EMPATHY and INTELLIGENCE AS PREDICTORS of COGNITIVE COMPLEXITY in COUNSELING STUDENTS a Dissertation Submitted to the Kent Stat

Home , Cognitive complexity, Human intelligence

EMPATHY AND INTELLIGENCE AS PREDICTORS OF COGNITIVE COMPLEXITY IN COUNSELING STUDENTS

A dissertation submitted to the Kent State University College of Education, Health and Human Services in partial fulfillment of the requirements for the degree of Doctor of Philosophy

Staci A. Hayes

May 2019

ii A dissertation written by

Staci A. Hayes

B.A., University of Akron, 2004

ME.D., Kent State University, 2011

Ph.D., Kent State University, 2019

Approved by

______, Co-director, Doctoral Dissertation Committee Jason McGlothlin

______, Co-director, Doctoral Dissertation Committee Martin Jencius

______, Member, Doctoral Dissertation Committee Dianne Kerr Accepted by

______, Director, School of Lifespan Development Mary M. Dellman-Jenkins and Educational Sciences

______, Dean, College and Graduate School of James C. Hannon Education, Health and Human Services

iii

HAYES, STACI A., Ph.D., May 2019 LIFESPAN DEVELOPMENT AND EDUCATIONAL SCIENCES

EMPATHY AND INTELLIGENCE AS A PREDICTOR OF COGNITIVE COMPLEXITY IN COUNSELING STUDENTS (165 pp.)

Co-Directors of Dissertation: Jason McGlothlin Ph.D. Martin Jencius Ph.D.

Counseling is a complicated and difficult process. Clinical issues often present as ambiguous and incomplete (Welfare & Borders, 2010a), challenging counselors to become comfortable with ambiguity and move forward with confidence. Many factors have been suggested to contribute to the success of counselors despite this perceived deficit. The concepts of empathy and cognitive complexity may be the most effective way of making this difficult and sometimes awkward transition. Simple linear regressions were utilized to determine if age, number of client hours, empathy and intelligence of participants could we accurately predict their level of cognitive complexity. In running of the univariate research design, it was concluded that this was not a predictive linear regression. A high effect size was established with alpha at .05 and power .80. 50 participants were utilized. The predictive variables do not accurately predict cognitive complexity. However, it was determined that there is a slight correlation between how this study defines and measure empathy and cognitive complexity.

ACKNOWLEDGMENTS

There are so many people that contributed to my ability to complete this arduous, challenging and fun task. First of all, to my Participants, thank you so for being so open and giving with your time. This project would have never been possible without your kindness and enthusiasm for the topic. Drs. Miller and Storlie, your mentorship this year has been invaluable and you both are so amazing. The time I spent with you both taught me so much of who I want to be as a counselor educator and I cherish being able to talk openly and honestly and look forward to our continued professional relationship.

In honor of Dr. Betsy Page, I know that you would proud, but I just keep thinking about how much you taught me and how much I still had to learn from you. I thank you for your integral role in this project and all you did for me. To my committee, your kindness and patience chartered me through this long, crazy endeavor and I appreciate all you have done. Dr. McGlothlin, your ability to navigate seemingly complex and difficult situations with grace never ceases to amaze me. Thank you for helping me break this process down into manageable and concrete steps. Dr. Marty Jencius, you came in at such a difficult time and your enthusiasm, sense of humor and thoughtfulness were like a breath of fresh air. Dianne Kerr, thank you for your feedback and support. Your continued commitment to this project has meant so much.

To my Mom, almost everything good in me either came from or has been conscientiously cultivated by you. I am so glad that I understand and appreciate all you have done for me, both big and small. And, all that I am is because of what you have taught and sacrificed for me. I love you so much. To my husband Brandon. I cannot

iv imagine my life without you and you make my heart smile. Thank you for making me laugh and always believing in me. You have turned into such an amazing teammate and have taught me to keep moving forward and believe in myself even if I am not exactly sure what I am doing or where I am going. But, I am just grateful that we get to go there together. To Riley and Emmi, being your mom is the greatest honor of my life. I would have never imagined that you both would be so incredibly strong, funny and kind. With hard work and a little luck, you can do anything and know that I will always have your back. To my sister Shawna, you may not have always understood what or why I was trying to accomplish with this project, but you blindly supported and assumed that I would get it done with grace and ease. And lastly, to my mom and dad Fred and Cheryl, you are there, you show up and I see you both. You are so precious to me and I know you like me better than your son. You both give so much, and I feel like I can never express how much all you do is appreciated. I love you both and thank you for all you have done to help me during this project.

