the relationship between learning style and personality type of extension community development

Copyright by Gregory A. Davis

2004

ii

ABSTRACT

Little research has been conducted that examines the learning style and

personality type preferences of Extension Community Development Educators. This

descriptive correlational study examines the relationship between learning style and

personality type preferences of Extension Community Development program

professionals in Ohio. In addition, the study explores the presence of relationships of

learning style and personality type preferences to primary work assignment, length of

tenure, academic major, educational attainment, age, and gender.

More than 56 percent of the 67 Extension Community Development program

professionals involved in this study favored a field dependent learning style, as

measured by the Group Embedded Figures Test (GEFT). The mean GEFT score for

the sample was 10.40, below the national mean of 11.4. Females were more field

dependent. Subjects with academic backgrounds in the physical sciences were more

field independent. Subjects with longer tenure in Extension were more field

dependent. Nearly 24 percent of study participants indicated a preference for the

ISTJ personality type as measured by the Personal Style Inventory (PSI). Males were

more than three times more likely to prefer gathering information using their senses

(sensing). Twice the number of female subjects (18) preferred gathering information

iii

through use of their unconscious (intuition) over males (9). Males preferred reacting

to information with logic (thinking). Females preferred reacting to information with

personal reflection and consideration for others (feeling). There was a negligible

level of association between learning style and personality type subscales.

The GEFT and PSI were used to gather data from Ohio State University

Extension Community Development program professionals that attended district-

level program meetings, completed the instruments, and provided usable data. While

study results were generalized only to those providing usable data, a sampling of non-

respondents revealed that non-respondent characteristics did not vary significantly

from the accessible population.

iv

ACKNOWLEDGMENTS

I would like to recognize Jamie Cano for his confidence in my abilities; Nikki

Conklin and Susie Whittington for their support and direction; Annie Berry for her

assistance with data analysis; my family for all they have sacrificed so that I might

accomplish this task, and my Extension friends for their ability to keep me motivated

throughout this learning process.

v

VITA 1989………………………………… Bachelor of Arts, The University of Findlay. 1993………………………………… M.P.A. Public Administration, Bowling Green

State University. 2004 to present……………………… Program Coordinator and Assistant Professor,

Community Development, Ohio State University Extension & Dept of AEDE

2001- 2004 ………………………… District Specialist and Assistant Professor,

Community Development, Ohio State University Extension, West District

1996 – 2001………………………… Extension Agent, Community Economic

Development, Ohio State University Extension, Crawford County

1993 - 1996………………………… Senior Lecturer, Department of Political

Science, University of Findlay

PUBLICATIONS Davis, G. A. (2004). Learning style preferences of extension educators in Ohio. The Ohio Journal of Science, 104(1). Davis, G. A. (2003). [Review of the book John Nolen and Mariemont: Building a new town in Ohio]. The Community Development Journal, 34(1). Thomas, J. R., Davis, G. A., & Sharp, J. (2003). Ohio Survey of Food, Agriculture, and Environmental Issues. The Ohio Journal of Science, 103(1). Davis, G. A. (2003). Using a retrospective pre-post questionnaire to determine program impact. Journal of Extension, 41(4).

vi

Davis, G. A. (2002). [Review of the book Emotional Impact: Passionate leaders and corporate transformation]. Leadership Link, A quarterly publication of the Ohio State University Leadership Center, Summer 2002. Davis, G. A., & Thomas, J. R. (1999). Industrial attraction: The experience of the Crawford County (Ohio) Development Board. In P. Schaeffer and S. Loveridge (Eds.), Small town and rural economic development: A case studies approach. (pp. 98-103). Westport, CT: Greenwood Publishing Group, Inc. Davis, G. A. (1999). Organizing for central business district renewal. Journal of Extension, 33(2).

FIELDS OF STUDY Major Field: Human & Community Resource Development Dr. Garee Earnest Area of Emphasis: Extension Education Dr. Scott Scheer Minor Areas: Research and Statistics Dr. Joseph Gliem Community Development Dr. Jeff Sharp

vii

TABLE OF CONTENTS

Page

Abstract ...................................................................................................................... ..ii

Acknowledgments ..................................................................................................... .iv

Vita.............................................................................................................................…v

List of Tables .............................................................................................................…x

List of Figures ............................................................................................................xiii

Chapters:

1. Introduction ...........................................................................................................…1 Problem Statement .........................................................................................…7 Research Hypotheses .....................................................................................…8 Purpose and Objective of the Study ..............................................................…8 Definition of Terms .........................................................................................10 Limitations of the Study...................................................................................11 Need for the Study ...........................................................................................12

2. Review of Literature ..............................................................................................13 Purpose of the Study ........................................................................................13 Learning Style..................................................................................................13 Learning Style Defined ....................................................................................14 Learning Style Models.....................................................................................15 The Kolb Model...................................................................................15 The Myers-Briggs Model.....................................................................18 The Witkin Model................................................................................27 Field Dependence & Field Independence ........................................................29 Witkin Early Measures of Field Dependence/Independence...........................31 Characteristics and Behaviors of Field Dependence .......................................32 Field Dependent Teaching Style......................................................................34 Characteristics and Behaviors of Field Independence .....................................36

viii

Field Independent Teaching Style ...................................................................38 Factors Related to Learning Style....................................................................41 Age.......................................................................................................41 Gender..................................................................................................41 Intelligence...........................................................................................43 Academic Achievement .......................................................................43 Vocational Interest ...............................................................................44 Academic Interest ................................................................................45 Research Using Witkin’s Model to Determine Preferred Style.......................47 Learning Styles of Extension Educators ..............................................47 Learning Styles of Agricultural Educators ..........................................49 Summary of Learning Style .............................................................................50 Personality Type ..............................................................................................53 Personality Type Defined ................................................................................54 Personality Type Models .................................................................................55 Myers-Briggs Model............................................................................55 Keirsey Temperament Theory .............................................................64 True Colors Type Model......................................................................65 Measures of Personality Type..........................................................................65 Myers-Briggs Type Indicator...............................................................65 Personal Style Inventory ......................................................................67 Research Using the Myers-Briggs Model to Determine Preferred Style.........69 Preferred Style of Extension Educators ...............................................72 Preferred Style of Agricultural Educators............................................73 Learning Style an Personality Type.................................................................75 Summary of Personality Type .........................................................................76 Learning Style as Related to Personality Types ..............................................79 Summary of Review of Literature ...................................................................80 3. Methodology ...................................................................................................…. ..84 Purpose.....................................................................................................…. ..84 Research Hypotheses ...............................................................................……85 Objectives ................................................................................................……86 Population ................................................................................................……87 Instrumentation .......................................................................................…. ..87 GEFT ......................................................................................................……88 PSI............................................................................................................……89 Data Collection .......................................................................................…....91 Data Analysis ..........................................................................................……92

ix

4. Findings............................................................................................................…103 Purpose and Objectives............................................................................…103 Research Hypotheses ...............................................................................…103 Objectives ...............................................................................................…104 Limitations ...............................................................................................…105 Sample Characteristics.............................................................................…106 Learning Style..........................................................................................…109 Personality Type ...................................................................................... ...112 Relationship Between Learning Style and Personality Type ..................…117 Correlates of Learning Style and Demographic Characteristics..............…117 Correlates of Personality Type and Demographic Characteristics ..........…119 5. Conclusions, Implications and Recommendations .........................................…122 Summary .................................................................................................…122 Sample Characteristics.............................................................................…122 Learning Style..........................................................................................…123 Personality Type ...................................................................................... ...124 Relationship Between Learning Style and Personality Type ..................…124 Relationship Between Learning Style and Selected Characteristics .......…125 Relationship Between Personality Type and Selected Characteristics ....…126 Conclusions and Implications .................................................................…128 Recommendations....................................................................................…142 General Recommendations ......................................................................…144 References ...........................................................................................................…146 Appendix A – Group Embedded Figures Test.....................................................…152 Appendix B – Personal Style Inventory...............................................................…155 Appendix C – Personal Style Inventory Scoring Sheet .......................................…158 Appendix D – Subject Characteristics Questionnaire..........................................…160 Appendix E – Analysis Of Extension Community Development Support Staff .…162 Appendix F – Non-Respondent Characteristics...................................................…171

x

LIST OF TABLES

Table Page

2.1: Sixteen Possible Type Combinations ................................................................19

2.2: Relationship Of MBTI Dimensions To Common Learning Style Measures.....27

2.3: Learning Style Characteristics And Behaviors..................................................40

2.4: Sixteen Personality Type Combinations............................................................57

3.1: Description of Variables ....................................................................................85

3.2: Adjectives Used To Describe Measures Of Association ...................................93

3.3: Variables And Statistical Procedure Used In Testing Hypothesis 1..................93

3.4: Variables And Statistical Procedures Used In Testing Hypothesis 2 ................95

3.5: Variables And Statistical Procedures Used In Testing Hypothesis 3 ................97

4.1: Frequency and Distribution of Sample Characteristics of Extension Community

Development Program Professionals in Ohio (n=67) ...............................................107

4.2: Frequency and Distribution of Sample Characteristics of Extension Community


4.3: Scores for Extension Community Development Program Professionals in Ohio

on the Group Embedded Figures Test (GEFT) (n=67) .............................................110

xi

4.4: Frequency of GEFT Scores by Gender of Extension Community Development

Program Professionals in Ohio (n=67) .....................................................................111

4.5: Frequency of GEFT Scores by Primary Work Assignment of Extension

Community Development Program Professionals in Ohio (n=67) ...........................112

4.6: Frequency of GEFT Scores by Academic Major of Extension Community


4.7: MBTI Opposite Scores of Extension Community Development Program

Professionals in Ohio (n=67) ....................................................................................114

4.8: MBTI Combination Distributions of Extension Community Development

Program Professionals in Ohio (n=67) ......................................................................115

4.9: MBTI Function Combination Distributions of Extension Community

Development Program Professionals in Ohio (n=67)................................................116

4.10: MBTI Opposite Distributions of Extension Community Development Program

Professionals in Ohio (n=67) ....................................................................................117

4.11: Correlation Between MBTI and GEFT of Extension Community Development


4.12: Correlation Between GEFT and Selected Characteristics of Extension

Community Development Program Professionals in Ohio (n=67)............................118

4.13: Intercorrelation Between GEFT and Selected Characteristics of Extension

Community Development Program Professionals in Ohio (n=67)............................119

4.14: Correlation Between GEFT and Gender of Extension Community Development


xii

4.15: Intercorrelations Between MBTI Preference Subscales and Gender of

Extension Community Development Program Professionals in Ohio (n=67)...........120

4.16: Intercorrelations Between MBTI Preference Subscales and Selected

Characteristics of Extension Community Development Program Professionals in Ohio

(n=67) ........................................................................................................................121

4.17: Intercorrelations Between MBTI Preference Subscales and Selected

Characteristics of Extension Community Development Program Professionals in Ohio

(n=67) ........................................................................................................................122

xiii

LIST OF FIGURES

Figure Page

2.1: Kolb’s Learning Style Theory ...........................................................................16

2.2: Conceptual Framework......................................................................................83

4.1: MBTI Opposite Scores of Extension Community Development Program

Professionals in Ohio .................................................................................................114

1

CHAPTER 1

INTRODUCTION

Extension, as an arm of the land grant university, has been involved in

informal education at the local community-level since the early 20th century. What

started with Seaman Knapp’s boll weevil problems in Texas and Louisiana, and local

businessmen’s willingness to pay for assistance, led to the development of the

Extension education system we know today. With the passage of the Smith-Lever

Act in 1914, the idea of placing Extension educators in every county to work with

local committees, organizations, and residents in conducting programming to address

local needs became a reality. The 450 extension agents in 455 counties of twelve

southern states in the early 1900s grew to roughly 16,000 Extension educators in 50

states and 4 U.S. territories by the end of the century (Seevers, Graham, Gamon, &

Conklin, 1997). Through the federal, state, and local partnership, communities have

had access to a variety of resources, research-based information, and campus-based

personnel for nearly a century.

The educational model used in Extension outreach is unique in that it involves

communities, stakeholders, and universities in ongoing conversations to define issues

and problems on which educational programming can focus. In 2002, Peters referred

2

to this as “practical public work” and stated that this was the predominant mode of

Extension outreach for the first fifty years of Extension’s history.

A key component of this “practical public work” involves an active local

constituency engaged with the local agent in “planning and developing programs,

non-formal teaching, facilitating meetings and community forums, providing

technical expertise, and applying research-based knowledge to the problems of

individuals, families, businesses, and communities” (Peters, 2002, p. 1).

The philosophy of educational outreach enables three distinct types of

learning to take place: instrumental, communicative, and emancipatory (Habermas,

1971; Cranton, 1998; in Peters, 2002). Engaging local residents in programming

designed to enhance their understanding of how tax incentive programs work, or how

to go about forming a community improvement corporation, or how to develop a

tourism and visitors’ bureau would all be examples of instrumental learning.

Communicative learning takes place when one is involved in activities or exercises

that lead to better understanding of “each other's views, problems, hopes, and

interests” (Peters, p. 2). Last, when members of a community are engaged in

programming that enhances their “leadership, confidence, and courage and enable[s]

them to act together to change the world in ways that further[s] their values and

ideals,” they have experienced emancipatory learning (Peters, p. 2).

For these types of learning to take place, Extension educational programming

must take into account the individual needs and differences among learners

(Birkenholz, 1999). A better understanding of the differences among learners can be

3

gained through a better understanding of the literature on learning styles and

personality type preferences.

There is a great deal of research related to learning styles. The term learning

styles has been described in the literature as “the manner in which learners sort and

process information” (Cano & Garton, 1994, p. 6). Learning style is also referred to

as “the predominant and preferred manner in which individuals take-in, retain,

process, and recall information” (Whittington & Raven, 1995, p. 10). These traits are

used to characterize how learners typically learn best and describe the way that

individuals gain, process, and use information (Rollins, 1990, p. 64).

A number of models have been put forth to better explain the ways in which

people receive and respond to information. Reviewed in this study include: Kolb’s

Learning Styles Model, measured by Kolb’s Learning Styles Inventory (LSI);

Witkin’s Field Dependent/Independent concept, measured by the Group Embedded

Figures Test (GEFT); and Jung’s theory of psychological type, measured by the

Myers-Briggs Type Indicator (MBTI) and Personal Styles Inventory (PSI).

The Kolb model (1984), which helps to explain how information is gained and

how it is processed, is widely known and cited (Birkenholz, 1999; Buch & Bartley,

2002; Sadler-Smith, 2001). According to Buch and Bartley, Kolb’s model provides

for two types of information processing and two ways for acquiring information:

“active experimentation and reflective observation” and “concrete experience and

abstract conceptualization” respectively (p. 6). From this perspective, Kolb named

four learning styles: convergers, divergers, accommodators, and assimilators. Using

4

Kolb’s Learning Styles Inventory, one’s preferred learning style can be determined

(Sadler-Smith, p. 611).

Witkin’s field dependent/independent conceptualization shared in 1962,

measured by the highly reliable Group Embedded Figures Test (GEFT), is widely

cited in agricultural education learning style research (Cano, 1999; Cano & Garton,

1994; Cano, Garton, & Raven, 1992; Marrison & Frick, 1994; Raven, Cano, Garton,

& Shelhamer, 1993). The instrument purports to measure an individual’s preference

for learning in relation to the surrounding field. Witkin’s GEFT evolved from earlier

tests such as the Embedded Figures Test (EFT), Rod and Frame Test (RFT) and Body

Adjustment Test (BAT). The GEFT, built upon these early measures (RFT, EFT),

was developed for use in group settings (see Appendix A).

Learners whose mode of perception is influenced by the surrounding field are

considered field dependent. Field dependency is represented by a GEFT score in the

1-11 range. Learners who are less influenced by the surrounding field are considered

field independent and are represented by a GEFT score in the 12-18 range.

To better explain personality differences among learners, many researchers

have employed the use of Carl Jung’s theory of psychological types (Cano, 1993;

Cano, Garton, & Raven, 1993). To better understand personality differences, the 20th

century psychologist Carl Jung sought to identify an objective psychology founded on

observation and experience (Jung, 1976, p. 8). Studying medieval and middle age

philosophies, works by 18th century philosopher Friedrich Schiller, 19th century

philosopher Nietzsche, and others; Jung put forth a personality or character typology

consisting of personality type opposites.

5

Jung’s typology theory is based upon the premise that an individual’s

orienting psychological functions include “a particular form of psychic activity that

remains the same in principle under varying conditions” (Jung, p. 436). The

psychological functions describe the way in which one gathers information and

makes sense of life’s events. Information is gathered or perceived using the senses,

described as Sensing (S) or through use of ones’ unconscious, described as Intuition

(N). These preferences describe the way in which one becomes “aware of things,

people, events, or ideas” (Myers, McCaulley, Quenk, & Hammer, 1998, p. 12). One

makes sense of this information or these perceptions through use of logic and

objectivity, described as Thinking (T) or through use of personal reflection and

consideration for others, described as Feeling (F). These functions help individuals

focus their mental activity toward a variety of ends (Myers et al., p. 13).

From this pyschological type theory, the Myers-Briggs Type Indicator

(MBTI) was developed (Myers et al., 1998). Myers and Briggs developed an

instrument that measures “preferences or strengths that persons use in gathering

information and making decisions” with extremely high reliability (Rollins, 1990, p.

65). Myers and Briggs theorized that there were specific learning activities that best

met the needs of individuals with specific learning styles (Rollins, 1990). According

to Myers et al., (1998) the MBTI provides a method by which Jung’s theory of

personality type can be put to practical use (p. 1). The MBTI “enables us to expect

specific differences in specific people and to cope with the people and their

differences more constructively than we otherwise could” (Myers et al., p. 11). The

MBTI attempts to measure opposites of personality characteristics that included what

6

Jung referred to as functions and attitudes. The functions describe how information is

gathered and treated. Attitudes describe how we relate to the surrounding

environment. Myers et al., indicated that “together, these functions and orientations

influence how a person perceives a situation and decides on a course of action” (p.

19). Rollins (1990) explained that in light of the Myers and Briggs theory, activities

for learning should take into account the individual’s preferred learning style.

A number of variations of the Myers-Briggs Type Indicator (MBTI) have

been developed since its inception in the early 1950s, one of which is the Personal

Styles Inventory (PSI) developed by Hogan and Champagne (1980) (see Appendix

B). The PSI is an abbreviated version of the MBTI, containing 32 items arranged in

pairs. Each member of the pair represents a preference for attitude (E-I), perceiving

function (S-N), judging function (T-F), and orientation (J-P). The PSI requires

subjects to rate their preference for each member of the pair by assigning each

member of the pair a score from 0 to 5.

A variety of learning styles and personality types exist. When more is known

of learners’ various learning and personality type preferences, learning can be

enhanced (Cano, 1993; Cano, Garton, & Raven, 1993, Witkin, Moore, Goodenough,

& Cox, 1977).

Understanding individual needs and differences in learners enables one to

develop better, more meaningful and effective educational programs (Myers et al.,

1998). Furthermore, realizing that teachers tend to teach the way they prefer to learn

(Dunn & Dunn, 1979; Gregorc, 1979; Witkin, 1973), reinforces the need to better

understand differences among teachers and learners as well. When instructors

7

recognize the learning and personality types of themselves and their learners,

instructors can target learners’ styles and attempt to provide flexibility in instructional

style to better meet learners’ needs (Powell, 1996 in Hudson, 1997).

Problem Statement

Extension and its clientele base continue to change. The changes in Extension

Community Development program professionals, Extension clientele, as well as

recent technological advances require Extension educators to re-think traditional

programming delivery methods and formats.

A better understanding of learners and how they prefer to learn is essential to

effectively redesign Extension program delivery methods and formats. Research by

Cano, Garton, and Raven (1992), Raven, Cano, Garton and Shelhamer (1993), and

Whittington and Raven (1995) involving preservice agricultural education teachers

revealed that a learner-centered approach to teaching was most preferred. Research

focusing on Extension educators found that a majority of agriculture and natural

resources Extension educators were what Witkin (1973) referred to as field

dependent, preferring to learn in group settings involving group projects, for example

(Sparks, 2001). Hudson (1997) found that Extension educators, in general, possessed

mixed learning styles.

The problem is that too little is known about the differences among Extension

Community Development program professionals and Extension clientele. For

learning to more effectively take place, it is of fundamental importance for Extension

Community Development educators to gain a better understanding of themselves.

8

Research Hypotheses

1. There is no relationship between learning style preferences as measured by

GEFT scores and personality type preferences as measured by PSI scores of

Extension Community Development program professionals employed in Ohio

during the time period April to July, 2004.

2. There is no relationship between learning style preferences as measured by

GEFT scores and primary work assignment, length of tenure, academic major,

educational attainment, age, or gender of Extension Community Development

program professionals employed in Ohio during the time period April to July,

2004.

3. There is no relationship between personality type preferences as measured by

PSI scores and primary work assignment, length of tenure, academic major,



2004.

Purpose and Objectives

The purpose of this study was to examine the relationship between learning

style and personality type preferences of Extension Community Development

program professionals in Ohio. In addition, the study aimed to better explore the

presence of relationships of those measures to primary work assignment, length of

tenure, academic major, educational attainment, age, and gender.

9

The following research objectives were addressed:

1. Describe learning style preferences as measured by Group Embedded Figures

Test (GEFT) scores of Extension Community Development program

professionals employed in Ohio during the time period April to July, 2004,

including: support staff, program assistants, educators, specialists, and

administrators.

2. Describe personality type preferences as measured by Personal Style Inventory

(PSI) scores of Extension Community Development program professionals

employed in Ohio during the time period April to July, 2004, including: support

staff, program assistants, educators, specialists, and administrators.

3. Describe the relationship between learning style preferences as measured by



during the time period April to July, 2004, including: support staff, program

assistants, educators, specialists, and administrators.



educational attainment, age, and gender of Extension Community Development


2004, including: support staff, program assistants, educators, specialists, and

administrators.

10

5. Describe the relationship between personality type preferences as measured by





administrators.

Definition of Terms

The following terms have been defined for the purpose of this study.

Learning Style - traits characterizing how one typically prefers to gain,

process, and use information (Rollins, 1990) as measured by the Group Embedded

Figures Test (GEFT) (Witkin, Oltman, Raskin, & Karp, 1971).

Personality type - a behavior displayed in a characteristic way and being

characteristic to other groups (Jung, 1976) as measured by the Personal Style

Inventory (PSI) (Hogan & Champagne, 1980), a variation of the Jungian Myers-

Briggs Type Indicator instrument.

Field Dependence/Field Independence - the two extremes on a continuum

which measures an individual’s ability to separate themselves from the surrounding

field as indicated by the Group Embedded Figures Test (GEFT) (Witkin, Oltman,

Raskin, & Karp, 1971).

Extension Community Development program professional - a program

assistant, educator, specialist, administrator, or support staff member employed by

11

Ohio State University Extension with some level of effort focused in Community

Development programming.

Length of tenure - years employed as an Extension program professional.

