, F

lori

da,

Geo

rgia

,

Mar

yla

nd, N

ort

h C

aroli

na,

South

Car

oli

na,

Vir

gin

ia, W

est

Vir

gin

ia,

Ala

bam

a, K

entu

cky, M

issi

ssip

pi,

Ten

nes

see,

Ark

ansa

s, L

ouis

ian

a, O

kla

hom

a, T

exas

)

o

Wes

t (A

rizo

na,

Colo

rado,

Idah

o, N

ew M

exic

o, M

onta

na,

Uta

h,

Nev

ada,

Wyom

ing,

Ala

ska,

Cal

iforn

ia, H

awai

i, O

regon,

Was

hin

gto

n)

11)

Hav

e you a

tten

ded

an

y c

onti

nuin

g e

ducat

ion p

rogra

ms

ov

er t

he

pas

t 5

yea

rs t

hat

wer

e sp

ecif

ical

ly f

ocu

sed o

n (

chec

k a

ll t

hat

apply

):

o

The

No C

hil

d L

eft

Beh

ind A

ct

o

Acc

ounta

bil

ity f

or

studen

t ac

adem

ic a

nd b

ehav

iora

l outc

om

es

o

The

pro

vis

ion o

f co

unse

ling s

ervic

es

o

Evid

ence

-bas

ed b

ehav

iora

l in

terv

enti

ons

o

Dat

a-bas

ed d

ecis

ion m

akin

g

o

Res

ponse

to I

nte

rven

tion

o

Oth

er (

ple

ase

spec

ify)

12)

How

man

y y

ears

has

it

bee

n s

ince

you r

ecei

ved

your

last

deg

ree?

o

0-5

o

6-1

0

o

>10

13)

Was

your

gra

duat

e p

rogra

m a

ccre

dit

ed b

y (

chec

k a

ll t

hat

apply

):

o

NA

SP

o

AP

A

o

NC

AT

E

o

Your

stat

e

o

Not

accr

edit

ed

14)

What

is

the

hig

hes

t deg

ree

that

you h

ave

earn

ed?

o

MA

/MS

o

Cer

tifi

cate

/Spec

iali

st

o

PhD

/Psy

D/E

dD

241

242 242

242

Res

earc

h Q

ues

tion

C

orr

espondin

g S

urv

ey Q

ues

tion(s

) D

ata

Anal

yse

s

o

Oth

er (

ple

ase

spec

ify)

15)

Did

your

gra

duat

e ac

adem

ic t

rain

ing i

ncl

ude

spec

ific

cours

ewo

rk i

n

the

foll

ow

ing a

reas

(ple

ase

chec

k a

ll t

hat

apply

):

o

Aca

dem

ic i

nte

rven

tions

o

Beh

avio

ral

inte

rven

tions

o

Counse

ling a

nd P

sych

oth

erap

y w

ith c

hil

dre

n

o

Counse

ling c

hil

dre

n w

ith

dev

elopm

enta

l dis

abil

itie

s

o

Gro

up c

ounse

lin

g

o

Mult

icult

ura

l co

unse

ling

242

243

APPENDIX F: NON-SIGNIFICANT CHI-SQUARE RESULTS

Chi Square Analysis Comparing Use of Problem Validation and Psychologist:Student

Ratio

Psychologist:Student Ratio

1:<500

1:500

-999

1:1000

-1499

1:1500-

2000

1:>2000

Total

Validate the Problem

Observed 27.0 55.0 51.0 39.0 21.0 193

Expected 27.7 55.4 54.5 33.6 21.8 193

Std. Residual -0.1 -0.1 -0.5 0.9 -0.2

Do Not Validate the Problem

Observed 6.0 11.0 14.0 1.0 5.0 37

Expected 5.3 10.6 10.5 6.4 4.2 37

Std. Residual 0.3 0.1 1.1 -2.1 0.4

Total

Observed 33.0 66.0 65.0 40.0 26.0 230

Expected 33.0 66.0 65.0 40.0 26.0 230

Note: χ2= 7.22, df=4, Sig.=0.125

Chi Square Analysis Comparing Use of Behavioral Goals and Psychologist:Student Ratio