v TABLE OF CONTENTS

Page

ACKNOWLEDGMENTS ...... iv

LIST OF TABLES ...... ix

CHAPTER

I. INTRODUCTION ...... 1 Purpose and Rationale...... 3 Definitions...... 5 Review of the Literature ...... 6 Cognitive Complexity ...... 6 History of cognitive complexity ...... 7 Detecting and measuring cognitive complexity ...... 11 Empathy ...... 15 History of empathy ...... 16 Detecting and measuring empathy ...... 21 Research in empathy ...... 21 Intelligence ...... 26 History of intelligence ...... 26 Detecting and measuring intelligence ...... 27 Research in intelligence ...... 30 Chapter Summary ...... 31

II. METHODOLOGY ...... 32 Participants ...... 33 Procedures ...... 33 Research Methodology ...... 36 Multiple Regression ...... 36 Instrumentation ...... 37 Demographic sheet ...... 37 The Counselor Cognition Questionnaire ...... 37 The Hayes Scale of Empathy for Supervisors...... 42 The Kaufman Brief Intelligence Test of Empathy ...... 45 Data Analysis ...... 48 Delimitations ...... 50 Chapter Summary ...... 51

III. RESULTS ...... 52 Sampling ...... 53 Data Analysis ...... 53 vi Demographic Data ...... 54 Sex assigned at birth / current gender identity ...... 54 Client hours ...... 54 Age ...... 55 Race / Ethnicity ...... 55 Course enrolled ...... 55 Education previous Master ...... 55 Testing Instruments ...... 57 Descriptive Statistics ...... 57 KBIT-2 ...... 58 HSES ...... 58 Preliminary data implications ...... 58 Effect size ...... 59 Modifications ...... 59 Linear Regression for Cognitive Complexity—Differentiation Score ...... 60 Linear Regression for Cognitive Complexity-Integration Score ...... 64 Conclusion ...... 68

IV. DISCUSSION ...... 69 Discussion of Findings ...... 69 Sample Characteristics ...... 70 Discussion of Statistical Analysis ...... 70 Cognitive Complexity, Empathy, and Intelligence ...... 71 Cognitive Complexity ...... 72 Empathy ...... 73 Intelligence ...... 74 Emotional Intelligence ...... 75 Millennials ...... 76 Spirituality ...... 79 Limitations ...... 82 Cognitive complexity ...... 82 Empathy ...... 83 Client hours / practicum enrollment ...... 84 Population ...... 84 Recommendations for Future Research ...... 84 Implications for Counseling, Supervision and Counselor Education ...... 87 Counseling ...... 87 Supervision ...... 88 Counselor education ...... 89 Conclusion ...... 92 Summary ...... 92