Educational attainment - one of three levels of education; Undergraduate

degree, Graduate degree, or Doctoral degree.

Academic major - one or more of ten areas of education: Business,

Economics, Education, Law, Planning, Political Science, Psychology, Public

Administration, Sociology, or Other.

Primary work assignment - one of Extension’s four geographic areas of

assignment: County, District, State, or Other.

Limitations of the Study

Correlational and descriptive studies such as this do not allow the researcher

to predict outcomes (Fraenkel & Wallen, 1999). As a result, the study sought only to

describe characteristics and examine hypothesized relationships among characteristics

of the population.



assistants, educators, specialists, and administrators comprised the population.

Therefore, the results and conclusions were generalizable to the population of

Extension Community Development program professionals that were employed in

Ohio during the time period April to July, 2004 and that completed both standardized

instruments and provided useable data.

12

Need for the Study

Extension can no longer operate from an educational paradigm that is based

upon simply providing information to clientele. Today’s information-based society

dictates that Extension add value to information if Extension is to survive. Truly

connecting with learners can provide Extension a competitive advantage. One way to

improve this connection is by improving our understanding of learning styles and

personality types of Extension program professionals and Extension clientele.

This knowledge can be useful to better understand Extension educators’

styles, but such information only begins to scratch the surface. Effective educational

programming requires a greater awareness of learning and personality types of the

educator and the learner, and most importantly, the methods in which this awareness

can be generated.

13

CHAPTER 2

REVIEW OF LITERATURE

Purpose of the Study

The purpose of this descriptive correlational study was to describe the

relationship between learning style and personality type preferences of Extension

Community Development program professionals in Ohio. In addition, the purpose of

the study was to describe Extension Community Development program professionals

employed in Ohio during the time period from April to May 2004, in terms of

primary work assignment, length of tenure, academic major, educational attainment,

age, and gender.

Learning Style

Learning style refers to the way in which an individual prefers to take in and

process information (Cano & Garton, 1994; Garger & Guild, 1985; Rollins, 1990;

Whittington & Raven, 1995). A number of models have been put forth to better

describe learning styles. The following review of literature on learning style focuses

on defining learning style and models to describe learning style. In addition, the

learning style literature review focuses specifically on Witkin’s field

14

dependence/independence conceptualization of learning style including: field

dependence/independence instruments, measurement of field

dependence/independence, characteristics and behaviors of field

dependence/independence, and field dependent/independent teaching styles.

Moreover, the following involves a review of factors related to learning style such as

age, gender, intelligence, academic achievement, and vocational and academic

interest. Finally, the review of learning style literature summarizes research using

Witkin’s model to determine preferred learning style of agricultural educators and

Extension educators, in particular.

Learning Style Defined

The term learning style has been described in the literature as “the manner in

which learners sort and process information” (Cano & Garton, 1994, p. 6). Garger

and Guild (1985) referred to learning style as “…stable and pervasive characteristics

of an individual, expressed through the interaction of one’s behavior and personality

as one approaches a learning task” (in Raven, Cano, Garton, & Shelhamer, 1993, p.

41). Learning style was also referred to as “the predominant and preferred manner in

which individuals take-in, retain, process, and recall information” (Whittington &

Raven, 1995, p. 10). Keefe (in Marrison & Frick, 1994), defined learning styles as

“cognitive, affective, and physiological traits that serve as relatively stable indicators

of how learners perceive, interact with, and respond to the learning environment” (p.

26). These traits are used to characterize how learners typically learn best and

describe the way that individuals gain, process, and use information (Rollins, 1990, p.

15

64). For the purposes of this study, learning style was defined as traits characterizing

how one typically prefers to gain, process, and use information (Rollins, 1990) as

measured by the Group Embedded Figures Test (GEFT) (Witkin, Oltman, Raskin, &

Karp, 1971).

Learning Style Models

The Kolb Model

The Kolb model (1984), widely known and cited (Birkenholz, 1999; Buch &

Bartley, 2002; Huelsman, 1983; Kitchel, 1999; Sadler-Smith, 2001), helps to explain

how information is gained and how it is processed. According to Buch and Bartley,

the Kolb model describes bipolar preferences for acquiring information and

processing information (see Figure 2.1).

16

Figure 2.1: Kolb’s Learning Style Theory

According to the Kolb model, individuals gain information through one of two

modes: concrete experience or abstract conceptualization. Acquiring information

through concrete experience (CE) requires one to connect with people. Concrete

experience involves “feeling, as opposed to thinking” (Tendy & Geiser, 1997, p. 6 in

Kitchel, p. 11). Gathering information through thinking as opposed to feeling

describes the abstract conceptualization (AC) mode. Abstract conceptualization (AC)

involves the use of logic and ideas, understanding unique, specific areas (Tendy &

Geiser, p. 6 in Kitchel, p. 12).

Reflective Observation

Assimilators

Accommodators

Divergers

Convergers

Concrete

Experience

Abstract Conceptualization

Active Experimentation

17

Processing the information one gathers through feeling and thinking takes

place in two modes according to the Kolb model: active experimentation (AE) and

reflective observation (RO). Processing information through the active

experimentation (AE) mode requires one to be actively engaging others and

themselves in practical application of knowledge (Tendy & Geiser, 1997, p. 6 in

Kitchel, p. 12). Learning through observation, rather than actively engaging in

practical experience, describes Kolb’s reflective observation (RO) mode. Reflective

observation involves examining issues and information from different perspectives,

acknowledging the value of varying points of view (Tendy & Geiser, p. 6 in Kitchel,

p. 12).

These dimensions can be viewed as polar opposites from which four learning

styles can be described: convergers, divergers, accommodators, and assimilators.

Assimilators

Individuals with preferences toward abstract conceptualization (AC) and

reflective observation (RO) have the ability to formulate theory via the integration of

individual observations, and are termed assimilators. Such individuals “tend toward

the basic sciences rather than the applied sciences” (Hudson, 1966 in Huelsman, p.

30).

Accommodators

Preferences toward concrete experience (CE) and active experimentation (AE)

are termed accommodators. Taking risks, engaging in new experiences, and working

closely with people are characteristics of the accommodator learning style (Hudson,

1966 in Huelsman, p. 30).

18

Divergers

Individuals with preferences toward reflective observation (RO) and concrete

experience (CE) are termed divergers and have the ability to work with people and

see diverse points of view. The diverger learning style involves emotion and

creativity (Hudson, 1966 in Huelsman, p. 29).

Convergers

Preferences toward active experimentation (AE) and abstract

conceptualization (AC) enable one to apply practical solutions to situations.

Individuals with the converger learning style preference “are relatively unemotional,

preferring to deal with things rather than people” (Hudson, 1966 in Huelsman, p. 30).

The Myers–Briggs Model

Myers and Briggs developed an instrument that identifies “preferences or

strengths that persons use in gathering information and making decisions” with

extremely high reliability (Rollins, 1990, p. 65). According to Rollins, Myers and

Briggs theorized that there were specific learning activities that best met the needs of

individuals with specific learning styles. Based upon Carl Jung’s theory of

psychological type, Isabel Myers and Katherine Briggs developed a multiple bi-polar

model to identify attitude, perception, judgment, and function preferences. The

MBTI “enables us to expect specific differences in specific people and to cope with

the people and their differences more constructively than we otherwise could”

(Myers, McCaulley, Quenk, & Hammer, 1998, p. 11).

19

According to the Myers-Briggs model, characteristics of learners can be

described using the four bi-polar dimensions grouped by function or process and

attitude or orientation. Myers et al., (1998) indicated that “together, these functions

and orientations influence how a person perceives a situation and decides on a course

of action” (p. 19)

The type opposites model enables one to better identify specific characteristics

of learners’ individual preferences in attitude, perception, judgment, and function.

The model asserts that individuals possess particular personality characteristics with

respect to how they prefer to gather information and relate to their surrounding

environment. The dimensions are described as either sensing or intuition (S-N) and

thinking or feeling (T-F) – the information gathering functions – and extraversion or

introversion (E-I) and judging or perceiving (J-P) – the relating orientations (Myers et

al., 1985).

Relationships among the four dichotomous scales provide for 16 possible

types: ISTJ, ISFJ, INFJ, INTJ, ISTP, ISFP, INFP, INTP, ESTP, ESFP, ENFP, ENTP,

ESTJ, ESFJ, ENFJ, and ENTJ (see Table 2.1). Each type of person will exhibit

different characteristics.

ISTJ ISFJ INFJ INTJ ISTP ISFP INFP INTP ESTP ESFP ENFP ENTP ESTJ ESFJ ENFJ ENTJ

Table 2.1: Sixteen Possible Type Combinations.

20

Sensing/Intuition (S-N)

The manner in which we take in information and make sense of life’s events is

described in our preference toward sensing-intuition (S-N). Information is gathered

or perceived using the senses, described as sensing (S) or through use of one’s

unconscious, described as intuition (N). These preferences describe the way in which

one becomes “aware of things, people, events, or ideas” (Myers et al., 1998, p. 12).

One who has a preference for understanding the world around them through

use of their senses would be considered an “S” type on the MBTI. Such individuals

are said to enjoy the present moment and could be characterized as realistic, practical,

and attentive to detail (Myers et al., 1998, p. 12). An individual considered an “N”

type on the MBTI would be more apt to prefer understanding things, people, events

and ideas by way of insight or the unconscious (p. 12). Such individuals have a

preference for focusing on the possibilities rather than those things that are actual.

Oftentimes, such individuals are characterized as “imaginative, abstract, and future

oriented” (p. 12).

Individuals with a preference for sensing (S) prefer to gather information

using their senses of smell, sight, touch, feel and hearing. Individuals with a

preference for sensing characteristically:

Focus on what is real and actual Value practical applications Notice detail, factual, and concrete Observe and remembers sequentially Are oriented in the present Prefer information step-by-step Trust experience

(Myers, 1993, p. 4)

21

Individuals with a preference for intuition (N) prefer to gather information

using their sense of intuition. Individuals with a preference for intuition

characteristically:

Focus on the “big picture” and possibilities Value imagination and insight Are abstract and theoretical See patterns and meaning in facts Possess a future-orientation Appear scatter-brained Trust inspiration

(Myers, 1993, p. 4)

Thinking/Feeling (T-F)

Each of us reacts to the information that we sense or feel in different ways.

One makes sense of this information or these perceptions through use of logic and

objectivity, described as thinking (T). Such individuals are said to be concerned with

justice and fairness and could be characterized as analytical, logical, and objective

(Myers et al., 1998, p. 12). The opposite type of reaction involves personal reflection

and consideration for others, described as feeling (F). An individual considered an

“F” type on the MBTI has a preference for people, compassion, and sympathy rather

than logic and objectivity. As such, “F” type-individuals “have an understanding of

people and a concern with the human aspects of problems” (p. 13). The thinking (T)

and feeling (F) functions help individuals focus their mental activity toward a variety

of ends (Myers et al., 1998, p. 13).

Individuals with a preference for thinking prefer to make decisions using

logic. Individuals with a preference for thinking characteristically:

Use cause-and-effect reasoning

22

Strive for personal, objective truth Address problem-solving using logical analysis Are reasonable, fair, and “tough-minded” Value practical applications

(Myers, 1993, p. 5)

Individuals with a preference for feeling prefer to make decisions keeping the

welfare of others in mind. Individuals with a preference for feeling characteristically:

Assess the impact of their decisions have on other people Strive for harmony and personal validation Are guided by personal values Are sympathetic, compassionate, accepting, and “tender-hearted”

(Myers, 1993, p. 5)

In addition to the sensing-intuition (S-N) and thinking-feeling (T-F) functions,

the model asserts that individuals possess particular personality characteristics with

respect to how they prefer to relate to their surrounding environment. These

attitudinal dimensions are described as either extraversion or introversion (E-I) and

judging or perceiving (J-P).

One’s attitudes toward life involve “a readiness of the psyche to act or react in

a certain way” (Jung, 1976, p. 414). The attitudes describe the way in which one

relates to the world around them. One provides energy to objects and people of the

surrounding environment, defined as extraverted (E) or one takes energy and interest

from the surrounding environment, described as introverted (I). An orientation

toward life that is “open, curious, and interested” is described as a perceptive attitude,

described as perception (P) (Myers et al., 1998, p. 14). One whose orientation toward

life is characteristically ordered, structured, and decisive is considered to be a judging

type, described as judging (J).

23

Extraversion/Introversion (E-I)

One’s attitudes toward life involve “a readiness of the psyche to act or react in

a certain way” (Jung, p. 414). The attitude we have toward interacting with the world

around us is described in our preference toward extraversion-introversion (E-I). One

provides energy to objects and people of the surrounding environment, defined as

extraverted (E) or one takes energy and interest from the surrounding environment,

described as introverted (I).

An individual considered an “E” type on the MBTI would be more interested

in the stimulation of the surrounding environment. Such individuals are said to have

“a desire to act on the environment, to affirm its importance, to increase its affect” (p.

13). Such individuals could be characterized as action-oriented, sociable, and easily

able to communicate (p. 13). Conversely, one who is internally motivated and

perceives the outside world to be of secondary importance would most likely be

considered an “I” type on the MBTI. Such individuals could be characterized as

thoughtful, contemplative, and desirous of quiet time and privacy (Myers et al., 1998,

p. 13).

Individuals with a preference for extraversion prefer to share their energy with

the surrounding environment; the outer world of people and things. Individuals with

a preference for extraversion characteristically:

Prefer to communicate by talking Learn best by doing or discussing Tend to speak first, reflect later Take initiative toward work and interpersonal relationships Possess a breadth of interests Are sociable, expressive, and attuned to the external environment

(Myers, 1993, p. 4)

24

Individuals with a preference for introversion prefer to gain their energy from

the surrounding environment; the inner world of thoughts and reflections. Individuals

with a preference for introversion characteristically:

Prefer to communicate in writing Learn best by reflection and mental “practice” Tend to reflect before acting or speaking Possess a depth of interest Are private, contained, and easily focus

(Myers, 1993, p. 4)

Judging-Perceiving (J-P)

One’s orientations or attitudes toward the outer world are described in terms

of preferences toward organization and structure or spontaneity. These behaviors are

described in our preference toward judgment-perception (J-P).

One whose orientation toward life is characteristically ordered, structured, and

decisive is considered to be a judging type, described as judging (J). Such individuals

have a preference for “making decisions, seeking closure, planning operations, or

organizing activities” and would be considered a “J” type on the MBTI (Myers et al.,

1998, p. 14). Conversely, an orientation toward life that is “open, curious, and

interested” is described as a perceptive attitude, described as perception (P) (Myers et

al., 1998, p. 14). An individual considered a “P” type on the MBTI would be more

apt to dislike structure and planning, favoring spontaneity and flexibility. Such

individuals are characterized as “adaptable, open to new events and changes, and

aiming to miss nothing” (p. 14).

25

Individuals with a preference for judging are viewed as outworldly structured

and organized. Individuals with a preference for judging characteristically:

Prefer closure Prefer to plan Prefer to have things decided Avoid last-minute stresses Are scheduled, systematic, and methodical Prefer to communicate in writing

(Myers, 1993, p. 5)

Individuals with a preference for perceiving are viewed as outworldly flexible

and spontaneous. Individuals with a preference for perceiving characteristically:

Prefer things loose and open to change Prefer to leave things open-ended Feel energized by last-minute pressures Are casual and adaptive

(Myers, 1993, p. 5)

A number of studies (Drummond & Stoddard, 1992; Elliott & Sapp, 1988;

Fourqurean, Meisgeier, & Swank, 1990; Gordon, Coscarelli, & Spears, 1986; Hinkle,

1986; Holsworth, 1985; Luh, 1991; Penn, 1992 in DiTiberio, 1996) have been

conducted relating the MBTI to various measures of learning style preference.

According to Hinkle (1986) and Gordon, Coscarelli, & Spears (1986, in

DiTiberio) the MBTI Extraversion (E) preference correlated with Kolb’s LSI

Concrete Experience (CE) and Active Experimentation (AE). Further, the MBTI

Introversion (I) preference correlated with the LSI Reflective Observation (RO), and

the MBTI Perceiving (P) preference correlated with the LSI Active Experimentation

(AE) (Hinkle, 1986 in DiTiberio). The MBTI Extraversion (E) preference correlated

with Kolb’s LSI combination Concrete Experience (CE) and Active Experimentation

26

(AE) according to Gordon, Coscarelli, and Sears (1986). The 1985 MBTI Manual

found the Sensing (S), Feeling (F), Perceiving (P) preferences correlated with the

LSI’s Concrete Experience (CE). The 1985 MBTI Manual found the Intuitive (N),

Thinking (T), Judging (J) preferences correlated with the LSI’s Abstract

Conceptualization (AC).

According to a study involving college students by Holsworth (1985, in

DiTiberio, 1986) the MBTI Extraversion (E), Sensing (S), and Feeling (F)

preferences correlated with Witkin’s Field Dependent learning style preference. The

MBTI Introversion (I), Intuition (N), Thinking (T), and Judging (J) preferences

correlated with Witkin’s Field Independent learning style preference. Canning (1983)

found a relationship between the MBTI Sensing (S) and Thinking (T) preferences and

Witkin’s Field Dependent learning style preference. The MBTI Intuitive (N) and

Feeling (F) preferences correlated with Witkin’s Field Independent learning style

preference.

To better understand the how the MBTI relates to Kolb’s Learning Styles

Inventory and Witkin’s Group Embedded Figures Test, see Table 2.2.

27

Myers-Briggs Type Indicator Preferences

Measures of Learning Style E-I S-N T-F J-P Combinations

Concrete Experience E S F P

Active Experimentation E P

Reflective Observation I

Learning Style Inventory (Kolb, 1984)

Abstract Conceptualization N T J

Field Independence N T IJ, NT, NF GEFT (Witkin et al., 1971) Field Dependence F ESF, ST

Table 2.2: Relationship of MBTI Dimensions to Common Learning Style Measures.

The Witkin Model

While learning styles have been conceptualized a number of ways (Marrison

& Frick, 1994) no model has been more widely studied than Witkin’s field

dependent/independent model (Estadt, 1997; Garger & Guild, 1984). Witkin’s field

dependent/independent conceptualization, measured by the highly reliable Group

Embedded Figures Test (GEFT) is widely cited in agricultural education learning

style research (Cano, 1999; Cano, 1993; Cano & Garton, 1994; Cano, Garton, &

Raven, 1992; Estadt, 1997; Hudson, 1997; Kitchel, 1999; Marrison & Frick, 1994;

McCutcheon, 1997; Raven et al., 1993; Sparks, 2001; Torres, 1993).

The model dates back to the 1940s when Herman Witkin studied factors

related to perception of the upright (Witkin, 1948; Witkin & Asch, 1948; Witkin,

28

1949). Witkin was particularly interested in how some airplane pilots were able to

maintain level flight without sight or instrumentation and other pilots were not. Pilots

able to maintain level flight without sight or instrumentation would be later labeled

field independent. Pilots unable to maintain level flight without sight or

instrumentation would fall into the field dependent category.

The terms field dependence and field independence as they relate to learning

style were coined by Herman Witkin (Kirby, 1979). The terms represent a bipolar

continuum which describes one’s orientation to the surrounding field. The continuum

is value neutral and does not have a clear high or low end (Witkin et al., 1977).

Furthermore, one’s orientation as measured by the continuum is not inherently better

or worse than that of another (Witkin et al.). The continuum does not purport to

measure two types of persons, but rather to provide a description of an individual’s

position on the continuum relative to the mean (Claxton & Ralston, 1978).

Witkin described field independence as a tendency to separate objects and

figures from an embedded context. Messick (1970) likened field independence to

consistently approaching one’s environment from an analytical perspective.

Field dependence was characterized by Witkin as a tendency “to experience

events globally in an undifferentiated fashion”(in McCutcheon, p. 20). According to

Messick (1970), consistently approaching one’s environment from a global

orientation is considered field dependence.

The Witkin model has provided a framework from which to study how

learning environments and instructional formats influence the cognitive and affective

development of learners. Furthermore, the Witkin model has “had major implications

29

for college admissions and faculty members who made decisions about those

environments and practices” (Chickering, 1976 in Torres, 1993).

Field Dependence & Field Independence

The terms field dependence and field independence describe each end of a

bipolar, value-neutral continuum which describes one’s orientation to the surrounding

field. Witkin (1973) defined field dependence/independence as:

“The extent to which a person is able to deal with a part of a field separately from

the field as a whole, or the extent to which he [sic] is able to disembed items from

organized context or, to put it everyday language, determines how analytical he

[sic] is. Because at one extreme of the performance range perception is strongly

dominated by the surrounding field, we speak of that mode as ‘field dependent.’

For the other extreme, where the person is able to deal with an item independently

of the surrounding field, we use designation ‘field independent’” (p. 5).

Two variables have been found to influence one’s learning style preference,

(Garton, 1993). Learning style preferences can potentially be linked to gender

differences (Cano & Garton, 1994; Garger & Guild, 1984; Hudson, 1997; Torres &

Cano, 1994; Witkin, 1976) and child-rearing experiences (Witkin, 1976).

Witkin (1976) found that the learning style preference was influenced to some

degree by the type and nature of relationship children had with their mothers.

Furthermore, Cano and Garton (1994, Garger and Guild (1984), Hudson (1997),

30

Torres and Cano (1994), Witkin (1976) and Witkin et al. (1977) have found that

females were more likely to prefer a field dependent learning style than males.

Witkin Early Measures of Field Dependence/Independence

Numerous standardized instruments to measure the field

dependence/independence learning style dimension have been developed by Witkin

and his associates since the 1940s. These instruments began in psychological

laboratories and have since evolved into today’s group and individual paper-and-

pencil tests.

The early field dependence assessment, the Rod and Frame Test (RFT),

involved an illuminated rod inside an illuminated frame, both of which could be

adjusted independently. Subjects would be placed in a darkened room and asked to

orient the rod in a vertical position, independent of the orientation of the frame.

Subjects who were able to orient the rod vertically, independent of the orientation of

the frame were described as field independent. Subjects who were unable to orient

the rod vertically, independent of the orientation of the frame were described as field

dependent. The RFT was found to be a valid and reliable measure for describing field

dependence/independence (Witkin, 1948).

The Body Adjustment Test (BAT) involved seated subjects and was similar to

the RFT in that subjects were asked to orient themselves in a chair to an upright

position, independent of the orientation of the room. The room was capable of being

tilted such that a subject described as field independent would orient themselves in

31

the chair in an upright position irregardless of the orientation of the room surrounding

them. Subjects who were unable to orient themselves in the chair in an upright

position irregardless of the orientation of the room surrounding them were described

as field dependent. The RFT was found to be a valid and reliable measure for

describing field dependence/independence (Witkin, 1948).

The RFT and BAT led the way for the development of the Embedded Figures

Test (EFT) and Group Embedded Figures Test (GEFT). The EFT and GEFT are

paper-and-pencil measures for describing field dependence/independence developed

by Witkin and associates in the early 1970s (Witkin, Oltman, Raskin, & Karp, 1971;

Oltman, Raskin, & Witkin, 1971).