Psychologist:Student Ratio

1:<500

1:500-

999

1:1000-

1499

1:1500-

2000

1:>2000

Total

Set behavioral goal(s)

Observed 27.0 59.0 52.0 29.0 22.0 189

Expected 27.0 54.0 54.0 32.7 21.3 189

Std. Residual 0.0 0.7 -0.3 -0.7 0.2

Do Not Set behavioral goal(s)

Observed 6.0 7.0 14.0 11.0 4.0 42

Expected 6.0 12.0 12.0 7.3 4.7 42

Std. Residual 0.0 -1.4 0.6 1.4 -0.3

Total

Observed 33.0 66.0 66.0 40.0 26.0 231

Expected 33.0 66.0 66.0 40.0 26.0 231

Note: χ2= 5.43, df=4, Sig.=0.246

244

Chi Square Analysis Comparing Use of Intervention Plans and Years of Experience

Years of Experience

0-5 6-10 >10 Total

Use Behavioral Intervention Plan

Observed 76.0 37.0 81.0 194

Expected 81.7 37.4 74.9 194

Std. Residual -0.6 -0.1 0.7

Do Not Use Behavioral

Intervention Plan

Observed 20.0 7.0 7.0 34

Expected 14.3 6.6 13.1 34

Std. Residual 1.5 0.2 -1.7

Total

Observed 96.0 44.0 88.0 228

Expected 96.0 44.0 88.0 228

Note: χ2= 6.04, df=2, Sig.=0.049

Chi Square Analysis Comparing Use of Decision-Making Plans and Time Spent

Counseling

% of Time Spent Counseling

Low

(0-9%)

Medium

(10-24%)

High

(25-100%)

Total

Use Decision-Making Plans

Observed 41.0 76.0 37.0 154

Expected 45.3 67.6 41.1 154

Std. Residual -0.6 1.0 -0.6

Do Not Use Decision-

Making Plans

Observed 24.0 21.0 22.0 67

Expected 19.7 29.4 17.9 67

Std. Residual 1.0 -1.6 1.0

Total

Observed 65.0 97.0 59.0 221

Expected 65.0 97.0 59.0 221

Note: χ2= 6.15, df=2, Sig.=0.046

245

Chi Square Analysis Comparing Use of Problem Analysis and Graduate Degree Earned

Degree Level

MA, MS,

Specialist,

Certificate

PhD/PsyD/

EdD

Total

Use Problem Analysis

Observed 122.0 40.0 162

Expected 122.5 39.5 162

Std. Residual 0.0 0.1

Do Not Use Problem Analysis

Observed 55.0 17.0 72

Expected 54.5 17.5 72

Std. Residual 0.1 -0.1

Total

Observed 177.0 57.0 234

Expected 177.0 57.0 234

Note: χ2= 0.03, df=1, Sig.=0.859

Chi Square Analysis Comparing Use of Problem Analysis and Years of Experience

Years of Experience

0-5 6-10 >10 Total

Use Problem Analysis

Observed 68.0 30.0 64.0 162

Expected 69.2 30.5 62.3 162

Std. Residual -0.1 -0.1 0.2

Do Not Use Problem Analysis

Observed 32.0 14.0 26.0 72

Expected 30.8 13.5 27.7 72

Std. Residual 0.2 0.1 -0.3

Total

Observed 100.0 44.0 90.0 234

Expected 100.0 44.0 90.0 234

Note: χ2= 0.24, df=2, Sig.=0.885

246

Chi Square Analysis Comparing Students Served in Counseling and Years of Experience

Years of Experience

Students 0-5 6-10 >10 Total

Special Education

Observed 33.0 12.0 16.0 61

Expected 26.1 11.9 23.0 61

Std. Residual 1.4 0.0 -1.5

General Education

Observed 0.0 0.0 3.0 3

Expected 1.3 0.6 1.1 3

Std. Residual -1.1 -0.8 1.8

Special and General Education

Observed 70.0 35.0 72.0 177

Expected 75.6 34.5 66.8 177

Std. Residual -0.6 0.1 0.6

Total

Observed 103.0 47.0 91.0 241

Expected 103.0 47.0 91.0 241

Note: χ2= 9.76, df=4, Sig.=0.045

247

Chi Square Analysis Comparing Number of Students Discontinued From Counseling and

