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Correlates and predictors of cognitive complexity among counseling and social work students in graduate training programs

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Title:
Correlates and predictors of cognitive complexity among counseling and social work students in graduate training programs
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English
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Simmons, Christopher
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University of South Florida
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Tampa, Fla
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Learning Environment Preferences
Intellectual development
Perry scheme
Age
Gender
Dissertations, Academic -- Psychological and Social Foundations -- Doctoral -- USF   ( lcsh )
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non-fiction   ( marcgt )

Notes

Abstract:
ABSTRACT: For this study, a web-based survey method was used as a means of collecting data to test a predictive model of education, supervised clinical experience (SCE), age, human services experience (HSE) and cognitive complexity. The theoretical framework for the study was Perry's (1970; 1999) scheme of intellectual development. The sample consisted of 332 counseling and social work students in graduate training programs in four different regions of the United States. The instruments used in the study were a researcher-developed demographic questionnaire and the Learning Environment Preferences (LEP) instrument (Moore, 1987). The results of the hierarchical regression analysis indicated that education and human services experience predicted a significant proportion of the variance in cognitive complexity. However, age and supervised clinical experience did not significantly predict any of the variance in cognitive complexity. Additional analyses were conducted to examine the effects of gender, ethnicity, programs, and earned degrees on a measure of cognitive complexity. Results of the Analyses of Variance (ANOVAs) did not reveal significant gender, ethnicity, program differences; however, as expected there were differences in terms of previously earned degree. Students who previously earned master's degrees had significantly higher cognitive complexity scores than students who had only earned a bachelor's degree. This study provided partial support for Perry's theory of intellectual development. The study also has implications for supervision, education and training of students in counseling and related fields.
Thesis:
Dissertation (Ph.D.)--University of South Florida, 2008.
Bibliography:
Includes bibliographical references.
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Mode of access: World Wide Web.
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System requirements: World Wide Web browser and PDF reader.
Statement of Responsibility:
by Christopher Simmons.
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Title from PDF of title page.
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Document formatted into pages; contains 111 pages.
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Includes vita.

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usfldc doi - E14-SFE0002542
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ABSTRACT: For this study, a web-based survey method was used as a means of collecting data to test a predictive model of education, supervised clinical experience (SCE), age, human services experience (HSE) and cognitive complexity. The theoretical framework for the study was Perry's (1970; 1999) scheme of intellectual development. The sample consisted of 332 counseling and social work students in graduate training programs in four different regions of the United States. The instruments used in the study were a researcher-developed demographic questionnaire and the Learning Environment Preferences (LEP) instrument (Moore, 1987). The results of the hierarchical regression analysis indicated that education and human services experience predicted a significant proportion of the variance in cognitive complexity. However, age and supervised clinical experience did not significantly predict any of the variance in cognitive complexity. Additional analyses were conducted to examine the effects of gender, ethnicity, programs, and earned degrees on a measure of cognitive complexity. Results of the Analyses of Variance (ANOVAs) did not reveal significant gender, ethnicity, program differences; however, as expected there were differences in terms of previously earned degree. Students who previously earned master's degrees had significantly higher cognitive complexity scores than students who had only earned a bachelor's degree. This study provided partial support for Perry's theory of intellectual development. The study also has implications for supervision, education and training of students in counseling and related fields.
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Correlates and Predictors of Cognitive Comp lexity among Counseling and Social work Students in Graduate Training Programs by Christopher Simmons A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Psychological and Social Foundations College of Education University of South Florida Co-Major Professor: Herbert Exum, Ph.D. Co-Major Professor: Debbie Osborn, Ph.D. Roger Boothroyd, Ph.D. Wilma Henry, Ed.D. Carlos Zalaquett, Ph.D. Date of Approval: July 1, 2008 Keywords: Learning Environment Preferences, Intellectual Development, Perry Scheme, Age, Gender, Ethnicity, Epistemological De velopment, Clinical Supervision, Human Services Experience, Education Copyright 2008, Christopher Simmons

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Dedication This dissertation is dedicated to my wi fe Siria and our daughter Imani. You have been my inspiration and you’ve taught me to live and finish strong.

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Acknowledgements This dissertation is the culmination of a lo t of hard work and sacrifice. It could not have been accomplished without much prayer, the guidance of some great professors, and the support of family and friends. First, I ow e my sincerest gratitude to my dissertation co-chairs, Dr. Herbert Exum and Dr. De bbie Osborn and my dissertation committee members, Dr. Roger Boothroyd, Dr. Wilma He nry and Dr. Carlos Zalaquett. Thank you for your support and challenge. Because of you, I am a not only a better researcher, I am a better person. I would like to thank my family especially my wife, Siria Simmons, her mother and father Lelis and Milquiya Pi mentel and my parents Victor and Joyce Simmons. Thank you for your support and prayers. Finally, I am grateful to my friends and colleagues, Dr. Lee Teufel and Dr. Jo se Coll, for the many wonderful times we shared in graduate school.

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i Table of Contents List of Tables................................................................................................................. ....iii List of Figures................................................................................................................ ....iv Abstract....................................................................................................................... ........v Chapter One: Introduction..................................................................................................1 Background.............................................................................................................1 Statement of the Problem........................................................................................5 Purpose of the Study...............................................................................................6 Research Question..................................................................................................8 Hypotheses..............................................................................................................8 Definitions of Major Terms..................................................................................10 Limitations of the Study........................................................................................12 Summary...............................................................................................................13 Chapter Two: Literature Review......................................................................................15 Cognitive Complexity and Counseling.................................................................16 The Perry Scheme: Cogn itive Development.........................................................25 The Perry Scheme: Review and Evaluation of the Literature...............................29 Critical Analysis....................................................................................................38 Chapter Three: Methodology............................................................................................41 Organization..........................................................................................................41 Logic, Structure and Design of Study...................................................................41 Research Question................................................................................................42

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ii Hypotheses............................................................................................................43 Instrumentation.....................................................................................................47 Ethical Considerations..........................................................................................51 Methodological assumptions................................................................................52 Chapter Four: Results.......................................................................................................54 Description of the Sample.....................................................................................54 Perry Positions of Participants..............................................................................56 Pearson Product-Moment Correlations.................................................................58 Predictive Model Testing......................................................................................60 Additional Analyses..............................................................................................65 Chapter Five: Discussion..................................................................................................72 Purpose of the Study.............................................................................................72 Summary of the Predictive Model........................................................................73 Summary of Analyses of Variance (ANOVAs)....................................................77 Conclusions...........................................................................................................79 Limitations............................................................................................................81 Implications...........................................................................................................82 Recommendations for Future Research................................................................85 Appendices..................................................................................................................... ...98 Appendix A: The Perry Scheme of Cognitive Development...............................99 Appendix B: Demographic Questionnaire.........................................................100 Appendix C: The Learning Environment Preferences Instrument....................103 Appendix D: Online Informed Consent Script..................................................109 Appendix E: IRB Letter.....................................................................................110 About the Author .………………………………………………………………..End Page

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iii List of Tables Table 1 Frequencies and Percentages by Program for All Participants............................44 Table 2 Frequencies and Percentages by Ethni city and Gender for All Participants.......45 Table 3 Course Matrix for Po tential Study Participants...................................................46 Table 4 CCI Score Ranges as Related to Perry Positions.................................................51 Table 5 Frequencies and Percentages by Et hnicity and Gender for Final Sample...........55 Table 6 Frequencies and Percentage s by Program for Final Sample................................56 Table 7 Correlations between Model Variables................................................................58 Table 8 Squared Correlations between Model Variables.................................................59 Table 9 Results for Cognitive Complexity Regression Model.........................................63 Table 10 Summary of Hierarchical Regression Analysis for Variables Predicting Cognitive Complexity........................................................................................65 Table 11 Mean CCI Scores by Ethnicity and Gender.......................................................66 Table 12 Within-Group Comparisons of Mean CCI Scores by Programs........................67 Table 13 Between-Group Comparisons of Mean CCI Scores by Programs.....................68 Table 14 Cross-Level Group Comparisons of Mean CCI Scores by Programs...............69 Table 15 Mean CCI Scores and Results of An alysis of Variance by Earned Degree......70

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iv List of Figures Figure 1. Hypothesized rela tionships among variables....................................................10 Figure 3. Study Participants’ Perry Positions...................................................................57 Figure 2. Histogram Distributions of CCI Scores.............................................................61

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v Correlates and Predictors of Cognitive Comp lexity among Counseling and Social Work Students in Graduate Training Programs Christopher Simmons ABSTRACT For this study, a web-based survey method was used as a means of collecting data to test a predictive model of education, supe rvised clinical experience (SCE), age, human services experience (HSE) and cognitive comp lexity. The theoretical framework for the study was Perry’s (1970; 1999) scheme of intellectual development. The sample consisted of 332 counseling and social work students in graduate training programs in four different regions of the United States. The instruments used in the study were a researcher-developed demographic questi onnaire and the Learning Environment Preferences (LEP) instrument (Moore, 1987). Th e results of the hierarchical regression analysis indicated that education and human services experience predicted a significant proportion of the variance in cognitive complex ity. However, age and supervised clinical experience did not significantly predict any of the variance in cognitive complexity. Additional analyses were conducted to exam ine the effects of gender, ethnicity, programs, and earned degrees on a measure of cognitive complexity. Results of the Analyses of Variance (ANOVAs) did not reve al significant gender, ethnicity, program differences; however, as expected there were differences in terms of previously earned

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vi degree. Students who previously earned master’s degrees had significantly higher cognitive complexity scores than students who had only earned a bachelor’s degree. This study provided partial support for Perry ’s theory of intellectual development. The study also has implications for supervis ion, education and training of students in counseling and related fields.

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1 Chapter One Introduction This chapter provides background inform ation on cognitive complexity across different developmental levels in counseling and related fields. This chapter outlines the statement of the problem and significance and purpose of the study. An outline of the organization of the remainder of the dissertati on is provided at the e nd of the chapter. Background Cognitive development was defined as movement from simplistic ways of viewing external events to more complex wa ys of viewing external events (Perry, 1970, 1999). Moreover, it was the ability to beco me more adaptive in terms of reasoning and behaviors (Brendel, Kolbert, & Foster, 2002). Cognitive development could be conceptualized as an increase in cognitive comp lexity, which was defined as the extent to which individuals differentiate (i.e., unders tand and analyze) and integrate (i.e., make meaning) external events (Streufert & Swezey, 1986). Therefore, as individuals become more complex in their thinking (i.e., cognitive development), they often seek out more complex situations to master. Research sugge sted that individuals with low levels of cognitive complexity think and behave differe ntly than individuals at higher levels (Brendel et al., 2002; Granello, 2002; Rapa port, 1984; Stoltenberg & Delworth, 1987; Thompson, 1999). Furthermore, individuals wi th high cognitive complexity might be better suited for professions, su ch as counseling and related fi elds, that call for complex problem-solving capabilities (Stoltenberg & Delworth, 1987). On the other hand,

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2 individuals with low cognitive complexity might encounter problems with various aspects of the counseling process, such as empathy and nonjudgmental attitudes toward their clients, because these individuals ar e concrete and inflexible in thinking and behaviors (Brendel et al., 2002). Helping professionals in counseling, so cial work, psychology and related fields have the task of facilitating behavioral ch ange among individuals, groups, families and communities. However, this is no simple task. At minimum, it requires that the practitioner is capable of unde rstanding the complex nature of behavior and behavioral change among diverse groups of clients. There was sufficient evidence that cognitive complexity was an important counselor variable, positively linked to psychological functioning (Brendel et al., 2002), confiden ce and focus on counseling effectiveness (Birk & Mahalik, 1996), empathic understand ing (Alcorn & Torney, 1982; Benack, 1988; Lovell, 1999a; Lyons & Hazler, 2002) and more sophisticated descriptions of client characteristics (Borders, 1989). Although signi ficant cognitive growth might occur after students’ training, the goal of training programs was for st udents to demonstrate higher levels of cognitive complexity by the end of their programs (cf. Skovholt & Ronnestad, 1992). Research provided support for the premis e that students were able to develop in terms of cognitive complexity during their pr ogram (Brendel et al., 2002; Fong, Borders, Ethington, & Pitts, 1997; Gran ello, 2002). Brendel, Kolbert, and Foster (2002) cited the importance of cognitive developmental theory in explaining cognitive complexity among counselors. Perry’s (1970; 1999) scheme wa s the cognitive developmental framework used in this study. It provided a general fr amework for describing where students were in terms of cognitive complexity and explained how developmental changes might occur,

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3 thus providing a cognitive map for developmen t. The Perry scheme consisted of nine different positions that outlined intellectual an d ethical development. The nine positions were often grouped into four categories. These categories were dualism, multiplicity, relativism, and commitment w ithin relativism. The scope of this study was limited to exploring intellectual or epistemological development, which encompassed dualism, multiplism, and relativism. According to the scheme, as students developed cognitively, they moved from an absolutist view of the world (i.e., dualism) to a pluralistic view (i.e., relativism) to a constructivist view (i.e., commitment within relativism) (Hofer & Pintrich, 1997; Perry, 1970, 1999). Cognitive developmental theory might explain occurrences of cognitive developmental processes in supervision (B locher, 1983). This developmental assumption spawned questions concerning the relationship between cognitive complexity and the training of graduate students. One important question worth exploring was what training variables were related to student cognitiv e complexity (Bernard & Goodyear, 2004). To address this question, it was important first to identify two major types of training variables in graduate training programs in the helping professions. Training variables could be divided into two types of experiences: didactic experience and field experience ( cf. Blocher, 1983). Didactic experience i nvolved instructional processes that occurred in a classroom environment and mi ght include simulated client practices. The classroom environment usually dictated that st udents interact with the professor and other students in a classroom. According to Perry (1999), didactic experience would be sufficient to bring about changes in cognitive complexity because the instructor and peers would provide the necessary support and challe nge. Field experience, on the other hand,

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4 was direct practice under supervision with actua l clients. This type of experience went beyond role-play or simulated client scenar ios and offered the trainee “real world” experience under supervision of a trained s upervisor. Although both didactic experience and field experience operated as a whole unit for practitione rs-in-training, much thought was given to the strength of each variable in predicting cognitive complexity (Bernard & Goodyear, 2004). In terms of the field experien ce and cognitive complexity, there were differing assumptions concerning whether work with actual clients or supervision was responsible for changes in cognitive complex ity. Blocker (1983) defined supervision as specialized instructional process in which the supervisor attempts to facilitate the growth of a counsel or-in-training using as the primary educational medium the student’s inte raction with real clients for whose welfare the student has some degree of professional, ethical, and moral responsibility. (p. 27) However, because much of the literature “a ssumes that experience under supervision and cognitive development enjoy a symbiotic re lationship” (Bernard & Goodyear, 2004, p. 108). It was important to consider Be rnard and Goodyear’s (2004) definition of supervision, an intervention provided by a more senior member of a profession to a more junior member or members of th at same profession. The relationship is evaluative; extends over time; and has the simultaneous purposes of enhancing the professional functioni ng of the more junior person(s), monitoring the quality of professional services offered to the clients that

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5 she, he, or they see, and serving as a gatekeeper for those who are to enter the particular profession. (p. 7) The former emphasized the importance of work with actual clients as a mechanism for cognitive development, while the latter em phasized supervision under close scrutiny as the mechanism for change. Statement of the Problem Much thought was given to the relati onship between training variables and cognitive complexity. The discussions genera ted several assumptions concerning whether work experience with actual clients or superv ised experience was re sponsible for student gains in cognitive complexity. However, these claims were not adequately tested to assure that the effects of othe r variables, such as age and ed ucation, were controlled. To this end, education and age were underemphasi zed in supervision research; however, they could not be ruled out as impor tant factors in student cognitive complexity. In order to get a clear understanding of th e role of variables that mi ght be catalysts for cognitive development among students in the helping fiel ds, work experience with actual clients was operationalized as the amount of empl oyment, practicum, internship and volunteer experiences in months of work providing direct services to individuals, families or groups (i.e., human services experience ), and s upervision was operationalized as the amount supervision received while working in hu man services (i.e., clinical supervised experience). These and other operational definitions will be discussed further in Chapter 3.

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6 Significance of the Study This research was important for instit utions of higher education because it extended the knowledge base regarding training variables (i.e., education, HSE and SCE) and a demographic variable (i.e., age) that might contribute to cognitive complexity among students in graduate training programs. Given that cognitive complexity was essential to the helping fields, as researcher s argued, facilitating th e types of experiences that increased cognitive complexity might he lp training programs to teach students to become effective practitioners. A general und erstanding of student cognitive complexity level might also enable instructors to provi de the best environment to enhance student growth intentionally, instead of leaving student development to chance (Fong et al., 1997). Purpose of the Study The purpose of this study was to test a predictive model among demographic variables and cognitive complexity of gra duate students using William Perry’s (1970; 1999) theory of intellectual development as the central framework. Perry’s scheme was a cognitive developmental model that focused on internal structures that determined how individuals perceived, organize d and evaluated external even ts and how they coped with those events (Rapaport, 1984; Thompson, 1999) Although Perry’s theory was criticized for being gender-biased because it was deve loped using a sample of male students (Belenky, Clinchy, Goldberger, & Tarule, 1997), it was widely referenced in the literature and provided a good framework for adult cognitive development. Extensive research was conducted using Pe rry’s scheme of cognitive development with undergraduate college students (B axter Magolda, 1992; Belenky, Clinchy,

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7 Goldberger, & Tarule, 1986; Felder & Bren t, 2004; Gottlieb, 2007; King & Kitchener, 1994; Markwell & Courtney, 2006; Perry, 1970) and graduate students in counseling (e.g., Granello, 2002; Knefelkamp & Slepit za, 1976; Lovell, 1999a, 1999b; McAuliffe & Lovell, 2006). Cognitive developmental theo rists (e.g., Benack, 1988; Blocher, 1983; Brendel et al., 2002; Granello, 2002; Hood & Deopere, 2002; Lovell, 1999a) argued that cognitive complexity was essential for stude nts in the helping professions to become effective practitioners; therefore, the participants selected for this study were graduate students in training programs in the helpi ng professions. Data co llection involved the used of a researcher-designed demographic questionnaire and the Learning Environment Preferences Scale (LEP, Moore, 1987), an inst rument that measured Perry’s (1970; 1999) scheme. The demographic questionnaire was based on previous research (See Chapter 2). The questionnaire contained the following demo graphic items: age, gender, ethnicity, education experience, HSE, SCE and practicum internship, or prac tice setting (Appendix B). The LEP had a Cognitive Complexity Index (CCI) calculated into a single score that corresponded to the five Perry (1970; 1999) positions to explain intellectual or epistemological development (Appendix C). Th ese were administered online using a web-based survey tool to provide some advant ages over mail-out surv eys in terms of cost savings, short time frame for the collection of responses, increased responses, ease of transferring data into a database for analys is and the possibility of a wider geographic coverage area (Lefever, Dal, & Ma tthasdttir, 2007; Mertler, 2001).