The EFT and GEFT, considered standardized instruments tested for validity

and reliability, require subjects to locate previously seen simple geometric figures

embedded within a larger complex context within a 20 minute time frame (Witkin,

Oltman, Raskin, & Karp, 1971). Subjects able to locate 12 or more of the simple

geometric figures embedded within the more complex figures are described as field

independent. Subjects unable to discern more than 11 of the simple figures are

described as field dependent. Individual scores above the national GEFT mean of

11.4 are considered field independent.

Characteristics and Behaviors of Field Dependence/Field Independence

Field dependence/field independence describes one’s orientation to the

surrounding field. The following review of literature on learning style discusses

32

different behavioral characteristics associated with field dependence and field

independence.

Characteristics and Behaviors of Field Dependence

Individuals whose mode of perception is strongly dominated by the

surrounding field are described as field dependent (Cano, 1993; Garger & Guild,

1984; Witkin et al., 1977). Individuals with a field dependent preference perceive in

a global perspective framed by personal surroundings. Field dependent individuals

are able to make broad, general distinctions among concepts and prefer social

contexts and orientations (Cano, 1993; Garger & Guild, 1984; Witkin, 1973; Witkin,

1976; Witkin et al., 1977).

Cano (1993) found that field dependent learners experienced a greater

difficulty breaking down complex learning tasks into more manageable components

and as a result, became more easily frustrated when tasked with such. In general,

field dependent learners are less able to perform analytical problem solving in

subjects such as math and science (Cano, 1993; Witkin, 1976). Field dependent

learners are not unable to succeed in subjects requiring analytical problem solving

such as math and science, field dependent learners are simply more challenged by

such subjects (Cano, 1993).

Field dependent learners have a tendency to give up easily, and quickly

become uninterested in tasks requiring problem solving skills (Cano, 1993). Field

dependent learners are able to make broader distinctions among concepts and are able

to see relationships (Garger & Guild, 1984), however when faced with tasks requiring

33

analytical problem solving skills, field dependent learners should be provided

structure and explicit instructions for such problem solving tasks.

Field dependent learners prefer to know exactly what is expected of them with

respect to learning tasks. Such externally defined goals enable field dependent

learners to work toward success (Witkin, 1976). According to Cano (1993), field

dependent learners tended to be highly organized. As such, structure and

organization in the teaching and learning process is of critical importance to field

dependent learners.

Field dependent learners require positive reinforcement from others to become

motivated to learn (Cano, 1993). Field dependent learners are extrinsically motivated

(Witkin, Moore, Goodenough, & Cox, 1977) and prefer instructors provide regular

guidance, modeling, and rewards that express support for their accomplishments

(Garger & Guild, 1984; Witkin, 1976). Individuals with a field dependent preference

take the opinions of others into account before making decisions (Cano, 1993;

Witkin, 1973; Witkin, 1976; Witkin et al., 1977).

Field dependent individuals possess effective social skills that are developed

naturally and instinctively. Typically extroverted, individuals with a field dependent

preference are highly sensitive, attuned to social environments, and influenced by

peer groups and authority figures. Individuals with a field dependent preference

prefer to be physically close to others (Witkin, 1973; Witkin, 1976; Witkin et al.,

1977).

Field dependent learners are more easily influenced by authority figures and

peer groups than field independent learners (Cano, 1993; Witkin, 1976). Cano found

34

that field dependent learners were more likely to express positive feelings toward

their instructors, view their instructors as role models, and become highly motivated

when given an opportunity to work individually with the instructor.

Field dependent learners like to be physically near other people (Holley, 1972)

and prefer to be socializing with other learners rather than actively engaged in

learning (Cano, 1993). Furthermore, field dependent learners have a tendency to

spend more time literally looking at the faces of those with whom they interact

(Witkin et al., 1977). As a result, such learners tend to get along well with others and

are more responsive to social cues than field independent learners (Cano, 1993).

Cano (1993) found field dependent learners preferred the spectator approach

to learning and preferred the teacher provide the answers rather than being part of an

active learning environment where they were required to learn by doing. Field

dependent learners would rather not be called upon by the instructor to provide

information related to subject matter (Cano, 1993). Field dependent learners prefer

learning activities that include small group work, lecture (Cano, 1993) and general

classroom discussion (Witkin, 1976). When faced with group work, Cano indicated

that field dependent learners preferred the task be divided equally among the

members of the group and group members’ feelings be taken into consideration more

so than field independent learners.

Field Dependent Teaching Style

Educators tend to teach the same way that they themselves prefer to learn

(Cano, 1993; Dunn & Dunn, 1979; Gregorc, 1979; Witkin, 1976). Cano (1993) found

35

consistency between field-dependent learning style and teaching style behaviors and

characteristics. Witkin found that one’s preferred learning style influenced the way in

which they preferred to teach. Lyons (1984) studied preservice teachers’ preferred

teaching styles and found preferred teaching style to be consistent throughout

teachers’ teaching careers.

Field dependent educators value relationships with learners. Cano (1993)

found that field dependent educators looked for positive qualities in learners and

shared positive reinforcement on a regular basis. Field dependent educators rely

more on positive reinforcement than negative feedback (Witkin, et al., 1977). To

foster relationships with learners, and to assist in motivating learners, field dependent

educators allocate time for informal discussion as part of the instruction (Cano, 1993).

According to Cano, this informal discussion time also permitted learners to relate

concepts learned to personal experience.

Because field dependent educators are focused on ensuring learner success,

field dependent educators pay particular emphasis to clearly organizing the learning

objectives (Cano, 1993). Furthermore, learners who experience difficulty are readily

identified by field dependent educators and provided needed guidance and personal

assistance. Koppleman (1980) found that field dependent educators provided more

direction, required more verbal participation of learners, and paid particular attention

to maintaining classroom control than field independent educators.

Educators approach the learning process differently depending upon their

learning style preference (Cano, 1993; Koppleman, 1980; Mahlios, 1981). According

to Cano, field dependent educators were more likely to promote a learning

36

environment characterized as one thinking unit. Conversely, field independent

educators were more likely to encourage learners to work in small groups or

independently (Mahlios, 1981). Witkin, et al. (1977) found that educators preferring

a field dependent teaching style were more likely to employ the discussion method of

instruction than lecture or problem solving. Furthermore, field dependent educators

were more apt to use questioning techniques to check on student learning than field

independent educators (Moore, 1973).

Characteristics and Behaviors of Field Independence

Field independence is characterized as a tendency to separate discrete parts of

the picture from the total picture or surrounding field (Cano, 1993; Garton, 1993;

Witkin, 1973). Individuals whose mode of perception is largely unaffected by the

surrounding field are described as field independent (Cano, 1993; Garger & Guild,

1984; Witkin et al., 1977). Field independent individuals are able to see individual

component parts, prefer the abstract, analytical thought and problem solving (Cano,

1993; Garger & Guild, 1984; Witkin, 1973; Witkin, 1976; Witkin et al., 1977).

Field independent learners are better able to make specific distinctions

(Garger & Guild, 1984) and solve problems (Witkin, 1976). This analytical approach

to learning enables field independent learners to perform well in subjects such as

math and science (Cano, 1993).

Cano (1993) found that field independent learners preferred an ‘inquiry’

approach to learning and independent studies. Because of their proficiency in

37

structuring the learning themselves, field independent learners require less guidance

when learning using the problem solving method, rarely seek physical interaction

with the instructor and others for personal motivation, and learn easier when they are

provided an opportunity to develop their own strategies (Cano, 1993; Garger & Guild,

1984; Witkin et al., 1977).

Field independent learners possess an impersonal orientation and are less

sensitive to the emotions and needs of others (Cano, 1993; Garger & Guild, 1984;

Witkin, 1976). According to Cano, field independent learners preferred a

professional distance to the instructor, rarely seeking physical contact, and preferred

tasks that allowed them to work independently, free of the constraints of the social

environment. Cano found that the social skills of field independent learners were less

developed than the social skills of field dependent learners. Unlike their field

dependent counterparts, individuals with a field independent preference possess less

effective social skills, developed through effort, necessity, and demand. Typically

introverted, individuals with a field independent preference are most often

unconcerned with the social environment and unresponsive to social reinforcement

(Witkin, 1973; Witkin, 1976; Witkin et al., 1977).

Field independent learners are intrinsically motivated, relying upon their

problem-solving ability, preference for the ‘inquiry’ approach, and comfort with

independent studies as motivational forces (Witkin et al., 1977). Rather than be

taught by an instructor, field independent learners would rather learn a task through

personal trial and error (Cano, 1993). According to Cano, Garger and Guild (1984)

and Witkin (1976), field independent learners were motivated by having a choice of

38

activities, and the ability to structure the learning process. Field independent learners

are motivated to compete with themselves more than others. The need to compete

felt by field independent learners is directed inwards – knowing that the task is

completed is the reward for field independent learners (Cano, 1993).

Field independent learners tend to be more individual-focused than field

dependent learners. Witkin (1976) found that in conversation, field independent

learners were more likely than field dependent learners to use personal pronouns and

active verbs such as “I wrote this” as opposed to “this happened to me”. Individuals

with a field independent preference are more likely to make their decisions without

the influence of others (Cano, 1993; Witkin, 1973; Witkin, 1976; Witkin et al., 1977).

Field independent learners, preferring an independent study, trial and error

approach to learning, have a tendency to want to work ahead of the class, beginning

new assignments and projects (Cano, 1993). According to Cano, working ahead on

projects and assignments without instructor guidance was preferred by field

independent learners more so than field dependent learners.

Field Independent Teaching Style

Similar to educators with a preference for field dependent learning style, field

independent educators tend to teach the same way that they themselves prefer to

learn. Similarities in teaching and learning preference apply to approach to personal

relationships, use of feedback, and instructional techniques and behaviors (Garger &

Guild, 1984; Mahlios, 1981; Witkin et al., 1977).

39

Unlike field dependent educators, field independent educators place more

emphasis on the cognitive aspects of teaching than interpersonal relationships with

learners (Witkin et al., 1977). Field independent educators were more likely to

interact with learners from an academic rather than social perspective, according to

Mahlios (1981). Witkin et al. found that field independent educators were more

likely to encourage learning through the application of principles and concepts than

through the recitation of facts. Mahlios found that field independent educators were

more likely to employ the use of analysis- and higher-level questioning than lower-

level comprehension type questioning.

Field independent educators tend to view negative feedback as an effective

instructional method (Witkin, et al., 1977). According to Mahlios (1981), field

independent educators believed that informing learners of mistakes and using the

mistake as an opportunity to further learning was an effective instructional technique.

Further, educators with a field independent preference were likely to provide more

corrective feedback to learners than field dependent educators.

Rather than clearly organizing the learning objectives, field independent

educators are more likely to promote a problem solving or inquiry approach to

teaching and learning (Garger & Guild, 1984; Witkin, et al., 1977). According to

Koppleman (1980), field independent educators provided learners experiencing

difficulty only enough guidance necessary to help the learner determine the solution

themselves.

Characteristics and behaviors associated with field independent and field

dependent learning style preferences are summarized in Table 2.3.

40

Fiel

d In

depe

nden

t Lea

rnin

g St

yle

Lear

ning

inde

pend

ent f

rom

the

surr

ound

ing

field

. Sc

ores

11.

5- 1

8.0

cons

ider

ed fi

eld

inde

pend

ent.

Cha

ract

eris

tics

Perc

eive

s and

pro

cess

es d

iscr

ete

parts

. A

ble

to se

e in

divi

dual

co

mpo

nent

par

ts.

Pref

ers t

he a

bstra

ct, a

naly

tical

thou

ght a

nd

prob

lem

solv

ing.

Im

pers

onal

ly o

rient

ed:

mor

e in

divi

dual

istic

and

inse

nsiti

ve to

th

e em

otio

ns o

f oth

ers.

Les

s eff

ectiv

e so

cial

skill

s are

de

velo

ped

thro

ugh

effo

rt, n

eces

sity

, and

dem

and.

Int

rove

rted,

an

d un

conc

erne

d w

ith th

e so

cial

env

ironm

ent a

nd

unre

spon

sive

to so

cial

rein

forc

emen

t. In

trins

ical

ly m

otiv

ated

: pr

efer

s com

petit

ion,

cho

ice

of

activ

ities

, and

abi

lity

to d

esig

n st

udie

s and

wor

k st

ruct

ure.

M

ore

likel

y to

mak

e th

eir d

ecis

ions

with

out t

he in

fluen

ce o

f ot

hers

. Le

arns

in a

n in

depe

nden

t con

text

. Pr

efer

s ind

ivid

ual s

tudi

es,

proj

ects

, and

wor

k.

Plac

es a

hig

her p

riorit

y on

the

lear

ning

env

ironm

ent t

han

soci

al e

nviro

nmen

t. Fa

vors

‘inq

uiry

app

roac

h’ to

lear

ning

. Pr

efer

s to

sit i

n th

e fr

ont o

f the

cla

ssro

om.

Rar

ely

seek

s phy

sica

l int

erac

tion

with

th

e in

stru

ctor

and

oth

ers f

or p

erso

nal m

otiv

atio

n.

Pref

ers t

o st

ruct

ure

indi

vidu

al le

arni

ng ta

sks a

nd p

roce

sses

w

ith li

ttle

dire

ctio

n fr

om th

e in

stru

ctor

. Pr

efer

s to

self-

desi

gn

goal

s and

dire

ctio

n.

Fiel

d D

epen

dent

Lea

rnin

g St

yle

Lear

ning

dep

ende

nt u

pon

the

surr

ound

ing

field

. Sc

ores

0.

0-11

.4 c

onsi

dere

d fie

ld d

epen

dent

Cha

ract

eris

tics

Perc

eive

s in

a gl

obal

per

spec

tive

fram

ed b

y pe

rson

al

surr

ound

ings

. A

ble

to m

ake

broa

d, g

ener

al d

istin

ctio

ns a

mon

g co

ncep

ts.

Pref

ers s

ocia

l con

text

s and

orie

ntat

ions

. So

cial

ly o

rient

ed:

feel

s a n

eed

to in

tera

ct w

ith o

ther

s.

Effe

ctiv

e so

cial

skill

s, de

velo

ped

natu

rally

and

ins

tinct

ivel

y.

Plac

es h

igh

impo

rtanc

e on

eye

con

tact

and

ver

bal m

essa

ges.

Ex

trove

rted,

and

hig

hly

sens

itive

and

attu

ned

to so

cial

en

viro

nmen

ts. I

nflu

ence

d by

pee

r gro

ups a

nd a

utho

rity

figur

es.

Pref

ers t

o be

phy

sica

lly c

lose

to o

ther

s. Ex

trins

ical

ly m

otiv

ated

: pr

efer

s soc

ial r

einf

orce

men

t, se

eks

guid

ance

and

rew

ards

from

the

inst

ruct

or a

nd o

ther

s. T

akes

the

opin

ions

of o

ther

s int

o ac

coun

t bef

ore

mak

ing

deci

sion

s. Le

arns

in a

soci

al c

onte

xt.

Pref

ers g

roup

stud

ies,

proj

ects

, and

w

ork.

Pl

aces

a h

ighe

r prio

rity

on th

e so

cial

env

ironm

ent t

han

lear

ning

en

viro

nmen

t. Fa

vors

‘spe

ctat

or a

ppro

ach’

to le

arni

ng.

Pref

ers t

o si

t in

the

back

of t

he c

lass

room

. R

elie

s on

mot

ivat

ion

to le

arn

from

ou

tsid

e so

urce

s (in

stru

ctor

, pee

rs, e

tc).

Pr

efer

s stru

ctur

ed, o

rgan

ized

lear

ning

. Pr

efer

s tha

t ins

truct

or

defin

es sp

ecifi

c di

rect

ions

, goa

ls a

nd o

utco

mes

.

Ove

rall

Orie

ntat

ion

to S

urro

undi

ngs

Soci

al O

rient

atio

n M

otiv

atio

nal

Orie

ntat

ion

App

roac

h to

Le

arni

ng

Table 2.3: Learning Style Characteristics and Behaviors

41

Factors Related to Learning Style

A review of the literature revealed several factors related to learning style.

Among these factors were age, gender, intelligence, academic achievement, and

vocational interest and academic interest.

Age

One’s learning style preference becomes more field independent between 10-17

years of age according to Witkin et al., (1954). After one’s preference becomes

established, Witkin et al. (1977) found that over time, one’s field

dependence/independence preference was relatively stable. However, as age increases,

both genders become generally more field dependent (Crosson, 1984). Raven, Cano,

Garton, and Shelhamer (1993) studied preservice agriculture teachers at Montana State

University (MSU) and The Ohio State University (OSU) and found that students 25 years

or older were more likely to prefer a field dependent learning style.

Gender

According to Witkin, et al., (1977a) beginning in early adolescence, learning

styles of males and females begin to differ. While the difference between the genders is

small (Witkin et al.) a number of researchers (Cano & Garton, 1994; Garger & Guild,

1984; Hudson, 1997; Torres & Cano, 1994; Witkin, 1976, Witkin et al., 1977a) have

found that males were less field dependent than females. However, more recent studies

(Garton, Spain, Lamberson, & Spiers, 1999; Raven, Cano, Garton, & Shelhamer, 1993;

42

Whittington & Raven, 1995) involving male and female college of agriculture students

revealed mixed results.

Hudson (1997) examined preferred learning styles of Extension Educators in Ohio

State University Extension’s Northeast District. Hudson concluded that a higher

percentage of male educators favored the field independent learning style than females

(65 percent and 50 percent, respectively).

Cano and Garton (1994) studied preservice teachers majoring in agricultural

education at The Ohio State University and enrolled in a teaching methods course during

the academic years 1990, 1991, and 1992. Cano and Garton learned that a greater

percentage of male preservice teachers favored the field independent learning style than

females (60 percent and 55 percent, respectively). Torres and Cano (1994) found that a

greater percentage of male college of agriculture seniors favored the field independent

learning style than females (71 percent and 50 percent, respectively).

Garton, Spain, Lamberson, and Spiers (1999) studied the learning styles of first-

year animal science majors at the University of Missouri. Garton et al. learned that an

equal percentage of male and female students favored the field independent learning

style.

Whittington and Raven (1995) described the preferred learning style of preservice

agricultural educators at the University of Idaho and Montana State University who had

student taught in Spring of 1993 and Fall, 1992 and 1993. Whittington and Raven

concluded that field dependence was favored by 33 percent of the males and none of the

females.

43

Raven, Cano, Garton, and Shelhamer (1993) studied preservice agriculture

teachers at Montana State University (MSU) and The Ohio State University (OSU).

Females at both institutions were more likely to prefer a field independent learning style

than males.

Intelligence

While one’s learning style preference is not an indication of intelligence, a

number of researchers (Goodenough & Karp, 1961; Karp, 1963; Witkin et al., 1971;

Witkin, 1976) have found evidence of relationships between field independence and level

of intelligence. Specifically, a positive correlation was found between the analytical

factor of the Wechsler IQ test and the field dependent/independent dimension (Witkin,

1976). Reiff (1992) and Witkin (1976) found that field independent learners possessed a

greater level of cognitive flexibility than field dependent learners.

Academic Achievement

With respect to the field dependent/independent dimension’s influence on

academic achievement, Witkin, et al. (1977b) found no relationship between verbal SAT

scores of community college students and their learning style preference. Findings from

the same study however, revealed a slightly higher correlation with students’ learning

style preference and their SAT math scores (Witkin et al., 1977b). Furthermore, Witkin

et al. found little correlation between male or female high school or college GPA and

preferred learning style. More recent research (Cano, 1999; Cano & Garton, 1994;

44

Garton, Spain, Lamberson, & Spiers, 1999) has shown a positive correlation between

field independence and academic achievement.

Cano (1999) examined the relationship between learning style and academic

performance of incoming freshmen enrolled in The Ohio State University’s College of

Food, Agricultural, and Environmental Sciences and discovered a relationship existed

between GEFT scores and academic disciplinary actions. Students that preferred a field

independent learning style were less likely to require formal disciplinary actions for poor

academic performance. Students with a field dependent preference were more likely to

experience academic disciplinary action.

Garton, Spain, Lamberson, and Spiers (1999) studied the relationship between

course achievement and instructors’ teaching performance of first-year animal science

majors at the University of Missouri. Garton et al. determined that student learning styles

had a “low positive relationship with their preferred way of learning” (p. 11).



the academic years 1990, 1991, and 1992. Cano and Garton found that preservice

teachers leaning toward a field independent learning style were more likely to achieve

greater scores in the teaching methods course as measured by microteaching lab and final

course scores.

Vocational Interest

Witkin (1976) and Witkin et al., (1977a, 1977b) found that field independent

students were drawn to vocational areas involving analytical skills. Field dependent

45

students avoided vocational areas requiring analytical skills preferring instead people-

oriented vocational fields where social skills could be exercised (Witkin, 1976; Witkin et

al., 1977a, 1977b).

Field independent students were more likely to favor vocational areas involving

theory such as mathematics, engineering, chemistry and the biological sciences, and

technical and mechanical activities (Witkin, 1976; Witkin et al., 1977a). With respect to

areas of instruction, Witkin (1977a) found that field independent students were more

likely to favor teaching in the areas of mathematics, science, industrial arts and

vocational agriculture.

Vocational areas involving regular and direct contact with others such as

elementary teaching and teaching in the social sciences, counseling, and sales and

advertising were more likely to draw field dependent students (Witkin, 1977a). In

addition, vocational areas of an administrative nature involving direct contact with other

people were also found to be vocational areas favored by field dependent students

(Witkin, 1977a).

Academic Interest

Similar to vocational interest, the field dependent/independent dimension was

found to influence students’ preference for college majors (Garton, Spain, Lamberson, &

Spiers, 1999; Torres & Cano, 1994; Witkin et al., 1977b). Areas of study involving

analytical skills were more likely to draw field independent students (Witkin, 1976;

Witkin et al., 1977a, 1977b).

46

Frank (1986) found that college majors preparing preservice teachers in the areas

of the social sciences, humanities, family and child development and home economics

were more likely to draw field dependent students than the natural sciences, mathematics

and business, for example. Torres and Cano (1994) examined field

dependent/independent preferences of college of agriculture seniors academic major and

found that agricultural communication majors favor a field dependent learning style most

(mean GEFT score 8.8).

Garton, Spain, Lamberson, and Spiers (1999) studied the learning styles of first-

year animal science majors at the University of Missouri. Garton et al., learned that 56

percent favored the field independent learning style, 22 percent favored the field

dependent style, and 22 percent were classified as field neutral. The students’ mean

GEFT score was 13.4.

Estadt (1997) studied preservice educators enrolled in an agricultural education

methods class at The Ohio State University. Estadt found that nearly 70 percent of the

students preferred the field independent learning style. The students’ mean score was

12.4.

Torres and Cano (1994) described the learning style of college of agriculture

seniors and found that 39 percent of the college of agriculture seniors leaned toward the

field dependent learning style and 61 percent favored the field independent learning style.