vii APPENDICES ...... 94 APPENDIX A. SCRIPT FOR IN-PERSON RECRUITMENT TO SUPERVISORS ...... 95 APPENDIX B. SCRIPT FOR IN-PERSON RECRUITMENT TO STUDENT PARTICIPANTS ...... 97 APPENDIX C. E-MAIL RECRUITMENT FOR INTERNSHIP SUPERVISORS (KSU) ...... 99 APPENDIX D. E-MAIL RECUITMENT FOR INTERNSHIP SUPERVISORS (YSU) ...... 101 APPENDIX E. E-MAIL RECRUITMENT FOR INTERNSHIP STUDENTS (YSU) ...... 103 APPENDIX F. INFORMED CONSENT DOCUMENT PRACTICUM SUPERVISORS (KSU) ...... 106 APPENDIX G. INFORMED CONSENT DOCUMENT PRACTICUM STUDENTS (KSU) ...... 110 APPENDIX H. INFORMED CONSENT DOCUMENT INTERNSHIP Student (KSU) ...... 114 APPENDIX I. INFORMED CONSENT DOCUMENT INTERNSHIP STUDENT (YSU) ...... 118 APPENDIX J. QUALTRICS RECRUITMENT E-MAIL FOR SUPERVISORS (KSU) ...... 122 APPENDIX K. QUALTRICS RECRUITMENT E-MAIL FOR SUPERVISORS (YSU) ...... 124 APPENDIX L. REMINDER E-MAIL TO SUPERVISOR (KSU) ...... 126 APPENDIX M. REMINDER E-MAIL TO STUDENT PARTICIPANTS (KSU) ...... 128 APPENDIX N. REMINDER E-MAIL TO STUDENTS (YSU)...... 130 APPENDIX O. DEMOGRAPHIC QUESTIONNAIRE ...... 132 APPENDIX P. REMINDER EMAIL FOR SUPERVISORS TO COMPLETE HSES ...... 135 APPENDIX Q. COUNSELOR COGNITIONS QUESTIONNAIRE CONTACT INFORMATION FOR INQUIRIES ...... 137 APPENDIX R. HAYES SCALE OF EMPATHY FOR SUPERVISORS ...... 139

REFERENCES ...... 141

viii LIST OF TABLES

Table Page

1. Counselor Cognitions Questionnaire Items, Descriptions, and Score Range (Welfare & Borders, 2007) ...... 39

2. Hayes Scale of Empathy for Supervisors Based on Watson (2002) ...... 43

3. Univariate Analysis ...... 49

4. Multiple Regression Analysis ...... 50

5. Demographic Data Including Sex Assigned at Birth, Current Gender Identity, Client Hours, Age, and Race Ethnicity ...... 56

6. Descriptive Statistics for Cognitive Complexity: Differentiation and Integration ...... 58

7. Hayes Scale of Empathy for Supervisors as Completed by Research Participants ...... 60

8. Descriptive Statistics for Linear Regression Differentiation ...... 61

9. Model Summary Differentiation ...... 62

10. ANOVA Differentiation ...... 62

11. Correlations for Linear Regression Differentiation ...... 63

12. Coefficients Differentiation ...... 64

13. Descriptive Statistics for Linear Regression for Integration...... 65

14. Model Summary Integration ...... 65

15. ANOVA Integration...... 66

16. Correlations for Linear Regression Integration ...... 67

17. Coefficients Integration ...... 68

ix 1

CHAPTER I

INTRODUCTION

Counseling is a complicated and difficult process. Clinical issues often present as ambiguous and incomplete (Welfare & Borders, 2010a), challenging counselors to provide treatment with potentially incongruent or circumscribed information. Many factors have been suggested to contribute to the success of counselors despite this perceived deficit. Two of these factors well substantiated in the literature are cognitive complexity and empathy.

Cognitive complexity can be defined as the ability to create and utilize multiple constructs to define social reality (Crockett, 1965; Kelly, 1955). It is the ability of human beings to make sense of the world around them by utilizing their experiences.

Counselors with high levels of cognitive complexity are more effective in counseling

(Blocher, 1983; Stoltenberg, 1981), because they can form a more comprehensive understanding of their clients (Welfare & Borders, 2010a). When counselors are able to connect this cognitive information to their counseling skills, cognitive complexity is expressed within the counseling relationship (Kindsvatter & Desmond, 2013). This includes bringing what they think they know about their client to what they do in the counseling relationship in a way that promotes better understanding and therapeutic conditions.

The existence of empathy in the counseling relationship is the best predictor of progress in therapy (Watson, 2002). Empathy can be described as a way of being with others to promote healing (Kohut, 1971, 1977; Rogers, 1961, 1975). It is also the ability

2 to understand this experience, and to communicate this in a meaningful way back to the client (Rogers, 1957; Truax & Carkhuff, 1967). Empathy is both an emotional and a cognitive process (Watson, 2002) and research has suggested that individuals operating at higher levels of cognitive development are better able to internalize and communicate empathy for others (Ivey, 1991; Kohlberg; 1984; Loevinger, 1976; Lyons & Hazler,

2002; Schlossberg, Lynch, & Chickering, 1989).

Another variable that may be an imperative contributor is intelligence.