Time Spent Counseling

% of Time Spent Counseling

Number of Students

Low

(0-9%)

Medium

(10-24%)

High

(25-100%)

Total

0

Observed 8.0 15.0 11.0 34

Expected 8.3 16.2 9.5 34

Std. Residual -0.1 -0.3 0.5

1-3

Observed 37.0 57.0 26.0 120

Expected 29.3 57.1 33.5 120

Std. Residual 1.4 0.0 -1.3

4-6

Observed 9.0 18.0 17.0 44

Expected 10.8 20.9 12.3 44

Std. Residual -0.5 -0.6 1.3

≥7

Observed 2.0 19.0 10.0 31

Expected 7.6 14.8 8.7 31

Std. Residual -2.0 1.1 0.5

Total

Observed 56.0 109.0 64.0 229

Expected 56.0 109.0 64.0 229

Note: χ2= 12.06, df=6, Sig.=0.061

248

APPENDIX G: LOGISTIC REGRESSION MODELS

Stepwise Logistic Regression Predicting Use of a Behavioral Definition From Graduate

Degree Earned, Time Spent Counseling, Years of Experience, Grade Levels Served, and

Psychologist:Student Ratio

Block 0

Variable B (SE) df Sig.

Variables Included in the Model

Constant -1.95 (.195) 1 .000

Variables Not Included in the Model

Graduate Degree 0.83 1 .363

Low Counseling 3.24 1 .072

Medium Counseling 0.50 1 .480

High Counseling 3.40 2 .183

0-5 yrs of Experience 0.01 1 .905

6-10 yrs of Experience 0.02 1 .892

>10 yrs of Experience 0.06 2 .972

Elementary School 2.35 1 .125

Middle/Junior High School 0.37 1 .541

High School 0.07 1 .789

1:<500 1.43 1 .232

1:500-999 0.31 1 .580

1:1000-1499 0.40 1 .525

1:1500-2000 0.49 1 .484

1:>2000 2.43 4 .657

Overall Statistics 11.29 12 .505

Block 1

Step

Number

-2 Log

Likelihood

Cox and

Snell R2

Nagelkerke

R2

Hosmer and Lemeshow Test

χ2

df Sig

1 169.129 .049 .092 6.949 8 .542

2 169.136 .048 .092 6.632 8 .577

3 169.274 .048 .091 6.373 8 .606

4 169.625 .047 .088 6.033 8 .644

5 174.016 .029 .055 2.833 6 .829

6 175.434 .023 .044 2.359 4 .670

7 178.945 .009 .017 .000 0

8 181.116 .000 .000 .000 0

249

Stepwise Logistic Regression Predicting Collection of Baseline Data Points From

Graduate Degree Earned, Time Spent Counseling, Years of Experience, Grade Levels

Served, and Psychologist:Student Ratio

Block O

Variable B (SE) df Sig.