The college of agriculture seniors’ mean GEFT score was 12.4. When examined by

major, the academic major most favoring a field independent learning style was

agricultural education (mean GEFT score 15.6).

47

Research has shown that a learning style congruent with area of vocational study

enables one to more easily focus on learning (Witkin et al., 1977b). Witkin et al. (1977b)

found that students who chose a college major compatible with their preferred learning

style were less likely to switch college major in the course of their college career.

Research Using Witkin’s Model to Determine Preferred Style

Learning style research in the area of agricultural education has identified

learning styles of Extension educators (Hudson, 1997; Sparks, 2001), preservice

agriculture teachers (Cano & Garton, 1994; Cano & Garton, 1992; Cano, Garton, &

Raven, 1991; Estadt, 1997; Raven, Cano, Garton, & Shelhamer, 1993; Whittington &

Raven, 1995), agriculture teacher educators (Foster & Horner, 1985), and agricultural

education students (Howard & Yoder, 1987; Rollins, Miller, & Kahler, 1988).

Learning Styles of Extension Educators

Little research specifically involving Witkin’s model to describe the learning

styles of Extension educators has been conducted. However, findings from studies

involving Extension educators (Hudson, 1997; Sparks, 2001) were similar to behaviors

and characteristics of field dependence/independence identified by other researchers

(Cano & Garton, 1994; Crosson, 1984; Garger & Guild, 1984; Torres & Cano, 1994;

Witkin, 1976, Witkin et al., 1977a, 1977b).

Congruent with findings by Crosson (1984), Hudson (1997) and Sparks (2001)

found that Extension educators’ field dependence increased with age. Describing

learning styles of West Virginia agriculture and natural resource Extension educators,

48

Sparks (2001) identified mean GEFT scores ranging from 14.4 for the age group 24-33 to

7.0 for the age group 54-63. Hudson described the learning styles of Extension educators

working in agriculture and natural resources, family and consumer sciences, community

development, and 4-H youth development in Ohio State University Extension’s Northeast

District. Hudson identified mean GEFT scores ranging from 12.8 for the age group 25-

30 to 9.4 for the age group 50-58.

Recent findings by Hudson (1997) and Sparks (2001) suggested that female

Extension educators tended to be more field dependent than male Extension educators.

Hudson found a mean female GEFT score of 11.2 compared to a mean male GEFT score

of 12.4. These findings are similar to the findings of others (Cano & Garton, 1994;

Garger & Guild, 1984; Torres & Cano, 1994; Witkin, 1976, Witkin et al., 1977a) who

found that females tended to be more field dependent than males.

Witkin (1976) and Witkin et al., (1977a, 1977b) found that field dependent

students avoided vocational areas requiring analytical skills, preferring instead people-

oriented vocational fields where social skills could be exercised. Congruent with

findings of Witkin (1976) and Witkin et al. (1977a, 1977b), Hudson (1997) and Sparks

(2001) found that Extension educators working in program areas requiring more regular

social contact with clientele such as family and consumer science (formerly known as

home economics) and 4-H youth development also tended to be more field dependent.

Similar to the influence of learning style preference on vocational interest,

academic major is also influenced by one’s learning style preference (Garton, Spain,

Lamberson, & Spiers, 1999; Torres & Cano, 1994; Witkin, 1976; Witkin et al., 1977b).

Hudson’s (1997) findings with respect to learning style preference and academic major

49

were also similar to findings of other researchers (Garton, Spain, Lamberson, & Spiers,

1999; Torres & Cano, 1994; Witkin et al., 1977b). Extension educators who had majored

in agriculture or science had mean GEFT scores of 11.1 or 16.3, respectively. Extension

educators who had majored in education or family and consumer sciences (formerly

known as home economics) had mean GEFT scores of 9.5 or 10.9, respectively (Hudson,

1997).

Learning Styles of Agricultural Educators

Researchers have found that preservice agricultural education instructors are more

field independent than field dependent (Cano, 1999; Cano & Garton, 1994; Estadt, 1997;

Whittington & Raven, 1995). These findings are congruent with those of Witkin (1977a)

who found that field independent students were more likely to favor teaching analytical

sciences such as vocational agriculture.

Estadt (1997) studied preservice educators enrolled in an agricultural education

methods class at The Ohio State University. Estadt found that nearly 70 percent of the

students preferred the field independent learning style. The students mean score was

12.4.

Whittington and Raven (1995) described the preferred learning style of preservice

agricultural educators at the University of Idaho and Montana State University who had

student taught in Spring of 1993 and Fall, 1992 and 1993. Whittington and Raven

concluded that 74 percent favored the field independent learning style and 26 percent

were classified as field dependent. The students’ mean GEFT score “was approximately

13” (p. 13).

50



the academic years 1990, 1991, and 1992. Cano and Garton learned that 41 percent of

the preservice teachers leaned toward the field dependent learning style and 59 percent

favored the field independent learning style. The preservice teachers’ mean GEFT score

was 11.9.

In 1993, Raven, Cano, Garton, and Shelhamer studied preservice agriculture

teachers at Montana State University (MSU) and The Ohio State University (OSU).

Raven et al. found that 67 percent of subjects at MSU favored the field independent

learning style. Field independence was the most popular learning style of subjects at

OSU at 56 percent. The MSU preservice teachers’ mean GEFT score was 12.4. The

OSU preservice teachers’ mean GEFT score was 11.5.

Summary of Learning Style

The term learning style has been described in the literature as “the manner in

which learners sort and process information” (Cano & Garton, 1994, p. 6). A variety of

models have been developed to describe learning style preference, but none have been

studied more widely than Witkin’s field dependence/independence model (Estadt, 1997;

Garger & Guild, 1984; Witkin et al., 1977a). Witkin’s field dependence/independence

conceptualization of learning style describes how one prefers to learn using a value

neutral, bipolar scale that describes an individual as field dependent or field independent.

51

The scale or continuum does not have a clear high or low end and one orientation is not

inherently better or worse than another (Witkin et al., 1977).

Witkin (1971) described field independence as a tendency to separate objects and

figures from an embedded context. To approach one’s environment from more analytical

terms is a preference of field independent individuals (Messick, 1970). Field dependence

was characterized by Witkin as a tendency “to experience events globally in an

undifferentiated fashion”(in McCutcheon, 1997, p. 20). To approach one’s environment

from a more global orientation is a preference of field dependent individuals (Messick,

1970).

Individuals with a field independent preference are less concerned with the

emotions of others, tend to be more introverted, and are less concerned with the social

environment. Conversely, individuals with a field dependent preference favor

relationships with others, tend to be extraverted, and are more comfortable in social

situations. Individuals with a field independent preference are intrinsically motivated.

Individuals with a field dependent preference are extrinsically motivated.

Learners with a field independent preference favor an ‘inquiry approach’ to

learning, prefer to structure their own learning, and place a higher priority on the learning

environment than the social environment. Field independent learners prefer individual

studies, projects, and work. Learners with a field dependent preference favor a ‘spectator

approach’ to learning, prefer the instructor provide structured, organized learning

activities, and place a higher priority on the social environment than the learning

environment. Field dependent learners prefer group studies, projects, and work.

52

Research has shown that one teaches others in the way that one prefers to learn.

The characteristics of field dependent and field independent learning styles are consistent

with field dependent and independent teaching styles. Teachers with a field independent

preference favor an ‘inquiry approach’ to instruction, prefer students structure their own

learning, and place a higher priority on the learning environment than the social

environment. Field independent teachers prefer individual studies, projects, and work.

Teachers with a field dependent preference favor a structured, organized instructional

environment, involving group projects and discussion. Personal relationships with

learners are important to field dependent instructors.

Furthermore, research has shown that age, gender, intelligence, academic

achievement, and vocational and academic interest are related to learning style. Research

findings are inconsistent with respect to the relationship between one’s learning style

preference and age as well as one’s learning style preference and gender. With respect to

learning style and level of intelligence, research has shown that field independent learners

possess a greater level of cognitive flexibility than field dependent learners. Field

independent learners are more likely to perform at a higher academic level and require

fewer academic disciplinary actions than field dependent learners. Fields requiring a

higher level of social interaction tend to draw individuals with field dependent learning

styles, whereas individuals with field independent learning styles are more apt to be

drawn to ‘hard science’ fields such as engineering, mathematics, and chemistry for

example.

Little research specifically involving Witkin’s model to describe the learning

styles of Extension educators has been conducted. However, findings from studies

53

involving Extension educators were similar to behaviors and characteristics of field

dependence/independence identified by other researchers with respect to subjects’ age,

gender, and vocational and academic interest.

Relative to research involving Witkin’s model to describe the learning styles of

Extension educators, a substantial amount of research involving Witkin’s model to

describe the learning styles of agricultural education students has been conducted.

Findings from studies involving agricultural educators were similar to behaviors and

characteristics of field dependence/independence identified by other researchers with

respect to subjects’ age, gender, and vocational and academic interest.

Personality Type

Personality type refers to the characteristic way in which an individual approaches

life’s experiences (Jung, 1971). At least three models (Myers-Briggs Type Model,

Keirsey Temperament Theory, True Colors Type Model) have been put forth to better

describe personality type. The following review of literature focuses on defining

personality type and models to describe personality type. In addition, the personality

type literature review focuses specifically on Jung’s character typology comprised of type

opposites and the Myers and Briggs multiple bi-polar model to identify attitude,

perception, judgment, and function preferences, including: Jungian type opposites

instruments such as the Myers-Briggs Type Indicator (MBTI) and an abbreviated version

of the MBTI, the Personal Style Inventory (PSI). Moreover, the following involves a

review of research related to personality types of preservice, adult, university, Extension

and agricultural educators. Finally, the review of personality type literature summarizes

54

research using Jungian type opposites instruments such as the Myers-Briggs Type

Indicator (MBTI) to determine personality type preferences.

Personality Type Defined

The 20th century psychologist Carl Jung sought to identify an objective

psychology founded on observation and experience (Jung, 1976, p. 8). Based upon the

premise that all human behavior was of a logical, orderly and consistent character, Jung’s

objective was to explain inherent differences in human behavior resulting from a few

basic mental functions and attitudes.

Studying medieval and middle age philosophies, works by 18th century

philosopher Friedrich Schiller, 19th century philosopher Nietzsche, and others, Jung put

forth a character typology consisting of type opposites. The essence of Jung’s theory is

the characteristic way in which individuals approach life’s experiences, something Jung

referred to as functions and attitudes. The functions describe how information is gathered

and treated. The functions – sensing, intuition, feeling, and thinking – describe an

individual’s preference toward dealing with self and the surrounding environment

through use of perception and judgment (Hammer McCaulley, Myers, & Quenk, 1998).

The attitudes – judging, perceiving, extraversion, and introversion - describe how

individuals relate to the surrounding environment (Jung, 1976).

Jung’s theory of psychological type has been one of the most comprehensive

theories with respect to personality type differences to date (Lawrence, 1982). From

Jung’s pyschological type theory, the Myers-Briggs Type Indicator (MBTI) was

developed (Myers et al., 1998).

55

For the purposes of this study, personality type was defined as a behavior

displayed in a characteristic way and being characteristic to other groups (Jung, 1976).

Personality type was measured by the Personal Style Inventory (PSI) (Hogan &

Champagne, 1980), a variation of the Jungian Myers-Briggs Type Indicator instrument.

Personality Type Models

Myers-Briggs Model

Myers and Briggs developed an instrument that identifies “preferences or

strengths that persons use in gathering information and making decisions” with extremely

high reliability (Rollins, 1990, p. 65). According to Rollins, Myers and Briggs theorized

that there were specific learning activities that best met the needs of individuals with

specific learning styles. Based upon Carl Jung’s theory of psychological type, Isabel

Myers and Katherine Briggs developed a multiple bi-polar model to identify attitude,

perception, judgment, and function preferences.

According to the Myers-Briggs model, characteristics of learners can be described

using the four bi-polar dimensions grouped by function and attitude. Myers et al., (1998)

indicated that “together, these functions and attitudes influence how a person perceives a

situation and decides on a course of action” (p. 19).

The type opposites model enables one to better identify specific characteristics of

learners’ individual preferences in attitude, perception, judgment, and function. The

model’s four dichotomous scales for attitude, perception, judgment, and function enables

one to describe individuals’ preference for extraversion (E) or introversion (I), sensing

56

(S) or intuition (N), thinking (T) or feeling (F), and judging (J) or perception (P) using

four separate indices (Myers et al., 1998). Relationships among the four dichotomous

scales provide for 16 possible types: ISTJ, ISFJ, INFJ, INTJ, ISTP, ISFP, INFP, INTP,

ESTP, ESFP, ENFP, ENTP, ESTJ, ESFJ, ENFJ, and ENTJ (see Table 2.4).

57

Source: Myers, I.B. & McCaulley, M.H. (1985). Manual: A Guide to the development and use of the Myers-Briggs Type Indicator. Palo Alto, CA: Consulting Psychologists Press. Table 2.4: 16 Personality Type Combinations.

58

One’s orienting psychological functions include “a particular form of psychic

activity that remains the same in principle under varying conditions” (Jung, 1976, p.

436). The functions describe the way in which one gathers information and makes sense

of life’s events. Information is gathered or perceived using the senses, described as

sensing (S) or through use of ones’ unconscious, described as intuition (N). These

preferences describe the way in which one becomes “aware of things, people, events, or

ideas” (Myers et al., 1998, p. 12). One makes sense of this information or these

perceptions through use of logic and objectivity, described as thinking (T) or through use

of personal reflection and consideration for others, described as feeling (F). These

functions help individuals focus their mental activity toward a variety of ends (p. 13).

The model posits that two separate functions or processes describe how an

individual gathers and treats information. These dimensions are described as either

sensing or intuition (S-N) and thinking or feeling (T-F).

Sensing/Intuition (S-N)

The manner in which we take in information and make sense of life’s events is

described in our preference toward sensing-intuition (S-N). Information is gathered or

perceived using the senses, described as sensing (S) or through use of one’s unconscious,

described as intuition (N). These preferences describe the way in which one becomes

“aware of things, people, events, or ideas” (Myers et al., 1998, p. 12).

One who has a preference for understanding the world around them through use

of their senses would be considered an “S” type on the MBTI. Such individuals are said

to enjoy the present moment and could be characterized as realistic, practical, and

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attentive to detail (Myers et al., 1998, p. 12). An individual considered an “N” type on

the MBTI would be more apt to prefer understanding things, people, events and ideas by

way of insight or the unconscious (p. 12). Such individuals have a preference for

focusing on the possibilities rather than those things that are actual. Oftentimes, such

individuals are characterized as “imaginative, abstract, and future oriented” (p. 12).

Individuals with a preference for sensing (S) prefer to gather information using

their senses of smell, sight, touch, feel and hearing. Individuals with a preference for

sensing characteristically:

Focus on what is real and actual Value practical applications Notice detail, factual, and concrete Observe and remembers sequentially Are oriented in the present Prefer information step-by-step Trust experience

(Myers, 1993, p. 4)

Individuals with a preference for intuition (N) prefer to gather information using

their sense of intuition. Individuals with a preference for intuition characteristically:

Focus on the “big picture” and possibilities Value imagination and insight Are abstract and theoretical See patterns and meaning in facts Possess a future-orientation Appear scatter-brained Trust inspiration

(Myers, 1993, p. 4)

Thinking/Feeling (T-F)

Each of us reacts to the information that we sense or feel in different ways. One

makes sense of this information or these perceptions through use of logic and objectivity,

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described as thinking (T). Such individuals are said to be concerned with justice and

fairness and could be characterized as analytical, logical, and objective (Myers et al.,

1998, p. 12). The opposite type of reaction involves personal reflection and consideration

for others, described as feeling (F). An individual considered an “F” type on the MBTI

has a preference for people, compassion, and sympathy rather than logic and objectivity.

As such, “F” type-individuals “have an understanding of people and a concern with the

human aspects of problems” (p. 13). The thinking (T) and feeling (F) functions help

individuals focus their mental activity toward a variety of ends (Myers et al., 1998, p. 13).

Individuals with a preference for thinking prefer to make decisions using logic.

Individuals with a preference for thinking characteristically:

Use cause-and-effect reasoning Strive for personal, objective truth Address problem-solving using logical analysis Are reasonable, fair, and “tough-minded” Value practical applications

(Myers, 1993, p. 5)

Individuals with a preference for feeling prefer to make decisions keeping the

welfare of others in mind. Individuals with a preference for feeling characteristically:

Assess the impact of their decisions have on other people Strive for harmony and personal validation Are guided by personal values Are sympathetic, compassionate, accepting, and “tender-hearted”

(Myers, 1993, p. 5)

In addition to the sensing-intuition (S-N) and thinking-feeling (T-F) functions, the

model asserts that individuals possess particular personality characteristics with respect to

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how they prefer to relate to their surrounding environment. These attitudinal dimensions

are described as either extraversion or introversion (E-I) and judging or perceiving (J-P).

One’s attitudes toward life involve “a readiness of the psyche to act or react in a

certain way” (Jung, 1976, p. 414). The attitudes describe the way in which one relates to

the world around them. One provides energy to objects and people of the surrounding

environment, defined as extraverted (E) or one takes energy and interest from the

surrounding environment, described as introverted (I). An orientation toward life that is

“open, curious, and interested” is described as a perceptive attitude, described as

perception (P) (Myers et al., 1998, p. 14). One whose orientation toward life is

characteristically ordered, structured, and decisive is considered to be a judging type,

described as judging (J).

Extraversion/Introversion (E-I)

One’s attitudes toward life involve “a readiness of the psyche to act or react in a

certain way” (Jung, p. 414). The attitude we have toward interacting with the world

around us is described in our preference toward extraversion-introversion (E-I). One

provides energy to objects and people of the surrounding environment, defined as

extraverted (E) or one takes energy and interest from the surrounding environment,

described as introverted (I).

An individual considered an “E” type on the MBTI would be more interested in

the stimulation of the surrounding environment. Such individuals are said to have “a

desire to act on the environment, to affirm its importance, to increase its affect” (p. 13).

Such individuals could be characterized as action-oriented, sociable, and easily able to

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communicate (p. 13). Conversely, one who is internally motivated and perceives the

outside world to be of secondary importance would most likely be considered an “I” type

on the MBTI. Such individuals could be characterized as thoughtful, contemplative, and

desirous of quiet time and privacy (Myers et al., 1998, p. 13).

Individuals with a preference for extraversion prefer to share their energy with the

surrounding environment; the outer world of people and things. Individuals with a

preference for extraversion characteristically:

Prefer to communicate by talking Learn best by doing or discussing Tend to speak first, reflect later Take initiative toward work and interpersonal relationships Possess a breadth of interests Are sociable, expressive, and attuned to the external environment

(Myers, 1993, p. 4)

Individuals with a preference for introversion prefer to gain their energy from the

surrounding environment; the inner world of thoughts and reflections. Individuals with a

preference for introversion characteristically:

Prefer to communicate in writing Learn best by reflection and mental “practice” Tend to reflect before acting or speaking Possess a depth of interest Are private, contained, and easily focus

(Myers, 1993, p. 4)

Judging-Perceiving (J-P)

One’s orientations or attitudes toward the outer world are described in terms of

preferences toward organization and structure or spontaneity. These behaviors are

described in our preference toward judgment-perception (J-P).

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One whose orientation toward life is characteristically ordered, structured, and

decisive is considered to be a judging type, described as judging (J). Such individuals

have a preference for “making decisions, seeking closure, planning operations, or

organizing activities” and would be considered a “J” type on the MBTI (Myers et al.,

1998, p. 14). Conversely, an orientation toward life that is “open, curious, and

interested” is described as a perceptive attitude, described as perception (P) (Myers et al.,

1998, p. 14). An individual considered a “P” type on the MBTI would be more apt to

dislike structure and planning, favoring spontaneity and flexibility. Such individuals are

characterized as “adaptable, open to new events and changes, and aiming to miss

nothing” (p. 14).

Individuals with a preference for judging are viewed as outworldly structured and

organized. Individuals with a preference for judging characteristically:

Prefer closure Prefer to plan Prefer to have things decided Avoid last-minute stresses Are scheduled, systematic, and methodical Prefer to communicate in writing

(Myers, 1993, p. 5)

Individuals with a preference for perceiving are viewed as outworldly flexible and

spontaneous. Individuals with a preference for perceiving characteristically:

Prefer things loose and open to change Prefer to leave things open-ended Feel energized by last-minute pressures Are casual and adaptive

(Myers, 1993, p. 5)

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The work of Isabel Myers and Katharine Briggs served as one of the earliest

models by which Jung’s theory of psychological types could be tested and better

understood (Myers et al., 1998). Since its development, other models based upon Jung’s

theory and the Myers and Briggs Model have been put forth to help explain personality

types as well.

Keirsey Temperament Theory

The Keirsey Temperament Theory developed by David Keirsey aims to better

explain character through application of Jung’s personality opposites. Unlike the Myers

and Briggs model, Keirsey’s model, involves only three of the four sets of opposites,

omitting the attitude pair extraversion-introversion (E-I). Using the MBTI, temperament

assessments can be made based upon the pairings of the six possibilities of opposites –

sensing-perceiving (SP), sensing-judging (SJ), intuition-thinking (NT), and intuition-

feeling (NF) (Keirsey & Bates, 1984).

Keirsey (1984) described four temperaments. The Dionysian Temperament was

based upon the sensing-perceiving (SP) pairing and included ISTP, ESTP, ISFP, and

ESFP MBTI combinations. The Epimethean Temperament was based upon the sensing-

judging (SJ) pairing and included ISTJ, ESTJ, ISFJ, and ESFJ MBTI combinations. The

Promethean Temperament was based upon the intuition-thinking (NT) pairing and

included INTP, ENTP, INTJ, and ENTJ combinations. The Apollonian Temperament

was based upon the intuition-feeling (NF) pairing and included INFJ, ENFJ, INFP, and

EFNP MBTI combinations (Keirsey & Bates, 1984).

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True Colors Type Model

The True Colors personality type model, based upon Keirsey’s Temperament

Theory, attempts to describe character through application of Jung’s personality

opposites as well. The True Colors model renames Keirsey’s temperaments to colors.

The Keirsey model’s Dionysian Temperament becomes the Orange type in the True

Colors model. The True Colors Gold type is the Keirsey model’s Epimethean

Temperament. The Keirsey model’s Promethean Temperament becomes the Green type

in the True Colors model. The True Colors Blue type is the Keirsey model’s Apollonian

Temperament (True Colors Communication Group, 1998).

Measures of Personality Type

Myers-Briggs Type Indicator

The Myers-Briggs type opposites model enables one to better identify specific

characteristics of learners’ individual preferences in attitude, perception, judgment, and

function. The model’s four dichotomous scales for attitude, perception, judgment, and

function enables one to describe individuals’ preference for extraversion (E) or

introversion (I), sensing (S) or intuition (N), thinking (T) or feeling (F), and judging (J) or

perception (P) using four separate indices (Myers et al., 1998). Strong relationships

among the four dichotomous scales provide for 16 possible types: ISTJ, ISFJ, INFJ,

INTJ, ISTP, ISFP, INFP, INTP, ESTP, ESFP, ENFP, ENTP, ESTJ, ESFJ, ENFJ, and

ENTJ.