Intelligence tests have long been used to identify students who were at-risk for having significant difficulty or inability to complete current school-based curriculum. Over the years, the traditional intelligence tests have been exchanged for aptitude, standardized, and skill tests (Bledstein, 1994).

Looking at the predictive ability of intelligence in its relationship to both empathy and cognitive complexity could unveil the accuracy of the long-standing tradition of academic aptitude being the hallmark for prediction of successful completion of educational requirements (Smaby, Maddux, Richmond, Lepkowski, & Packman, 2005), most specifically to counseling education, where academic and practical success are not mutually exclusive. Further investigation into the relationship between intelligence, empathy, and cognitive complexity could provide evidence that cognitive complexity is a potential criterion for the admission of counseling students into graduate work (De La

Garza, 2013).

Purpose and Rationale

Although the concept of cognitive complexity has been investigated for many years, it is still unclear how it develops and if we as counselor educators are effectively promoting its development. The major priority of counseling programs is that they are preparing and graduating caring and efficacious counselors with the intent of improving society and communities (Boland, 2004). To fulfill this endeavor, greater attention should be paid to not only instilling the theories and basic skills but also to promoting the development of cognitive complexity (Brendel, Kolbert, & Foster, 2002). After the release of 2016 Council for Accreditation of Counseling and Related Educational

Programs [CACREP] standards, counselor educators and researchers were expected to gain a better understanding of how counselors-in-training develop to ensure that recent graduates are entering the field with an established, professional counselor identity and exhibiting a mastery of both knowledge and skill (CACREP, 2016). CACREP has also clearly delineated the importance of self-evaluation as a constant process, further exacerbating the debate within the field on how we are measuring and determining the existence and growth of cognitive complexity.

Research has suggested that significant growth has occurred during graduate school (Brendel et al., 2002; Duys & Hedstrom, 2000; Granello, 2002; Kendjelic & Eells,

2007; Little, Packman, Smaby, & Maddux, 2005; Welfare & Borders, 2010a), with cognitive complexity increasing when trainees apply their learned skills into actual practice (Kindsvatter & Desmond, 2013). Due to this detection of growth, this research is investigating students after they have had some clinical experience in either their

4 internship or practicum class. Another reason for assessing the cognitive development/empathy levels and intelligence of counselors-in-training could be to better promote the consideration of their developmental level when presenting the curriculum of counselor education programs (Granello & Hazler, 1998). It is with this study that there is hope in identifying the level of cognitive complexity among novice counselors and questioning whether they are entering the field as prepared as they can be.

Counselors with higher levels of cognitive complexity have been shown to use empathy most effectively and express higher understanding of the counselor-client relationship (Duys & Hedstrom, 2000; Lovell, 1999; Lyons & Hazler, 2002). Fiedler

(1950) found that neophyte counselors were more likely to understand their clients intellectually but not emotionally, revealing a gap in their ability to understand and effectively interact with their clients. This further expresses the importance of the integration of cognitive understanding and skill application. Could this potentially be ascertained through the application of empathy and cognitive complexity? Analyzing the relationship between these two variables in conjunction with intelligence will promote understanding of the perceived interaction and potential predictive values of these variables. This study investigates empathy, intelligence, and other demographic information as a predictor of cognitive complexity in Master level counseling students.

The research questions are as follows:

1. How accurately can empathy, intelligence, and number of client hours predict

the level of cognitive complexity (differentiation) in counseling students?

2. How accurately can empathy, intelligence, and number of client hours predict

the level of cognitive complexity (integration) in counseling students?

Definitions

Cognitive complexity can be defined as the ability to create and utilize multiple constructs to define social reality (Crockett, 1965; Kelly, 1955). Specific to counseling, it is the ability of counselors to discern relevant characteristics that contribute to the overall conceptualization of clients (Welfare & Borders, 2010b).

Cognitive development is the increase of cognitive complexity (Welfare, 2007a).

Conceptual system is the way in which people relate and make sense of their environment and events they experience (Harvey, Hunt, & Schroeder, 1961).

Differentiation is the number of constructs a counselor has available to understand his or her client. It is one of the two major components of cognitive complexity

(Crockett, 1965; Kelly, 1955). This is the total number of descriptors of the client provided by the counselor on the CCQ (Welfare, 2007b).