Variables Included in the Model

Constant -1.24 (.156) 1 .000

Variables Not Included in the Model

Graduate Degree 0.37 1 .543

Low Counseling 0.02 1 .896

Medium Counseling 3.18 1 .075

High Counseling 5.13 2 .077

0-5 yrs of Experience 0.31 1 .576

6-10 yrs of Experience 1.52 1 .217

>10 yrs of Experience 1.52 2 .467

Elementary School 3.63 1 .057

Middle/Junior High School 3.57 1 .059

High School 0.01 1 .937

1:<500 0.03 1 .853

1:500-999 0.06 1 .802

1:1000-1499 0.00 1 .987

1:1500-2000 0.41 1 .523

1:>2000 1.16 4 .885

Overall Statistics 13.89 12 .308

Block 1

Step

Number

-2 Log

Likelihood

Cox and

Snell R2

Nagelkerke

R2

Hosmer and Lemeshow Test

χ2

df Sig

1 237.406 .058 .088 10.059 8 .261

2 238.327 .054 .082 2.057 7 .957

3 238.546 .053 .081 2.067 8 .979

4 238.960 .051 .078 3.230 8 .919

5 240.588 .045 .068 5.744 7 .570

6 244.334 .030 .045 2.895 2 .235

250

Stepwise Logistic Regression Predicting Problem Validation From Graduate Degree

Earned, Time Spent Counseling, Years of Experience, Grade Levels Served, and

Psychologist:Student Ratio

Block O

Variable B (SE) df Sig.

Variables Included in the Model

Constant -1.641 (.180) 1 .000

Variables Not Included in the Model

Graduate Degree 0.20 1 .652

Low Counseling 0.20 1 .655

Medium Counseling 0.94 1 .334

High Counseling 0.95 2 .621

0-5 yrs of Experience 0.40 1 .527

6-10 yrs of Experience 0.30 1 .583

>10 yrs of Experience 0.50 2 .788

Elementary School 0.00 1 .983

Middle/Junior High School 0.13 1 .716

High School 0.16 1 .686

1:<500 0.11 1 .742

1:500-999 0.01 1 .909

1:1000-1499 1.89 1 .170

1:1500-2000 6.46 1 .011

1:>2000 7.07 4 .132

Overall Statistics 10.07 12 .610

Block 1

Step

Number

-2 Log

Likelihood

Cox and

Snell R2

Nagelkerke

R2

Hosmer and Lemeshow Test

χ2

df Sig

1 189.785 .053 .090 5.938 8 .654

2 190.096 .052 .088 4.113 8 .847

3 190.239 .051 .087 3.076 8 .930

4 191.422 .046 .079 3.305 8 .914

5 191.768 .045 .076 4.234 8 .835

6 192.169 .043 .073 2.594 6 .858

7 192.819 .040 .069 .000 3 1.000

251

Stepwise Logistic Regression Predicting Problem Analysis From Graduate Degree

Earned, Time Spent Counseling, Years of Experience, Grade Levels Served, and

Psychologist:Student Ratio

Block O

Variable B (SE) df Sig.

Variables Included in the Model

Constant -.800 (.143) 1 .000

Variables Not Included in the Model

Graduate Degree 0.12 1 .725

Low Counseling 0.46 1 .500

Medium Counseling 1.18 1 .278

High Counseling 1.18 2 .554

0-5 yrs of Experience 0.03 1 .859

6-10 yrs of Experience 0.13 1 .718

>10 yrs of Experience 0.26 2 .879

Elementary School 0.40 1 .529

Middle/Junior High School 2.68 1 .102

High School 0.13 1 .721

1:<500 0.25 1 .616

1:500-999 0.24 1 .628

1:1000-1499 0.02 1 .884

1:1500-2000 0.00 1 .972

1:>2000 0.41 4 .982

Overall Statistics 5.24 12 .950

Block 1

Step

Number

-2 Log

Likelihood

Cox and

Snell R2

Nagelkerke

R2

Hosmer and Lemeshow Test

χ2

df Sig

1 278.216 .023 .033 9.904 8 .272

2 278.217 .023 .033 7.155 8 .520

3 278.854 .020 .029 5.095 8 .747

4 278.993 .020 .028 6.365 8 .606

5 279.028 .020 .028 2.276 6 .893

6 279.326 .018 .026 0.188 4 .998

7 280.874 .012 .016 0.000 0

8 283.564 .000 .000 0.000 0

252

Stepwise Logistic Regression Predicting Setting Behavioral Goals From Graduate

Degree Earned, Time Spent Counseling, Years of Experience, Grade Levels Served, and

Psychologist:Student Ratio

Block O

Variable B (SE) df Sig.