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8 Research Question The research question examined whethe r age, education, HSE and SCE were associated with the criterion variable, c ognitive complexity. The study answered the following research question: To what exte nt do age, education, HSE and SCE predict cognitive complexity? This question was analyzed using a hierarchical multiple regression model. Based on an a priori power analysis (Algina & Olejnik, 2003), a sample size of 77 was required in order to ex ceed a statistical power of .80 using alpha = .05 and an effect size of f2 = .15. Hypotheses H0: No variables (education, SCE, age, HSE) will predict cognitive complexity. H1: Education, SCE, age and HSE will predict cognitive complexity. H2: The combination of education and SCE will predict more of the variance in cognitive complexity than education and age or education and HSE. These hypotheses were based on the assumption that education and experience were needed to increase cognitive complex ity among counseling students (Bernard & Goodyear, 2004). The hypotheses were analyzed using a hier archical multiple regression to test the predictive model of age, edu cation, experience and cognitive complexity. Education was entered into the model first because prior studies showed a positive relationship between education and cognitive complexity (e.g., Belenky et al., 1986, 1997; Perry, 1970, 1999; Wilson, 1995a, 1995b). SCE was entered next because recent studies have been interpreted as showing a pos itive relationship between SCE and cognitive complexity

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9 (Granello, 2002; Lovell, 1999b). Age was ente red next because there was inconsistent evidence concerning the relationship between age and cognitive development (Granello, 2002; Hood & Deopere, 2002; Wilson, 1995b). HSE was entered last because there were no studies that found a relationship between general experience in human services and cognitive complexity. Assumptions of the Study The following assumptions were made for this study: 1. Cognitive complexity was seen as a favorable counseling variable. 2. Effective counseling requires higher levels of cognitive complexity. 3. Participants would give honest and accurate responses on the LEP and demographic questionnaire. To enco urage honest responses, the LEP and demographic questionnaire were administered anonymously online. 4. The sample might be representative of gr aduate students in helping professions, such as professional counseling programs and social work. Conceptual Framework The conceptual framework presented in Figure 1 shows the relationships among variables under investig ation in this study. The aim of this research was to follow as many of the rules as possible for constructing goo d theory. That is, the research had to be important and practical to counseling and soci al work educators and supervisors, have few assumptions and account for considerable knowledge concerning cognitive complexity among graduate student in the helping professions.

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10 Figure 1 Hypothesized relationships among variables. Definitions of Major Terms The following definitions were used in this study (Note: definitions with no citations represent the researcher’s operationalization of terms): Advanced Standing : A classification given to elig ible graduates of baccalaureate social work programs allowing them to ente r the advanced level of the MSW program. Cognitive Complexity : The extent to which individuals differentiate and integrate external events (Streufert & Swezey, 1986). Cognitive Complexity Index (CCI): The single-score formula incorporating all the participants’ stage scores on the Learning E nvironment Preferences (LEP, Moore, 1987, 1989). This single-score ranged from 200-500 an d measured the complexity of thinking according to Perry’s (1970; 1999) positions two to five.

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11 Cognitive Development : Movement from dualistic, objectivistic view of knowledge to a more subjective, relativistic view, and then to a constructivist view of knowledge (Hofer & Pintrich, 1997; Perry, 1970, 1999). Counseling Students : Includes masters and doctora l programs in professional counseling, mental health counseling, reha bilitation counseling, counselor education, counseling psychology, marriage and family counseling and community counseling. Development: Movement of an individual from a lower position to a higher position based on Perry’s model. Education: Number of years of education completed. Empathy : The ability to take “multiple perspectives on phenomena [which facilitates] an enhanced ability to see a s ituation from another person’s point of view” (Lovell, 1999a, p. 196). Epistemology : The branch of philosophy concerne d with the nature of knowing. How individuals understand and make meaning of the world (Perry, 1970, 1999). Intellectual Development : The first five positions of the Perry scheme, which deal with the way in which individuals make meaning from simple to complex ways of thinking (Perry, 1970, 1999). Locus of Control: How students define themselves and their environment (i.e., internal or external factors) (Knefelkamp & Slepitza, 1976). Helping Professional : Refers to professionals in the field of psychology, social work and counseling such as professional c ounseling, counselor education, community counseling, marriage and family counseling, mental health counseling, pastoral counseling, school counseling, career c ounseling and rehabilitation counseling.

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12 Helping Profession : Refers to the field of psychology, social work and counseling, such as professional counseling, counselor education, community counseling, marriage and family counseling, mental hea lth counseling, pastoral counseling, school counseling, career counseling a nd rehabilitation counseling. Human Services Experience (HSE) : Total number of months worked in the helping profession providing direct services with individuals, families or groups (i.e., employment, practicum, internship and volunteer hours). Supervised Clinical Experience ( SCE): HSE with at least one hour of group or individual supervision (See de finition of supervision). Supervision : “ An intervention provided by a more senior member of a profession to a more junior member or members of that same profession. The relationship is evaluative, extends over time and has the simultaneous purposes of enhancing the professional functioning of the more junior person(s), monitoring the quality of professional services offered to the clients th at she, he, or they see and serving as a gatekeeper for those who are to enter the particular prof ession” (Bernard & Goodyear, 2004, p. 7). Limitations of the Study This study may be limited by the web-based survey method used. While webbased survey methods provide some advantages in terms of cost savings, short time frame for the collection of responses, ease of transfer ring data into a databa se for analysis and the possibility of a wider geographic covera ge area, they posed possible limitations. These were lack of a population list, a nonra ndom sample, inability to calculate response rate and computer access to the survey (M ertler, 2001). There might be a potential

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13 limitation of lowered response rates for web-based surveys (Converse, Wolfe, & Huang, 2008). Because data were gathered using only self-reports response bias posed a potential limitation (Ellis, La dany, Krengel, & Schult, 1996). A convenience sampling was employed; it was unknown whether responden ts to the survey were different from non-respondents (i.e., non-response error). An other potential limitation was the lack of demographic variability in the sample in te rms of gender and ethnicity because of the lack of diversity in counseling and so cial work programs (Granello, 2002). Summary This study was based on two prevalen t assumptions in the literature on supervision that were offered as explanations of why students might show an increase in cognitive complexity at the end of the program s: 1) supervision might be the catalyst for increasing cognitive complexity; and 2) expe rience involving actual clients might account for an increase in cognitive complexity. This study was designed to test a predictive model among age, education, HSE, SCE and cognitive complexity. The theoretical framework for this study was based on the Pe rry scheme. Perry’s (1970; 1999) theory of intellectual development offered a general description of how students progress from simple to complex ways of thinking. Thes e ways of thinking acted as filters through which the student gave meaning to his or her world. Cognitive development among college students was studied extensively using Perry’s scheme; however, no studies tested the assumptions regarding supervision an d work experience with actual clients, and their impact on cognitive complexity.

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14 Organization of the Study This dissertation is organized into five chapters. Chapter 1 provided an overview of the topics that will be discussed in the study. Chapter 2 provides the framework on which this study is grounded and the liter ature review. Chapter 3 provides a detailed description of the method used for this st udy, the instrument used and its psychometric properties and a description of the sample. Chapter 4 provide s the results of the study. Chapter 5 provides the discussion, including li mitations, of the theoretical and practical implications.

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15 Chapter Two Literature Review This chapter describes literature relevant to the research purposes of this dissertation. The review of the literature contains four major sections: 1. Cognitive Complexity and Counseling 2. Perry Scheme (1970, 1999), the central component of this study 3. Review and evaluation of the relevant literature 4. Summary Several definitional distinctions should be considered in this review. Unless otherwise specified, cognitive complexity refers to the extent to which individuals differentiated and integrated external events (Streufert & Swezey, 1986). Differentiation is the ability to unde rstand and analyze avai lable data; integrati on refers to how one interprets or makes meaning of the available data. Cognitive development refers to movement from a dualistic, object ive view of knowledge to a mo re subjective, relativistic view, and then to a constructive view of knowledge (Hofer & Pintrich, 1997; Perry, 1970, 1999). Cognitive development is an incr ease in cognitive complexity. Cognitive development is used interchangeably with epistemological development. Epistemology is a branch of philosophy concerned with the nature of knowing and how individuals understand and make meaning of the world (Perry, 1970, 1999).

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16 Cognitive Complexity and Counseling Cognitive complexity plays an important role in counselor development. An examination of the role of cognitive developm ental variables is essential to counseling and related fields. Usually, as students go through college and respond positively to the challenges of peers and instructors, th ey begin to develop gradually (i.e., disequilibration). That is, indi viduals interact wi th their environment and respond to information by assimilating the information into existing schemas or accommodating existing schemas to new information, thus, creating new schemas. Schemas determine how individuals organize and evaluate inco ming information. The knowledge constructed by individuals formed into chunks, which enables them to attend to details and inconsistencies (Sakai & Nasserbakht, 1997). Thus, complex reasoning and adaptive behaviors play an essential role in studen ts becoming competent counselors (Brendel et al., 2002). Counseling involves a higher level of cogni tive complexity, which was defined as the ability to take multiple perspectives – empathy, the ability to differentiate among alternatives, to manipulate facts and causes a nd to integrate and synt hesize large amounts of data – in a collaborative way with clie nts (Blocher, 1983). Severa l studies illustrated the importance of cognitive co mplexity in counseling. Benack, 1988. Using a small sample of college students, Benack (1988), in three separate studies, compared dualists and relativists on their ability to express empathy. Study 1 used a sample of 20 ( N = 7 relativists; N = 8 dualists; N = 5 mixed dualistic/relativistic) students in an introductory counseling course. Participants included 10 women and 10 men with an age range from 21-42 years. In Study 1, relativists had

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17 significantly higher scores on ove rall empathy than dualists ( m = 3.9 relativists, m = 2.9 dualists t (12) = 3.68, p < .01). The participants in Study 2 included 18 undergraduate students, between 19-22 years of age, who had no formal training in counseling. The participants completed the epistemology interview completed in Study 1. Six participants were rated as showing relativistic thought and 12 were rated as showing dualistic thought. They were given descriptions of seven hypotheti cal counseling situations and instructed to write a brief essay describing the client’s inner experience but not provide a helpful response. Results indicated that dualists attended to the problem situation more often, while relativists attended to the client’s experience more often but the difference was not significant. Study 3 participants were drawn from the same population as Study 2. They included 24 undergraduate stud ents (14 men and 10 women), ranging from 19-22 years. Relativists were significantly more likely than dualists to expre ss empathic understanding of their clients ( m = 1.92 for relativists, m = .56 for dualists t (22) = 1.85, p < .05). Benack concluded that the studies indicated “there is a strong tendency for people who think relativistically about epistemological issues to more frequently and accurately express empathic understanding of other people’s inner experience” (Benack, 1988, p. 229). The studies brought to the forefr ont the important relationship between epistemological development and empathic understanding among students with counseling experience and stude nts without counseling experience; however, the studies had limitations; for example, Benack (1988) us ed a small sample in each study, and she did not report differences in terms of cogni tive complexity or empathy among variables such as gender, age, ethnicity or education.

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18 Lovell, 1999. To replicate and extend Benack ’s study, Lovell (1999a) examined empathy and cognitive development using a national sample of counseling students ( N = 340). The sample was selected from a ra ndom, computer-selected, invitation pool of 2000 individuals based on their student member ship provided by the American Counseling Association (ACA). Eighty-one percent of the participants were female, 55% majored in liberal arts as undergraduates and 79% were pursuing a master’s degree. The mean age for all participants was 37.4 years with a st andard deviation of 9.3 years. Lovell did not report whether different ethnicities were represented in his sample. Lovell’s (1999a) study was based on adult cognitive-developmental theory, using the schemes in Perry’s model of intellectual development as a framework. The purpose of the study was to investigate thr ee different research aims usi ng a large national sample of counseling students. First, Lovell was intere sted in the correlation between counselor epistemic-cognitive development and empathy. He defined empathy as the ability to take “multiple perspectives on phenomena [which facilitates] an enhanced ability to see a situation from another person’s point of view” (Lovell, 1999a, p. 196). Second, he investigated whether relativists scored highe r on a measure of empathy than students at lower Perry positions. This would replicat e Benack’s (1988) study that found a link between relativism and empathy. Third, he de termined if differences on a measure of the criterion variable, empathy among groups of part icipants (categorized by four of Perry’s intellectual positions) would be found in the pred icted (positive) direction. That is, would empathy increase as supervisee development increased? Empathy was measured by the Hogan Empathy Scale (EM) (Hogan, 1969, as cited in Lovell, 1999). The EM measured both cognitive empathy (mental perspective taking) and empathic disposition (cognitive

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19 and affective empathy). The alpha coefficient for the EM was reported as high as .71 and the test-retest reliability was reported as high as .84. Cognitive complexity was measured by the LEP. For hypothesis 1, Lovell (1999a) used a correlational design to examine the relationship between empathy and cognitive co mplexity. The correlation between the EM and cognitive complexity was reported as being moderate ( r = .31; p < .001). Lovell argued that correlation statistics did not fit adequately with the theoretical underpinnings of the stage theory, showing empathy at the different levels of cognitive complexity. To investigate hypothesis 2, he tested the samples using a nonparametric test. Results of the nonparametric Mann-Whitney test indicated that rela tivists (i.e., students that held the epistemological belief that al l knowledge was contextual) were higher on the EM ( M = 25.23, SD = 3.72) than those at lower epistemological positions ( M = 23.77, SD = 4.01, U = 11432.00, p < .001). Hypothesis 3 was tested by disaggregating the participants into four epistemic positions: dualism ( N = 20); early multiplicity ( N = 85); late multiplicity ( N = 69); relativism ( N = 166). The results indicated that high levels of cognitive complexity, based on Perry’s schemes, were associated w ith higher levels of empathy: dualism ( M = 21.7, SD = 3.90); early multiplicity ( M = 23.46; SD = 3.82); late multiplicity ( M = 24.75; SD = 4.03); relativism ( M = 25.23, SD = 3.72). relativists scored higher on the EM (( M = 25.23, SD = 3.72, p < .001) than those at lower positions, confirming Benack’s (1988) earlier findings of significant differences between cognitive levels on measures of empathy.

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20 The results indicated that high levels of cognitive complexity were associated with higher levels of empathy (Lovell, 1999a). Relativists possessed greater empathy than dualists and multiplists, confirming Ben ack’s (1988) earlier findings. The study used a random sample selected from the American Counseling Association student member list. However, there were several limitations worth noting. Only student members were able to participate in the study; thus, it is unknown whether nonmembers were different from members. In addition, the research er did not report cognitive complexity for variables, such as age, gender, ethnicity, education and experience, which could have a significant effect on the results of this study. Lyons and Hazler, 2002. In a related study, Lyons a nd Hazler (2002) conducted a cross-sectional study examining cognitive development and empathy among 162 1stand 2nd-year master’s-level counseling students with ages ranging from 21 to 55 years (M = 31 years). Eighty-one percent of the particip ants were women and 19% were men: eightyfour percent were European American, 9% were African American 1% was Latino and 6% were either other or not American citizen s. The majority of the participants (76%) were community counseling students. Nine pe rcent were school counseling students, 6% were rehabilitation students and 3% were career counseli ng students. Students were administered two measures of empathy and the LEP. To measure affective empathy, they used the Questionnaire Measure of Emotional Empathy (QMEE; Mehrabian & Epstein, 1971 as cited in Lyons & Hazler, 2002). To measure cognitive empathy, they used the Empathic Understanding Scale (EUS; Carkhuff, 1969 as cited in Lyons & Hazl er, 2002). The LEP was used to measure cognitive complexity (Moore, 1987).

