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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
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.
159
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
Deborah Kundert
Dissertation Chair
School Psychology
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 [email protected].
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
Deborah Kundert, PhD
Dissertation Chairperson
School Psychology
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
Deborah Kundert, PhD
Dissertation Chairperson
School Psychology
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