Variables Included in the Model

Constant -1.493 (.171) 1 .000

Variables Not Included in the Model

Graduate Degree 1.36 1 .244

Low Counseling 0.10 1 .754

Medium Counseling 0.12 1 .735

High Counseling 0.14 2 .934

0-5 yrs of Experience 0.13 1 .723

6-10 yrs of Experience 0.57 1 .452

>10 yrs of Experience 0.57 2 .753

Elementary School 0.68 1 .408

Middle/Junior High School 0.28 1 .599

High School 0.00 1 .980

1:<500 0.00 1 .980

1:500-999 3.70 1 .054

1:1000-1499 0.51 1 .475

1:1500-2000 3.05 1 .081

1:>2000 5.63 4 .229

Overall Statistics 9.02 12 .701

Block 1

Step

Number

-2 Log

Likelihood

Cox and

Snell R2

Nagelkerke

R2

Hosmer and Lemeshow Test

χ2

df Sig

1 208.998 .040 .064 7.951 8 .438

2 209.272 .038 .063 2.504 7 .927

3 209.308 .038 .062 3.101 8 .928

4 209.835 .036 .059 2.627 7 .917

5 210.390 .034 .055 3.125 8 .926

6 211.354 .030 .048 .634 6 .966

7 212.531 .025 .040 .000 3 1.000

8 218.246 .000 .000 .000 0

253

Stepwise Logistic Regression Predicting Use of an Intervention Plan From Graduate

Degree Earned, Time Spent Counseling, Years of Experience, Grade Levels Served, and

Psychologist:Student Ratio

Block O

Variable B (SE) df Sig.

Variables Included in the Model

Constant -1.745 (.189) 1 .000

Variables Not Included in the Model

Graduate Degree 1.48 1 .224

Low Counseling 0.20 1 .652

Medium Counseling 0.25 1 .620

High Counseling 1.05 2 .592

0-5 yrs of Experience 5.02 1 .025

6-10 yrs of Experience 0.01 1 .907

>10 yrs of Experience 5.84 2 .054

Elementary School 1.53 1 .217

Middle/Junior High School 0.16 1 .691

High School 0.50 1 .479

1:<500 0.23 1 .631

1:500-999 2.53 1 .112

1:1000-1499 1.10 1 .295

1:1500-2000 0.27 1 .605

1:>2000 3.71 4 .446

Overall Statistics 14.03 12 .299

Block 1

Step

Number

-2 Log

Likelihood

Cox and

Snell R2

Nagelkerke

R2

Hosmer and Lemeshow Test

χ2

df Sig

1 172.042 .064 .112 12.237 8 .141

2 172.059 .064 .112 6.218 8 .623

3 173.155 .059 .104 4.505 8 .809

4 176.717 .044 .077 2.321 7 .940

5 177.392 .041 .072 1.727 6 .943

6 178.388 .036 .064 .094 3 .993

7 180.595 .027 .047 .000 1 1.000

254

Stepwise Logistic Regression Predicting Use of a Measurement Plan From Graduate

Degree Earned, Time Spent Counseling, Years of Experience, Grade Levels Served, and

Psychologist:Student Ratio

Block O

Variable B (SE) df Sig.

Variables Included in the Model

Constant -1.280 (.165) 1 .000

Variables Not Included in the Model

Graduate Degree 0.02 1 .154

Low Counseling 0.26 1 .608

Medium Counseling 3.36 1 .067

High Counseling 3.74 2 .154

0-5 yrs of Experience 0.72 1 .397

6-10 yrs of Experience 0.30 1 .582

>10 yrs of Experience 1.71 2 .425

Elementary School 0.02 1 .894

Middle/Junior High School 1.61 1 .204

High School 2.26 1 .133

1:<500 0.20 1 .655

1:500-999 0.12 1 .728

1:1000-1499 0.07 1 .790

1:1500-2000 0.21 1 .650

1:>2000 1.33 4 .857

Overall Statistics 10.33 12 .587

Block 1

Step

Number

-2 Log

Likelihood

Cox and

Snell R2

Nagelkerke

R2

Hosmer and Lemeshow Test

χ2

df Sig

1 215.504 .049 .075 7.937 8 .440

2 215.560 .049 .075 7.285 8 .506

3 215.645 .048 .074 7.975 8 .436

4 217.615 .039 .061 6.484 8 .593

5 218.773 .034 .053 1.350 8 .995

6 219.825 .030 .045 .590 4 .964

7 224.011 .011 .016 .000 0

8 226.301 .000 .000 .000 0

255

Stepwise Logistic Regression Predicting Use of a Decision-Making Plan From Graduate

Degree Earned, Time Spent Counseling, Years of Experience, Grade Levels Served, and

Psychologist:Student Ratio

Block O

Variable B (SE) df Sig.