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21 Participants were categorized as low or high cognitive complexity based on their LEP scores. Students who responded to most items representing POS/2 and POS/3 were categorized as having low cognitive comple xity and students who responded to most items representing POS/4 and POS/5 were categorized as having high cognitive complexity. They conducted a series of 2 x 2 Analyses of Variance (ANOVAs). The first 2 x 2 ANOVA found a significant different between 1st – ( M = 54) and 2nd – ( M = 46) year students on the QMEE instrument F (1, 160) = 5.953, p < .05, suggesting that 2nd-year students had higher affective empathy than 1st-year students. However, no significant difference was found on the QMEE when comparing students with low cognitive complexity ( M = 47) and students with high cognitive complexity ( M = 52) F (1,158) = .177, p = .68. The second 2 x 2 ANOVA found a si gnificant difference between 1st( M = 50) and 2nd – ( M = 42) year students on the EUS (lower scores represent higher cognitive empathy) instrument F (1, 160) = 14.564, p < .05, suggesting that 2nd-year students had higher cognitive/skill-based empathy than 1st-year students. However, no significant difference was found on the EUS when comparing students with low cognitive complexity and students with high cognitive complexity F (1,158) = 2.238, p = .14. The third and fourth 2 x 2 ANOVAs was run after re-categorizing the cognitive complexity groups by removing cognitive complexity scores that fell within the middle range. Since most of the students fell within th is range, only 53 students were used for the third and fourth procedures. The third 2 x 2 ANOVA found a signifi cant difference in QMEE scores for low ( M = 45) and high ( M = 57) cognitive complexity, suggesting that

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22 students with high cognitive complexity had higher affect trait-base empathy than students with low cognitive complexity F (1, 51) = 6.04, p < .05. The fourth 2 x 2 ANOVA found no significant differences between low ( M = 47) and high ( M = 41) cognitive complexity and cognitive/skill-based empathy F (1, 51), p = .11. As noted, the sample size for the last two procedures was sma ll. Therefore, the results of this study did not confirm to or refute earlier studies; howev er, the results demonstrated that, even with a small sample, a relationship between cognitive complexity and empathy could be found (Lyons & Hazler, 2002). On the other hand, the study did not report experience of the students, which could have played a role in the amount of cognitive complexity and empathy displayed. Granello, 2002. Granello (2002) conducted a cross-sectional analysis of counseling students from 13 colleges and univers ities in nine states who were at the beginning ( N = 66), middle ( N = 74), and end ( N = 65) of their training ( N = 205). Participants were mostly women ( N = 167) and European American ( N = 185). Other participants included ten African Americans, two Hispanics, two Asian Americans and six other. Students were enrolled in community mental health ( N = 83), clinical mental health ( N = 27), school ( N = 68), rehabilitation ( N = 9) and marriage and family ( N = 14) counseling programs. The mean age of participants was 32.74 year ( SD = 9.23) with a range from 21-57. The results of the study indicated th at students made more gains in cognitive complexity, per CCI scores, from the middle ( M = 361.39) to the end ( M = 377.06) of their training than they made from the beginning ( M = 359.39) to the middle of their training ( p < .05). Granello (2002) addressed the confounding nature of education on

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23 experience by examining prior HSE, that is, experience gained before entering graduate school. She found no relationship between pr ior HSE and cognitive complexity. This finding might provide some support for the argu ment that experience alone might not be sufficient to bring about changes in cogn itive complexity; however, prior HSE was a broad concept that might or might not include direct practice with i ndividuals or groups or a chance for guided reflection. Granello reported that students made more gains in cognitive complexity while in their internships. Fong et al. (1997) reported similar results in an earlier study. It was argued that increases in cognitive complexity during internships might be due to students working with actual clients (Fong et al., 1997). Th is was consistent with Blocker’s (1983) assumptions. However, Lovell (1999b) conducte d a study with master’s level counseling students ( N = 83) found that supervised clinical experience was related to counselor cognitive development. According to the la tter view, counseling students with more SCE should have higher levels of cognitive complexity. Studies found that experience, however, might not be the most critical factor related to increases in cognitive complexi ty. These studies are presented below. Holloway and Wolleat, 1980. Holloway and Wolleat (1980) investigated complexity level in counseling students us ing a semi-projective instrument, which measured conceptual level ( N = 37). They showed that cognitive complexity was related to more effective clinical hypot heses describing their client’s problem regardless of their experience level. Borders, Fond, and Neimeyer, 1986. Borders, Fong, and Neimeyer (1986) found that experienced counselors might be simplistic in their conceptualiz ations of clients,

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24 while inexperienced counselors could be comple x in their conceptualizations of clients. However, student experience did not play a role in students’ perceptions of their clients. That is, students with lower ego levels were more simplistic and concrete in their descriptions of their clients than stude nts at higher ego levels, who used more sophisticated and interactive descriptions of their clients (Borders et al., 1986). Borders, 1989. However, in a later study, Border s (1989) investigated in-session cognitions among first-year practicum students ( N = 27). She found that experience was related to cognitive complexity, which was in consistent with Borders et al.’s (1986) findings. She reported that, desp ite their ego developmental levels, students at the same experience levels (first-practicum) exhibi ted black and white thinking. This was consistent with the developmental models of supervision that posite d that students at a low experience level thought and behaved diffe rently from students at high experience levels (Loganbill, Hardy, & Delworth, 1982; McNeill, Stoltenberg, & Romans, 1992; Ronnestad & Skovholt, 1997; Stoltenberg & Delworth, 1988). Summary. Cognitive Complexity might play a sign ificant role in student empathya necessary counseling variable. Experience ma y be factor in cognitive development. However, there is no agreed upon definition of experience, and the ways in which experience have been operationalized in the counseling literature have not addressed the confounding nature of education on the experien ce variable being tested. In the studies presented, it was difficult to separate trai ning, education and e xperience into three different variables (Bernard & Goodyear, 2004). In fact, training, education, and experience were, at times, used interchangeab ly in the literature. Both training and supervision might be needed to bring about an increase in cognitive complexity of

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25 students in counseling programs (Bernard & Goodyear, 2004); however there could be other variables that account for the changes in student cognitive complexity. The Perry Scheme: Cognitive Development In the previous section, relevant coun selor cognitive complexity studies were reviewed. This section discusses the Perry scheme—the theoretical framework used in this study. William Perry (1970) conducted a lo ngitudinal study of liberal arts students from Harvard and Radcliffe. He examined how students viewed knowledge and learning by devising an instrument called the Check list of Educational Values (CLEV). An example of one question from the CLEV was “The best thing about science courses is that most problems have only one right answ er.” Perry administered the CLEV to a random sample of 313 first-year students in 1954-1955. He reinte rviewed 31 of these students (27 men and 4 women), annually. One of the questions he asked was, “Would you like to say what has stood out for you during the year?” Initially, Perry sought personality variables that would emerge fr om the interviews but what he found were schemes of cognitive developmental processe s. Perry conducted a similar study with a random sample of 109 first-year student (85 men and 24 women) that began in 19581959. From this research, Perry developed a stage model with nine positions. The first five positions (basic dualism, full dualism, early multiplism, late multiplism and relativism) described epistemology and inte llectual development and the last four positions (pre-commitment, commitment, challenges to commitment and post commitment) described ethical and identity development (Finster, 1989). The last four positions were important in cognitive development; however, they will not be addressed in this study, which sought an understanding of cognitive complexity according to the

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26 first five positions of Perry scheme. Descriptions of the five positions that make up epistemological development are as follows. Students in basic dualism (POS/1) were dependent on authority to make decisions for them. Students with dualistic thinking beli eved there were righ t and wrong answers to all questions and authorities (e.g., instructor s, professors, supervisors) had the right answers. The tasks for students in this stag e were to learn the right answers and ignore all others. In full dualism (POS/2), students believed that some authorities disagreed on subjects like psychology and philosophy but othe rs agreed on subjects like math and science. The task for the student was to lear n to find the right answers (Rapaport, 1984). Students who adopted a dualistic epistemo logy preferred structure, which they saw as giving them the right answers. For example, if an instructor had different views than other instructors or view s expressed in the text, the st udent noted this as conflict among authorities. The student might also feel hostility towards the inst ructor if he or she did not give the right answers or appeared vague (Rapaport, 1984). In early multiplism (POS/3), students might believe there are conflicting answers; therefore, they might trust their own intuiti on and not external authority. Students in this position believed there were two kinds of que stions: those with answers that we know now and those with answers that we do not know yet. Therefore, they believed that some authorities had the right answers and others did not yet know the right answers. Another view of early multiplism was that there we re right and wrong ways to find answers to questions. Here, the students might believe th e authority’s role was to teach them proper methods to find the right answers instead of gi ving them the right answers. Thus, students might feel their task was to learn the right way to find the correct answers (Rapaport,

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27 1984). In late multiplism (POS/4), students hold the belief that most problems have no known answers and everyone has a right to his or her own opinion, known as the less cynical form of late multiplism. They might believe that some problems are unsolvable; therefore, it did not matter which solu tion was chosen. This was known as a more cynical form of late multiplism. In contextual relativism (POS/5), students believed that all proposed solutions must be supported by reasoning. They understood that instructors were not asking for the right answers but only for those answers that could be supported (Rapaport, 1984). Within a certain context, there could be righ t and wrong answers. Hence, there were rules for good thinking. Moreover, there were righ t and wrong answers; some answers were better than others but depended on context. Students that adopted a relativistic epistemology believed that their task was to learn to evaluate answers. Much attention was given to transitio ns from one stage to another (e.g., Commons, 2002; Commons & Richards, 2002; F ong et al., 1997; Hess, 1987; Holloway, 1987). Perry made note of horizontal decala ge, a Piagetian term meaning horizontal movement within a stage. Perry did not believ e that individuals regressed to earlier stages when they were learning something new. He believed that individuals operated from more than one stage at a given time but ha d a dominant stage. Movement within or between stages was accomplished by an i nnate inclination toward autonomy and a supportive but challenging envir onment (Perry, 1970, 1999). Perry’s model laid the groundwork fo r other adult intellectual development theories (Baxter Magolda, 1992; Belenky et al., 1986, 1997; King & Kitchener, 1994). Perry’s scheme was important to this study because it provided a framework for

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28 describing adult cognitive development levels. Perry’s (1970; 1999) stages were flexible and adequately described indi viduals who might be proficie nt in one area but were learning something new. The novice might be at a low position until he or she was able to assimilate and accommodate the new experiences of a higher position. Measuring Perry’s Scheme. Perry’s research methods were time-consuming. He conducted only two studies over a 10-year pe riod; however, other researchers (Baxter Magolda & Porterfield, 1985; Erwin, 1983; Knefelkamp, 1974; Moore, 1987) discovered less time-consuming ways for measur ing intellectual development. Several instruments were designed as alternatives to Perry’s original interview format. The first instrument created was a written protocol deve loped by Knefelkamp (1974) and Widick (1975) that eventua lly became the Measure of Intellectual Development (MID, Moore, 1990). The MID was a production-task measure consisting of sentence stems and semi-structured essay questions (Moore, 1990). Another production-task measure, based on the Perry Scheme, was the Measure of Epistemological Reflection (MER) (Baxter Ma golda & Porterfield, 1985). Both the MID and the MER measured positions 1-5 of Perry’s scheme. Production-task measures were more cost-effective than interviews; however it was difficult to achieve inter-rater reliability unless the raters were well trained (Moore, 1990). A more cost-effective alternative to production-task m easures are questionnaires. Two scales used to assess the Perry scheme were also developed. The Scale of Intellectual Development (SID), developed by Erwin (1983), was based on Perry’s cognitive developmental model. The instrume nt consisted of 119 items rated on a fourpoint Likert scale and measured duality, re lativism, commitment and empathy. Erwin

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29 argued that cognitive development con tinued beyond young adulthood; therefore, an empathy subscale was added to reflect this continued adult deve lopment beyond Perry’s stages. Another measure of Perry’s scheme wa s the Learning Environment Preferences (LEP) instrument (Moore, 1987, 1989). The LEP was an objective measure of cognitive development, according to the Perry scheme that included 65 items containing five sentence stems. The five sentence stems corre sponded to content in five domains: view of knowledge and learning; role of instructor; role of student and peers in the classroom; classroom atmosphere and activities; and ro le of evaluation and grading (Moore, 1987). The instrument yielded a general score of overall cognitive development, the Cognitive Complexity Index (CCI). The LEP also offe red four percentage scores showing the degree of preference for each of four Perry positions: full dualism (POS/2), multiplicity: early (POS/3) and late (POS/4) and contextual relativism (POS/5). The alpha coefficients for the LEP were .72 to .84 and the test -retest reliability was .89 (Moore, 1989). The LEP was an acceptable measure of in tellectual development according to the Perry scheme. The LEP measured the four of Perry’s positions. The SID did not have a multiplism measure, which made up two of Pe rry’s positions. The SID was criticized for not being theoretically grounded in Perry’s model (Moore, 1989). However, the LEP was a good measure for this study. The inventory ha d a CCI subscale, which was essential for the proposed cognitive complexity model. The Perry Scheme: Review and Evaluation of the Literature In the previous section, a review of Pe rry’s scheme and the measurements used to operationalize the schemes were presented. Th is section reviews the literature related to

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30 intellectual development, according to Perry scheme. The review consists of two broad sections: (a) the effects of ethnicity and gender on cognitive complexity and (b) the effects of age, experience and educati onal level on cognitive complexity. Effects of ethnicity and ge nder on cognitive complexity. Perry (1970) studied mostly Caucasian men from an elite university in his original research; however, gender and ethnicity received some attention in the lit erature. As a critique of Perry’s original study, Belenky et al. (1986) studied cognitive development in women. They found that the ways in which women made meaning of th eir experiences were different from men’s, suggesting that Perry’s model might not acc ount for women’s epistemology. In later research, gender-related patterns were repor ted in one study (Baxter Magolda, 1992) but no gender differences were found in a study conducted two ye ars later (King & Kitchener, 1994). However, gender issues in cognitive developmental research remained an unresolved issue (Hofer & Pintrich, 1997). Ethnicity was considered in a diss ertation study (Johnson, 1999). Significant differences in cognitive complexity we re found between African American and Caucasian undergraduate students (Johnson, 1999). In this study, cognitive complexity was greater for Caucasians than for Afri can Americans except when socio-economic status (SES) was controlled. When SES wa s controlled, there were no significant differences. On the other hand, when gende r was controlled, there were significant differences in cognitive complexity between African American and Caucasian freshmen students. Caucasians were higher in cogniti ve complexity than African Americans. No differences in cognitive complexity were f ound for seniors when gender was controlled. The author inferred that both African Ameri can and Caucasian students progressed from

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31 simplistic to complex ways of knowing; how ever, the Perry scheme may not explain African American epistemology (Johnson, 1999). Cross-cultural studies were almost nonexi stent in the literature with regard to Perry’s theory. However, Zhang (1999; 2004; Zhang & Hood, 1998; Zhang & Watkins, 2001) conducted cross-cultural studies with As ian students. Repeated studies showed that the (average sample size of 426) cognitive development of Chinese students progressed in an opposite direction, calling into questi on the universality of Perry’s model (Zhang, 1999; Zhang & Watkins, 2001). In these stud ies, Chinese students progressed from relativistic thought to du alistic thought, showing that diffe rent cultures might progress in different patterns than the Perry scheme and that this progression could be the result of social-political factors. Effects of age, experience, and educa tional level on cognitive complexity. Other variables, such as age, experience, and edu cational level, received some attention in the adult cognitive development literature. However, as noted in much of the research on adult cognitive development according to the Perry scheme, most studies were conducted with undergraduate college students. As a result, little was known about development beyond undergraduate education. More recent studies extende d Perry’s theory to graduate students (Benack, 1988; Granello, 2002; Hood & Deopere, 2002; Lovell, 1999a, 1999b, 2002; McAuliffe & Lovell, 2006), to nursing professionals in the field (Rapps, Riegel, & Glaser, 2001) and to community members with varying education levels (Hood & Deopere, 2002). In two of these studies, age was not related to cognitive complexity(Granello, 2002; Wilson, 1995b). However, Hood and Deopere (2002) did find age-related differences among

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32 levels of cognitive complexity. Experience, when it was defined as work experience beyond college, was related to cognitive comple xity (Rapps et al., 2001); however, when it was defined broadly, it was not related to cognitive complexity (Granello, 2002; Hood & Deopere, 2002). Therefore, the problem with much of the literature examining the relationship between cognitive complexity and experience might be the result of the way in which experience was defined (Granello, 2002). Education level, on the other hand, was found to have a positive relationship on intellectual cognitive complexity across several studies for both males and females and different ethnicities in the United States (e.g., Granello, 2002; Hood & Deopere, 2002; Perry, 1970, 1999). Hood and Deopere (2002) examined th e role of age in adult cognitive development among 165 adults from a sample of community members and college students (88 were from the community samp le), while statistically controlling for educational level and intelligence. The commu nity sample included 5% adults who had not graduated from high school; 18% who ha d graduated from high school but did not attend college; 10% with some college; a nd 19% who were college graduates. As occupations, 15% were laborers; 25% were cl erical workers; 40% were professionals; and 20% were retired. The other 77 participan ts were selected from a state university. The university participants were either freshmen or sophomores enrolled in an introductory psychology course. Fifty-six percent of the pa rticipants were women and 44% were men. Their ages ranged from 18 to 87 years with a mean age of 36 years. The researchers did not report the et hnicities of participants. Hood and Deopere argued that much of the research on adult cognitive development focused on the role of education in increasing cognitive complexity and

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33 little research examined the role of age. Hood and Deope re hypothesized whether the independent variables of age, education level, intelligence or life experience played a role in the dependent variable, adult cognitive development. They used the Scale of Intellectual Development (SID) (Erwin, 1983) to measure Perry’s scheme of intellectual and ethical development. The Quick Test (Q T) (Ammons, & Ammons, 1962, as cited in Hood & Deopere, 2002) was used to control for varying intelligence levels. The researchers also gathered demographic data on age, occupation, marital status, religious preferences and information about life experien ces, such as educational level, church and community activity and travel experiences to develop the Life Experience Survey. Results from the hierarchical regression analysis revealed that age was predictive of dualistic thinking (Hood & Deopere, 2002). Th at is, dualistic thinking increased with age; however, when education was controlled age accounted for only 1.2% of the scores associated with dualism. When IQ was cont rolled, age continued to make a significant contribution, accounting for 14.3% of the vari ance associated with dualism. When both IQ and education were held constant, age accounted for 4.8% of the variance. Age was also negatively related to relativism ( r = -.39, p < .01). Age made a significant contribution to the variance with relativism when IQ was controlled (12.2%). When education was controlled, age accounted for 12.5% of the variance with relativism. Education was negatively related to dualism ( r = -.48, p < .01). When age was controlled, education accounted for 17.4% of the variance. When IQ was controlled, education accounted for 18.6% of the varian ce. Education accounted for 9.1% of the variance when both age and IQ were held c onstant. Education was positively related to relativism ( r = .17, p < .05) but did not make any significant contributions to the

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34 regression coefficients with either age or IQ alone or when they were both held constant. However, education was significan tly related to commitment. IQ was negatively related to dualism ( r = -.30, p < .01). IQ accounted for 16.8% of the variance with dualism when age was controlled and 4.9% of the variance when educational level was controlled. When bot h age and education we re controlled, IQ accounted for 8.5% of the variance. IQ was negatively related to relativism ( r = -.18, p < .05). IQ contributed 4.6% to the variance wh en educational level was controlled but did not contribute to variance when age was he ld constant. Community activity and church activity were not significantly related to dualism and commitment but were negatively related to relativism. Travel was not signifi cantly related to relativism and commitment but was negatively related to dualism. In sum, age was negatively related to e ducation; education was positively related to IQ; and IQ was positively related to ag e. Education level s howed a strong negative correlation with dualism and a weak positive correlation with relativism. IQ scores were negatively related to dualism and negativel y related to relati vism. Education and commitment showed a small association; how ever, neither IQ nor age was related to commitment. The results suggested that age and participation in community or church activities was negatively related to cogn itive development; however, intelligence and education were positively rela ted to cognitive development. Less dualistic thinking and more relativistic thinking were positively related to education attainment. They also found that dualistic thinking incr eased with age even after cont rolling for intelligence and education.