Ego development is the growth of the person’s ability to make sense of his or her world.

Epistemology is a theory of knowledge. It is the study of learning and can lead to better practices in teaching.

Empathy is a way of being with others to promote healing (Kohut, 1971, 1977;

Rogers, 1965, 1975), to understand this experience, and to communicate this in a meaningful way back to the client (Rogers, 1957; Truax & Carkhuff, 1967). Empathy is both an emotional and a cognitive process (Watson, 2002).

Integration is how created constructs are activated and connected to form an overall understanding of others. It is one of the two major components of cognitive complexity (Crockett, 1965; Kelly, 1955). This is one of the scores produced on the

CCQ (Welfare, 2007b). This is the way the counselor organizes and prioritizes the descriptors of the client.

Intelligence is the ability to take experience and previously learned information to adapt to novel situations and manipulate and interact with current environment.

Review of the Literature

The following sections are dedicated to providing an overview of three of the main concepts included in this paper. Cognitive complexity, empathy, and intelligence are discussed. Each section summarizes the history of each construct, how each construct is measured and detected, and relevant research.

Cognitive Complexity

It is exceedingly important that counselors are able to have a workable and malleable conceptualization of clients, as this is continually shifting and morphing not only between, but also, within sessions. New information is constantly being presented and cognitive complexity empowers the counselor to respond appropriately and select the therapeutic intervention that would be most effective in moving towards a therapeutic direction. Effective counselors should be able to integrate the cognitive, emotional, behavioral, and spiritual components within the context of their relationship with the client, and to discern the client’s needs at that time (Welfare, 2007a).

History of cognitive complexity. It has been substantiated over many years that the predominant goal of any counselor education program is to facilitate cognitive development (Blocher, 1983), and this concept has been investigated and researched through varying perspectives and by many approaches. Whether it is referred to as cognitive complexity (Bieri, 1955; Crockett, 1965; Kelly, 1955), conceptual system

(Harvey et al., 1961), or as ego development (Loevinger, 1976), this ability to connect cognitive information to practical application is exceedingly important. This has sparked many investigations within the field on how this construct would be conceptualized and defined. The available research provides both discordant findings and a disjointed compilation of conceptualizations. Spengler and Strohmer (1994) posited that the field has either been investigating different constructs or could be measuring varying aspects of the same construct. The use of counselor-specific measures and more general measures may have also contributed to the differences in the current results (Welfare &

Borders, 2010a). The following paragraphs provide a very brief overview of the most relevant theories to this dissertation with a breakdown of the current research by correlating measure.

Kelly, Bieri, and Crockett. The first theory to be discussed is Kelly’s theory of personality. Kelly’s theory conceptualized and laid the framework of who we were in relationship to others. It had a direct relationship to counseling because it attempted to explain how to make sense of one’s own existence, by considering who a person is in relationship to the world around him or her. Kelly posited that, in order to act accordingly to perceived stimuli, individuals delve into complex filing systems called

8 constructs or schema. These constructs or schemas are complex interpretations of our own life events (Kelly, 1963). This theory is essentially a way of responding to the world based on our experiences.

Following this initial work, Bieri expanded this concept to add important aspects that constructed the accepted view of cognitive complexity today. Bieri expounded these constructs to posit that these created schemas were predominantly used to predict the behavior of others within our social interactions. He referred to this information processing as social judgment and stated that this was an individual’s capacity to interpret this social behavior in a multi-dimensional way (Bieri et al., 1966). Bieri utilized Kelly’s

Construct Grid (1955) to gain an overall score for cognitive complexity. Bieri counted the total number of constructs provided by the respondent and equated this score to the number of constructs the person had available in their total cognitive system. This approach becomes limited by the context of the question. Welfare (2007a) provided an example stating that if the respondents are asked to discuss and reflect on family relationships then only cognitive complexity within that context will be represented.

Although both Kelly (1963) and Bieri et al. (1966) mentioned the ability of these cognitive systems to become more complex with the introduction of new stimuli,

Crockett (1965, 1982) spoke to the domain-specific nature of cognitive complexity.