Variables Included in the Model

Constant -.821 (.148) 1 .000

Variables Not Included in the Model

Graduate Degree 0.81 1 .369

Low Counseling 2.38 1 .123

Medium Counseling 6.58 1 .010

High Counseling 6.59 2 .037

0-5 yrs of Experience 0.95 1 .329

6-10 yrs of Experience 0.63 1 .429

>10 yrs of Experience 1.12 2 .572

Elementary School 0.13 1 .717

Middle/Junior High School 0.07 1 .786

High School 0.61 1 .433

1:<500 0.01 1 .943

1:500-999 0.01 1 .912

1:1000-1499 1.55 1 .214

1:1500-2000 0.46 1 .499

1:>2000 2.96 4 .564

Overall Statistics 13.01 12 .368

Block 1

Step

Number

-2 Log

Likelihood

Cox and

Snell R2

Nagelkerke

R2

Hosmer and Lemeshow Test

χ2

df Sig

1 252.367 .061 .086 3.826 8 .872

2 252.546 .060 .085 3.964 8 .860

3 253.469 .056 .079 7.805 7 .350

4 253.735 .055 .077 3.337 8 .911

5 257.253 .039 .055 2.218 6 .899

6 258.111 .035 .050 .375 3 .945

7 259.178 .031 .043 .000 1 1.000

256

Stepwise Logistic Regression Predicting Collection of Progress Monitoring Data From

Graduate Degree Earned, Time Spent Counseling, Years of Experience, Grade Levels

Served, and Psychologist:Student Ratio

Block O

Variable B (SE) df Sig.

Variables Included in the Model

Constant -1.569 (.186) 1 .000

Variables Not Included in the Model

Graduate Degree 1.59 1 .207

Low Counseling 0.51 1 .477

Medium Counseling 0.45 1 .504

High Counseling 2.16 2 .340

0-5 yrs of Experience 0.04 1 .847

6-10 yrs of Experience 0.55 1 .460

>10 yrs of Experience 0.87 2 .649

Elementary School 0.41 1 .520

Middle/Junior High School 1.61 1 .205

High School 0.58 1 .448

1:<500 2.32 1 .128

1:500-999 0.05 1 .829

1:1000-1499 0.02 1 .888

1:1500-2000 0.27 1 .606

1:>2000 3.11 4 .540

Overall Statistics 9.59 12 .652

Block 1

Step

Number

-2 Log

Likelihood

Cox and

Snell R2

Nagelkerke

R2

Hosmer and Lemeshow Test

χ2

df Sig

1 176.323 .050 .082 10.425 8 .236

2 176.364 .049 .082 11.635 8 .168

3 179.016 .037 .061 4.335 8 .826

4 179.350 .035 .059 6.920 8 .545

5 181.450 .025 .042 7.453 8 .489

6 182.281 .021 .035 .899 4 .925

7 185.016 .008 .013 .000 0

8 186.635 .000 .000 .000 0

257

Stepwise Logistic Regression Predicting Problem Analysis From Graduate Degree

Earned, Time Spent Counseling, Years of Experience, Grade Levels Served, and

Psychologist:Student Ratio

Block O

Variable B (SE) df Sig.