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35 The findings suggested that formal edu cation is an important variable in developing cognitive complexity. However, th e Perry position of commitment remained unclear from the results of this study. Perh aps, objective measures, such as the SID, do not adequately measure commitment. One important issue worth noting was the authors’ definition of experience. They operationalized experience as life experience in general rather than experience in a specific area. Life experience alone may not be enough to bring about cognitive development. However, experience gained after formal education may provide a better understanding of th e role of cognitive development postbaccalaureate. Rapps et al. (2001) tested whether knowledge base, critical thi nking skills, critical thinking dispositions and experience were pr edictive of adult cogni tive development in nurses using Perry’s scheme of intellectual and ethical development, specifically dualism, relativism, and commitment. They defined experience as experience gained after nursing school. They argued that critical thinking skills and critical thinking dispositions might not be sufficient to understand the critical thinker. They posited that adding a third variable, cognitive development, might present a more complete picture of the critical thinker. The purpose of their study was to test the proposition that cri tical thinking skills, dispositions and cognitive development occu r during the educationa l process; however, experience is more salient in producing critic al thinkers. Perry’s ( 1970; 1999) theory of intellectual development provided a th eoretical framework for the study. Rapps et al. (2001) tested two different hypotheses in a sample of nurses in the field and nursing students ( N = 290). Hypothesis 1 was that experience, critical thinking

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36 skills and critical thinking dispositio ns contributed significantly to cognitive development. Hypothesis 2 was that the comb ination of experience, critical thinking skills and critical thinking dispositions e xplained more of the variance in commitment than at either the dualistic or the relati vistic levels of cognitive development. Cognitive development was measured using the Scale of Intellectual Development IV (SID-IV), an objective m easure of Perry’s scheme of intellectual development (Erwin, 1981). The alpha coeffici ent reliability was .84 for dualism, .70 for relativism and .72 for commitment. Empa thy was not considered in the study. The population was graduates from a Sout hern California baccalaureate-nursing program who completed all of their basic nursing education at the institution. They used a non-probability sample of 290 registered nurse s who were either working in nursing or pursuing a postgraduate degree. For sample cons ideration, the nurses had to work at least 20 hours per week for a minimum of two years in the same general practice setting. The sample included 91.8% were female, 66.4% were married 85% were baccalaureate-prepared, 53.4% were certified and 62.1% were working in an acute care setting. The mean age of participants was 34.8 ( SD = 6.5). The mean number of hours worked was 36.5 ( SD = 10.3). However, the authors fa iled to include information on ethnicity and socioeconomic class. A hierarchical regression model was used to test the predictiveness of the variables – knowledge base, cr itical thinking skills, cri tical thinking disposition and experience – on the criterion variable, cognitive development. Three separate hierarchical regression analyses were run on SID-IV c ognitive developmental factors, dualism, relativism and commitment. A set of four vari ables (knowledge base, skills, dispositions

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37 and experience) were entered into the model. Based on previous studies that showed a positive relationship between knowledge and critical thinking, knowledge base was entered first. Skills and dispositions were entered simultaneously due to a lack of evidence that supported one developing before the other. Experience was entered last. The researchers’ justificati on was that the other variable s were the foundations of experience. Knowledge base was not a significant predictor of either cognitive developmental level. Knowledge base accounted for 1.2% of the variance in dualism; 0.6% of the variance in relativism; and .08% of the variance in commitment. Critical thinking disposition was a significant predictor of all three levels of cognitive development, dualism, relativism and commitment. Critical thinking skill was a significant predictor in dualism. For cognitive development, nurses in this study reached the commitment level and achieved higher scores on commitment than found in a previous study (Erwin, 1981) conducted with college students. The results suggested that cr itical thinking skills were re lated to dualism; critical thinking dispositions were related to dualism, relativism, and commitment; and experience was related to commitment. Accordin g to the authors, critical thinking was a function of both time and experience, calling into question the current measure of critical thinking as an outcome of formal education. Critical thinking dispos itions might be an essential ingredient of cognitive development, regardless of the development level achieved. The study provided some support for cognitive development beyond an undergraduate education (Erwin 1983); however, there were pot ential threats to external

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38 validity because the sample was homogeneous consisting of nurses only. It is unknown whether these findings would generalize to other helping fields like counseling and social work. Taken together, the studies by Hood and Deopere (2002) and Rapps et al. (2001) provided important information on the role of experience in intellectual development. Age and life experience alone might be inad equate to bring ab out cognitive growth according to Perry’s scheme. Nursing profe ssionals (Rapps et al., 2001) continued to develop cognitively, reaching relativistic levels according to Perry’s scheme; however, the community sample (Hood & Deopere, 2002) declined in c ognitive abilities. Intellectual development in adults occurred with formal education, perhaps because the college environment was set up to enhance th is type of growth. The support from and challenges of the college environment stim ulated cognitive growth (Hood & Deopere, 2002). As students were met with diverse situ ations to master con ceptually, they were compelled to become more flexible in their thinking. To be sure, not all students readily accepted the challenge provided by the college environment and challenges may begin what Perry called alienation According to Perry (1999), th ree forms of alienation were retreating, escaping, and temporizing. That is, students might opt to retreat to an earlier position, escape by dropping out or te mporize to avoid being challenged. Critical Analysis The research supported the argument that the cognitive complexity of students was related to favorable counseling variable s like psychological functioning (Brendel et al., 2002), confidence and focus on counseli ng effectiveness (Bir k & Mahalik, 1996), empathic understanding (Alcorn & Torney 1982; Benack, 1988; Lovell, 1999a) and

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39 more sophisticated descriptions of client characteristics (Borders, 1989); therefore, cognitive complexity might be essential in counseling and related fi elds (Brendel et al., 2002; Fong et al., 1997; Granello, 2002; Knefelkamp, 1974; Lovell, 1999a, 2002; Widick, 1977). The research also noted that cognitive complexity in students increased significantly at the end of their fieldwor k (Brendel et al., 2002; Fong et al., 1997; Granello, 2002; Kohlberg, 1976). However, mi xed information existed concerning what actually caused this increase. If students made greater gains in cognitive complexity at the end of their programs, was it due to th e education they rece ived (Perry, 1999), the work with actual clients (F ong et al., 1997), the clinical s upervision (Lovell, 1999b), their chronological age (Granello, 2002; Hood & Deopere, 2002; Wilson, 1995b) or various combinations of these variables (Bernard & Goodyear, 2004)? What are the critical factors that contribute to cognitive comple xity among graduate students in helping professions? The role of demographic variables on cognitive complexity among graduate students provided mixed results. Age remained important in the literature but ethnicity was not factored-out as an important variab le. Experience also pr ovided mixed results, especially when confounded with education. This study tested a predictive model of age, education, SCE, HSE and cognitive complexity among graduate students in help ing professions. This study answered the following question: to what extent do age, education, HSE and SCE predict cognitive complexity? This research has implicati ons for graduate training programs in the different helping professions and supervis ion of students in helping professions.

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40 Chapter 2 presented research on coun selor cognitive complexity, the Perry Scheme (1970; 1999), related research on the th eoretical framework used in the study and more current research on Perry’s scheme in counseling and related fields. Chapter 3 describes the design and me thodology of the study.

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41 Chapter Three Design and Methodology Organization This chapter describes the design and me thodology of the study. The chapter is divided into the following sections: 1. Logic, structure and design of study 2. Limitations 3. Research question 4. Hypotheses 5. Description of sample 6. Data collection procedures 7. Instrumentation 8. Ethical Considerations 9. Methodological assumptions 10. Analyses Logic, Structure and Design of Study The purpose of this study was to test a predictive model of demographic variables (education, supervised clinic al experience, age and human services experience) and cognitive complexity. To test the predicti ve model among variable s, a descriptive and web-based survey research design was used. The study employed the LEP’s (Moore,

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42 1988) Cognitive Complexity Index (CCI) scores to determine the cognitive complexity of graduate students in the helping professions. Limitations As stated in Chapter 1, this study might have been limited by the web-based survey method. While web-based survey met hods provided some advantages in terms of cost savings, short time frame for the collec tion of responses, ease of transferring data into a database for analysis and the possibility of a wider geographic coverage area, they posed possible limitations. These included a la ck of a population list, nonrandom sample, inability to calculate response rate and co mputer access to the survey (Mertler, 2001). There might be a potential limitation of lo wered response rates for web-based survey relative to mail survey s (Converse et al., 2008). Because data were gathered using only self-reports, response bias posed a potential limitation (Ell is et al., 1996). The study was corre lational and only meant to find relationships between variables; therefore causa l inferences could not be made from this study. In addition, it was unknown whether res pondents to the survey were different from non-respondents (i.e., non-response error) because a convenience sample was employed. Another potential limitation was the lack of demographic vari ability in the sample in terms of gender and ethnicity. Research Question The research question was posed to exam ine whether the demographic attributes of participants had an effect on the criteri on variable. The study answered the following research question: to what exte nt do education, supervised cl inical experience (SCE), age, and human services experience (HSE) predict cognitive complexity? This question was

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43 analyzed using a hierarchical multiple regression model. Based on a power analysis (Algina & Olejnik, 2003), a sample size of 77 wa s required in order to exceed a statistical power of .80 using alpha = .05 and an effect size of f2 = .15. Hypotheses H0: No demographic variables (education, SCE, age and HSE) will contribute to cognitive complexity. H1: Education, SCE, age and HSE will predict cognitive complexity. H2: The combination of education and SCE will predict more of the variance in cognitive complexity than educati on and age or education and HSE. The hypothesis was based on the assumption that education and supervision were needed to increase cognitive complexity among c ounseling students (Bernard & Goodyear, 2004). The hypothesis was analyzed using a hierar chical multiple regression to test the predictive model of education, HSE, age, and SCE, and cognitive complexity. Education was entered into the model first because prior studies evidenced a positive relationship between education and cognitive complexity (Belenky et al., 1986, 1997; Perry, 1970, 1999). SCE was entered next because prior st udies also showed a positive relationship between SCE and cognitive complexity (Granello, 2002; Lovell, 1999b). Age was entered as a third variable since there was inconsistent evidence that age related to cognitive complexity (Granello, 2002; Hood & Deopere, 2002; Wilson, 1995b). HSE was entered last because no studies found a rela tionship between general experience in human services and cognitive complexity.

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44 Description of Sample The participants consisted of 366 graduate students in helping professions, such as social work and various forms of counseling (S ee Table 1). Participants were a least 22 years old ( M = 35.02, SD = 10.96; N =362). The mean number of hours of supervision Table 1 Frequencies and Percentages by Program for All Participants FrequencyPercent Valid Percent Cumulative Percent Community Counseling 17 4.6 4.7 4.6 Counseling Psychology 15 4.1 4.1 8.7 Counselor Education 21 5.7 5.8 14.4 Marriage and Family Counseling 7 2.1 2.1 16.5 Mental Health Counseling 65 17.8 17.8 34.3 School Counseling 17 4.7 4.7 39.0 Social Work 214 58.5 58.6 97.6 Other 10.0 2.7 2.7 100.0 Total 366 100.0 100.0 received was 36.14 hours ( SD = 52.79) and the mean number of months of human services experience was 45.7141 months ( SD = 59.01). The majority of the participants were Caucasian and female and a small percentage was African American/Black, Hispanics, other ethnicities and male. Table 2 gives the frequencies and percentages by ethnicity and gender. Because a convenie nce sample was used in this study, generalizations to the larger population of students in helpin g professions should be made with extreme caution.

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45 Table 2 Frequencies and Percentages by Ethnicity and Gender for All Participants Frequency Percent Valid Percent Cumulative Percent Ethnicity African American 24 6.6 6.6 6.6 Asian American/ Pacific Islander 13 3.6 3.6 10.2 Caucasian/White 290 79.2 79.7 89.8 Latino/Hispanic 20 5.5 5.5 95.3 Other 17 4.6 4.6 100.0 Missing 2 .6 Total 366 100.0 Gender Female 315 86.1 86.8 86.8 Male 48 13.1 13.2 100.0 Missing 3 .8 Total 366 100.0 Data Collection Procedures Participants were recruited in one of th ree ways, in person, by email or through listservs. Presentations were made to six of 12 cl asses at the Univers ity of South Florida (USF), whose instructors responded to an ema il request to use a few minutes of their class time to explain the purpose of the study and to solicit volunteers, as noted in Table 3 below.

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46 Table 3 Course Matrix for Potential Study Participants Program Pre-Practicum Practicum Internship Social Work SOW 6305 Fundamentals of social work practice SOW 6534 Field instruction I SOW 6536 Field instruction III School Counseling MHS 6006 Trends and principles of the counseling profession MHS 6800 Practicum in counseling adolescents and adults SDS 6820 Internship in school guidance Counselor Education Mental Health Counseling MHS 6006 Trends and principles of the counseling profession MHS 6800 Practicum in counseling adolescents and adults MHS 6885 Internship in community agency counseling Rehabilitation and Mental Health Counseling MHS5020 Foundations of mental health counseling RCS 6803 Practicum in counseling RCS 6825 Internship Sixty-six invitations to participate in the study were given out to students at USF. Invitations to participate (inc luding the link to the survey) were also sent via email and posted on two counseling listservs and one soci al work listserv. Emails with link to the survey were sent to 250 counseling students and approximately 100 social work students. The listservs had a combined membership of 2,095 The Association of Baccalaureate Social Work Program Directors (BPD – 800), American Counseling Association (COUNSGRAD 1,000), and International A ssociation of Marriage and Family Counselors (IAMFC – 295 members). Approxi mately 2,500 potential participants

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47 received invitations to participate in the study and 366 students responded to the survey. The response rate was approximately 15%. D illman et al. (2001) reported a web-based survey response rate of 13%. It is important to note that not all of the listserv members were students that qualified for the study, part icipants enrolled in a counseling or social work graduate program; therefore, th e response rate was based on a lower bound estimate. Potential participants who received the invitation to participate in the study were instructed to follow a link to the web-based su rvey. The first page of the survey contained the an informed consent script approved by the university’s institutional research review board (Appendix D). The script outlined the stud y concerns, student factors that might be related to cognitive complexity and the pur pose of the study. All students electing to participate were instructed to “click ‘yes’ or cl ick ‘no’ ”if they did not want to participate. If participants clicked “no,” they were take n to the last page. Th e last page offered participants a chance to enter a drawing to win an Apple iPod Nano by sending an email to the principal investigator. All students ha d the chance to enter the drawing whether or not they participated in the survey. Email addr esses were not used to identify individual data. All data were reported as group data. Confidentiality was maintained to the degree permitted by the technology used. The web-base d survey tool used in the study allowed only one survey entry per Inte rnet protocol (IP) address. Instrumentation Two instruments were used to colle ct information for this study: the Demographic Questionnaire and the Learni ng Environment Preferences (LEP, Moore, 1987).