Crockett stated that it is the process of forming impressions of others that these complicated and hierarchical systems are created. He also presented the workable definition of cognitive complexity that acknowledges the complexity of these cognitive

9 systems. They can be truncated into two differing categories: integration and differentiation. Further discussion of these two concepts is provided in Chapter 2.

The Role Category Questionnaire (RCQ; Crockett, 1965) is the instrument created from this theory. The RCQ does provide for a score resulting in the total number of interpersonal characteristics for both a liked and a disliked peer. Although this instrument has been hallmarked as a measure of social cognition, it has both concerns surrounding test-retest reliability and time constraints (O’Keefe, Shepherd, & Streeter,

1982). The RCQ does not provide an assessment of how the individual connects or organizes the produced constructs.

The instrument utilized in this dissertation is the Counselor Cognitions

Questionnaire (CCQ: Welfare, 2007a). It was developed from a four-phase process in which modifications were made to the Role Category Questionnaire (RCQ; Crockett,

1965). The modifications and rationalizations for the changes are explained in Chapter 2.

Harvey, Hunt, and Schroeder. Harvey et al. (1961) also provided a theory of personality that referred to the concept of cognitive complexity as a conceptual system.

The theory of personality organization is a developmental system intended to analyze the relationship between personality and social cognition as it applies to classroom environments. This included the behaviors of both the student and the teacher. They posited 4 belief systems in which individuals could be classified. These belief systems are multi-dimensional and conceptualized in developmental terms. As the individual interacts with the environment and depending which of the 4 levels they most closely align, behaviors could be predicted. The correlating instrument to this theory is Harvey’s

Conceptual System Test (HCST; Harvey & Hoffmeister, 1967), and the Paragraph

Completion Method (PCM; Hunt, Butler, Noy, & Rosser, 1977). Many of the same limitations apply as to the other general measures. It does not encapsulate conceptual level as it pertains specifically to counselors (Welfare, 2007a).

Loevinger. Loevinger’s concept of ego development is a much more comprehensive construction incorporating components of moral development, socialization, character structure and even cognitive development (Loevinger, 1976).

Through this conceptualization, Loevinger attempted to describe and encapsulate cognitive development in eight stages. Progression through these stages was determined by an existence of shared characteristics, not chronological age. The correlating measure that emerged was the Sentence Completion Test (SCT; Loevinger & Wessler, 1970) and later the Washington University Sentence Completion Test (WUSCT; Hy & Loevinger,

1996). Although the SCT and the WUSCT do collect information about the specific systems for perceiving self, others, and relationship, they negate to ascertain cognitions specific to the client, the counseling relationship, or the counseling process (Welfare,

2007a).

Perry. Perry’s model (1970) was created after a series of interviews that were conducted with 500 undergraduates from Harvard University. Perry found that as students progressed their education, their perceptions changed dramatically. Their assumptions about the nature of knowledge, as well as their expectations of the instructors and of themselves as learners, changed. These epistemological assumptions uncovered the underlying philosophical perceptions about how these students perceived

11 knowledge attainment. It also affected how they were organizing and evaluating these ideologies (Granello, 2010). This model can best be summarized as a “students’ evolving sense of others” (Lyons & Hazler, 2002).

This information prompted Perry to group these assumptions into categories that he referred to as cognitive structures. There are nine developmental positions that can be condensed into four major categories. The correlating instrument is the Learning

Environment Preferences (LEP; Moore, 1989). Although Perry’s model (1970) has been a great contributor to the body of research suggesting a change in graduate students’ level of cognitive development (Duys & Hedstrom, 2000; Granello, 2002, 2010; McAuliffe &

Lovell, 2006), it is a general measure of cognitive complexity and still shares many of the same criticisms.

Detecting and measuring cognitive complexity. In her review of the literature,

Granello (2010) proposed that the current literature can be truncated into different categories including research intended to: map cognitive complexity within a graduate curriculum (Fong, Borders, Ethington, & Pitts, 1997; Granello, 2002; Lyons & Hazler,

2002); develop methods to enhance cognitive complexity (Choate & Granello, 2006;

Duys & Hedstrom, 2000, Fong et al., 1997; Granello & Underfer-Babalis, 2004; Lovell,

2002); link cognitive complexity to enhanced counseling outcomes (Birk & Mahalik,

1996; Borders, 1989); or skill acquisition (Eriksen & McAuliffe, 2006; McAuliffe &

Lovell, 2006). With each theory previously discussed, motivating the development of different measures, the following paragraphs are intended to provide a brief overview of the available research.