Variables Included in the Model

Constant -.221 (.142) 1 .121

Variables Not Included in the Model

Graduate Degree 5.68 1 .017

Low Counseling 0.11 1 .741

Medium Counseling 0.03 1 .861

High Counseling 0.11 2 .947

0-5 yrs of Experience 8.86 1 .003

6-10 yrs of Experience 3.17 1 .075

>10 yrs of Experience 9.15 2 .010

Elementary School 2.24 1 .135

Middle/Junior High School 1.33 1 .249

High School 1.00 1 .318

1:<500 0.00 1 .969

1:500-999 0.02 1 .880

1:1000-1499 1.54 1 .214

1:1500-2000 0.90 1 .342

1:>2000 2.38 4 .666

Overall Statistics 19.36 12 .080

Block 1

Step

Number

-2 Log

Likelihood

Cox and

Snell R2

Nagelkerke

R2

Hosmer and Lemeshow Test

χ2

df Sig

1 254.468 .097 .130 7.948 8 .439

2 255.408 .093 .124 5.826 8 .667

3 255.864 .090 .121 8.885 8 .352

4 256.531 .087 .117 10.141 8 .255

5 260.551 .069 .092 2.498 6 .869

6 261.442 .065 .087 .589 4 .964

258

Stepwise Logistic Regression Predicting Measurement of Treatment Integrity From

Graduate Degree Earned, Time Spent Counseling, Years of Experience, Grade Levels

Served, and Psychologist:Student Ratio

Block O

Variable B (SE) df Sig.

Variables Included in the Model

Constant -1.295 (.172) 1 .000

Variables Not Included in the Model

Graduate Degree 0.15 1 .703

Low Counseling 0.06 1 .813

Medium Counseling 0.38 1 .539

High Counseling 0.39 2 .821

0-5 yrs of Experience 3.10 1 .078

6-10 yrs of Experience 0.75 1 .386

>10 yrs of Experience 3.12 2 .210

Elementary School 0.03 1 .863

Middle/Junior High School 2.18 1 .140

High School 0.92 1 .337

1:<500 0.26 1 .613

1:500-999 0.87 1 .354

1:1000-1499 1.93 1 .164

1:1500-2000 0.13 1 .716

1:>2000 2.94 4 .568

Overall Statistics 10.67 12 .558

Block 1

Step

Number

-2 Log

Likelihood

Cox and

Snell R2

Nagelkerke

R2

Hosmer and Lemeshow Test

χ2

df Sig

1 197.222 .053 .083 3.024 8 .933

2 197.236 .053 .082 2.845 8 .944

3 197.527 .052 .080 11.255 8 .188

4 198.253 .049 .075 12.713 8 .122

5 202.547 .028 .043 4.808 7 .683

6 202.850 .026 .041 .456 4 .978

7 206.013 .011 .017 .000 0

8 208.203 .000 .000 .000 0

259

Stepwise Logistic Regression Predicting Summative Assessment From Graduate Degree

Earned, Time Spent Counseling, Years of Experience, Grade Levels Served, and

Psychologist:Student Ratio

Block O

Variable B (SE) df Sig.

Variables Included in the Model

Constant -.824 (.154) 1 .000

Variables Not Included in the Model

Graduate Degree 0.26 1 .611

Low Counseling 0.00 1 .954

Medium Counseling 0.11 1 .744

High Counseling 0.19 2 .910

0-5 yrs of Experience 0.58 1 .445

6-10 yrs of Experience 0.01 1 .910

>10 yrs of Experience 0.64 2 .728

Elementary School 1.51 1 .219

Middle/Junior High School 0.46 1 .500

High School 0.89 1 .346

1:<500 0.42 1 .518

1:500-999 0.03 1 .855

1:1000-1499 1.02 1 .313

1:1500-2000 0.39 1 .534

1:>2000 2.93 4 .570

Overall Statistics 6.97 12 .860

Block 1

Step

Number

-2 Log

Likelihood

Cox and

Snell R2

Nagelkerke

R2

Hosmer and Lemeshow Test

χ2

df Sig

1 238.921 .035 .049 11.665 8 .167

2 239.114 .034 .048 9.287 8 .319

3 239.428 .032 .046 4.209 8 .838

4 240.599 .027 .038 3.654 8 .887

5 243.481 .013 .018 3.380 4 .496

6 244.019 .010 .014 .177 2 .915

7 244.439 .008 .011 .000 0

8 246.017 .000 .000 .000 0