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48 Demographic Questionnaire. The Demographic Questionnaire was a one-page, researcher-developed demographic survey. Th e demographic questionnaire contained the following items: students’ age, gender, ethnic ity, education experience, human services experience (practicum, internship or practice se tting) and supervised clinical experience (See Appendix B). Education was assessed based on three questions: 1. Highest degree earned (bach elors or master’s degree) 2. Current degree program (i.e., M.A., M. Ed., M.S., M.S.W., Ph.D., Ed.D., other) 3. Year in current degree program. For example, a student with a B.A. and currently in his or her first year of a M.Ed. program would have a total of 16 years of educational experience. Likewise, a student with an M.S.W. and currently in his or her third year of a Ph.D. program would have 20 years of educational experience. Calculations were made by the researcher. HSE was operationally defined as the total number of human services experience in months. This was assessed by a single question: “What types of human services experience (i.e., employment, practicum, intern ship or volunteer experience) have you had in providing direct services to individuals, families or groups?” Respondents chose from a list of job titles (e.g., case manager, caseworker, counselor, psych tech, intern, social worker, volunteer, therapist) and settings (e.g., child welfare, career center, health care, mental health, school). Pa rticipants were inst ructed to choose the job title that most closely represented their HSE. They entered years and months of service, hours worked per week, hours of weekly individual supe rvision and hours of w eekly group supervision for each work experience. After the data we re collected, the following formula was used

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49 to calculate total work experiences in th e human services field. The formula for calculating work experience was based on a full-time work week or 40 hours. The formula was as follows: 5-10 hours/week = .25; 15-20 hours/week = .5; 25-30 hours/week = .75; and 35-40 hours/week = 1.00. The total number of months was multiplied by the hours per week percentage to obtain the numerical value for adjusted work experience. This numerical value was co mputed for each of the participant’s work experience. The total number of HSE was calculated as the total number of work experiences in the human serv ices field after adjusting fo r hours worked per week. For example, if a participant had a total of 2 years and 3 months HSE, the number entered into the spreadsheet under HSE was 27 months However, seven of those months were gained while in an internship when the student worked only 20 hours per week. As a result, the student received half credit for thos e hours, resulting in a total of 3.5 months. Consequently, the student would have a total of 23.5 months of HSE. SCE was operationalized as the amount of HSE when th e student received supervision. As stated earlier, supervision was assessed by two items on the demographic questionnaire (i.e., hours of weekly individual supervision and hours of weekly group supervision). The amount of HSE was converted into weeks a nd multiplied by the number of supervision hours reported. Learning Environment Preferences (LEP) The LEP was an objective measure of cognitive development according to the Perry scheme. The instrument consisted of 65 questions divided into five domains: (1) view of knowledge and course content, (2) role of the instructor, (3) role of the student and peers in the classroom, (4) the classroom atmosphere and (5) the role of evaluation. Each domain contained 13 statements, which

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50 participants rated as significant or important on a 4-point Likert-type scale. These ratings were used for item-response analysis and re quired to obtain a general score of overall cognitive development. At the end of each domain, participants ranked the three most important statements of that domain. These rankings yielded the Cognitive Complexity Index (CCI) and four percentage scores s howing degree of prefer ence for each of four Perry positions: Dualism (Position 2), Early Multiplicity (Position 3), Late Multiplism (Position 4), and Relativism (Position 5). Th e CCI index offered a single numerical score along a continuous scale of cognitive devel opment from 200 – 500 that corresponded to the Perry positions and transitioned betw een the positions (See Table 4). The alpha coefficients for the LEP were reported as .72 to .84 (Moore, 1 988). The test-retest reliability was .89 (Moore, 1988). Criterion, concurrent and construct validity were found to be acceptable (Moore, 1987). Significant CCI meant criterion group di fferences were shown across educational levels. In terms of concurrent validity, the LEP showed a moderate correlation with the Measure of Intellectual Development (M ID, Moore, 1990). Construct validity was determined by examining whether the LEP measured underlying factors that corresponded with positions 2-5 and whether the LEP measured cognitive development. Factor analysis yielded negative correlations between factor 2 and factors 1 and 3, supporting the reliability and validity of the LEP as a measure of Perry’s scheme. The LEP was modified to reflect the experiences of counseling and social work students. For example, “To learn counseling” was added to the original stem “My ideal learning environment would,” creating: “To learn c ounseling, my ideal learning environment would” (See Appendix C). For social work students, “social work” replaced “counseling”

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51 in the stem. The adapted version was sent to the scale’s author and approved in a previous study (Granello, 2002). The LEP was further modified by eliminating the itemby-item response ratings to reduce non-responsi ve biases. Participants were asked to rate their top three choices for each of the domains. As stated above, these were the ratings needed to obtain CCI sores and their related Perry positions. Table 4 CCI Score Ranges as Related to Perry Positions CCI Score Ranges Perry Positions 200-240 POS/2 – Full Dualism 241-284 Transition 2/3 285-328 POS/3 – Early Multiplism 329-372 Transition 3/4 373-416 POS/4 – Late Multiplism 417-460 Transition 4/5 461-500 POS/5 – Contextual Relativism Note CCI score ranges do not include Perry positions 1, 6, 7, 8 and 9. Ethical Considerations All participants were informed of the purpose of this research project before their participation. Names of students were not collected. Before data collection, the research study was submitted for approval to the Univers ity of South Florida Institutional Review Board (See Appendix E). The Institutional Review Board stipulated that specific procedure be followed to protect the rights of human subjects. It was the researcher’s

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52 responsibility to ensure that each particip ant understood the objective and scope of the research. Methodological assumptions The assumptions for the study reviewed in Chapter 1 were as follows: It is necessary to assume that participants gave honest and accurate responses on the LEP and demographic questionnaire. To encourage honest responses, the LEP and demographic questionnaire was administered anonymously online. It was assumed that the sample would be representative of graduate students in counseling and social work. Analyses After data were collected, each completed survey was entered into a statistical spreadsheet. Each completed survey was th en coded (e.g., 1001, 1002, etc.). The total LEP scores were computed using a scoring spreadsheet provided by the instrument’s author. Item numbers for the top three choi ces across all domains were converted to keyed Perry positions. Total points were calcu lated for each Perry position using a pre-set weighted scale and converted to proportions based on the total numbe r of possible points. The proportions were then converted to percen tages (and rounded to in tegers), reflecting position sub-scores. Finally, the individual su b-scores were entered into a formula and weighing factor based on position numbers. This final step calculated the overall Cognitive Complexity Index (CCI), which was a specific numerical score on a continuous scale of 200-500, comparable to Perry’s POS/2 through POS/5 (Regira, 2006).The CCI scores and corresponding Perry positions were entered into a statistical spreadsheet and matched to the demographi c portion of the survey for analyses.

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53 Analysis of Demographics. Percentages were computed and reported along with the number of cases in each category for dem ographic data measured at a nominal level (ethnicity, gender, graduate program) and score interval data. Means and standard deviations were computed for equal interv al data (age, education, HSE and SCE). Major analyses. To test the hypotheses, a hierarchi cal multiple regression was conducted with the predictor variables (age, education, HSE and SCE) and the criterion variable, cognitive complexity. Descrip tive statistics were calculated for predictor and criterion variables. Chapter 3 explained the methodology used in the study, including a description of the sample and the instrument psychometric pr operties. Chapter 4 outlines the results of the study.

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54 Chapter Four Results This chapter outlines the results of the study. The purpose of this study was to test a predictive model among demographic variab les and cognitive complexity of graduate students using William Perry’s (1970; 1999) th eory of intellectual development as the central framework. Cognitive theorists (e.g., Benack, 1988; Blocher, 1983; Brendel et al., 2002; Granello, 2002; Hood & Deopere, 2002; Lovell, 1999a) argued that cognitive complexity was essential for students in the helping professions to become effective practitioners; therefore the part icipants in this study consis ted of graduate students in helping professions. These students were in tr aining to become helping professionals in either counseling or social work. The me thodology for the present study involved a webbased survey research method. Description of the Sample Participants were comprised of 366 gradua te students from fo ur different regions in the United States who responded to th e face-to-face, email or online research invitation. Of the participants, 18.7% were from Midwestern states, 4.9% were from Northeastern states, 54.1% were from Sout hern states, and 22.3% were from Western states. Of the individuals responding to th e research invitation, 344 (94%) respondents completed the survey. Twelve surveys were discarded because their cognitive complexity index (CCI) scores were inva lid. The LEP contained five meaningless items (one per

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55 domain), which sound complex but were improbabl e in terms of learni ng. If three of the five items were endorsed, the test was fla gged as invalid on the scoring matrix (Moore, 1988). Participants in the final sample include d only graduate students in counseling and social work programs. The age of the part icipants ranged from 22 years to 66 years ( N = 332; M = 35; SD = 11.07). The education experience of the participants ranged from 16 years to 26 years ( N = 332; M = 17.61; SD = 1.86). The supervised clinical experience (SCE) for participants ranged from zero hours to 10,305.05 hours ( N = 332; M = 751.17; SD = 1,445.77). The human services experience (HSE) of participants ranged from zero months to 336 months ( N = 332; M = 44.49; SD = 53.56). As shown in Table 5, participants were predominantly Caucasian/White females. Table 5 Frequencies and Percentages by Ethnicity and Gender for Final Sample Frequency Percent Valid Percent Cumulative Percent Ethnicity African American 22 6.6 6.6 6.6 Asian American/ Pacific Islander 12 3.6 3.6 10.2 Caucasian/White 261 78.6 78.6 88.9 Latino/Hispanic 20 6.0 6.0 94.9 Other 17 5.1 5.1 100.0 Total 332 100.0 100.0 Gender Female 290 87.3 88.1 88.1 Male 39 11.7 11.9 100.0 Missing 3 100.0 100.0 Total 332 100.0

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56 The highest degrees held by participants were bachelor’s (73.8%) and master’s (26.2%). Participants were seeking ma ster’s degrees in social work (45.8%) and counseling programs (35.5%) or doctoral degrees (18.7% ) in either counseling or social work. Thirty-nine percent of students seeking a master’s degree in social work reported they had advanced standing in their social wo rk program. Social work students made up 58.4% of the total sample. The other 41.6% were counseling student s. The frequencies and percentages are shown in Table 6. Table 6 Frequencies and Percentages by Program for Final Sample Frequency Percent Valid Percent Cumulative Percent Community Counseling 14 4.2 4.2 4.2 Counseling Psychology 13 3.9 3.9 8.1 Counselor Education 19 5.7 5.7 13.9 Marriage and Family Counseling 7 2.1 2.1 16.0 Mental Health Counseling 63 19.0 19.0 34.9 School Counseling 17 5.1 5.1 40.1 Social Work 194 58.4 58.4 98.5 Other 5 1.5 1.5 100.0 Total 332 100.0 100.0 Perry Positions of Participants The framework used for this study was Pe rry’s (1999) intellectual developmental scheme. Perry’s scheme was previously used with college undergraduates and more recently with graduate students. According to Perry (1999), as students developed, they moved from an absolutist view (i.e., dualism) to a pluralistic view (i.e., relativism), to a constructivist view (i.e., commitment w ithin relativism) (Perry, 1999). The Perry

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57 positions of the participants are illustrated in Figure 3 below. The findings in this study are as follows: 1. Only one (.3%) participant in the sample was in POS/2 (full dualism), which was consistent with Gran ello’s (2002) sample. 2. Eight participants (2.4%) were in transition between POS/2 and POS/3. 3. Forty (12%) participants were in POS/3 (early multiplism). 4. The majority of the participants were in POS/4 (late multiplism; 38.9%) or transitioning between POS/3 (33.4%) a nd POS/4 (38.9%), according to the Perry (1970; 1999) scheme. This was c onsistent with previous research (Eriksen & McAuliffe, 2006; Gr anello, 2002; Moore, 1990). 5. Only three participants (.9%) were in POS/5 (contextual relativism); however, 40 participants (12%) were in transition between POS/4 and POS/5. Figure 2 Study Participants’ Perry Positions.

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58 Pearson Product-Moment Correlations Pearson Product-Moment correlations were calculated to determine the direction and strength of the relations hip among variables. Based on a priori power analysis (Algina & Olejnik, 2003), a sample size of 67 wa s required in order to exceed a statistical power of .80 using alpha = .05 and an effect size of r = .3. Correlations among variables are presented in Table 7. All variables, with the exception of supervised clinical experience (SCE), showed a low but signi ficant positive correlation with CCI scores. SCE showed a positive correlati on with CCI scores but the correlation was very low and insignificant (r = .040). Table 7 Correlations between Model Variables HSE (months) Age SCE (hours) Education (years) CCI CCI --Education (Years) --.221* SCE (hours) --.122* .040 Age --.135* .339* .122* HSE (months) --.322* .649* .335* .168* Note SCE = supervised clinical experience; H SE = human services experience; CCI = Cognitive Complexity Index, p < .05 (1-tailed), N = 332.

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59 There were low to moderate intercorrelations between predictor variables. That is, education, human services experience and ag e were related to cognitive complexity. The finding that education was relate d to cognitive complexity was consistent with the finding of previous research (Brende l et al., 2002; Granello, 2002; Perry, 1970; Wilson, 1995a). The finding that human services experience and age were significantly related to cognitive complexity was inconsistent with the findings of previous studies that found no significant relationship between the two va riables and cognitive complexity among counselors (Granello, 2002) a nd technical school instructor s (Wilson, 1995b). However, the finding regarding the relationship betw een age and cognitive complexity was consistent with a previous study that f ound a significant correlation between age and cognitive complexity (Hood & Deopere, 2002). Th e finding regarding clinical supervised experience was surprising and inconsistent w ith earlier assumptions that supervised experience is related to cognitive co mplexity (Bernard & Goodyear, 2004). To determine the magnitude of the effect s of the variables, squared correlations was computed (See Table 8). One squared correlation worth noting was between human services experience (HSE) and SCE ( r2 = .42). There was a 42% overlap between the two variables, which was the highest shared contribution in the model. Multicollinearity was investigated. As a rule of thumb, intercorrelations.80 and above might signify multicollinearity problem s (Mertler, Meriter, & Vannatta, 2001). Because all intercorrelations were well below .80, multicollinearity was not deemed to be a problem. In addition, variance inflation factors (VIF) and tolerance (1/VIF) were carefully analyzed. VIF’s of 5 or greater and tolerance of .10 or less would signify potential collinearity prob lems (Stevens, 1999). VIF’s were between 1.0 and 2.1.

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60 Tolerance was between .48 and 1.0. Give n these findings, it was concluded that multicollinearity was not a major problem. Table 8 Squared Correlations between Model Variables HSE (months) Age SCE (hours) Education (years) CCI CCI --Education (Years) --.048 SCE (hours) --.015 .002 Age --.018 .115 .015 HSE (months) --.104 .421 .112 .028 Note SCE = supervised clinical experience; H SE = human services experience; CCI = Cognitive Complexity Index, N = 332. Predictive Model Testing Several assumptions had to be met in orde r to test a linear regression model. It is important to note that the assumptions of linear regression models were based on the population and not the sample (Cohen, Cohe n, West, & Aiken, 2003). The distribution for the criterion variable, cognitive comple xity, was examined to assess for normality assumption. The Kolmogorov-Smirnov (K-S) test was used to test the assumption of normality ( p = .259). The results of the test showed that CCI scores did not significantly

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61 depart from normality. Visual inspection of the histogram distribution confirmed the results of the K-S test ( M =1.16; SD = 0.99), illustrated in Figure 2. Figure 3 Histogram Distributions of CCI Scores. However, the test for normality with the pred ictor variables revealed significant P values for all predictor variables, which signified that the sample might not have come from a Gaussian population. However, it is important to note that it is common to get significant findings in tests of normality in large samples (Pedhazur, 1997). Because of the robustness of the linear regression models, infe rences could still be made without error when there was moderate violations to these assumptions (Cohen et al., 2003). To test the hypotheses, a hierarchical regression analysis was conducted. The regression solution was assessed for outliers a nd influential points using standardized

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62 residuals, hat elements and Cook’s Distan ce. There were no outliers beyond the acceptable levels (Pedhazur, 1997). The research questions were posed to examine whether participant demographic attributes predicted the criterion variable This study answered the following research question: To what extent do age, ed ucation, HSE, and SCE predict cognitive complexity? Two hypotheses were formulated. Hypothesis 1 was that education, SCE, age, and HSE, would predict cognitive complexity. Hypothesis 2 was that the combination of education and SCE would pr edict more of the variance in cognitive complexity than education and age or edu cation and HSE. This hypothesis was based on the assumption that education and expe rience are needed to increase cognitive complexity among counseling students (B ernard & Goodyear, 2004). These hypotheses were analyzed using a hierarchical multiple regression to test the predictive model of age, education, experience and cogni tive complexity. The results of the regression model are presented in Table 9. Education was entered into the model first because prior studies revealed a positive relationship between education and cognitive complexity (Belenky et al., 1986, 1997; Perry, 1970, 1999). Education accounted for 4.9 % of the variance in cognitive complexity F (1, 330) = 16.968, p < .05. As expected, educat ion significantly predicted cognitive complexity in this study. This was c onsistent with prior re search that found that education experience might have a significant effect on cognitive complexity (Brendel et al., 2002; Granello, 2002; Lovell, 20 02; Perry, 1970; Wilson, 1995a).

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63 Table 9 Results for Cognitive Complexity Regression Model Change Statistics Model R R Square Adjusted R Square Std. Error of the Estimate R Square Change F Change df1 df2 Sig. F Change 1 .218a .048 .045 41.67 .048 16.54 1 330 .000 2 .219b .048 .042 41.73 .000 .07 2 329 .787 3 .224c .050 .042 41.74 0.00 .83 3 328 .363 4 .251d .063 .051 41.53 .013 4.40 4 327 .037 a. Predictors: (Constant ), Education experience b. Predictors: (Constant), Education e xperience, Supervised experience (hours) c. Predictors: (Constant), Education experience, Supervi sed experience (hours), Age d. Predictors: (Constant), Education experience, Supervised experience (hours ), Age Human services experience (months) SCE was the second step because prior studi es showed a positive relationship between SCE and cognitive complexity (Granello, 2002 ; Lovell, 1999b). In this study, however, SCE did not account for any of the variance cognitive complexity F (2, 329) = .063, p = .803, r2 change = .000. This finding was incons istent with the study hypothesis and also inconsistent with the assumptions that c ognitive complexity and SCE shared a close relationship (Bernard & Goodyear, 2004). Age was entered at the third step because there was inconsistent evidence that age was relate d to cognitive complexity (Granello, 2002; Hood & Deopere, 2002). Age accounted for only an additional .2% of the variance F (3, 328) = .820, p = .366, r2 change = .002. In terms of the predictive model, this finding was inconsistent with the hypot hesis and Hood and Deopere’s (2002) study which found that age was predictive of cognitive complexity. HSE was entered at the last step because no studies that found a relationship between ge neral experience in human services and

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64 cognitive complexity. HSE accounted for an a dditional 1.2% of the variance in cognitive complexity ( = .159, t = 2.078, p < .05). As hypothesized, HSE significantly predicted cognitive complexity in this study. This findi ng was inconsistent with Granello’s (2002) study that found no relationship between HSE and cognitive complexity. However, the findings were consistent with the assumption that work experience with actual clients might be a good predictor of cognitive complexity. The summary table of the regression model is presented in Table 10. For hypothesis 1, education, SCE, age a nd HSE will predict cognitive complexity, the combined model accounted for 6.4% of the variance in cognitive complexity. Education and human services significan tly predicted cognitive complexity among graduate students in the study. Contrary to expectations, age and SCE did not significantly predict cognitive complexity among graduate students in the study. Hypothesis 2, the combination of edu cation and SCE will predict more of the variance in cognitive complexity than education and age or education and HSE, was not significant. This hypothesis was based on the assumption that education and supervision experience were needed to increase cogni tive complexity among counseling students (Bernard & Goodyear, 2004). Contrary to th e expectation, the combination of SCE F (1, 329) = .063, p = .803, r2 change = .000 and education F (1, 330) = 16.968, p < .05 did not account for most of the variance in the model. Interestingly, the combination of HSE ( = .159, t = 2.078, p < .05) and education accounted for most of variance in the model, 6.1%. According to the findings, work with actual clients and edu cation might predict cognitive complexity among graduate students in the helping fields.