Learning Environment Preferences (LEP; Moore, 1989). This first section is intended to describe some of the research utilizing the Learning Environment Preferences

(LEP; Moore, 1989). This assessment is intended to assess the learner’s level of cognitive development by rating 65 items. Granello (2002) utilized the LEP to conduct a cross-sectional study on counseling students in the beginning, middle, and end of their program. She concluded that, although small, there were significant differences in cognitive development between the beginning and end of the counseling program. Lovell in 2002 conducted a longitudinal study and provided support for trainee development. In that same year, Lyons and Hazler looked at empathy, cognitive complexity, and year in the program. Their cross-sectional study let them question whether empathy was a prerequisite to cognitive development (Lyons & Hazler, 2002). Eriksen and McAuliffe’s correlational study of 119 counseling students posited that the moral development of the student explained 18% of the student’s ability to perform counseling skills (Eriksen &

McAuliffe, 2006). A qualitative study correlated developmental epistemology and counseling behavior and proposed teaching methods in counseling based on developmental level (McAuliffe & Lovell, 2006).

Sentence Completion Test (SCT; Loevinger & Wessler, 1970); Washington

University Sentence Completion Test (WUSCT; Hy & Loevinger 1996). The Sentence

Completion Test (SCT; Loevinger & Wessler, 1970) assigns ego development levels by assessing general cognitive complexity. Stemming from the work of Loevinger, it consists of 36 sentence stems. When utilizing the SCT, Borders found that supervisees with higher levels of ego development reported fewer negative thoughts about both

13 themselves and of their clients. They reported that these individuals expressed more

“neutral in-session processing” (Borders, 1989).

Harvey’s Conceptual Systems Test (CST; Harvey & Hoffmeister, 1967).

Harvey’s Conceptual Systems Test (Harvey & Hoffmeister, 1967) is an objective measure used to estimate the conceptual level of a participants’ conceptual system. It consists of 67 statements ranked across a 6-point scale. Goldberg posited that conceptual level may be an important component to counselor selection and that more directive verbal behavior was observed in participants with lower conceptual level (Goldberg,

1974).

Paragraph Completion Method (PCM; Hunt et al., 1977). Utilizing the

Paragraph Completion Method (PCM; Hunt et al., 1977), Holloway and Wolleat suggested that the experience of the student was not related to the overall quality of the clinical hypothesis, but the conceptual level was significantly related to the overall quality and clarity of the hypothesis (Holloway & Wolleat, 1980). A meta-analysis of 24 studies investigating the effects of counselor environment and counselor performance found that individuals with low conceptual level performed better in highly structured environments and those with high conceptual level performed better in low structured environments (Holloway & Wampold, 1986). A longitudinal study of 30 counseling students at three points in their education found that they had achieved a significant gain on the PCM (Brendel et al., 2002).

Role Category Questionnaire (RCQ; Crockett, 1965). Utilizing the RCQ, a pre- and post-test design, Duys and Hedstrom (2000) found that it was possible to measure developmental changes over time. They suggested that following the basic counseling skills course “enhanced” the cognitive development of counseling students

(Duys & Hedstrom, 2000). Further exploring the use of the underpinnings of the RCQ,

Spengler and Strohmer investigated whether there was a relationship between clinical judgment error and level of cognitive complexity. They used vignettes and a modified version of Bieri et al.’s (1966) assessment. Spengler and Strohmer posited that the lower the cognitive complexity level the more likely the individual had to express clinical bias

(Spengler & Strohmer, 1994). This projects a potential correlation between cognitive complexity and accuracy of clinical judgment.