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The Myers-Briggs type opposites model dimensions are measured by the highly

reliable Myers-Briggs Type Indicator (MBTI). According to Myers et al., (1998) the

MBTI provides a method by which Jung’s theory of personality type can be put to

practical use (p. 1). The MBTI “enables us to expect specific differences in specific

people and to cope with the people and their differences more constructively than we

otherwise could” (p. 11).

The MBTI attempts to measure opposites of personality characteristics that

included what Jung referred to as functions - using perception and judgment to gather and

treat information; and attitudes - how one relates and responds to the surrounding

environment as a result of one’s attitudes. Myers et al., (1998) indicated that “together,

these functions and orientations influence how a person perceives a situation and decides

on a course of action” (p. 19).

The MBTI was first developed in the 1950s and is most suitable for use with

English-speaking high school students and adults. The MBTI instrument has been

refined and revised over the years and can now be administered in one of three formats.

The MBTI has been used extensively in higher education to improve the learning process

for over 25 years (Estadt, 1997, p. 4). Moreover, the personality type indicator has

become a reliable tool for determining learning style preference as well as in academic

advising, career and psychological counseling (Lawrence, 1984). Jung (1971) found that

attitudes, perceptions, judgment and other personality characteristic preferences described

by the MBTI influence learning style preferences.

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Personal Style Inventory

Hogan and Champagne (1980) developed The Personal Style Inventory (PSI) to

provide researchers an alternative to the MBTI. The PSI is an abbreviated version of the

MBTI, containing only 32 items. Similar to the MBTI, the PSI aims to measure a

person’s Jungian typology, generating preference scores for four separate dimensions of

personality type - extraversion (E) or introversion (I), sensing (S) or intuition (N),

thinking (T) or feeling (F), and judging (J) or perception (P). The PSI’s 32 items are

arranged in pairs with each member of the pair representing a preference for attitude (E-

I), perceiving function (S-N), judging function (T-F), and orientation (J-P).

Subjects are asked to rate their preference for each member of the pair by

assigning each member a score from 0 to 5. The sum of the members in each pair,

however, must total 5. Assigning one member of the pair a score of 5 would require the

subject to assign the other member of the pair a 0, and would indicate the subject prefers

that member of the pair very strongly, much more than the other member of the pair.

Because fractions are not permitted in rating preferences for the members in the paired

items, subjects are forced to indicate a preference for one member of the pair.

Preference scoring is explained in the following example. Without using

fractions, subjects are required to assign a total of 5 points to the following paired item,

for example: 1a) I most often am quietly friendly and reserved, or 1b) I most often attract

others to me by being outgoing. Assume the subject followed directions and assigned a

total of 5 points between the two members in the paired item; 3 points to 1a and 2 points

to 1b. In this manner of assigning points, a preference is indicated for item 1a.

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The PSI is a self-scoring instrument. However, unlike the MBTI, the PSI can be

self-administered in as little as 15 minutes. After assigning a total of 5 points between

the two members in each paired item, subjects are asked to record the score assigned to

each item to a scoring sheet containing eight columns – two columns for each of the four

dichotomies. After subjects double check each score, the columns are summed,

providing a total of eight scores – one score for each of the eight preferences. The

member of each pair with the greater score indicates the preference for that dimension.

The PSI may be scored using four continuous scales. PSI scores are treated as

interval-level data and converted to a continuous scale score using 100 as the base.

Myers et al., (1998) indicated that “for E, S, T, or J preference scores, the continuous

score is 100 minus the numerical portion of the preference score. For I, N, F, or P

preference scores, the continuous score is 100 plus the numerical portion of the

preference score” (p. 9). In other words, E, S, T, and J preferences are represented by

scores less than 100. Scores greater than 100 represent subjects’ preferences for I, N, F,

and P dimensions. A score is recorded for each subject for each dimension of the

typology, resulting in four scores per subject.

To determine instrument reliability, Hogan and Champagne (1980) compared

subjects’ estimated scores with subjects’ actual PSI scores to find acceptable Pearson

product-moment correlation scores. Reliability coefficients were .60, .74, .66, and .61 for

the four dichotomies measured by the PSI - attitude (E-I), perceiving function (S-N),

judging function (T-F), and orientation (J-P) dimensions, respectively.

To determine instrument validity, Hogan and Champagne (1980) generated Phi

correlations for the model’s four dichotomous scales for attitude, perception, judgment,

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and function. Phi correlations were .78, .55, .90, and .71 for the four dichotomies

measured by the PSI - attitude (E-I), perceiving function (S-N), judging function (T-F),

and orientation (J-P) dimensions, respectively.

Research Using the Myers-Briggs Model to Determine Preferred Style

Are individuals of certain personality types more apt to become educators than

others? One’s preference with respect to the perception domain - sensing (S) and

intuition (N) - and the judgment domain – thinking (T) and feeling (F) can be an

influencing factor on vocational choice and satisfaction (Myers, 1963). Myers found that

sensing-feeling (SF) individuals were motivated most by an observable reality and made

judgments based on feeling more than thinking; and as such, (SF) individuals were

ideally suited for a practical and people-oriented vocation such as teaching (Myers,

1963).

Since the early 1960s, the MBTI has been used in personality types research

related to preservice educators and educators at the preschool, elementary, adult, junior

college, and university levels (Hammer, 1996; Myers et al., 1998; Sears, Kennedy, &

Kaye, 1997). More recently, researchers in the field of agricultural education have

employed use of the MBTI to describe type of Extension educators, and preservice

agriculture teachers (Cano, Garton & Raven, 1992a; Cano, Garton & Raven, 1992b;

Estadt, 1997; Garton, Thompson & Cano, 1997; Kitchel, 1997; Raven, Cano, Garton &

Shelhamer, 1993).

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An examination of thirty years of research by Lawrence (1993) supported the

premise that sensing-feeling (SF) individuals are ideally suited for educational vocations.

More educators in the MBTI research subjects database have indicated a preference for

sensing-feeling (SF) than sensing-thinking (ST), intuitive-feeling (NF) or intuitive-

thinking (NT). Furthermore, the sensing-feeling (SF) preference was more prevalent

among educators at the preschool, elementary, and middle and high school levels than

among adult, junior college, and university educators (Lawrence, 1993).

A review of university educators’ MBTI preferences contained in the MBTI

research subjects database, specifically university teachers’ MBTI preferences was

unable to support this notion. University-level educators were least likely to indicate a

preference for sensing-feeling (SF) as compared to preschool, elementary, middle and

junior, high school, adult and junior college level educators. University-level educators

were more likely to indicate a preference for intuitive-feeling (NF) (32.7 percent) and

intuitive-thinking (NT) (31.2 percent) than sensing-thinking (ST) (22.5 percent) or

sensing-feeling (SF) (13.9 percent) (Lawrence, 1993).

A review of adult educators’ MBTI preferences contained in the MBTI research

subjects database revealed that adult educators were more likely to indicate a preference

for sensing-feeling (SF) (32.9 percent), intuitive-feeling (NF) (31.1) and sensing-thinking

(ST) (28.6 percent) than intuitive-thinking (NT) (13.2 percent) (Lawrence, 1993).

Lawrence (1993) found that educators in general were more likely to indicate a

preference for judging (J) than perceiving (P). A review of adult educators’ MBTI

preferences contained in the MBTI research subjects database revealed that adult

educators were more likely to indicate a preference for judging (J) (68 percent) than

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perceiving (P) (32 percent). Similarly, university-level educators were more likely to

indicate a preference for judging (J) (65.8 percent) than perceiving (P) (34.2 percent).

When paired with the extroversion-introversion (E-I) dimension, adult educators were

more likely to prefer extroversion-judging (E-J) (40.8 percent) than introversion-judging

(I-J) (27.2 percent). Conversely, university-level educators were less likely to prefer

extroversion-judging (E-J) (28.5 percent) than introversion-judging (I-J) (37.3 percent)

(Lawrence, 1993).

Overall, more (13.6 percent) adult educators identified themselves as Extroverted

(E), Intuitive (N), Feeling (F), Judgers (J) (ENFJ) than any other type combination. The

second most (11.4 percent) frequently reported types for adult educators included

Extroverted (E), Intuitive (N), Feeling (F), Perceivers (P) (ENFP), Extroverted (E),

Sensing (S), Feeling (F), Judgers (J) (ESFJ), and Introverted (I), Sensing (S), Feeling (F),

Judgers (J) (ISFJ) at 11.4 percent. The least popular types for adult educators were INFJ,

INTJ, INTP, at 3.1, 2.6, 1.8 percent respectively (Lawrence, 1993).

More (12.8 percent) university teachers identified themselves as Introverted (I),

Sensing (S), Thinking (T), Judgers (J) (ISTJ) than any other MBTI type. The second

most frequently reported type for university teachers was Introverted (I), Intuitive (N),

Thinking (T), Judgers (J) (INTJ) at 10.9 percent. The least popular types for university

teachers were ESTP, ISTP, ISFP, and ESFP at 1.2, 1.7, 1.7, and 1.7 percent respectively

(Lawrence, 1993).

Research involving preservice education majors supports the premise that

sensing-feeling (SF) individuals are ideally suited for educational vocations as well

(Lawrence, 1993). Grindler and Stratton (1990, in Hammer, 1996) found that the most

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frequent types among preservice teachers were ESFJ, ISFJ, and ENFP. Furthermore,

these findings agree with those of Lawrence who found that educators in general were

more likely to indicate a preference for judging (J) than perceiving (P).

A review of the more recent agricultural education literature reveals use of the

MBTI in research related to agricultural and Extension educators as well (Cano, Garton

& Raven, 1992a; Cano, Garton & Raven, 1992b; Estadt, 1997; Garton, Thompson &

Cano, 1997; Ishaya, Henderson, & McCracken, 1992; Kitchel, 1997; Raven, Cano,

Garton & Shelhamer, 1993; Watson & Hillison, 1991).

Preferred Style of Extension Educators

Extension educators conduct applied research as university-level educators and

design and deliver informal adult educational programs as adult educators. While a

myriad of research has been conducted on adult and university-level educators

(Lawrence, 1993) very little research specifically involving the Myers-Briggs model to

describe the personality type of Extension educators has been conducted. Findings by

Ishaya, Henderson, and McCracken (1992) involving use of the MBTI indicated that

personality types of Extension educators were similar to personality types of adult

educators found by other researchers (Lawrence, 1993).

Ishaya, Henderson, and McCracken (1992) described the range of MBTI

psychological types involved in a study of 116 Ohio State University Extension educators

participating in a managerial assessment center. Congruent with findings on adult

educators (Lawrence, 1993), most Extension educators were typed by the MBTI as

extroverts (E) (57 percent) with preferences toward sensing (S) (57 percent), thinking (T)

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(58 percent), and judging (J) (65 percent) (Ishaya et al., 1992). According to Ishaya et

al., Extension educators were more likely to indicate a preference for sensing-thinking

(ST) (35.4 percent) and intuitive-thinking (NT) (24.2 percent) than sensing-feeling (SF)

(22.3 percent) and intuitive-feeling (NT) ( 18.0 percent). These findings are incongruent

with preferences of university-level and adult educators shared by Lawrence (1993).

However, similar to adult and university-level educators (Lawrence, 1993),

Extension educators were more likely to indicate a preference for judging (J) (66.3

percent) than perceiving (P) (33.7 percent). When paired with the extroversion-

introversion (E-I) dimension, adult educators were more likely to prefer extroversion-

judging (E-J) (40.8 percent) than introversion-judging (I-J) (27.2 percent). Moreover,

Extension educators were also more likely to indicate a preference for extroversion-

judging (E-J) (40.8 percent). Similar results have been found with research involving

adult educators (Lawrence, 1993).

Preferred Style of Agricultural Educators

Researchers in the field of agricultural education have employed use of the MBTI

to describe personality type preferences of preservice agriculture teachers and agriculture

students (Cano, Garton & Raven, 1992a; Cano, Garton & Raven, 1992b; Estadt, 1997;

Garton, Thompson & Cano, 1997; Kitchel, 1997; Raven, Cano, Garton & Shelhamer,

1993; Watson & Hillison, 1991).

Extroverted and introverted preservice agricultural educators indicated

preferences for sensing (S), thinking (T), and judging (J) than any other type combination

(Cano & Garton, 1994; Cano, Garton, & Raven, 1992a). Female preservice agricultural

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educators have indicated a preference for feeling (F) as opposed to thinking (T) whereas

male preservice agricultural educators were more likely to be typed thinking (T) ) (Cano,

Garton, & Raven, 1992a). Cano and Garton indicated that this distribution of

psychological types was congruent with findings involving other agriculturally related

groups (Cano, Garton, & Raven, 1992a; and Bargar, Bargar & Clark, 1990; Barrett, 1985;

Barrett & Homer, 1987; Barrett, Sorensen & Hartung, 1987; McCann, Heird & Roberts,

1989 in Cano & Garton, 1994).

Similar to findings of preservice agricultural educators, students of agricultural

education indicated preferences for sensing (S), thinking (T), and judging (J) than any

other type combination (Estadt, 1997; Kitchel, 1999). Regardless of extroversion or

introversion preference, Kitchel found sensing-thinking-judging (STJ) type preferences

accounted for 36.8 percent of types. Moreover, Estadt found that sensing-thinking-

judging (STJ) type preferences accounted for 37.7 percent of types.

Watson and Hillison (1991) found agricultural educators’ type preferences were

similar to type preferences of agricultural education students. Watson and Hillison

examined psychological types of West Virginia agricultural educators in comparison to

norms of the general population norms and high school teacher norms. Watson and

Hillison found that 57.6 percent of the agricultural educators were typed sensing-judging

(SJ) by the MBTI. An additional 23.7 percent were typed sensing-perceiving (SP).

Agricultural education “attracts practical, action-oriented, realistic types” such as

sensing-judging and sensing-perceiving (Watson & Hillison, 1991, p. 28).

In contrast to these findings, Garton, Thompson, and Cano (1997) found high

school-level agricultural educators were typed ENTJ preferring “active learning and

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exercises that stimulated thought” (p. 38). High school-level agricultural students were

typed ISFJ preferring a “quiet learning environment and ‘real life’ illustrations” (p. 38).

Garton et al., shared that such results demonstrate a need for educators to be more

conscious of learner and educator learning style differences (p. 38).

Learning Style and Personality Type

What do we know of studies that have been done to relate Jungian personality

types indicators to the GEFT? A small but statistically significant relationship has been

found between the GEFT and the MBTI of Jungian personality types (Canning, 1983;

Carey, Fleming, & Roberts, 1989; Holsworth, 1985; Kitchel, 1999).

In 1989, Carey et al. found that MBTI scores for perception (either Sensing – S,

or Intuition – N) and orientation (either Judging - J, or Perception – P) were significantly

correlated with GEFT scores. Using multiple regression procedures, Carey et al. found

that the perception (S-N) and orientation (J-P) scales accounted for 17.8% of GEFT score

variance. Carey et al. shared that while a statistically significant relationship was found,

and individuals with intuitive and perception type preferences generally tend to be less

field dependent than sensing and judging types, relating perception and orientation scores

to GEFT scores should be done with caution.

In 1985, Holsworth (in DiTiberio, 1996) shared that Introverted (I) and Judging

(J) college students with preferences for Intuition (N) and Thinking (T) were more likely

to be field independent. Extroverted college students with preferences for Feeling (F)

and Sensing (S) were more likely to be field dependent.

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Canning’s 1983 study (in DiTiberio, 1996) focused on high school students, using

the function pairs to determine relationships. Canning found that field dependent

students were more likely to prefer Sensing (S) and Thinking (T). Field independent

students were more like to prefer Intuition (N) and Feeling (F).

Kitchel (1999) examined college students attending The Ohio State University

between 1990-1999, majoring or minoring in agricultural education. Similar to the Carey

et al. study (1989), Kitchel found a low, but statistically significant relationship between

perception (either Sensing – S, or Intuition – N) and orientation (either Judging - J, or

Perception – P). Kitchel found that students with field dependent learning styles were

more likely to prefer Sensing (S) and Judging (J).

Summary of Personality Type

Personality type refers to the characteristic way in which an individual approaches

life’s experiences (Jung, 1971). A number of models have been put forth to better

describe personality type, but none have been studied more widely than Jungian

psychological typology and related Myers-Briggs Type Theory (Lawrence, 1982). The

essence of Jung’s theory are the characteristic functions and attitudes which guide the

way in which individuals approach life’s experiences. Functions describe how

information is gathered and treated. The functions – sensing, intuition, thinking, and

feeling – describe an individual’s preference toward dealing with self and the surrounding

environment through use of perception and judgment (Myers et al., 1998). The attitudes

– judging, perceiving, extraversion, and introversion - describe how individuals relate to

the surrounding environment (Jung, 1976).

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Myers and Briggs believed there were specific learning activities that best met the

needs of individuals with specific learning styles (Rollins, 1990). Building upon Carl

Jung’s theory of psychological type, Isabel Myers and Katherine Briggs developed a

multiple bi-polar model to identify an individual’s attitude, perception, judgment, and

function preferences.

The model’s four dichotomous scales for attitude, perception, judgment, and

function enable one to describe individuals’ preference for extraversion (E) or

introversion (I), sensing (S) or intuition (N), thinking (T) or feeling (F), and judging (J) or

perception (P) using four separate indices (Myers et al., 1998). Relationships among the

four dichotomous scales provide for 16 possible types: ISTJ, ISFJ, INFJ, INTJ, ISTP,

ISFP, INFP, INTP, ESTP, ESFP, ENFP, ENTP, ESTJ, ESFJ, ENFJ, and ENTJ.

Orienting psychological functions describe the way in which one gathers

information and makes sense of life’s events. Information is gathered or perceived using

the senses, described as sensing (S) or through use of ones’ unconscious, described as

intuition (N). These preferences describe the way in which one becomes “aware of

things, people, events, or ideas” (Myers et al., 1998, p. 12). One makes sense of this

information or these perceptions through use of logic and objectivity, described as

thinking (T) or through use of personal reflection and consideration for others, described

as feeling (F). These functions help individuals focus their mental activity toward a

variety of ends (p. 13).

The Myers-Briggs psychological types model asserts that individuals possess

particular personality characteristics with respect to how they prefer to relate to their

78

surrounding environment. These dimensions are described as either extraversion or

introversion (E-I) and judging or perceiving (J-P).

Orientations or attitudes toward life involve “a readiness of the psyche to act or

react in a certain way” (Jung, 1976, p. 414). The attitudes describe the way in which one

relates to the world around them. One provides energy to objects and people of the

surrounding environment, defined as extraverted (E) or one takes energy and interest

from the surrounding environment, described as introverted (I). An orientation toward

life that is “open, curious, and interested” is described as a perceptive attitude, described

as perception (P) (Myers et al., 1998, p. 14). One whose orientation toward life is

characteristically ordered, structured, and decisive is considered to be a judging type,

described as judging (J).

The MBTI has been used in personality types research related to preservice

educators and educators at the preschool, elementary, adult, junior college, and university

levels for over forty years (Hammer, 1996; Myers et al., 1998; Sears, Kennedy, & Kaye,

1997). More recently, researchers in the field of agricultural education have employed

use of the MBTI to describe Extension educators and preservice agriculture teachers’

personality type (Cano, Garton & Raven, 1992a; Cano, Garton & Raven, 1992b; Estadt,

1997; Garton, Thompson & Cano, 1997; Kitchel, 1997; Raven, Cano, Garton &

Shelhamer, 1993).

Researchers (Grindler & Stratton, 1990, in Lawrence, 1993; Lawrence, 1993;

Myers, 1963) found that individuals with a preference for sensing-feeling (SF), motivated

by an observable reality and favoring judgments based on feeling, were ideally suited for

the teaching field. This finding holds true for all levels of instruction except for

79

university-level educators who were more likely to indicate a sensing-thinking (ST)

preference (Lawrence, 1993).

Research involving Extension educators found personality type preferences

congruent with findings on adult educators. Extension educators were typed by the

MBTI as extroverts (E) with preferences toward sensing (S), thinking (T), and judging (J)

(Ishaya, Henderson, & McCracken, 1992). Similar findings on the preferences of

agricultural educators were shared by Cano and Garton (1994) and Cano, Garton, and

Raven (1992a). Educators in general were more likely to indicate a preference for judging

(J) than perceiving (P) and overall, adult educators identified themselves as extroverted

(E), intuitive (N), feeling (F), judging (J) (ENFJ) more than any other type combination.

Learning Style as Related to Personality Types

The preferred manner in which individuals sort and process information from

teaching and learning perspectives vary widely (Cano, Garton, & Raven, 1992a; Raven,

Cano, Garton, & Shelhamer, 1993). Such personal preferences - personality

characteristics, attitudes, perception, and judgment - are influencing factors in the manner

in which learners sort and process information (Jung, 1971). Individual personality

characteristics, attitudes, perceptions, and judgments play a deciding role in the way one

learns and responds in a learning situation (Myers & Myers, 1995, p. 139).

Little research exists that examines the relationship between the MBTI and

measures of field dependence/independence (Carey, Fleming, & Roberts, 1989; Hammer,

1996). A moderate relationship (.375 r coefficient) was found between the MBTI

80

sensing-intuitive (SN) scale and the GEFT. A similar relationship (.377 r coefficient)

was found between the MBTI judging-perceiving (JP) scale and GEFT scores (Carey,

Fleming, & Roberts, 1989). Studies described by Lawrence (1993, in Hammer, 1996)

indicated relationships between field independence and intuition (N), thinking (T),

introversion-judging (IJ), and intuitive-thinking (NT). Further, it was shared that a

relationship between a preference for field dependence and preferences for feeling (F)

and extraversion-sensing-feeling (ESF) existed. Kitchel (1999) found low levels of

association between GEFT scores and the MBTI sensing-intuition (SN) and judging-

perceiving (JP)scales.

Summary of Review of Literature

Learning style can be described as “the manner in which learners sort and process

information” (Cano & Garton, 1994, p. 6). Personality type refers to the characteristic

way in which an individual approaches life’s experiences. A number of models have

been put forth to better describe learning style and personality type. Two of the most

widely studied models used to explain learning style and personality type are Witkin’s

field dependence/independence model, measured by the Group Embedded Figures Test

(GEFT), and Myers-Briggs Jungian psychological typology, measured by the Myers-

Briggs Type Indicator (MBTI).

The Myers-Briggs model characterizes learners using four bi-polar dimensions.

Myers et al. (1998) indicated that the dimensions that comprise the type opposites model

“influence how a person perceives a situation and decides on a course of action” (p. 19).

The model enables one to better identify specific characteristics of learners’ individual

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preferences in attitude, perception, judgment, and function. The dimensions are

described as either sensing or intuition (S-N) and thinking or feeling (T-F) – the

information gathering functions – and extraversion or introversion (E-I) and judging or

perceiving (J-P) – the relating orientations.