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65 Table 10 Summary of Hierarchical Regression Analysis for Variables Predicting Cognitive Complexity (N = 332) Variable B SE B Step 1 Education 4.87 1.18 .221* Step 2 Education 4.829 1.19 .219* SCE .000 .002 .014 Step 3 Education 4.46 1.26 .202* SCE .000 .002 .009 Age .202 .223 .052 Step 4 Education 3.74 1.30 .170* SCE -.003 .002 -.087 Age .096 .227 .025 HSE .127 .061 .159* Note SCE = supervised clinical experience; HSE = human services experience; R2 = .049 for Step 1; R2 = .000 for Step 2; R2 = .002 for Step 3; R2 = .012 for Step 4; p < .05. Additional Analyses One-way Analyses of Variance (ANOVAs) were conducted for the variables ethnicity and gender to examine their eff ects on cognitive complexity. Although these variables were not part of the major analysis, it was important to examine their influence on CCI scores because of inconsistent results in prior studies. Means and standard deviations for ethnicity and gender are presen ted in Table 11. There were no significant differences between different ethnicities on mean CCI scores F (4, 327) = .460, p = .498.

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66 Table 11 Mean CCI Scores by Ethnicity and Gender N Mean Standard Deviation F ES Power Ethnicity .460 .12 .34 African American/Black 22 354.41 47.24 Asian American/Pacific Islander 12 373.25 49.38 European American/White 261 373.49 42.24 Latino/Hispanic 20 367.20 37.30 Other 17 371.94 43.26 Gender 3.250 .31 1.0 Female 290 370.03 42.81 Male 39 383.13 41.03 Total 329 371.58 42.75 Note : CCI = Cognitive Complexity Index; ES = Effect Size. The total N for Gender was 329 due to missing demographic data. Effect sizes were calculated to determine the magnitude of the relationship between variables (Cohen, 1998). There was a medium effect size for European Americans and African Americans (Cohen’s d = .41). There was a small effect size for European Americans and Latino Americans (Cohen’s d = .14). Gender also revealed no significant differences in cognitive complexity F (1, 327) = 3.25, p = .072. The gender effect size was small to medium (Cohen’s d = .31). ANOVAs were also conducted for program degree (Master’s, Ph.D.) and graduate program to examine their effects on cognitive complexity. Within group comparison were conducted for counseling and social work programs. No significant differences were found between doctoral and maste r’s level counseling students F (1, 136) = 3.88, p = .051. Social work doctoral students showed higher mean CCI scores than social work master’s students F (1,192) = 16.68, p < .05. Since counseling consisted of different

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67 programs, these groups were compared. No significant differences were found among counseling doctoral students F (1, 16) = .132, p = .877; and no significant differences were found among counseling master’s students F (6, 112) = 3.27, p = .093. Table 12 illustrates within-group comparisons. Table 12 Within-Group Comparisons of Mean CCI Scores by Programs Variable N Mean Standard Deviation F ES Power Counseling 3.88 .49 .98 Master’s 119 364.63 43.24 1.86 .32 .99 Community Counseling 14 362.79 35.36 Counseling Psychology 10 379.10 36.04 Counselor Education 7 385.43 29.01 Marriage and Family Counseling 7 360.71 30.03 Mental Health Counseling 59 369.36 45.74 School Counseling 17 336.94 37.47 Other 5 355.80 67.73 Total 119 364.63 43.24 Doctorate 19 385.37 38.34 .132 .13 .07 Counseling Psychology 3 389.33 15.95 Counselor Education 12 381.83 38.30 Mental Health Counseling 4 393.00 55.92 Total 138 367.49 43.07 Social Work 16.68* .29 .98 Master’s 151 368.68 38.52 Doctorate 43 397.07 47.16 Total 194 374.78 42.18 Note : CCI = Cognitive Complexity Index; ES = Effect Size; N =332; p < .05

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68 Between-groups comparisons were con ducted for counseling and social work students. Table 13 shows between-groups comp arisons of programs. Counseling students did not differ from social work students on measures of cognitive complexity when both master’s and doctoral levels were combined F (1, 330) = 1.78, p = .183. When doctoral counseling students were compared with docto ral social work students, no significant difference was found F (1, 60) = .903, p = .346. There were no significant differences between counseling and social work students at the master’s level F (1, 268) = .579, p = .447. Table 13 Between-Group Comparisons of Mean CCI Scores by Programs Variable N Mean Standard Deviation F ES Power Combined Levels 1.78 .18 .90 Counseling Doctoral/Master’s 138 367.49 43.07 Social Work Doctoral/Master’s 194 374.78 42.18 Total 332 371.14 42.63 Doctoral Students .903 .28 .58 Counseling 19 385.37 38.34 Social Work 43 397.07 47.16 Total 62 391.22 42.75 Master’s Degree .579 .10 .37 Counseling 119 364.63 43.24 Social Work 151 368.68 38.52 Total 270 366.68 40.88 Note : CCI = Cognitive Complexity Index; ES = Effect Size; N = 332

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69 Between-groups comparisons were also conducted among counseling and social work students at different levels. Counseling doctoral students did not differ significantly from social work master’s student s in terms of mean CCI scores F (1, 168) = 3.27, p = .072. However, social work doctoral student s had significantly higher mean CCI scores than counseling master’s students F (1, 160) = 16.93, p < .05. Table 14 shows the cross level group comparisons. Table 14 Cross-Level Group Comparisons of Mean CCI Scores by Programs Variable N Mean Standard Deviation F ES Power Comparison 1 3.27 .42 1.0 Counseling Doctoral 19 385.37 38.34 Social Work Master’s 151 368.68 38.52 Total 170 377.03 38.43 Comparison 2 16.93* .71 1.0 Counseling Master’s 119 364.63 43.24 Social Work Doctoral 43 397.07 47.16 Total 162 377.03 45.20 Note : CCI = Cognitive Complexity Index; ES = Effect Size; N = 332; *p < .05 Post hoc comparisons revealed signifi cant differences between graduate degree programs. Social work doctoral students ha d significantly higher CCI mean scores than master’s level students in mental he alth, school counseli ng and social work F (11, 320) = 3.175, p < .05. School counselors had the lowest mean CCI score of all the programs; however, this difference was not statistically significant when compared to students in master’s level programs. However, when two master’s programs, mental health

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70 counseling and school counseling, were comp ared, the effect size was large (Cohen’s d = .74). The effect size for social work and sc hool counseling master’s programs was also large (Cohen’s d = .65). This finding was consistent with prior research. Granello (2002) found that school counselors had lower CCI sc ores than other students and that school counselors showed a decrease in cognitive complexity, while other students made gains in cognitive complexity. When school counseli ng was compared to the combined groups (master’s and doctoral programs) counselor education, counseling psychology and social work had significantly higher mean CCI scores, F (7, 324) = 2.375, p < .05. There were no significant differences between social work doctoral students a nd counseling doctoral students. Because some doctoral students had not yet earned master’s degrees and some master’s students had previously attained mast er’s degrees in other areas, further analysis examined the effects of earned degrees on cognitive complexity. It was found that students who had earned a master’s degree had significantly higher CCI scores than students who had earned only a bachelor’s degree F (1, 330) = 21.90, p < .05. This is illustrated in Table 15. Table 15 Mean CCI Scores and Results of Analysis of Variance by Earned Degree Variable N Mean Standard Deviation F ES Power Earned Degree 21.90*.58 1.0 Bachelor’s 245 365.42 40.15 Master’s 87 389.57 44.59 Total 332 371.75 42.64 Note. CCI = Cognitive Complexity Index; ES = Effect Size; N = 332; *p < .05

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71 This was consistent with the findings in the predictive model. More education was related to higher cognitive complexity. Chapter 4 presented the findings of this study, discussed whether the findings were consistent with the research hypothese s and prior research. Chapter 5 will discuss the findings in more detail along with impli cation for practice and further research. The limitations of the study will also be discussed.

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72 Chapter Five Discussion This chapter provides a detailed discussion of the findings. The chapter is divided into the following sections: 1. Purpose of the Study 2. Perry’s Positions of Participants in the Study 3. Summary of the Predictive Model 4. Summary of Analyses of Variance (ANOVAs) 5. Conclusions 6. Limitations 7. Implications 8. Recommendations for Future Research Purpose of the Study Given the findings from prior research that students make more gains in cognitive complexity at the end of their graduate pr ograms (Brendel et al., 2002; Fong et al., 1997; Granello, 2002; Kohlberg, 1976; Lovell, 1999b), the question remained of what variables contributed to that increase? Given education was the only cons tant in past models, it was important to control for the effects of educat ion in the research design, while examining whether work with actual clients (Fong et al ., 1997), the amount of supervision received (Lovell, 1999b), chronological age (Granello, 2002; Hood & Deopere, 2002) or various

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73 combinations of these variables (Bernard & Goodyear, 2004) contributed to cognitive complexity. The research question posed for th is study was: to what extent do education, supervised clinical experience (SCE), age, and human services experience (HSE) predict cognitive complexity? Two hypotheses were formulated based on this question. 1. Hypothesis 1: Education, SCE, age and HSE will predict cognitive complexity. 2. Hypothesis 2: The combination of educa tion and SCE will predict more of the variance in cognitive complexity than education and age or education and HSE. To answer this question and test the hypothe ses, a web-based survey was conducted with a sample of counseling and social work students in graduate training programs. A hierarchical multiple regression analysis was used to test the predictive model of education, SCE, age, HSE and cognitive complexity. One-way analyses of variance (ANOVAs) were also conducted to examine the effects of gender, ethnicity and program variables on a measure of cognitive complexity. Summary of the Predictive Model Hypothesis 1 was education, SCE, ag e and HSE would predict cognitive complexity. Contrary to the hypothesis, SCE and age were not significant predictors of cognitive complexity in this study; alt hough, age showed a significant and positive correlation with cognitive complexity. As hypothesized, education and HSE were significant predictors of cogni tive complexity. A discussion of the regression solution is provided below.

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74 Education Consistent with a more recent study (Hood & Deopere, 2002), education accounted for a signi ficant proportion of the vari ance in cognitive complexity in the present model, which might be taken as support for Perry’s (1970; 1999). That is, higher education was related to higher cogni tive complexity. Other studies have found a similar relationship between education and cognitive complexity (Granello, 2002; Wilson, 1995a). A majority of the graduate st udents in the study were in early to late multiplism, according to Perry’s model. This was consistent with the results of other research. Moore (1990) examined psychology gra duate students and found that they also exhibited multiplistic thinking. Granello ( 2002) examined cognitive complexity among counseling graduate students and found that they enter counseling programs at early multiplism and progress to late multiplism by the end of their programs. Eriksen and McAuliffe (2006) also examined counseling students and found that they ranged from early multiplism to transitioning between late multiplism and contextual relativism. For students in multiplism, there is a decrease in reliance on authority and an increase in autonomous thinking. Authority is seen as th e authority on the proper methods to find the right answers instead of havi ng the right answers. Supervised Clinical Experience. Surprisingly, SCE show ed a non-significant correlation with cognitive complexity, and as a result did not account for any additional variance in predicting cognitive complexity. This suggests that clinical supervision is not directly related to cognitive complexity. Th e finding in the present study was inconsistent with a previous study that found a significant relationship between supervisory experience and cognitive complexity (Lovell, 1999b).

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75 One possible explanation for the finding in this study is that respondents did not distinguish between clinical supervision and administrative supervision. In a more recent dissertation study, individuals reported they were receivi ng clinical supervision (i.e., supervision to increase counseling skills) when they were receiving administrative or managerial supervision (i.e., supervision to increase organization goals) (Teufel, 2007). Teufel (2007) observed supervision sessions and found that what was being reported as clinical supervision was very different from what was actually occurring in the supervision session. She observed that much of time in the session was spent on reporting productivity as opposed to the supervisees’ de velopment. Therefore, what was reported might have depended heavily on whether student s viewed formal supervision as clinical, administrative, or both. Age. Age was significantly and positively correlated with cognitive complexity. This was inconsistent with previous studi es that found no relationship between age and cognitive complexity (Granello, 2002; Wilson, 1995b). On the other hand, Hood and Deopere’s (2002) found a correla tion between age and cognitive complexity, albeit in the opposite direction. They found a significant positive correlation betw een age and dualism and a significant negative correlation be tween age and relativism. Their findings suggested that age was negatively related to cognitive complexity, which was contradictory to the findings in the present study. In the regression model, age accounted fo r a small but insignificant change in cognitive complexity in the present study. Age was entered into the regression equation at the third step and did not contribute to any unique variance that was not already accounted for by education, which was entered at the first step. If age were entered into

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76 the equation as the first step, it could have accounted for a signifi cant proportion of the variance until education was entered. This was inconsistent with Hood and Deopere’s (2002) study in which age accounted for a si gnificant proportion of the variance when both education and intell igence were controlled. Human Services Experience HSE showed a significant positive relationship with human cognitive complexity and explained a small proportion of the variance in cognitive complexity. The results of this study were consistent with the findings of Rapps et al. (2001) that work experience was re lated to cognitive complexity. However, Granello (2002) found no relationship between HSE and cognitive complexity. One possible explanation for the inconsistent findi ngs was that, in the previous study, prior human services experience might have been t oo broad to capture part icipants’ work with actual clients. In the curren t study, the question regarding human services experience was specific to direct practice. In order to capture participants’ work with actual clients, HSE was operationalized by a single question on th e demographic questionnaire: “What type of human services experience (i.e., employme nt, practicum, internsh ip, and/or volunteer experience) have you had in providing direct services with individuals, families, or groups?” One study found that cogn itive complexity increased significantly after students began practicing in the field (Rapps et al., 2001). The findings in the current study provided some support for this notion. Ther efore, human services experience after training might be a variable worth further examination. Hypothesis 2 was that the combination of education and SCE would predict more of the variance in cognitive complexity th an education and age or education and HSE would. This hypothesis was based on the assu mption that education and experience were

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77 needed to increase cognitiv e complexity among counseling students (Bernard & Goodyear, 2004). The results showed that edu cation and HSE and not education and SCE explained the greatest proportion of the vari ance in the regression model. Conceptually, experience under supervision was an important factor in bringing about cognitive change but the data did not support this, possi bly for the reasons discussed earlier. Summary of Analyses of Variance (ANOVAs) Additional analyses regarding gender and ethnicity were conducted due to inconsistent findings regarding these variable s in the literature. In addition, analyses regarding cross-discipline analyses were conducted to examine difference between counseling and social work programs and program levels. Gender. The results of the analysis of ge nder revealed there was no significant gender difference. Although the finding was not significant, males were higher in cognitive complexity than females with a sma ll to medium effect si ze. In terms of Perry positions, males tended to be POS/4 and females tended to be transitioning between POS/3 and POS/4. However, it could be argued that the way in which university classrooms are set up often led to self-doubt and alienation among women; therefore, women sought to gain a voice instead of searching for tr uths (Belenky et al., 1986). Ethnicity The results of the analysis of et hnicity revealed that there were no significant ethnic-related differences for cognitive complexity in the present study, but reasonable effect sizes. European Americans were higher in cognitive complexity than Asian Americans, Latino American and African Americans, respectively. European and Asian Americans tended to be at POS/4 and African and Latino Americans tended to be transitioning between POS/3 and POS/4. Howeve r, it was important to note that cognitive

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78 development in the college years might be due to cultural ex pectations (Zhang & Watkins, 2001). That is, student cognitive comp lexity may reflect the beliefs, values, and traditions of the student’s racial/ethnic group, which may impact the ways in which they develop in the educational environment. Graduate Programs The results of the cross-discipline analysis of social work and counseling programs were as follows: 1. There was no significant difference be tween counseling and social work students at the master’s level and counse ling students and social work students at the doctoral level. They both tended to be transitioning between POS/3 and POS/4. 2. School counselors scored lower in terms of cognitive complexity than students in other programs. Although this was not a significant finding, it was important to note that this finding was consistent with prior research (Granello, 2002). 3. Social work doctoral students were si gnificantly higher in cognitive complexity than both master’s level social work students and master’s level counseling students. Social work doctoral students tended to be in POS/4 and master’s level students tended to be in tr ansition between POS/3 and POS/4. 4. There was no significant difference betw een counseling doctoral students and master’s level students. Counseling doctora l students tended to be in POS/4 and master’s level students tended to be in transition between POS/3 and POS/4 as stated earlier; however, the mean cogn itive complexity scores of counseling doctoral students were lower than social work doctoral students. One possible explanation for this finding was the sm all sample size of doctoral counseling

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79 students included students who had not rece ived master’s degrees; whereas, all doctoral social work students had received master’s degrees. Previously Earned Degree To further explore the effect of prior education, means were examined for earned degrees. The analys is of earned degrees showed that students who had received a master’s degree scored hi gher on cognitive complexity than students who had bachelor’s degrees. This lent some support to the importance of educational experience in cognitive development. It sta nds to reason that students who have more education are exposed to mo re diversity, especially in counseling where there is ambiguity. Therefore, students might become increasingly flexible in their worldview, embracing diversity and more complex situations. Conclusions The findings in the current study combin ed with previous research provided partial support for Perry’s scheme and it’ s applicability to graduate students. Consistently, studies have found that most graduate students enter master’s programs beyond dualistic thinking. On average, maste r’s level students in the current study were in transition between early and late multiplism. However, there is evidence that they might not reach relativistic thinking before they are at the end of their programs or beyond graduate school (Skovholt & Ronnestad, 1992). Relativist ic thinking represents a fundamental shift in thinking (Perry, 1999). Therefore, it wa s not surprising that only doctoral students in the present study endor sed relativistic thought. What was surprising was that only three doctoral students were at this stage, which meant that the majority of the doctoral students had not yet made that shift in thinking. On average, doctoral students were firmly in late multiplism.

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80 Education level was found significantl y to predict cognitive complexity. Education was the only variable that was significant at each stage of the regression model. In Perry’s study of Harvard underg raduate students, he found that students reached relativism at about their senior ye ar. However, more recently, Granello (2002) and Eriksen and McAuliffe (2006) found that students in counseling master’s programs were multiplistic in their thinking. Simila r results were found for psychology graduate students (Moore, 1990). The results of the pr esent study confirmed these findings. Some students were beyond multiplism in the study; ho wever, they were mostly Ph.D. students and second year master’s level students. Ma ster’s students were in early to late multiplism, both within and between program s, which provided some evidence that graduate students in different programs in the helping professions possessed similar levels of cognitive complexity. In addition, higher levels of cognitive complexity, such as relativism, might occur when a student was at the end of their program or once he or she had completed training (Skovholt & Ronnestad 1992). This further suggested that Perry’s original sample of undergraduate st udents might be very different from the average college student. Age, as concluded in past studies (Granello, 2002; Wilson, 1995b), did not seem to have a significant effect on cognitive comp lexity. However, a zero-order correlation in the present study suggested that age had a di rect positive relationship with cognitive complexity. This was consistent with a previous study that showed that age was correlated with cognitive complexity and had predictive power when education was controlled (Hood & Deopere, 2002).

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81 Education was a better predictor of c ognitive complexity and accounted for more of the variance than human services experience, age, and supervised clinical experience combined. However, experience, specifically human services experience, could be an important factor in increasing cognitive comple xity in students and practitioners but more research is needed before reaching that conclu sion. On the other hand, supervised clinical experience did not account for any changes in cognitive complexity, suggesting that supervised clinical experience might not be a factor in student cognitive complexity. The results of the study warrant additional examin ation of experience-re lated variables and their relationship to cognitive complexity. Limitations Although the study confirmed the results of earlier studies, there were several limitations that must be noted. The study was co rrelational; therefore no causal inferences could be made from the results. The data were gathered from a homogeneous population of graduate students in counseling and soci al work. Because a convenience sample was used, it was unknown whether respondents to the survey were different from nonrespondents (i.e., non-response error). Additiona lly, there was a lack of demographic variability in the sample. Most of the sample consisted of European Americans and female participants. Although this might be a reflection of the counseling and social work fields in general, it limited the researcher’s ability to examine issues of gender and ethnicity more closely and to make meaningful infere nces (Granello, 2002). Web-based survey methods may pose possible limitations, for example, lack of a population list, nonrandom sample, inability to calculate response rate and computer access to the survey (Mertler, 2001). There mi ght be a potential limitation of lowered

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82 response rates for web-based survey rela tive to mail surveys (Converse et al., 2008). Because data were gathered using only self -reports, response bias posed a potential limitation (Ellis et al., 1996). Finally, the study might also be limited by the instruments used. As discussed earlier, the operationalization of SCE might not have measured the actual clinical supervision received. It could have m easured both clinical and administrative supervision. Implications Several implications were based on the findings that education experience and HSE significantly predicted cognitive co mplexity and previous research. Although there is much work to be done regarding our unders tanding of the true impact of baseline cognitive complexity on outcome measures, what has been speculated is that students with higher cognitive comple xity might be faster and more efficient at integrating new knowledge needed for furt her development (Granello, 2002; Perry, 1999). Given that experience is related to cognitive complexity, counseling admissions committees might consider work experience as criteria for admitting students into graduate programs. It might be important for counselor edu cators to have an understanding of adult cognitive development and cognitive complex ity so that they could provide the best learning environments to promote learning in the classrooms. Perry (1999) stated that development might occur in the educationa l environment because of the diversity on most college campuses. But how could educ ators assure cognitive development of students if it were left up to chance (Fong et al., 1997)? Earlier studies proposed

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83 developmental instruction as a way of helping student cognitive development (Granello & Hazler, 1998; Knefelkamp, 1974). Instructors with knowledge of cognitive development mi ght assess their students’ baseline cognitive complexity at the beginning of the class by using measures like the Learning Environment Preferences or the Meas ure of Intellectual Development. Based on the work of Knefelkamp (1974), Rapaport (1984 ) offered a creative way that instructors could challenge students and also support them once they have established a baseline for students in the class. Rapaport (1984) suggested that instructors offer students choice in assignments that support their development level but challenge them by requiring that they considered other ideas about one position above their own. This induces a disequilibrium, which might help students develop cognitive complexity. Students interacted with thei r environment and responded to challenges by assimilating to existing cognitive framework or accommodating th e framework itself (Piaget, 1967). Supervisors might also be trained to provide a challe nging and supportive supervisory environment. The supervisory en vironment has been a major area of study among counselor supervision researchers, and the importance the supervisory environment has been well-documented (e.g., Loganbill et al., 1982; Stoltenberg, 1981; Worthington & Roehlke, 1979). However, trainee models to promote cognitive complexity among students might be more e ffective if they are rooted in cognitive developmental theory and include chances for students to gain experience while working with actual clients. Brendel et al. (2002) st udied a counselor educa tion program that used a deliberate psychological educ ation (DPE) component to tr ain counselors. They found that cognitive complexity among students si gnificantly increased after two years.

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84 DPE is an educational model design ed specifically to enhance cognitive complexity among students. The model challeng es students’ perceptions of the world by providing conditions for developmental gr owth. The model in cludes role-taking experience in helping--take others’ poin t of view, guided re flection--focusing on meaning-making, critical-dialect ical analysis and self-eva luation, a balance between action and reflection through actual practice and clinical supervision, continuity-opportunities to practice and receive feedback and instru ction in supervision and a challenging and support environm ent (Brendel et al., 2002). It might be important for students to seek out educational models such as DPE. Students might also seek out more “hands on” experience with dive rse client populations. More interactions with clie nts in the field performing c ounseling tasks might aid in increasing cognitive complexity beyond training programs. Blocher (1983) and Fong et al. (1997) pointed to the importance of student work experience with actual clients as a mechanism for cognitive development. However, it is important that students advocate on their own behalf to get the ty pes of experiences that might help them develop cognitive skills needed to become effective practitioners; especially when internship settings do not allow for such experiences. Finally, the findings in this study, with regards to SCE, might magnify the need for more research on the two types of superv ision conducted in agenci es. It is well known that administrative supervision is readily available in agencies and clinical supervision is often neglected, although both are important. As Teufel’s (2 007) study pointed out, more administrative supervision was being conducted than clinical supervision. Another study (Page, Pietrzak, & Sutton, 2001) found that only 13% of school counselors were

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85 receiving individual clinical supervision and only 11% were receiving group clinical supervision as opposed to 100% who r eceived administrativ e supervision. Recommendations for Future Research Though, the results of the study add to the knowledge base of cognitive developmental research, there is still mu ch work to be done. The following are recommendation for future research: SCE was very hard to measure with objective measurements, mainly because supervisors, students and practit ioners might not distinguish between clinical supervision and administrative supervision. Therefore, it is recommended that, when constructing demographic surveys, it is important that the questions are not ambiguous. In order to minimize ambiguity, a cognitive interview durin g the piloting of the study is suggested. In the cognitive interview, the researcher might ask probing questions related to the questions on the survey or responses given by participants (Caspar, Lessler, & Willis, 1999). The interview might decrease survey error. A qualitative component might also serve to decrease survey erro r. A qualitative component might provide the researcher with the opportunity to ask participants que stions regarding their work and supervision experiences that are not possible with a dem ographic survey. In addition, a qualitative component could help to examine what types of activities are preval ent in supervision, and the impact these activities might have on cognitive complexity. This study and a previous study (Gra nello, 2002) found lower mean cognitive complexity scores for students in school counseling programs when compared to other counseling programs. To this end, it would be important to understand these findings.

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86 Therefore, future research might closely examine the experiences of school counseling students. Another recommendation for future research is to continue to conduct gender and ethnicity studies. Given the e ffect sizes of gender and ethnici ty in this study, cognitive complexity, as it relates to these variables, may be an area worth further investigation. More emphasis might be placed on comparisons between males and females and different ethnicities to continue to address the appl ication of the Perry scheme to women and ethnic minorities (Baxter Magolda, 1992; Be lenky et al., 1986; Johnson, 1999; King & Kitchener, 1994). However, the lack of variability in sampli ng in the graduate programs in counseling and social work may require that researchers employ an over sampling method to assure increased balance in terms of group sizes. In over sampling, members of underrepresented groups were invi ted to participate in larger numbers in the research, and enabled researchers to make infere nces from the data (Palta, 2003). Finally, cognitive complexity interacti ons might be a good direction for future research because it is not yet known what im pact interactions be tween supervisors and supervisees might have student learning or other outcomes. Supervisors’ level of cognitive complexity may play an important role in determining the cognitive complexity of students. That is, supervis ors with high cognitive comple xity may be better equipped to provide the right type of environment to stimulate cognitive growth in supervisees. On the other hand, supervisees with low cogniti ve complexity may hinder cognitive growth. That is, supervisors who are rigid and inflexible may not be able to make the necessary changes to allow for growth of the supervisee To this end, it would be important to examine how antecedents impact the process and outcome of supervision, such as

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87 working alliance and supervision satisfaction. This hopefully w ould lead to more research examining the impact of supervision on client outcomes. In closing, counselor cognitive comp lexity is a phenomenon worth further investigation. The regression model accounted for only about 6% of the variance in cognitive complexity; therefore more research is needed to examine other variables that might predict cognitive complexity in hopes of constructing a parsimonious cognitive developmental model of supervision.

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88 References Alcorn, L. M., & Torney, D. J. (1982). Counselor cognitive complexity of self-reported emotional experience as a predictor of accurate empathic understanding. Journal of Counseling Psychology, 29 534-537. Algina, J., & Olejnik, S. (2003). Sample size tables for correlation analysis with applications in partial correlation and multiple regression analysis. Multivariate Behavioral Research, 38 309-324. Baxter Magolda, M. B. (1992). Knowing and reasoning in college: Gender-related patterns in students' intellectual development San Francisco: Jossey-Bass. Baxter Magolda, M. B., & Porterfield, W. D. (1985). A new approach to assess intellectual developmen t on the Perry scheme. Journal of College Student Development, 26 343-351. Belenky, M. F., Clinchy, B. M., Goldberger, N. R., & Tarule, J. M. (1986). Women's ways of knowing: The development of self, voice, and mind New York: Basic Books. Belenky, M. F., Clinchy, B. M., Goldberger, N. R., & Tarule, J. M. (1997). Women's ways of knowing. (10th anniversary edition) New York: Basic Books. Benack, S. (1988). Relativistic thought: A c ognitive basis for empathy in counseling. Counselor Education and Supervision, 27 216-232.

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89 Bernard, J. M., & Goodyear, R. K. (2004). Fundamentals of clinical supervision. 3rd edition Boston: Pearson Education, Inc. Birk, J. M., & Mahalik, J. R. (1996). The infl uence of trainee concep tual level, trainee anxiety, and supervision evaluation on counselor developmental level. The Clinical Supervisor, 14 123-127. Blocher, D. H. (1983). Toward a cognitive developmental-approach to counseling supervision. Counseling Psychologist, 11 27-34. Borders, L. D. (1989). Developmental c ognitions of first practicum supervisees. Journal of Counseling Psychology, 36 163-169. Borders, L. D., Fong, M. L., & Neimeyer, G. J. (1986). Counseling students' level of ego development and perceptions of clients. Counselor Education & Supervision, 8 36-49. Brendel, J. M., Kolbert, J. B., & Foster V. A. (2002). Promoting student cognitive development. Journal of Adult Development, 9 217-227. Caspar, R. A., Lessler, J. T., & Willis, G. B. (1999). Reducing survey error through research on the cognitive and decision processes in surveys Paper presented at the Meeting of the American Statistical Association. Cohen, J. (1998). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates. Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahwah, NJ: Lawrence Erlbaum Associates.

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90 Commons, M. L. (2002). Introduc tion: Attaining a new stage. Journal of Adult Development, 9 155-157. Commons, M. L., & Richards, F. A. (2002). Organizing components into combinations: How stage transition works. Journal of Adult Development, 9 159-177. Converse, P. D., Wolfe, E. W., & Huang, X. (2008). Response rates for mixed-mode surveys using mail and e-mail/web. American Evaluation Association, 29 99-107. Cornfeld, J. L., & Knefelkamp, L. L. (1979). Combining student stage and type in the designing of learning environments: and integration of Perry stages and Holland typologies Paper presented at the American College Personnel Association. Dillman, D., Phelps, G., Tortora, R. D., Sw ift, K., Kohrell, J., & Berck, J. (2001). Response rates and measurement differences in mixed mode surveys using mail, telephone, interactive voice response, a nd the internet [El ectronic Version]. Retrieved June 24, 2008 from http://survey.sesrc.wsu.edu/dillman/ papers/Mixed%20Mode%20ppr%20_with%2 0Gallup_%20POQ.pdf Ellis, M. V., Ladany, N., Krengel, M., & Schu lt, D. (1996). Clinical supervision research from 1981 to 1993: A methodological critique. Journal of Counseling Psychology, 43 35-50. Eriksen, K. P., & McAuliffe, G. J. (2006) Constructive development and counselor competence. Counselor Education & Supervision, 45 180-192. Erwin, T. D. (1981). Manual for the scale in tellectual development Harrisburg, VA: Developmental Analytics.

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91 Erwin, T. D. (1983). The scale of intellect ual development: Measuring Perry's scheme. Journal of College Student Personnel, 24 6-12. Felder, R. M., & Brent, R. (2004). The intellectual developm ent of science and engineering students: Part 1. models and challenges. Journal of Engineering Education, 93 269-277. Finster, D. C. (1989). Developmental instruc tion: Part 1. Perry's model of intellectual development. Journal of Chemical Education, 66 659-661. Fong, M. L., Borders, L. D., Ethington, C. A., & Pitts, J. H. (1997). Becoming a counselor: A longitudinal study of student cognitive development. Counselor Education & Supervision, 37 100-114. Gottlieb, E. (2007). Learning how to believe: Epistemic development in cultural context. Journal of the Learning Sciences, 16 5-35. Granello, D. H. (2002). Assessing the cogniti ve development of counseling students: changes in epistemological assumptions. Counselor Education and Supervision, 41 279-293. Granello, D. H., & Hazler, R. J. (1998). A developmental rationale for curriculum order and teaching styles in counselor education programs. Counselor Education & Supervision, 38 89. Hess, A. K. (1987). Psychotherapy supervis ion: stages, Buber, and a theory of relationship. Professional Psychology: Research and Practice, 18 251-259. Hofer, B. K., & Pintrich, P. R. (1997). The development of epistemological theories: Beliefs about knowledge and knowing and their relation to learning. Review of Educational Research, 67 88-140.

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92 Holloway, E. L. (1987). Developmental models of supervision Is it development? Professional Psychology-Research and Practice, 18 209-216. Holloway, E. L., & Wolleat, P. L. (1980). Rela tionship of counselor conceptual level to clinical hypothesis formation. Journal of Counseling Psychology, 27 539-545. Hood, A. B., & Deopere, D. L. (2002). The re lationship of cognitive development to age, when education and intelligence are controlled for. Journal of Adult Development, 9 229-234. Johnson, J. B. (1999). A comparison of cognitive development between Whites and African Americans based on William Perry's scheme of intellectual and ethical development. Dissertation Abstracts International, 61 522. (UMI No: 9961423) King, P. M., & Kitchener, K. S. (1994). Developing reflective judgment San Francisco: Jossey-Bass. Knefelkamp, L. L. (1974). Developmental instru ction: Fostering intellectual and personal growth of college students. Unpublished Doctoral Dissertation. University of Minnesota. Knefelkamp, L. L., & Slepitza, R. (1976) Cognitive-development al model of careerdevelopment Adaptation of Perry scheme. Counseling Psychologist, 6 53-58. Kohlberg, L. (1976). Moral stages and mo ralization: The cognitive-developmental approach. In T. Lickona (Ed.), Moral development and behavior: Theory, research, and social issues (pp. 31-53). New York: Holt, Rinehart and Winston. Lefever, S., Dal, M., & Matthasdttir, . (2007). Online data collection in academic research: Advantages and limitations. British Journal of Educational Technology, 38 574–582.

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93 Loganbill, C., Hardy, E., & Delworth, U. (1982). Supervision a conceptual-model. Counseling Psychologist, 10 3-42. Lovell, C. (1999a). Empathic-cognitive deve lopment in students of counseling. Journal of Adult Development, 6 195-203. Lovell, C. (1999b). Supervisee cognitive comple xity and the Integrated Developmental Model. Clinical Supervisor, 18 191-201. Lovell, C. (2002). Development and disequilib ration: Predicting counselor trainee gain and loss scores on the Supervisee Levels Questionnaire. Journal of Adult Development, 9 235-240. Lyons, C., & Hazler, R. J. (2002). The in fluence of student development level on improving counselor student empathy. Counselor Education and Supervision, 42 119-130. Markwell, J., & Courtney, S. (2006). Cognitiv e development and the complexities of the undergraduate learner in the science classroom. Biochemistry and Molecular Biology Education, 34 267-271. McAuliffe, G., & Lovell, C. (2006). The in fluence of counselor epistemology on the helping interview: A qualitative study. Journal of Counseling and Development, 84 308-317. McNeill, B. W., Stoltenberg, C. D., & Romans, J. S. C. (1992). The integrated developmental model of supervision Scale development and validation procedures. Professional Psychology-Research and Practice, 23 504-508.