Relationships among the four bi-polar dimensions provide for 16 possible types:

ISTJ, ISFJ, INFJ, INTJ, ISTP, ISFP, INFP, INTP, ESTP, ESFP, ENFP, ENTP, ESTJ,

ESFJ, ENFJ, and ENTJ. Each individual exhibits different personality type

characteristics.

Witkin’s field dependence/independence model involves a bipolar, value-neutral

continuum which describes one’s orientation to the surrounding field. Individuals whose

mode of perception is strongly dominated by the surrounding field are described as field

dependent. Individuals with a field dependent preference perceive in a global perspective

framed by personal surroundings, are able to make broad, general distinctions among

concepts, and prefer social contexts and orientations. Individuals whose mode of

perception is largely unaffected by the surrounding field are described as field

independent. Field independent individuals are able to see individual component parts,

prefer the abstract, analytical thought and problem solving.

A small but statistically significant relationship has been found between learning

styles and personality types as measured by the GEFT and the MBTI. Relationships have

been found between GEFT scores and MBTI scores for perception (either Sensing – S, or

Intuition – N) and orientation (either Judging - J, or Perception – P). Relationships have

been found between field independence and Introverted (I) and Judging (J) students with

preferences for Intuition (N) and Thinking (T). And, relationships have been found

82

between field dependence and Extroverted (E) students with preferences for Feeling (F)

and Sensing (S).

The GEFT and MBTI have been used extensively in research involving educators.

Learning style research in the area of agricultural education has identified learning styles

of preservice agriculture teachers, agriculture teacher educators, and agricultural

education students. Further, a myriad of research has been conducted on adult and

university-level educators, however very little research specifically involving the GEFT

and MBTI to describe learning style and personality type preferences of Extension

educators has been conducted.

Extension program effectiveness is determined by a variety of interrelated factors,

among which are: the program professional’s and program participant’s personal and

interpersonal styles (learning style and personality type); the program professional’s level

of experience (age and length of tenure); geographic area of responsibility (primary work

assignment); academic training (academic major and educational attainment); and

compatibility with program participants (gender).

The following conceptual framework (Figure 2.2) illustrates the relationships

between learning style, personality type, primary work assignment, length of tenure,

academic major, educational attainment, age, and gender; and how these variables relate

to Extension program effectiveness.

83

Figure 2.2: Conceptual Framework

Academic Major

Educational Attainment

Length of Tenure

Learning Styl e

Personality Style

Age Primary Work Assignment

Gender

Extension Program

Effectiveness

84

CHAPTER 3

METHODOLOGY

Purpose

The purpose of this descriptive correlational study was to describe the relationship

between learning style and personality type preferences of Extension Community

Development program professionals in Ohio. In addition, the purpose of the study was to

describe Extension Community Development program professionals employed in Ohio

during the time period from April to July, 2004 in terms of primary work assignment,

length of tenure, academic major, educational attainment, and gender (see Table 3.1).

85

Variable Level

Learning Style (GEFT score) Ratio

Personality type (PSI score) Interval

Age Ordinal

Length of Tenure Ordinal

Educational Attainment Ordinal

Primary Work Assignment Nominal, Multichotomous

Academic Major Nominal, Multichotomous

Gender Nominal, Dichotomous

Table 3.1: Description of Variables.

Research Hypotheses

1. There is no relationship between learning style preferences as measured by GEFT

scores and personality type preferences as measured by PSI scores of Extension

Community Development program professionals employed in Ohio during the

time period April to July, 2004.


scores and primary work assignment, length of tenure, academic major,



2004.

86

3. There is no relationship between personality type preferences as measured by PSI




2004.

Objectives





administrators.












87




administrators.






administrators.

Population

The target population was all Extension Community Development program

professionals employed in Ohio during the time period April to July, 2004, including:

support staff, program assistants, educators, specialists, and administrators. The

accessible population was all Community Development program professionals that

attended the state program meetings, completed the instruments, and provided usable

data. While study results were generalized only to those providing usable data, a

sampling of non-respondents revealed that non-respondent characteristics did not vary

significantly from the accessible population (see Appendix F).

88

Instrumentation

The Group Embedded Figures Test (GEFT), the Personal Style Inventory (PSI),

and a one-page subject characteristics questionnaire were used. The GEFT measured

learning style preference. The Personal Style Inventory (PSI) measured personality type

preference. The researcher administered both instruments following the guidelines

provided by the originators of each instrument. The one-page subject characteristics

questionnaire was used to record subjects’ primary work assignment, length of tenure,

academic major, educational attainment, age, and gender (Appendix D).

GEFT

The GEFT is a standardized instrument designed to measure preference for field

dependence/independence (see Appendix A). The terms field dependence and field

independence represent a bipolar continuum which describes one’s orientation to the

surrounding field. The continuum is value neutral and does not have a clear high or low

end (Witkin, Oltman, Raskin, & Karp, 1971). Furthermore, one’s orientation as

measured by the continuum is not inherently better or worse than that of another (Witkin

et al.). The continuum does not purport to measure two types of persons, but rather to

provide a description of an individual’s position on the continuum relative to the mean

(Claxton & Ralston, 1978).

The three-section GEFT booklet requires subjects to find geometric figures

embedded in drawings within a 12-minute time period. The various figures embedded in

the instrument are shared on the back cover of the instrument. To orient subjects to the

instrument, subjects are given two minutes to identify seven simple practice items found

89

in Section I. More difficult to find geometric figures are found in Sections II and III.

Sections II and III have nine figures each and allow subjects five minutes to complete

each of these sections. Section III is the most difficult. Since Section I is practice and

not scored, subjects’ GEFT scores were based on nine items in Sections II and nine items

in section III (18 items total). Subjects scoring less (0-11) than the national mean for the

GEFT (11.4) were categorized as field dependent. Subjects scoring the national mean

(11.4) or greater (12-18) were categorized as field independent (Witkin, Oltman, Raskin,

& Karp, 1971).

The GEFT instrument evolved from Witkin’s earlier field dependence tests: the

Embedded Figures Test (EFT), Body Adjustment Test (BAT), and the Rod and Frame

Test (RFT). Using the GEFT with male and female subjects, Witkin (1971) used a

Spearman-Brown formula to determine a reliability coefficient of .82. Witkin also

established instrument validity against the Embedded Figures Test with correlation

coefficients for male and female university students of .82 and .79, respectively (Witkin

et al., 1971).

Personal Style Inventory (PSI)

The Personal Style Inventory (PSI) is an abbreviated version of the MBTI (Hogan

& Champagne, 1979) (see Appendix B). Like the MBTI, the PSI aims to measure a

person’s Jungian typology, generating preference scores for four separate dimensions of

personality type. The model asserts that individuals possess particular personality

characteristics with respect to how they prefer to gather information and relate to their

surrounding environment. The dimensions are described as either sensing or intuition (S-

90

N) and thinking or feeling (T-F) – the information gathering functions – and extraversion

or introversion (E-I) and judging or perceiving (J-P) – the relating orientations (Myers et

al., 1998).

The self-administering and self-scoring instrument requires approximately 15

minutes to complete. The instrument contains 32 items, arranged in pairs with each

member of the pair representing a preference for attitude (E-I), perceiving function (S-N),

judging function (T-F), and orientation (J-P). Subjects were asked to rate their preference

for each member of the pair by assigning each member a score from 0 to 5 with the sum

of the members in each pair totaling 5. Because fractions are not permitted in rating

preferences for the members in the paired items, subjects were forced to indicate a

preference for one member of the pair. This is explained further in the following

example: Without using fractions, subjects were required to assign a total of 5 points to

the following paired item: 1a) I most often am quietly friendly and reserved, or 1b) I most

often attract others to me by being outgoing. Assuming the subject followed directions

and assigned a total of 5 points between the two members in the paired item; 3 points to

1a and 2 points to 1b, a preference is indicated for item 1a.

To determine instrument reliability, Hogan and Champagne (1980) compared

subjects’ estimated scores with subjects’ actual PSI scores to find Pearson product-

moment correlation scores. Reliability coefficients were .60, .74, .66, and .61 for the

attitude (E-I), perceiving function (S-N), judging function (T-F), and orientation (J-P)

dimensions, respectively. To determine instrument validity, Hogan and Champagne

(1980) generated Phi correlations of .78, .55, .90, and .71 respectively, for the four

dichotomies measured by the PSI.

91

The PSI is a self-scoring instrument. After assigning a total of 5 points between

the two members in each paired item, subjects were asked to record the score assigned to

each item to a scoring sheet containing eight columns – two columns for each of the four

dichotomies. After subjects double checked each score, the columns were summed,

providing a total of eight scores – one score for each of the eight preferences. The

member of each pair with the greater score indicated the preference for that dimension.

Profiles for each subject were determined in this manner (see example in Appendix C).

Data Collection Process

Prior to involving Extension program personnel in this study, necessary approvals

were obtained from The Ohio State University’s Office of Human Subjects Review and

Ohio State University Extension’s Administrative Cabinet. Data collection was

coordinated in conjunction with district meetings, Spring Conferences, and Community

Development program area inservices which took place between April and July, 2004.

Meeting, conference, and inservice organizers were informed of the study’s objectives

and up to 50 minutes of the inservice time was requested for actual data collection. A

written confirmation and thank you to each inservice coordinator followed the request.

Data were collected at a point in the meetings that were convenient for the

meeting, conference, and inservice coordinator(s) and participants. The researcher

provided a short overview of the study and the instruments being used to gather data.

Approximately 45-50 minutes was required to collect the data at each location. The

researcher was present during the collection of data to address any behaviors or activities

disruptive or unacceptable to the data collection process.

92

Data Analysis

Data were summarized, organized, and analyzed using the descriptive statistics

and measures of association features in the SPSS 12.0. GEFT scores were treated as

ratio-level data and coded in SPSS as a raw score (0-18). PSI scores were treated as

interval-level data and converted to a continuous scale score using 100 as the base.

Myers et al., (1998) indicated that “for E, S, T, or J preference scores, the continuous

score is 100 minus the numerical portion of the preference score. For I, N, F, or P

preference scores, the continuous score is 100 plus the numerical portion of the

preference score” (p. 9). In other words, E, S, T, and J preferences were represented by

scores less than 100. Scores greater than 100 represented subjects’ preferences for I, N,

F, and P dimensions. A score was recorded for each subject for each dimension of the

typology, resulting in four scores per subject.

Primary work assignment, academic major, and gender were entered as nominal

level data. Subjects’ age, length of tenure and educational attainment were entered as

ordinal level data.

To describe the strength of relationships among the GEFT, PSI, and demographic

information, the adjectives by Bartz (1999) as shown in Table 3.2 were used.

93

Value of r Adjective

.80 or higher Very High

.60 to .80 Strong

.40 to .60 Moderate

.20 to .40 Low

.20 or lower Very Low

Source: Bartz, 1999

Table 3.2: Adjectives Used To Describe Measures Of Association.

Research Hypotheses Analysis

To address the research hypotheses of this study, the following statistical analyses

were employed:

Hypothesis 1

Variables Level Statistical Procedure Used

Learning Style (GEFT score) Ratio

Personality type (PSI score) Interval Pearson’s R

Table 3.3: Variables And Statistical Procedure Used In Testing Hypothesis 1.





94

Pearson product-moment correlation coefficients were used to describe the

relationship between learning style preferences as measured by GEFT scores and

personality type preferences as measured by PSI scores. To use a Pearson product-

moment correlation coefficient, raw scores on the GEFT were considered ratio level data

and correlated with PSI scores, considered interval level data. A correlation coefficient ρ

(RHO) near 0 indicates that there is no presence of a relationship between learning style

preferences as measured by GEFT scores and personality type preferences as measured

by PSI scores. A correlation coefficient ρ (RHO) nearing either +1 or -1 indicates that

there is a very strong relationship between learning style preferences as measured by

GEFT scores and personality type preferences as measured by PSI scores. According to

Myers et al., (1998) positive correlations were associated with I, N, F, or P and negative

correlations were associated with E, S, T, or J.

The null hypothesis was: H0: ρ = 0 (there is no relationship)

The research hypothesis was: H1: ρ ≠ 0 (some relationship exists)

95

Hypothesis 2


Learning Style (GEFT score) Ratio (treated as Ordinal)

Age Ordinal



Kendall Tau

Learning Style (GEFT score) Ratio (treated as Ordinal)


Point Biserial

Learning Style (GEFT score) Ratio (treated as

Nominal, Multichotomous)



Cramer’s V

Table 3.4: Variables And Statistical Procedures Used In Testing Hypothesis 2.





2004.

Kendall Tau coefficients were used to describe the relationship between learning

style preferences as measured by GEFT scores and age, length of tenure, and educational

96

attainment. To use a Kendall Tau coefficient, ratio level GEFT scores were treated as

ordinal level data and correlated with age, length of tenure, and educational attainment,

also considered ordinal level data. A correlation coefficient ρ (RHO) near 0 indicates

that there is no presence of a relationship between learning style preferences as measured

by GEFT scores and length of tenure and educational attainment. A correlation

coefficient ρ (RHO) nearing either +1 or -1 indicates that there is a very strong

relationship between learning style preferences as measured by GEFT scores and length

of tenure and educational attainment.



Point-biserial correlations were used to describe the relationship between learning

style preferences as measured by GEFT scores and gender. To use a point-biserial

correlation coefficient, GEFT scores were considered ratio level and correlated with

gender, treated as dichotomous nominal level data. A correlation coefficient ρ (RHO)

near 0 indicates that there is no presence of a relationship between learning style

preferences as measured by GEFT scores and gender. A correlation coefficient ρ (RHO)

nearing either +1 or -1 indicates that there is a very strong relationship between learning

style preferences as measured by GEFT scores and gender.



Cramer’s V statistic was used to describe the relationships between learning style

preferences as measured by GEFT scores and primary work assignment and academic

major. To use the Cramer’s V statistic, GEFT scores were considered nominal level data

97

and correlated with primary work assignment and academic major, considered

multichotomous, nominal level data. A correlation coefficient ρ (RHO) near 0 indicates

that there is no presence of a relationship between learning style preferences as measured

by GEFT scores and primary work assignment and academic major. A correlation


relationship between learning style preferences as measured by GEFT scores and primary

work assignment and academic major.



98

Hypothesis 3


Personality type (PSI score) Interval (treated as Ordinal)

Age Ordinal



Kendall Tau

Personality type (PSI score) Interval (treated as Ordinal)


Point Biserial

Personality type (PSI score) Interval (treated as

Nominal, Multichotomous)



Cramer’s V

Table 3.5: Variables And Statistical Procedures Used In Testing Hypothesis 3.



educational attainment, or gender of Extension Community Development


2004.

99

Kendall Tau coefficients were used to describe the relationship between

personality type preferences as measured by PSI scores and age, length of tenure, and

educational attainment. To use a Kendall Tau coefficient, interval level PSI scores were

treated as ordinal level data and correlated with age, length of tenure, and educational

attainment, also considered ordinal level data. A correlation coefficient ρ (RHO) near 0

indicates that there is no presence of a relationship between personality type preferences

as measured by PSI scores and length of tenure and educational attainment. A correlation


relationship between personality type preferences as measured by PSI scores and length

of tenure and educational attainment.



Point-biserial correlations were used to describe the relationship between

personality type preferences as measured by PSI scores and gender. To use a point-

biserial correlation coefficient, PSI scores were considered interval level and correlated

with gender, treated as dichotomous nominal level data. A correlation coefficient ρ

(RHO) near 0 indicates that there is no presence of a relationship between personality

type preferences as measured by PSI scores and gender. A correlation coefficient ρ

(RHO) nearing either +1 or -1 indicates that there is a very strong relationship between

personality type preferences as measured by PSI scores and gender.



100

Cramer’s V statistic was used to describe the relationships between personality

type preferences as measured by PSI scores and primary work assignment and academic

major. To use the Cramer’s V statistic, PSI scores were considered nominal level data

and correlated with primary work assignment and academic major, considered

multichotomous, nominal level data. A correlation coefficient ρ (RHO) near 0 indicates

that there is no presence of a relationship between personality type preferences as

measured by PSI scores and primary work assignment and academic major. A

correlation coefficient ρ (RHO) nearing either +1 or -1 indicates that there is a very

strong relationship between personality type preferences as measured by PSI scores and

primary work assignment and academic major.



Research Objectives Analysis

To address the research objectives of this study, the following statistical analyses

were employed.





administrators.

101

Frequencies, percentages, means, standard deviations, and range were used to

describe subjects’ learning style preferences as measured by Group Embedded Figures

Test (GEFT) scores.





Frequencies, percentages, means, standard deviations, and range were used to

describe subjects’ learning style preferences as measured by Personal Style Inventory

(PSI) scores.






A Pearson product-moment correlation coefficient was used to describe the

relationship between learning style preferences as measured by GEFT scores and

personality type preferences as measured by PSI scores.





102


administrators.

Kendall’s Tau, Point Biserial, and Cramer’s V correlation coefficients were used

to describe the relationship between learning style preferences as measured by GEFT

scores and primary work assignment, academic major (Cramer’s V), length of tenure,

educational attainment, age (Kendall’s Tau), and gender (Point Biserial).






administrators.

Kendall’s Tau, Point Biserial, and Cramer’s V correlation coefficients were used

to describe the relationship between personality type preferences as measured by PSI

scores and primary work assignment, academic major (Cramer’s V), length of tenure,

educational attainment, age (Kendall’s Tau), and gender (Point Biserial).

103

CHAPTER 4

FINDINGS

Purpose and Objectives

The purpose of this study was to examine the relationship between learning style

and personality type preferences of Extension Community Development program

professionals in Ohio. In addition, the study aimed to better explore the presence of

relationships of those measures to primary work assignment, length of tenure, academic

major, educational attainment, and gender.

Research Hypotheses








104


2004.





2004.

Objectives





administrators.










105






administrators.






administrators.

Limitations of the Study

Correlational and descriptive studies such as this do not allow the researcher to

predict outcomes (Fraenkel & Wallen, 1999). As a result, the study sought only to

describe characteristics and examine hypothesized relationships among characteristics of

the population.


during the time period April to July, 2004, including: support staff, program assistants,

educators, specialists, and administrators comprised the population. Therefore, the

results and conclusions were generalizable to the population of Extension Community

Development program professionals that were employed in Ohio during the time period

106

April to July, 2004 and that completed both standardized instruments and provided

useable data.

Due to the Extension organizational restructuring, the population for this project

was expanded to include support staff with professional Community Development

program area responsibilities. Doing so increased the sample size by nearly 18 percent.

An analysis specific to support staff participating in this project is contained in Appendix

E.

Sample Characteristics

The target population was all Extension Community Development program

professionals employed in Ohio during the time period April to July, 2004, including:

support staff, program assistants, educators, specialists, and administrators. A profile of

the program professionals studied are summarized in Table 4.1 and Table 4.2. The

profile was developed to provide data necessary to interpret findings. Characteristics of

the population (n=67) included age, gender, length of tenure, educational attainment,

academic major, and primary work assignment.

Males comprised the majority of the sample at 55.2 percent (Table 4.1). Of the

sample, 17.9 were support staff and 82.1 percent were program assistants, educators,

specialists and administrators. The mean age was 45.1.

107

Characteristic Frequency Percent Gender Male 37 55.2 Female 30 44.8 Support Staff 12 17.9 Program Assistants, Educators, Specialists, and Administrators 55 82.1

Average Age 45.1

Table 4.1: Frequency and Distribution of Sample Characteristics of Extension Community Development Program Professionals in Ohio (n=67).

The mode for length of tenure was 11-20 years at 31.3 percent of the sample

(Table 4.2). The mode for educational attainment was graduate degree at 41.8 percent.

The mode for academic major was business at 17.9 percent. The mode for primary work

assignment was district at 53.7 percent (Table 4.2).

108

Characteristic Frequency Percent Length of Tenure 0-5 years 20 29.9 6-10 years 18 26.9 11-20 years 21 31.3 21+ years 8 11.9 Educational Attainment Undergraduate Degree 25 37.3 Graduate Degree 28 41.8 Doctoral Degree 14 20.9 Academic Major Business 12 17.9 Economics 8 11.9 Education 8 11.9 Law 2 3.0 Planning 1 1.5 Political Science 4 6.0 Public Administration 4 6.0 Natural Resources 9 13.4 Agriculture 7 10.4 Humanities 2 3.0 Computer Science 3 4.5 FCS 2 3.0 Engineering 1 1.5 Other 4 6.0 Primary Work Assignment County 12 17.9 District 36 53.7 State 15 22.4 Other 4 6.0

Table 4.2: Frequency and Distribution of Sample Characteristics of Extension Community Development Program Professionals in Ohio (n=67).

109

Learning Style Distributions

The overall GEFT score mean of the sample (n=67) was 10.40. The standard

deviation was 5.29. The mean indicated that the sample was field dependent compared to

the national mean of 11.4 (Witkin, Oltman, Raskin, & Karp, 1971). The mode of the

sample was 18 (see Table 4.3)

Table 4.3: Scores for Extension Community Development Program Professionals in Ohio on the Group Embedded Figures Test (GEFT) (n=67).

For the sample of Extension Community Development program professionals in

Ohio (n=67), 38 individuals scored 1-11 on the GEFT, which indicated a field dependent

learning style preference. A GEFT score of 12-18 was recorded by 29 individuals, which

GEFT Frequency Percent Cumulative Percent

Field Dependent

1 1 1.5 1.5 2 2 3.0 4.5 3 5 7.5 11.9 4 1 1.5 13.4 5 7 10.4 23.9 6 4 6.0 29.9 7 2 3.0 32.8 8 6 9.0 41.8 9 5 7.5 49.3 10 4 6.0 55.2 11 1 1.5 56.7 Field Independent

12 4 6.0 62.7 13 1 1.5 64.2 Mean = 10.40 14 4 6.0 70.1 S.D. = 5.29 15 3 4.5 74.6 Mode = 18 16 3 4.5 79.1 Minimum = 1 17 6 9.0 88.1 Maximum = 18 18 8 11.9 100.0 TOTAL 67 100.0 100.0

110

indicated a field independent learning style preference. Of those who indicated a field

independent learning style preference, 18 were males. There were 11 females who

indicated a field independent learning style preference (Table 4.4).

Male Female Total Field Dependent 1 0 1 1 2 2 0 2 3 1 4 5 4 1 0 1 5 4 3 7 6 2 2 4 7 2 0 2 8 2 4 6 9 3 2 5 10 1 3 4 11 1 0 1 Total 19 19 38 Field Independent 12 2 2 4 13 1 0 1 14 3 1 4 15 2 1 3 16 2 1 3 17 4 2 6 18 4 4 8 Total 18 11 29

Table 4.4: Frequency of GEFT Scores by Gender of Extension Community Development Program Professionals in Ohio (n=67).