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94 Mertler, C. A. (2001). Lessons learned from the administration of a web-based survey Paper presented at the Annual Meeting of the Mid-Western Education Research Association. Mertler, C. A., Meriter, C., & Vannatta, R. A. (2001). Advanced and multivariate statistical method Los Angeles: Pyrczak Publishing. Moore, W. S. (1987). The Learning Environm ent Preferences: Establishing preliminary reliablity and validity for an objective measure of the Perry scheme of intellectual development. Dissertation Abstracts, 49 1186A. (UMI No. 8808586) Moore, W. S. (1988). The Measure of Intell ectual Development: An instrument manual. Olympia, Washington: Center for th e Study of Intellectual Development. Moore, W. S. (1989). The Learning Environm ent Preferences: Exploring the construct validity of an objective measure of the Perry scheme of intellectual development. Journal of College Student Development, 30 504-514. Moore, W. S. (1990). Measure of intellectual devel opment (MID) instrument manual Olympia, WA: Center for the Study of Intellectual Development. Page, B. J., Pietrzak, D. R., & Sutton, J. M. (2001). National survey of school counselor supervision. Counselor Education and Supervision, 41 142-150. Palta, M. (2003). Quantitative methods in population health: Extensions of ordinary regression Hoboken: John Wiley & Sons, Inc. Pedhazur, E. J. (1997). Multiple regression in behavioral research: Explanation and predicition (3rd ed.). New York: Wa dsworth/Thomson Learning. Perry, W. G. (1970). Forms of intellectual and ethical de velopment in the college years: A scheme New York: Holt, Rinehart, and Winston.

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95 Perry, W. G. (1999). Forms of intellectual and ethical de velopment in the college years: A scheme San Francisco: Jossey-Bass. Piaget, J. (1967). Six psychological studies New York: Random House. Rapaport, W. J. (1984). Critical thinki ng and cognitive development [Electronic Version]. Proceedings and Addresses of the American Philosophical Association Retrieved March 21, 2006 from http://www.cse.buffalo.edu/~rapaport/perry.positions.html Rapps, J., Riegel, B., & Glaser, D. (2001). Te sting a predictive model of what makes a critical thinker. Western Journal of Nursing Research, 23 610-626. Regira, L. M. (2006). Applying intellectual development theory to college student drinking. Dissertation Abstracts International, 67 (UMI No: 3229245) Ronnestad, M. H., & Skovholt, T. (1997). The professional development and supervision of psychotherapists. Psychotherapeut, 42 299-306. Sakai, P. S., & Nasserbakht, A. (1997). Counselor development and cognitive science models of expertise: Possibl e convergences and divergences. Educational Psychology Review, 9 353-359. Skovholt, T. M., & Ronnestad, M. H. (1992). Themes in therapist and counselor development. Journal of Counseling & Development, 70 505-515. Stevens, J. (1999). Intermediate statisti cs: A modern approach (2nd ed.). Mahwah: Lawrence Erlbaum Associates, Publishers. Stoltenberg, C. D. (1981). Approaching supe rvision from a developmental perspective: The counselor complexity model. Journal of Counseling Psychology, 28 59-65.

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96 Stoltenberg, C. D., & Delworth, U. (1987). Supervising counselors and therapists: A developmental approach San Francisco: Jossey-Bass. Stoltenberg, C. D., & Delworth, U. (1988). De velopmental models of supervision It is development Response. Professional Psychology-Research and Practice, 19 134-137. Streufert, S., & Swezey, R. W. (1986). Complexity, managers, and organizations. New York: Academic Press. Teufel, L. (2007). Clinical s upervision of child an d adolescent counselors in residential foster care: A collective case study. Dissertation Abstracts International, 69 (Publication No. AAT 3306895) Thompson, J. M. (1999). Enhancing cognitive development in college classrooms: A review. Journal of Instructional Psychology, 26 56-64. Widick, C. (1975). An evaluation of developmen tal instruction in th e university setting. Unpublished Doctoral Dissertati on. University of Minnesota. Widick, C. (1977). Perry scheme Foundation for developmental practice. Counseling Psychologist, 6 35-38. Wilson, B. A. (1995a). Intellectual devel opment of technical college instructors. Journal of Vocational Education Research, 20 29-50. Wilson, B. A. (1995b). The relationship be tween the intellectual development of technical college instructors and age, e ducation, teaching experience, supervisory experience. Delta Pi Epsilon Journal, 37 95-106. Worthington, E. L., & Roehlke, H. J. (1979). Effective supervision as perceived by beginning counselors-in-training. Journal of Counseling Psychology, 26 64-73.

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97 Zhang, L. F. (1999). A comparison of US and Chinese university students' cognitive development: The cross-cultural applicability of Perry's theory. Journal of Psychology, 133 425-439. Zhang, L. F. (2004). The Perry scheme: Acro ss cultures, across approaches to the study of human psychology. Journal of Adult Development, 11 123-138. Zhang, L. F., & Hood, A. B. (1998). Cognitive development of students in China and the USA: Opposite directions? Psychological Reports, 82 1251-1263. Zhang, L. F., & Watkins, D. (2001). Cognitive development and student approaches to learning: An investigation of Perry's theory with Chinese and US university students. Higher Education, 41 239-261.

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98 Appendices

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99 Appendix A: The Perry Scheme of Cognitive Development Dualism Early Multiplism Late Multiplism Relativism View of Knowledge All knowledge is known; there are clear right/wrong answers Most knowledge is known; there are right/wrong ways to find answers Most knowledge is not known; therefore “everyone is entitled to own opinion” All knowledge is “contextual”; within a context there are right/wrong answers and rules for good thinking View of Instructor Source of knowledge Source of right way to get knowledge Source of the thinking process or irrelevant Source of expertise View of Student Role To receive knowledge; to demonstrate knowledge To learn how to learn, to work hard To learn to think for oneself, to support opinions To study different contexts, see different perspectives View of Peers Not a legitimate source of knowledge Peers are OK, but instructor is still the Authority Peers are a legitimate source of knowledge; all opinions are just as good (or bad) as others’ Peers are legit if they follow rules of adequacy Evaluation Wrong answers = bad person Evaluation is main issue; related to amount of time; quantity of work; fairness Independent thought deserves good grades Or “I’ll do what they want” Evaluation of work separate from evaluation of self; evaluation is part of learning (opportunity for feedback) Adapted from Cornfield and Knefelkamp (1979) and Belenky et al. (1986; 1997)

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100 Appendix B: Demographic Questionnaire 1. Your Age 1. Your Age 2. Gender 2. Gender Female Male 3. Predominant Ethnic Background African American Asian/Pacific Islander European American Latino/Hispanic Middle Eastern Native American Other (please specify) 4. Highest Degree Earned Thus Far BA/BS BSW MA/MS M.Ed MSW Other (please specify) 5. What was your undergraduate major? Business Communications Criminal Justice

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101 Appendix B (Continued) Education Psychology Social Work Sociology Other (please specify)Other (please specify) 6. In which graduate program are you currently enrolled? Community Counseling Counseling Psychology Counselor Education Marriage and Family Counseling Mental Health Counseling Professional Counseling Rehabilitation Counseling School Counseling Social Work Other (please specify)Other (please specify) 7. Current Degree Program: Ph.D/Ed.D/DSW MA/MS/Ed.M MSW Other (please specify) 8. Year in current degree progra m (e.g. indicate your 1st year by typing 1)

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102 Appendix B (Continued) 9. What type of human services experiences have you had where you have provided direct services with individuals, families, or groups? (i.e., employment, practicum, internship, and/or volunteer experience) Job Title (start with most recent) Setting Service (Years) Service (Months) Hours per week Hours of Weekly Formal Supervision (Individual) Hours of Weekly Formal Supervision (Group) 1 2 3 4 5

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103 Appendix C: The Learning Environment Preferences Instrument LEARNING ENVIRONMENT PREFERENCES This survey asks you to describe w hat you believe to be the most significant issues in your IDEAL LEARNING ENVIRONMENT in your graduate training program. Your opinions are important to us as we study how students think about teaching and learning issues. We ask, theref ore, that you take this task seriously and give your responses some thought. We appreciate your cooperation in sharing what you find most impor tant in a learning environment. The survey consists of five secti ons, each representing a different aspect of learning environments. In each section, y ou are presented with a list of specific statements about that particular area. Try not to focus on a specific class or classes as you think about these item s; focus on their significance in an ideal learning environment for you. We ask that you do two things for each section of the instrument: 1. Please rate each item of the section (u sing the 1-4 scale provided below) in terms of its significance or importance to your learning. 2. Review the list and rank the three most important items to you as you think about your ideal learning environment by writing the item numbers on the appropriate spaces at the bottom of the answer sheet. Please mark your answers on the separate answer sheet provided, and be sure to indicate both your rati ngs of individual items and your ranking of the top 3 items in each section It is very important that you indicate your top three choices for each question area by writing the ITEM NUMBER in the spaces provided (1st choice, 2nd choice, 3rd choice). Rating Scale: 1 2 3 4 Not at all Somewhat Moderately Very significant signifi cant significan t significant

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104 Appendix C (Continued) DOMAIN ONE: COURSE CONTENT/VIEW OF LEARNING TO LEARN COUNSELING, MY IDEAL LEARNING ENVIRONMENT WOULD: 1. Emphasize basic facts and definitions. 2. Focus more on having the right answers than on discussing methods or how to solve problems. 3. Insure that I get all the cour se knowledge from the professor. 4. Provide me with an opportunity to learn methods and solve problems. 5. Allow me a chance to think and reason, applying facts to support my opinions. 6. Emphasize learning simply for the sake of learning or gaining new expertise. 7. Let me decide for myself whether issues discussed in class are right or wrong, based on my own interpretations and ideas. 8. Stress the practical applic ations of the material. 9. Focus on the socio-psycho, cultur al and historical implications and ramifications of t he subject matter. 10. Serve primarily as a catalyst for re search and learning on my own, integrating the knowledge gained into my thinking. 11. Stress learning and thinking on my own, not being spoonf ed learning by the instructor. 12. Provide me with appropriate lear ning situations for thinking about and seeking personal truths. 13. Emphasize a good positive relati onship among the students and between the students and teacher. PLEASE BE SURE TO REVIEW THE ABOVE LIST AND MA RK YOUR THREE MOST SIGNIFICANT ITEMS (BY ITEM NUMBER) IN THE LINES PROVIDED ON THE ANSWER SHEET. __________________ __________________ _______________

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105 Appendix C (Continued) DOMAIN TWO: ROLE OF INSTRUCTOR TO LEARN COUNSELING, IN MY ID EAL LEARNING ENVIRONMENT, THE TEACHER WOULD: 1. Teach me all the facts and info rmation I am supposed to learn. 2. Use up-to-date textbooks and materials and teach from them, not ignore them. 3. Give clear directions and guidance fo r all course activities and assignments. 4. Have only a minimal role in the class, turning much of the control of course content and class discussions over to the students. 5. Be not just an instructor, but mo re an explainer, entertainer and friend. 6. Recognize that learning is mutual--indi vidual class members contribute fully to the teaching and learning in the class. 7. Provide a model for conceptualizing li ving and learning rather than solving problems. 8. Utilize his/her expertise to prov ide me with a critique of my work. 9. Demonstrate a way to think about t he subject matter and then help me explore the issues and come to my own conclusions. 10. Offer extensive comments and reactions about my performance in class(papers, exams, etc.). 11. Challenge students to present their own ideas, argue with positions taken, and demand evidence for their beliefs. 12. Put a lot of effort into the cla ss, making it interesting and worthwhile. 13. Present arguments on cour se issues based on his/her expertise to stimulate active debate among class members. PLEASE BE SURE TO REVIEW THE ABOVE LIST AND MA RK YOUR THREE MOST SIGNIFICANT ITEMS (BY ITEM NUMBER) IN THE LINES PROVIDED ON THE ANSWER SHEET. __________________ __________________ _______________

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106 Appendix C (Continued) DOMAIN THREE: ROLE OF STUDENT/PEERS TO LEARN COUNSELING, IN MY ID EAL LEARNING ENVIRONMENT, AS A STUDENT I WOULD: 1. Study and memorize the subject matt er--the teacher is there to teach it. 2. Take good notes on what's presented in class and reproduce that information on the tests. 3. Enjoy having my friends in the class, but other than that classmates don't add much to what I would get from a class. 4. Hope to develop my ability to reason and judge based on standards defined by the subject. 5. Prefer to do independent research allowing me to produce my own ideas and arguments. 6. Expect to be challenged to work hard in the class. 7. Prefer that my clas smates be concerned with in creasing their awareness of themselves to others in relation to the world. 8. Anticipate that my cla ssmates would contribute sign ificantly to the course learning through their own expertise in the content. 9. Want opportunities to think on my own, making connections between the issues discussed in class and other areas I'm studying. 10. Take some leadership, along with my classmates, in deciding how the class will be run. 11. Participate actively with my peer s in class discussions and ask as many questions as necessary to fully understand the topic. 12. Expect to take learni ng seriously and be personally motivated to learn the subject. 13. Want to learn methods and procedures related to the subject--learn how to learn. PLEASE BE SURE TO REVIEW THE ABOVE LIST AND MA RK YOUR THREE MOST SIGNIFICANT ITEMS (BY ITEM NUMBER) IN THE LINES PROVIDED ON THE ANSWER SHEET. __________________ __________________ _______________

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107 Appendix C (Continued) DOMAIN FOUR: CLASSROOM ATMOSPHERE/ACTIVITIES TO LEARN COUNSELING, IN MY ID EAL LEARNING ENVIRONMENT, THE CLASSROOM ATMOSPHERE A ND ACTIVITIES WOULD: 1. Be organized and well-structured--there should be clear expectations set (like a structured syllabus that's followed). 2. Consist of lectures(with a chance to ask questions) because I can get all the facts I need to know more efficiently that way. 3. Include specific, detailed instructions for all activities and assignments. 4. Focus on step-by-step procedures so th at if you did the procedure correctly each time, your answer would be correct. 5. Provide opportunities for me to pull together connections among various subject areas and then construct an adequate argument. 6. Be only loosely structured, with t he students themselves taking most of the responsibility for what structure there is. 7. Include research papers, since they demand that I consult sources and then offer my own interpretation and thinking. 8. Have enough variety in content areas and learning experiences to keep me interested. 9. Be practiced and internalized but be balanced by group experimentation, intuition, comprehens ion, and imagination. 10. Consist of a seminar format, providi ng an exchange of ideas so that I can critique my own perspective s on the subject matter. 11. Emphasize discussions of personal answers based on relevant evidence rather than just ri ght and wrong answers. 12. Be an intellectual dialogue and debate among a small group of peers motivated to learn for the sake of learning. 13. Include lots of projects and assignments with practical, everyday applications. PLEASE BE SURE TO REVIEW THE ABOVE LIST AND MA RK YOUR THREE MOST SIGNIFICANT ITEMS (BY ITEM NUMBER) IN THE LINES PROVIDED ON THE ANSWER SHEET. __________________ __________________ _______________

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108 Appendix C (Continued) DOMAIN FIVE: EVALUATION PROCEDURES TO LEARN COUNSELING, EVALUATION PROCEDURES IN MY IDEAL LEARNING ENVIRONMENT WOULD: 1. Include straightforward, not "tricky," tests, covering only what has been taught and nothing else. 2. Be up to the teacher, since s/he knows the material best. 3. Consist of objective-style tests bec ause they have clear-cut right or wrong answers. 4. Be based on how much students have improved in the class and on how hard they have worked in class. 5. Provide an opportunity for me to j udge my own work along with the teacher and learn from the criti que at the same time. 6. Not include grades, since there aren't really any objective standards teachers can use to evaluate students' thinking. 7. Include grading by a prearranged point system (homework, participation, tests, etc.), since I think it seems the most fair. 8. Represent a synthesis of internal and external opportunities for judgment and learning enhancing the quality of the class. 9. Consist of thoughtful criticism of my work by someone with appropriate expertise. 10.Emphasize essay exams, paper s, etc. rather than objecti ve-style tests so that I can show how much I've learned. 11.Allow students to demonstr ate that they can think on their own and make connections not made in class. 12.Include judgments of the quality of my oral and written work as a way to enhance my learning in the class. 13.Emphasize independent thinking by eac h student, but include some focus on the quality of one's argument s and evidence. PLEASE BE SURE TO REVIEW THE ABOVE LIST AND MA RK YOUR THREE MOST SIGNIFICANT ITEMS (BY ITEM NUMBER) IN THE LINES PROVIDED ON THE ANSWER SHEET. __________________ __________________ _______________

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109 Appendix D: Online Informed Consent Script This survey will ask you ques tions about your opinions of several aspects of your learning preferences. You will not be asked to identify yourself person ally, and no information about you other than what you report on the survey will be collected. The answers you provide will be useful in understanding more about cognitive development of graduate students in counseling and social work an d will be analyzed for the purpose of developing a cognitive complexity model of supervision. Upon completion of the survey you will be asked to provide your email address if you would like to enter a drawing for an Apple iPod Nano. If you understand the purposes of the study, you are a graduate student in a social work program and consent to provide your opinions, please click “yes” below. Otherwise, click “no” and you will be taken away from this page. Yes No

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110 Appendix E: IRB Letter

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111 Appendix E (Continued)

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About the Author Christopher Simmons received a B.S. in Psychology from the University of Louisiana, Lafayette in 1994 and a M.S.W. fr om Louisiana State University in 1997. He is an instructor and coordinator of the Child Welfare Training Program in the School of Social Work at the University of South Flor ida. He is also a Licensed Clinical Social Worker and a Qualified Clinical Supervisor He has provided supervision for Licensed Mental Health Counselor Interns and Licensed Cl inical Social Worker Interns; as well as counseling and social work graduate stude nts. His academic interests are cognitive development, clinical supervision, multicu ltural counseling, and direct practice with children, adolescents, families.