GEFT scores for the sample of Extension Community Development program

professionals in Ohio (n=67), in relation to primary work assignment, showed that 20

individuals with primary work assignment at the district level recorded a GEFT score

between 1-11, which indicated a field dependent learning style preference (Table 4.5).

111

There were 16 individuals with primary work assignment at the district level who

recorded a GEFT score between 12-18, which indicated a field independent learning style

preference.

Primary Work Assignment Field Dependent Field Independent County 4 5 District 20 16 State 8 7 Other 2 2 Total 37 30

Table 4.5: Frequency of GEFT Scores by Primary Work Assignment of Extension Community Development Program Professionals in Ohio (n=67).

GEFT scores for the sample of Extension Community Development program

professionals in Ohio (n=67), in relation to academic major, showed that there were 6

individuals with an academic major in business, education, or agriculture, and 4

individuals with an academic major in political science or public administration who

recorded a GEFT score between 1-11, which indicated a field dependent learning style

preference (Table 4.6). There were 8 individuals with an academic major in natural

resources, 6 individuals with an academic major in business, and 5 individuals with an

academic major in economics who recorded a GEFT score between 12-18, which

indicated a field independent learning style preference (Table 4.6).

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Academic Major Field Dependent Field Independent Business 6 6 Economics 3 5 Education 6 2 Law 0 2 Planning 1 0 Political Science 4 0 Public Administration 4 0 Natural Resources 1 8 Agriculture 6 1 Humanities 1 1 Computer Science 1 2 Family and Home Economics 1 1 Engineering 0 3 Other 1 1 Total 35 32

Table 4.6: Frequency of GEFT Scores by Academic Major of Extension Community Development Program Professionals in Ohio (n=67).

Personality Distributions by Type Opposites

For statistical analysis, each PSI standard score was treated as interval-level data

and converted to a continuous scale score using 100 as the base. Myers et al., (1998)

indicated that “for E, S, T, or J preference scores, the continuous score is 100 minus the

numerical portion of the preference score. For I, N, F, or P preference scores, the

continuous score is 100 plus the numerical portion of the preference score” (p. 9). In

other words, E, S, T, and J preferences were represented by scores less than 100. Scores

greater than 100 represented subjects’ preferences for I, N, F, and P dimensions.

The mean score of the extraversion-introversion opposite of the sample (n=67)

was 104.06 (Table 4.7). Individuals with a preference for introversion had their

introversion score added to 100, which indicated that the sample E-I mean score leaned

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toward introversion. The mean score of the sensing-intuition opposite was 93.40.

Individuals with a preference for sensing had their sensing score subtracted from 100,

which indicated that the sample S-N mean score leaned toward sensing. The mean score

of the thinking-feeling opposite was 92.28. Individuals with a preference for thinking

had their thinking score subtracted from 100, which indicated that the sample T-F mean

score leaned toward thinking. The mean score of the judgement-perception opposite was

85.10. Individuals with a preference for judging had their judgement score subtracted

from 100, which indicated that the sample J-P mean score leaned toward judgement

(Table 4.7, Figure 4.1).

MBTI Opposite Mean Standard Deviation Minimum Maximum

Extroversion - Introversion 104.06 23.31 69 131 Sensing - Intuition 93.40 24.28 66 137 Thinking - Feeling 92.28 23.96 64 133 Judgement - Perception 85.10 18.88 65 129

Table 4.7: MBTI Opposite Scores of Extension Community Development Program Professionals in Ohio (n=67).

Figure 4.1: MBTI Opposite Scores of Extension Community Development Program Professionals in Ohio (n=67).

100

E

J

T

S

I

N

F

P

104.0669

65

66

64

131

137

133

129

maxmin

93.40

92.28

85.10

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Personality Type Distributions by Type Combination

The mode for the MBTI distributions of Extension Community Development

program professionals in Ohio (n=67) was ISTJ (Table 4.8). The ISTJ group comprised

23.9 percent of the sample, followed by 11.9 percent for INTJ. Examined without E-I

preference, the STJ group comprised 32.9 percent of the type combinations. The next

largest group was NTJ with 16.4 percent. The ISFJ group comprised 10.4 percent of the

sample, followed by the ESTJ and ENFJ groups at 9.0 percent. The ISTP, INFP, INTP,

ESTP, ENFP, ESFJ, and ENTJ groups each comprised 4.5 percent. The INFJ, ISFP,

ESFP, and ENTP groups were each 3 percent or less (Table 4.8).

MBTI Combination Frequency PercentISTJ 16 23.9 ISFJ 7 10.4 INFJ 2 3.0 INTJ 8 11.9 ISTP 3 4.5 ISFP 0 0.0 INFP 3 4.5 INTP 3 4.5 ESTP 3 4.5 ESFP 1 1.5 ENFP 3 4.5 ENTP 0 0.0 ESTJ 6 9.0 ESFJ 3 4.5 ENFJ 6 9.0 ENTJ 3 4.5 TOTAL 67 100.0

Table 4.8: MBTI Combination Distributions of Extension Community Development Program Professionals in Ohio (n=67).

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Individuals with a preference toward sensing and thinking (ST) comprised 31.4

percent of the sample (Table 4.9). Individuals with a preference toward ST were

included in the ISTJ, ESTJ, ISTP, and ESTP groups. Individuals with a preference

toward intuition and thinking (NT) comprised 18.0 percent of the sample and were

included in the INTJ, ENTJ, INTP, and ENTP groups. Individuals with preferences

toward sensing and feeling (SF) and intuition and feeling (NF) each comprised 10 percent

of the sample (Table 4.9).

MBTI Function Frequency PercentSF 11 16.4 ST 28 41.9 NF 14 21.0 NT 14 20.9

Table 4.9: MBTI Function Combination Distributions of Extension Community Development Program Professionals in Ohio (n=67).

An analysis by type opposite revealed that 53.7 percent or 36 individuals

preferred introversion and 37.3 percent or 25 individuals preferred extraversion (Table

4.10). For the S-N opposite, 59.7 percent or 40 individuals preferred sensing and 34.3

percent or 23 individuals preferred intuition. For the T-F opposite, 61.2 percent or 41

individuals preferred thinking and 31.3 percent or 21 individuals preferred feeling. With

the J-P opposite, 74.6 percent or 50 individuals preferred judging and 16.4 percent or 11

individuals preferred perception (Table 4.10).

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MBTI Opposite Frequency PercentExtraversion 25 37.3 Introversion 42 62.7 Sensing 39 58.2 Intuition 28 41.8 Thinking 42 62.7 Feeling 25 37.3 Judgement 51 76.1 Perception 16 23.9

Table 4.10: MBTI Opposite Distributions of Extension Community Development Program Professionals in Ohio (n=67).

Relationship Between Learning Style and Personality Type

Of the Extension Community Development program professionals in Ohio

(n=67), the strongest correlation between GEFT scores and PSI scores was between the

sensing-intuition scores and GEFT scores, with an r coefficient of .095 (Table 4.11).

According to Bartz (1999), this indicated a very low association. Stronger correlations,

statistically significant at the .05 level, were found between sensing-intuition and

judgement-perception with an r coefficient of .340, which according to Bartz, indicated a

low correlation. Statistically significant correlations were also found between sensing-

intuition and thinking-feeling (r=.247), and extraversion-introversion and thinking-

feeling (r=-.302), which according to Bartz indicated a low correlation.

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GEFT E - I S - N T - F J - P

GEFT 1.000 -.038 .095 -.047 .092 E – I 1.000 -.181 -.302* -.025 S – N 1.000 .274* .340* T – F 1.000 .125 J – P 1.000

*significant at the .05 level Table 4.11: Correlation Between MBTI and GEFT of Extension Community Development Program Professionals in Ohio (n=67).

Correlates of Learning Style and Demographic Characteristics


(n=67), the strongest, statistically significant correlation between GEFT scores and

selected characteristics of Extension Community Development program professionals in

Ohio was between GEFT scores and academic major, with an r coefficient of .603 (Table

4.12). According to Bartz (1999), this indicated a strong association. A moderate,

statistically significant relationship was found between primary work assignment and

academic major with an r coefficient of .485, which according to Bartz (1999), indicated

a moderate association (Table 4.12).

*significant at the .05 level Table 4.12: Correlation Between GEFT and Selected Characteristics of Extension Community Development Program Professionals in Ohio (n=67).

GEFT Primary Work

Assignment

Academic Major

GEFT 1.000 .041 .603*

Primary Work Assignment 1.000 .485

Academic Major 1.000

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A statistically significant relationship was found between GEFT scores and age in

years with an r coefficient of -.147, which according to Bartz (1999), indicated a very low

association (Table 4.13). A statistically significant relationship was found between age in

years and length of tenure with an r coefficient of .420, which according to Bartz (1999),

indicated a moderate association. The association between age in years and educational

attainment was found to be very low (Bartz, 1999) with an r coefficient of .095.

GEFT Age in Years Length of Tenure Educational Attainment

GEFT 1.000 -.147* -.055 .057

Age in Years 1.000 .420* .095 Length of Tenure 1.000 .030

Educational Attainment 1.000

*significant at the .05 level Table 4.13: Intercorrelations Between GEFT and Selected Characteristics of Extension Community Development Program Professionals in Ohio (n=67). The association between GEFT scores and gender was very low (Bartz, 1999)

with an r coefficient of .015 (Table 4.14).

GEFT Gender

GEFT 1.000 .015 Gender 1.000

Table 4.14: Correlation Between GEFT and Gender of Extension Community Development Program Professionals in Ohio (n=67).

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Correlates of Personality type and Demographic Characteristics


(n=67), the strongest, statistically significant correlation between personality type and

selected characteristics of Extension Community Development program professionals in

Ohio was between the thinking-feeling preference and gender, with an r coefficient of

.453, which according to Bartz (1999), indicated a moderate association (Table 4.15).

The relationship between the sensing-intuition preference and gender was also

statistically significant, with an r coefficient of .362. According to Bartz (1999), this is a

low level of association (Table 4.15).

*significant at the .05 level Table 4.15: Intercorrelations Between MBTI Preference Subscales and Gender of Extension Community Development Program Professionals in Ohio (n=67).

A relationship with an r coefficient of -.257 was found between the thinking-

feeling preference and educational attainment, which according to Bartz (1999), indicated

a low level of association (Table 4.16). A relationship with an r coefficient of .243 was

found between the sensing-intuition preference and educational attainment, which

E - I S - N T - F J - P Gender

E – I 1.000 -.121 -.272* -.272* .174

S – N 1.000 .220 .220 .362*

T – F 1.000 .099 .453*

J – P 1.000 .042

Gender 1.000

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according to Bartz (1999), indicated a low level of association. A statistically significant

relationship was found between age in years and length of tenure with an r coefficient of

.420, which according to Bartz (1999), indicated a moderate relationship (Table 4.16).

E - I S - N T - F J - P Age in Years

Length of Tenure


E - I 1.000 -.121 -.272* -.272* .061 -.048 -.092

S - N 1.000 .220 .220 .092 -.002 .243

T - F 1.000 .099 .102 .177 -.257

J - P 1.000 ..019 .169 .033

Age in Years 1.000 .420* .095 Length of Tenure 1.000 .030


*significant at the .05 level Table 4.16: Intercorrelations Between MBTI Preference Subscales and Selected Characteristics of Extension Community Development Program Professionals in Ohio (n=67).

Statistically significant relationships were found between the thinking-feeling

preference and academic major, with an r coefficient of .303 (Table 4.17). According to

Bartz (1999), this indicated a low level of association. The relationship between the

judging-perceiving preference and primary work assignment was also statistically

significant with an r coefficient of -.189, but described a very low association between

the judging-perceiving preference and primary work assignment according to Bartz

(1999) (Table 4.17).

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*significant at the .05 level Table 4.17: Intercorrelations Between MBTI Preference Subscales and Selected Characteristics of Extension Community Development Program Professionals in Ohio (n=67).

E - I S - N T - F J - P Primary Work

Assignment

Academic Major

E – I 1.000 -.121 -.272* -.272* .020 .041

S – N 1.000 .220 .220 -.135 -.169

T – F 1.000 .099 .087 .303*

J – P 1.000 -.189* .035

Primary Work Assignment 1.000 .485

Academic Major 1.000

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CHAPTER 5

CONCLUSIONS, IMPLICATIONS, AND RECOMMENDATIONS

Summary

The purpose of this descriptive correlational study was to describe the relationship

between learning style and personality type preferences of Extension Community

Development program professionals in Ohio. In addition, the purpose of the study was to

describe Extension Community Development program professionals employed in Ohio

during the time period from April to July, 2004 in terms of primary work assignment,

length of tenure, academic major, educational attainment, and gender. Data were

collected through the use of a one-page subject characteristics questionnaire, the Group

Embedded Figures Test (GEFT), and Personal Style Inventory (PSI). Measures of

association were used to determine the relationships between learning style, personality

type, and the selected characteristics: primary work assignment, length of tenure,

academic major, educational attainment, and gender.

Sample Characteristics

Included in this study were primary work assignment, length of tenure, academic

major, educational attainment, and gender characteristics. Of the 67 Extension

Community Development program professionals in this study, 55.2 percent (37) were

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male and 44.8 percent (30) were female. Of the program professionals sample, 17.9 were

support staff and 82.1 percent were program assistants, educators, specialists and

administrators. The average age was 45.1 years. Data analysis specific to support staff

participating in the study sample can be found in Appendix E.

Study subjects with length of tenure of 5 years or less comprised 29.9 percent of

the sample. Nearly 12 percent had more than 21 years experience in Extension.

Approximately 60 percent of the Extension Community Development program

professionals had 6-20 years experience within Extension. More than 60 percent

possessed a graduate or doctoral degree.

Almost 30 percent of those studied had an academic background in business or

economics. Nearly one fourth (23.8 percent) had an academic background in agriculture

or natural resources. Roughly 12 percent of the sample held an academic background in

education. Similarly, roughly 12 percent of the sample held an academic background in

political science or public administration.

The majority (53.7 percent) of the Extension Community Development program

professionals studied had a district-level primary work assignment. Roughly one fourth

(22.4 percent) worked a state-level assignment. Only 17.9 percent had county-level

assignments.

Learning Style

The learning style of the 67 Extension Community Development program

professionals, as measured by the Group Embedded Figures Test (GEFT), leaned toward

field dependence. The mean GEFT score for the sample was 10.40. More program

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professionals (56.7 percent) preferred the field dependent learning style than those (43.3

percent) who preferred the field independent learning style.

Personality Type

The most common personality type (nearly 24 percent of the 67 Extension

Community Development program professionals) was the ISTJ type. Nearly 12 percent

were INTJ types. No one exhibited preferences for the ENTP type.

Introversion (53.7 percent) was preferred more than extraversion (37.3 percent).

Nearly 60 percent of the program professionals preferred the sensing function over

intuition. Over 60 percent preferred the thinking function over feeling. Nearly three

quarters (74.6 percent) indicated a preference for judging over perceiving.

Roughly one third (31.4 percent) of Extension Community Development program

professionals had preferences for ISTJ, ISTP, ESTP, or ESTJ. Individuals with

preferences for INTJ, INTP, ENTJ, or ENTP comprised 18.0 percent of the sample.

Approximately 10 percent of the sample was in the sensing and feeling cells (ISFJ, ISFP,

ESFP, or ESFJ) and 10 percent of the sample was in the intuitive and feeling cells (INFJ,

INFP, ENFP, or ENFJ).

Relationship Between Learning Style and Personality Type

In this study, the levels of association between learning style and personality type

subscales indicated a very low association (.095). It was concluded that there was a

negligible association between the GEFT and PSI.

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Relationship Between Learning Style and Selected Characteristics

One objective of this study was to describe the relationships between learning

style and the selected characteristics: primary work assignment, length of tenure,

academic major, educational attainment, and gender. Analysis by gender revealed that

more than 63 percent of females preferred the field dependent learning style. Just over

half of males preferred the field dependent learning style. There was a very low

association between learning style and gender.

Extension Community Development program professionals with academic

backgrounds in education, planning, political science, public administration and

agriculture preferred a field dependent learning style more than a field independent

learning style. Subjects with academic backgrounds in engineering, computer science,

natural resources and economics were more likely to prefer a field independent learning

style than a field dependent learning style. There was a strong association between

academic major and learning style.

Subjects working district- or state-level assignments preferred a field dependent

learning style more than a field independent learning style. The level of association

between primary work assignment and learning style preference was negligible.

Except for subjects within the youngest age group (24-34 years of age), study

subjects preferred the field dependent learning style. Preference for a field independent

learning style for subjects within the 24-34 years of age was nearly two to one.

Preference for a field dependent learning style was four to one for subjects within the 57-

66 age range. The level of association between age and learning style was very low.

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Except for subjects with length of tenure of 6-10 years, subjects preferred the field

dependent learning style more than a field independent learning style. Twelve subjects

with length of tenure in the 0-5 year range preferred the field dependent learning style

compared to eight subjects with length of tenure in the 0-5 year range who preferred a

field independent learning style. Subjects in the 21+ range were three times as likely to

prefer the field dependent learning style than the field independent learning style. There

was a statistically significant, moderate level of association between length of tenure and

learning style.

Roughly 57 percent of subjects with an undergraduate or graduate level of

educational attainment preferred the field dependent learning style. There were equal

numbers of subjects with a doctoral level of educational attainment who preferred a field

dependent or a field independent learning style. The level of association between

educational attainment and learning style was found to be very low.

Relationship Between Personality Type and Selected Characteristics

An objective of this study was to describe the relationships between personality

type and the selected characteristics: primary work assignment, length of tenure,

academic major, educational attainment, and gender. Nearly three times (30) as many

males preferred thinking than females (11). Males were more than four times more likely

to prefer thinking over feeling. Females preferred feeling to males 19 to 7. Males were

more than three times more likely (28:9) to prefer sensing over intuition. Twice the

number of female subjects (18) preferred intuition over males (9). Analysis by gender

revealed a statistically significant, moderate association between the thinking-feeling

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personality dimension and gender and a statistically significant, but low association

between the sensing-intuition personality dimension and gender. Other levels of

association between gender and dimensions of personality type were negligible.

Subjects with an undergraduate level of educational attainment were more likely

to prefer sensing over intuition and feeling over thinking than subjects with graduate or

doctoral levels of educational attainment. Subjects with graduate or doctoral levels of

educational attainment preferred thinking over feeling almost 3 to 1. The level of

association between educational attainment and the thinking-feeling personality

dimension was found to be low. The level of association between the sensing-intuition

personality dimension and educational attainment was also low. Other levels of

association between educational attainment and dimensions of personality type were

negligible.

There was a general preference across every age group toward introversion,

sensing, thinking, and judging. Levels of association between age and dimensions of

personality type were negligible.

There was a general preference across length of tenure categories 0-5, 6-10, and

11-20 toward introversion, sensing, thinking, and judging. Subjects with more than

twenty years experience in Extension shared preferences for extraversion and

introversion, sensing and intuition, and thinking and feeling in equal numbers. Levels of

association between length of tenure and dimensions of personality type were negligible.

Subjects with an academic background in natural resources preferred judging over

perceiving 8 to 1. Subjects with an academic background in business preferred judging

over perceiving 5 to 1. The level of association between academic major and the

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thinking-feeling personality dimension was found to be low, but statistically significant.

Other levels of association between academic major and dimensions of personality type

were negligible.

Subjects working a state-level assignment were 14 times more likely to prefer

judging than perceiving. Subjects working a county-level assignment preferred thinking

over feeling 5 to 1. While statistically significant, the level of association between

primary work assignment and the judging-perceiving personality dimension was found to

be very low. Levels of association between primary work assignment and all other

dimensions of personality type were negligible.

Conclusions and Implications

The following conclusions and implications are based upon the review of

literature and findings related to the research hypotheses and objectives of this study.

The conclusions may be applied only to subjects involved in this study.

Hypothesis 1

There is no relationship between learning style preferences as measured by GEFT


Community Development program professionals employed in Ohio during the time

period April to July, 2004.



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Conclusion 1

There was a negligible level of association between learning style and dimensions

of personality type, therefore the null hypothesis was accepted. Learning style and the

sensing-intuition personality dimension had the strongest association, with an r

coefficient of .095. This data did not support previous research findings (Holsworth,

1985; Canning, 1983; in DiTiberio, 1996) that found some level of association between

field independence and preference for intuition.

Implication 1

Interestingly, while nearly 60 percent of Extension Community Development

program professionals preferred a field dependent learning style, more than 60 percent

preferred introversion. The most common personality could be described as quiet,

serious, thorough, dependable, practical, matter of fact, realistic, logical, focused, and

organized; characteristics of the ISTJ type combination (Myers et al., 1998), which

comprised nearly one fourth of the program professionals.

Hypothesis 2

There is no relationship between learning style preferences as measured by GEFT

scores and primary work assignment, length of tenure, academic major, educational

attainment, age, or gender of Extension Community Development program professionals

employed in Ohio during the time period April to July, 2004.



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Conclusion 2

The null hypothesis was accepted. However, relationships were found between

academic background and learning style preference and length of tenure and learning

style preference of Extension Community Development program professionals. Data

suggested a strong, statistically significant association between academic major and

learning style, with an r coefficient of .603. This supports previous research that found

individuals with a field independent learning style preference were drawn to academic

areas involving analytical skills and field dependent preferences were drawn to academic

areas involving interaction with others (Garton, Spain, Lamberson, & Spiers, 1999;

Hudson, 1997; Torres & Cano, 1994; Witkin, 1976; Witkin et al., 1977a, 1977b). There

was also a relationship between length of tenure and learning style. A statistically

significant relationship between length of tenure and learning style was found, suggesting

that as length of tenure increases, subjects’ learning style preference becomes more field

dependent. The level of association was very low, with an r coefficient of -.147.

Implication 2

The data suggested that as length of tenure increases, Extension program

professionals’ prefer more group studies, projects, and work; a learning style preference

that is more field dependent. Are such professionals aware of the change in preference?

Is the change in preference evidenced in the way they deliver programming? Are their

clientele aware of the change in preference? Furthermore, are continuing education and

inservice trainings for these professionals with greater tenure designed to take into

account their more social orientation toward learning? An improved understanding of

this relationship can help program professionals understand their need for social

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interaction and help administrators better provide for program professional continuing

education and inservice needs.

A strong relationship was found between academic background and learning

style. Similarly, a low but statistically significant relationship was found between

academic background and preference for judging. Understanding this has implications for

Extension organizational hiring practices. Extension programming developed and

delivered by a professional with training in business to clientele with training in business

can make for an effective teaching and learning exchange. Similar personal orientations

to the outer world can enable the development of strong interpersonal relationships as

well. However, as Extension works to bridge ties across program areas and to deliver

programming to non-traditional audiences, the organization and the program professional

need to be aware of potential differences in academic background and resulting

differences in learning style and personal orientation to the outer world. Program

professionals with academic training in business or economics may very well possess

learning styles and orientations far different from audiences with academic backgrounds

in elementary education, for example. The potential for a disjointed teaching and

learning exchange as well as interpersonal conflict exists. Understanding the

implications of Extension Community Development program professionals’ academic

background can be useful in matching program professionals to program opportunities, as

well as in directing them to the programmatic and administrative teams that can realize

the benefits of their learning style and personality type preferences.

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Hypothesis 3

There is no relationship between personality type preferences as measured by PSI

scores and primary work assignment, length of tenure, academic major, educational

attainment, or gender of Extension Community Development program professionals

employed in Ohio during the time period April to July, 2004.



Conclusion 3

The null hypothesis was accepted. However, there were relationships between

gender and personality type preference and academic background and personality type

preference. Male Extension Community Development program professionals preferred

to make decisions using logic. Their female counterparts preferred to make decisions that

considered the decision’s impact on others. Data suggested a moderate, statistically

significant association between the thinking-feeling personality dimension and gender,

with an r coefficient of .453. A gender difference was also present in the sensing-

intuition subscale. Males preferred to trust their experiences and to focus on reality.

Females preferred to trust inspiration and focus on the future. These findings were

consistent with other research by Cano, Garton, and Raven (1992a). There was a low,

statistically significant association between the sensing-intuition personality dimension

and gender, with an r coefficient of .362. Academic areas requiring analytical and

organizational skills such as agriculture and business attracted individuals with a

preference for judging. The level of association between academic major and the

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thinking-feeling personality dimension was low, but statistically significant, with an r

coefficient of .303.

Implication 3

Congruent with other research (Cano, Garton, & Raven, 1992a), findings

indicated gender differences with respect to personality preference. Male Extension

Community Development program professionals preferred to trust their experiences, to

focus on reality, and make decisions using logic. Their female counterparts preferred to

trust inspiration, focus on the future, and make decisions that considered the decision’s

impact on others. These differences have implications for program planning and delivery

as well as the future of Extension Community Development.

Gender differences have implications for mixed-gender program planning and

delivery teams. Are such teams consciously aware of the differences in gender with

respect to how their members gather and makes sense of information? Are such

differences a source of conflict for existing teams? Are such differences one of the

reasons that more Extension Community Development programming is not undertaken

by mixed gender teams?

Gender differences have implications for the future of the Extension Community

Development program area. Gender differences with respect to how program

professionals gather and makes sense of information influence the manner in which

decisions are made that affect the short-, medium-, and long-range future of the program

area. If males with a sensing preference are oriented in the present (Myers, 1993) and

females with a intuitive preference are oriented in the future (Myers, 1993), the result is

quite possibly a disjointed sense of direction in which the Community Development

134

program area is headed. Similarly, if males with a thinking preference are “tough-

minded” and objective (Myers, 1993), and females with a feeling preference are “tender-

hearted” and guided by personal values (Myers, 1993), a potential conflict exists with

respect to how important decisions are made. Extension Community Development

program professionals need to be aware of these gender differences as they plan and

deliver programs and make decisions that could impact the future of the program area.

Extension Community Development program professionals overall had an

orientation toward life that is characteristically ordered, structured, and decisive.

Subjects with state-level work assignments were 14 times more likely to prefer judging

than perceiving. Such subjects had an orientation to the outer world that could be

characterized as scheduled, systematic, and methodical; characteristics of the judging

preference (Myers, 1993). Is the nature of Extension work, state-level Extension work in

particular, such that it draws individuals with these qualities and/or imparts such qualities

on individuals over time or do the majority of Extension Community Development

program professionals in this study simply have such qualities in common?

Conclusion 4

Extension Community Development program professionals preferred different

learning styles. Over half (56.7 percent) preferred a field dependent learning style. The

average Group Embedded Figures Test score for this group was 10.40.

Implication 4

The majority of Extension Community Development program professionals (56.7

percent) preferred a field dependent learning style, learning in a global perspective

framed by personal surroundings and making broad, general distinctions among concepts.

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They possessed effective social skills and preferred to learn in a social context (Cano,

1993; Garger & Guild, 1984; Witkin, 1973; Witkin, 1976; Witkin et al., 1977). The

characteristics and behaviors associated with this learning style preference have

implications for the formation of interdisciplinary program teams. Understanding these

differences is useful to program professionals across disciplines and program areas as

they go about the process of working with others to plan and deliver programs.

Understanding Extension Community Development program professionals’

learning style and personality type preferences has implications for those program

professionals, their clientele, and for the administrators who support them. The level of

association between learning style and sensing-intuition personality dimension was weak,

but supports other research findings (Holsworth, 1985; Canning, 1983; in DiTiberio,

1996). Possessing knowledge of this relationship can help Extension program

professionals better understand how information is gathered. Individuals with a

preference for a field dependent learning style or a sensing personality preference will

learn more with interaction and lively presentation techniques that convey detailed,

factual information in a step-by-step manner (Myers, 1993). Individuals with a

preference for a field independent learning style or an intuitive personality preference

will gain a great deal more from instructional methods that focus on the ‘big picture’ and

allow them to think to themselves in an abstract and theoretical manner as they process

information (Myers, 1993). Understanding these differences is useful to program

professionals who plan and deliver programs to internal and external audiences as well as

to the administrators who regularly communicate with them.

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Conclusion 5

Extension Community Development program professionals possessed a variety of

personality types. Every MBTI personality type was exhibited by except for the ENTP

combination.

The most common personality could be described as quiet, serious, thorough,

dependable, practical, matter of fact, realistic, logical, focused, and organized;

characteristics of the ISTJ type combination (Myers et al., 1998), which comprised nearly

one fourth of the program professionals. Previous research findings indicated university

teachers preferred the ISTJ type combination more than any other type (Lawrence, 1993).

The INTJ combination comprised the next largest group, roughly 12 percent of program

professionals. Interestingly, 62.7 percent of the sample indicated a preference for

introversion.

Extension Community Development program professionals could be

characterized as analytical, rigid, and present-focused; common characteristics of the

sensing and thinking dimensions (Myers, 1993). Subjects with preferences toward

sensing and thinking comprised nearly one third of the sample. This was consistent with

the 1992 research findings involving Ohio Extension professionals by Ishaya et al., as

well as research involving preservice agricultural educators conducted by Cano and

Garton (1994), and Cano, Garton, and Raven (1992a).

Implication 5

Extension Community Development program professionals possess a variety of

personality type preferences. Variation in personality type preference can lead to

137

dynamic program teams, however working together toward a common goal requires that

individuals understand and appreciate differences.

Conclusion 6

Females tended to prefer interaction, group projects, and a social environment

overall; characteristics of a field dependent learning style. While the association was

very low, interpretation of the data suggests that females, more than males, preferred a

field dependent learning style. This is supported by other research (Cano & Garton,

1994; Hudson, 1997; Torres & Cano, 1994) that found females tend to prefer a field

dependent learning style more than males.

Extension Community Development program professionals working on a regional

or state basis preferred to work on group projects utilizing effective interpersonal skills;

characteristics of a field dependent learning style. Subjects assigned to work at the

district or state level indicated a preference for a field dependent learning style. The level

of association between primary work assignment and learning style preference was very

low.

The youngest Extension Community Development program professionals (24-34

age group) were intrinsically motivated and preferred competition and the ability to

design their own work structure; characteristics of the field independent learning style.

The 24-34 age group preferred a field independent learning style nearly two to one,

however the level of association between age and learning style preference was very low.

There was a negligible association between level of educational attainment and

learning style preference of Extension Community Development program professionals.

138

Subjects with a doctoral level of educational attainment preferred a field dependent

learning style and a field independent learning style in equal numbers.

More experienced Extension Community Development program professionals

were more likely to place an emphasis on the social aspect of the learning environment; a

characteristic of a field dependent learning style preference. Subjects with more than 20

years of experience in Extension were three times as likely to prefer the field dependent

learning style. These data support research (Crosson, 1984) that found as age increases,

both genders become generally more field dependent. Hudson (1997) and Sparks (2001)

found that Extension educators’ field dependence increased with age. There was a

statistically significant, moderate level of association between length of tenure and

learning style. There was a strong association between academic major and learning

style.

Program professionals with academic backgrounds in education, planning,

political science, public administration and agriculture were more likely to prefer learning

activities with specific directions, and defined goals and outcomes; characteristics of a

field dependent learning style. Program professionals with backgrounds in engineering,

computer science, natural resources and economics were more likely to prefer an ‘inquiry

approach’ to learning, and to determine learning direction, goals, and outcomes

themselves. These data support the research (Hudson, 1997; Sparks, 2001; Witkin, 1976;

Witkin et al., 1977a) that found that technical and mechanical fields requiring analytical

skills draw individuals preferring a field independent learning style and fields requiring

more social interaction draw individuals preferring a field dependent learning style.

139

Implication 6

Differences in learning style preference cross dimensions of age, gender,

academic background, and length of tenure. Effective program teams will take these

differences into account. Effective program teams will require program leaders and

administrators to recognize the learning style differences among professionals as they

lead team formation efforts.

Conclusion 7

Subjects with graduate or doctoral levels of educational attainment preferred to

make decisions using logic rather than consideration for the decision’s impact on others.

Extension Community Development program professionals with graduate or doctoral

degrees preferred thinking over feeling almost 3 to 1.

Extension Community Development program professionals overall had an

orientation toward life that is characteristically ordered, structured, and decisive. More

subjects with a natural resource academic background preferred the judging dimension

than any other academic background. Subjects with state-level work assignments were

14 times more likely to prefer judging than perceiving. Such subjects had an orientation

to the outer world that could be characterized as scheduled, systematic, and methodical;

characteristics of the judging preference (Myers, 1993). While statistically significant,

the level of association between primary work assignment and the judging-perceiving

personality dimension was very low.

County-based program professionals preferred to react to the information around

them with logic and objectivity, and practical application. Subjects with county-level

appointments preferred thinking over feeling 5 to 1.

140

Implication 7

The program professionals’ scheduled, systematic, and methodical orientation to

the outer world provides for a stable and predictable orientation toward the work that is

performed. This orientation may come at the expense of the pursuit of new and

innovative programming. Opportunities to expand programming, clientele, and funding

sources may be overlooked as a result.

Conclusion 8

The majority of Extension Community Development program professionals were

male. Subjects’ could be described as middle-aged. Assuming a career in Extension lasts

thirty years, almost one third were at a mid-point in their Extension careers. A graduate

degree is the highest level of educational attainment and an academic background in

business is most common for program professionals. Most program professionals have a

regional orientation to their work, assigned to a district-level appointment.

Implication 8

A better understanding of the characteristics of Extension Community

Development program professionals has implications for Extension and the Extension

Community Development program area. In particular, implications exist for differences

in gender, age and length of tenure, educational attainment, academic background, and

primary work assignment.

The majority of Extension Community Development program professionals were

male. Is the nature of Extension Community Development work such that it is most

appropriate for males? What role do position requirements play in gender difference as

they relate to academic background?

141

Subjects’ could be described as middle-aged. How does this compare to

Extension program professionals in other states? How does this compare to Extension

program professionals in other program areas in Ohio? Should steps be taken to ensure

that a qualified pool of candidates will be available for future Extension Community

Development program professional positions?

A graduate degree is the highest level of educational attainment and an academic

background in business is most common for program professionals. Are these credentials

appropriate for the type of Extension work the organization will be facing in the future?

Should steps be taken to determine future Extension Community Development

programming directions and the competencies needed to most effectively meet those

needs? Assuming that future Extension organizational leadership is drawn from within

the organization, are these the credentials that will enable Extension Community

Development professionals to move into a position of organizational leadership?

Most program professionals are assigned a district-level appointment, working

with a regional orientation. What implications does this have for maintaining the county-

based support that is part of the cooperative nature of Extension? Should steps be taken

to ensure that county-based support for Extension programming is strengthened? Can it

be done with a regional orientation to Extension Community Development

programming?

142

Recommendations

The following recommendations for Ohio State University Extension and for

further study are the result of the review of literature, the findings of this study, and

resulting conclusions and implications. The recommendations are shared in no particular

order.

1. Realizing that Extension Community Development program professionals learn

differently, efforts must be made within the Community Development program

area and within Extension to take into account the various learning styles. In

order to become more effective educators, Extension Community Development

program professionals and Extension educators in general should learn more

about learning style differences via regular professional inservice training that

introduces and reinforces the concepts of learning style and personality type

differences.

2. Extension Community Development program professionals and Extension

educators in general should incorporate knowledge of learning style differences in

program planning efforts. Understanding learning style preferences of peers in

Extension could serve as one way to become familiar with differences and

provide background knowledge from which to draw should Extension educators

wish to involve their program participants in discovering their learning style

differences. Program evaluation should also take into account the efforts made by

program professionals to incorporate differences in learning style.

143

3. It is recommended that mechanisms be implemented that strengthen the

relationship between Extension Community Development and the county-based

supporters. With most Extension Community Development program

professionals assigned a district-level appointment, special efforts must be made

to connect with county-based financial supporters.

4. It is recommended that further research be conducted to understand the learning

style differences among all Extension program professionals working in all areas

(Agriculture & Natural Resources, Family & Consumer Science, and 4-H Youth

Development.)

5. As the Extension organization will be experiencing significant organizational

changes in the coming years, it is recommended that this study serve as the

beginning of a longitudinal study of Extension Community Development program

professionals to determine if learning style preferences and/or personality type

preferences as well as demographic characteristics of this group are impacted by

the organizational changes and evolving mission.

144

Other General Recommendations

1. As a result of the Extension organizational restructuring, the population for this

project was expanded to include support staff with professional responsibilities to

the Community Development program area. Doing so increased the sample size

by nearly 18 percent. However, the author hypothesizes that support staff prefer a

field dependent learning style and as such, the addition of this category to the

population decreased the mean GEFT score. Future learning styles research

should examine support staff separate from program assistants, educators,

specialists, and administrators.

2. Extension Human Resources should compare demographics of OSU Extension

professionals with those of Extension professionals in other states. Is the

organization aging such that an age gap will exist in the future? Are pools of

qualified candidates readily available. Administrators should forecast human

resource needs for the future and identify sources from which to recruit

candidates.

3. The formal teaching evaluation tool, Effective Evaluation Extension Teaching,

should be revised to take into account learning style preferences. It is

recommended that the instrument more precisely measure teaching methods

employed and provide program participants with an opportunity to share

information related to their preferred learning style.

145

4. Extension Human Resources should identify future Extension Community

Development programming directions and the competencies needed to most

effectively meet those needs. This should be done in collaboration with an

Extension Community Development strategic plan.

5. Extension Human Resources should identify future Extension organizational

leadership needs and begin to identify credentials needed for these administrative

position. Do Extension Community Development program professionals have the

appropriate credentials to move into a position of organizational leadership?

146

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APPENDIX A GROUP EMBEDDED FIGURES TEST (GEFT)

FRONT AND BACK PAGES

155

APPENDIX B PERSONAL STYLE INVENTORY (PSI)

FRONT AND BACK PAGES

158

APPENDIX C PERSONAL STYLE INVENTORY SCORING SHEET

160

APPENDIX D SUBJECT CHARACTERISTICS QUESTIONNAIRE

161

Participant Questionnaire A. Age _________ (years) C. Length of Extension Employment (total years in Ohio and other states)

1. 0-5 2. 6-10 3. 11-20 4. 21+ D. Educational Attainment (please circle one)

1. Undergraduate Degree 2. Graduate Degree 3. Doctoral Degree

E. Academic Major (please circle all that apply)

1. Business 2. Economics 3. Education 4. Law 5. Planning 6. Political Science 7. Psychology 8. Public Administration 9. Sociology 10. Other (please list) _____________________________________

F. Primary Work Assignment (please circle one)

1. County 2. District 3. State 4. Other (please list) _____________________________________

The Relationship Between Learning Style and Personality Type of Extension Community Development Program Professionals at The Ohio State University – Gregory A Davis

Code Number ________

B. Gender (please circle one)

1. Male 2. Female

162

APPENDIX E ANALYSIS OF EXTENSION COMMUNITY DEVELOPMENT SUPPORT STAFF

163

Characteristic Frequency Percent Gender Male 1 7.7 Female 12 92.3 Average Age 48.6

Frequency and Distribution of Sample Characteristics of Extension Community Development Support Staff in Ohio (N=13).

Characteristic Frequency Percent

Length of Tenure 0-5 years 20 29.9 6-10 years 18 26.9 11-20 years 21 31.3 21+ years 8 11.9 Educational Attainment Less than Undergraduate Degree 11 84.6 Undergraduate Degree 1 7.7 Graduate Degree 1 7.7 Academic Major Business 1 7.7 Education 1 7.7 None 11 84.6 Primary Work Assignment County 1 7.7 District 10 76.9 State 2 15.4

Frequency and Distribution of Sample Characteristics of Extension Community Development Support Staff in Ohio (N=13).

164

Scores for Extension Community Development Support Staff in Ohio on the Group Embedded Figures Test (GEFT) (N=13).

GEFT Frequency Percent Cumulative Percent

Field Dependent

3 3 23.1 23.1 5 2 15.4 38.5 Mean = 7.85 6 2 15.4 53.8 S.D. = 4.88 8 3 23.1 76.9 Mode = 3,8 Field Independent Minimum = 3 14 1 7.7 84.6 Maximum = 18 15 1 7.7 92.3 18 1 7.7 100.0 TOTAL 13 100.0 100.0

165

Male Female Total Field Dependent 1 0 0 0 2 0 0 0 3 0 3 3 4 0 0 0 5 0 2 2 6 0 2 2 7 0 0 0 8 0 3 3 9 0 0 0 10 0 0 0 11 1 0 0 Total 0 10 10 Field Independent 12 0 0 0 13 0 0 0 14 1 1 2 15 0 1 1 16 0 0 0 17 0 0 0 18 0 1 1 Total 1 3 13

Frequency of GEFT Scores by Gender of Extension Community Development Support Staff in Ohio (N=13).

Primary Work Assignment Field Dependent Field Independent County 1 0 District 7 3 State 2 0 Total 10 3

Frequency of GEFT Scores by Primary Work Assignment of Extension Community Development Support Staff in Ohio (N=13).

166

Academic Major Field Dependent Field Independent Business 1 0 Education 1 0 None 8 3 Total 10 3

Frequency of GEFT Scores by Academic Major of Extension Community Development Support Staff in Ohio (N=13).

MBTI Opposite Mean Standard Deviation Minimum Maximum

Extroversion - Introversion 99.92 23.95 70 127 Sensing - Intuition 87.69 21.32 71 125 Thinking - Feeling 117.85 16.31 74 133 Judgement - Perception 81.23 16.28 65 121

MBTI Opposite Scores of Extension Community Development Support Staff in Ohio (N=13).

167

MBTI Combination Frequency PercentISTJ 1 7.7 ISFJ 3 23.1 INFJ 2 14.4 INTJ 0 0 ISTP 0 0 ISFP 2 14.4 INFP 0 0 INTP 0 0 ESTP 0 0 ESFP 1 7.7 ENFP 0 0 ENTP 0 0.0 ESTJ 0 0 ESFJ 3 23.1 ENFJ 1 7.7 ENTJ 0 0 TOTAL 13 100.0

MBTI Combination Distributions of Extension Community Development Support Staff in Ohio (N=13).

MBTI Function Frequency PercentSF 7 53.9 ST 1 7.7 NF 5 38.5 NT 0 0

MBTI Function Combination Distributions of Extension Community Development Support Staff in Ohio (N=13).

168

MBTI Opposite Frequency PercentExtraversion 6 46.2 Introversion 7 53.8 Sensing 9 69.2 Intuition 4 29.8 Thinking 1 8.7 Feeling 12 91.3 Judgement 10 76.9 Perception 3 23.1

MBTI Opposite Distributions of Extension Community Development Support Staff in Ohio (N=13).

GEFT E - I S - N T - F J - P

GEFT 1 -.228 .164 .114 -.099E – I 1 .283 -.267 .141 S – N 1 .192 .426 T – F 1 .158 J – P 1

Correlation Between MBTI and GEFT of Extension Community Development Support Staff in Ohio (N=13).

Correlation Between GEFT and Selected Characteristics of Extension Community Development Support Staff in Ohio (N=13).

GEFT Primary Work Assignment Academic Major

GEFT 1.00 .429 -.359

Primary Work Assignment 1.00 -.380

Academic Major 1.00

169

GEFT Age in Years

Length of Tenure


GEFT 1.00 -.027 .373 -.173

Age in Years 1.00 .343 -.298

Length of Tenure 1.00 -.263 Educational Attainment 1.00

Intercorrelations Between GEFT and Selected Characteristics of Extension Community Development Support Staff in Ohio (N=13).

GEFT Gender

GEFT 1.00 -.276 Gender 1.00

Correlation Between GEFT and Gender of Extension Community Development Support Staff in Ohio (N=13).

Intercorrelations Between MBTI Preference Subscales and Gender of Extension Community Development Support Staff in Ohio (N=13).

E - I S - N T - F J - P Gender

E – I 1.00 .283 -267 .141 .312

S – N 1.00 .192 .426 .192

T – F 1.00 .158 -.083

J – P 1.00 .158

Gender 1.00

170

E - I S - N T - F J - P Age in Years

Length of Tenure


E - I 1.00 .283 -.267 .141 .124 -.201 .267

S - N 1.00 .192 .426 -.325 -.043 .433

T - F 1.00 .158 -.397 -.376 .083

J - P 1.00 -.398 -.285 .527

Age in Years 1.00 .343 -.298 Length of Tenure 1.00 -.263


Intercorrelations Between MBTI Preference Subscales and Selected Characteristics of Extension Community Development Support Staff in Ohio (N=13).

*significant at the .05 level Intercorrelations Between MBTI Preference Subscales and Selected Characteristics of Extension Community Development Support Staff in Ohio (N=13).

E - I S - N T - F J - P Primary Work

Assignment

Academic Major

E – I 1.00 .283 -.267 .141 -.164 .148

S – N 1.00 .192 .426 -.118 -.259

T – F 1.00 .158 .612* -.311

J – P 1.00 .097 .109

Primary Work Assignment 1.00 -.380

Academic Major 1.00

171

APPENDIX F NON-RESPONDENTS CHARACTERISTICS

172

* data unavailable

Sample OSUE HR Database Characteristic

Percent Percent Gender Male 55.2 60.9 Female 44.8 39.1 Average Age 45.1 48.2 Length of Tenure 0-5 years 29.9 22.8 6-10 years 26.9 25.5 11-20 years 31.3 26.5 21+ years 11.9 26.5 Educational Attainment Undergraduate Degree 37.3 10.8 Graduate Degree 41.8 77.0 Doctoral Degree 20.9 12.2 Academic Major Business 17.9 * Economics 11.9 * Education 11.9 * Law 3.0 * Planning 1.5 * Political Science 6.0 * Public Administration 6.0 * Natural Resources 13.4 * Agriculture 10.4 * Humanities 3.0 * Computer Science 4.5 * FCS 3.0 * Engineering 1.5 * Other 6.0 * Primary Work Assignment County 17.9 * District 53.7 * State 22.4 * Other 6.0 *

the relationship between learning style and personality type of extension community development

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