Counselor Cognitions Questionnaire (CCQ; Welfare, 2007a). Although a further explanation of the CCQ is provided in Chapter 2, it is important to remember that its inception was prompted by the concern that measuring cognitive complexity with anything other than a conception of clients would not be providing a score for cognitive complexity of counselors (Welfare, 2007a). Although general complexity is an important pre-cursor to the development of domain-specific complexity (Borders, 1989), the CCQ furthers Crockett’s theory (1965) that domain-specific measures provide more valuable information into the cognitions of counseling practitioner’s cognitions. In their 2010 study, Welfare and Borders (2010a) investigated both the general and domain-specific implications of cognitive complexity. One hundred twenty participants were given the

CCQ and the WUSCT. Results suggested that experience (counseling, supervisory, and

15 counseling education) and highest counseling degree completed were significant predictors of counselor cognitive complexity.

In a study conducted in 2013, researchers questioned whether counselors are able to identify the strengths of their clients and if cognitive complexity played a role.

Utilizing the CCQ, Welfare, Farmer and Lile (2013) stated that of the 120 case conceptualizations provided by both counselors in training and post-master level counselors, overall themes emerged. Clients with whom they felt more effective resulted in the participants identifying more positive than negative characteristics. Clients with whom they felt less effective resulted in the production of more negative than positive traits (Welfare et al., 2013).

Also utilizing the CCQ, Washburn investigated the role of cognitive complexity and the formation of the supervisory working alliance. He posited whether supervision cognitive complexity was a unique aspect of cognitive complexity that had capabilities of greatly affecting supervisory relationships and how supervisees conceptualized their supervisors. In his research with 42 participants, Washburn concluded that although his results were non-significant they did indicate a strong correlation between cognitive complexity and supervisory cognitive complexity (Washburn, 2015).

Empathy

The concept of empathy has been around for a long time. The field of social neuroscience has traced the evolutionary origin of empathy and has succeeded in identifying the use of empathy back to over a million years ago (Carter, Harris, & Porges,

2009). Early works in the field of psychotherapy determine that both Adler (Clark, 2016;

Watts, 1996) and Freud (Bordin, 1979) spoke about empathy in their work. The importance of expressing empathy has been recognized historically (Rogers, 1957; Truax,

1966; Truax & Carkhuff, 1967) and more recently (Bohart & Greenberg, 1997; Elliott,

Bohart, Watson, & Greenberg, 2011; Hazler, 1999; Wampold & Imel, 2015; Watson,

2002). It has also been a determining factor in creating a positive treatment relationship

(Lambert & Barley, 2002; Norcross, 2010).

If asked what empathy is, most individuals would probably speak to the more affective components. This can best be described as “feeling the feelings of another person and responding in a caring fashion” (Bohart, Elliott, Greenberg, & Watson, 2002, p. 89). Although this is essential to human interaction, more is required for the counseling relationship. This is only one of the two major components of the accepted conceptualizations of empathy utilized in this paper. Rogers (1959) emphasized empathy as both an emotional and a cognitive process.

History of empathy. Better distinguishing itself from sympathy, this acceptance of empathy is more focused on understanding where the client is coming from than actually experiencing what they are experiencing (Bohart et al., 2002). This perspective definitively shifts the clinician from only feeling what the client is feeling. It is also about attempting to acquire an understanding of how those feelings are affecting the client’s frame of reference and his or her internalization of the world around him or her.

This challenges the clinicians to move to “sensing” the meaning and feeling of the client without being consumed by the experience (Truax & Carkhuff, 1967).

There is much confusion between the concepts of empathy and sympathy and the danger of misuse in the counseling relationship can lead to damaging effects in the therapeutic process (Clark, 2010). As important as it is for counselors to respond in a kind and caring way, it is more important for them to utilize empathy in a way that benefits the development of the client. Clark made clear distinction between the two concepts by delineating that empathy has aim, appraisal, apprehension, and agreement.

He also stated that although sympathy has immense implications in human interactions, empathy is the factor that improves counseling outcomes (Clark, 2010).

True therapeutic empathy is extremely complex in application. To be able to align oneself with the intricacies of a client’s frame of reference and reflect this to them through both the words that are said and how they are expressed (Truax & Carkhuff,

1967) is very difficult. It is both a message sent and received, implying that, in order for a therapist to be empathic, the client must perceive them as expressing empathy.

Empathy goes beyond understanding what a person is experiencing and delves into knowing how things affect the person emotionally (Watson, 2002). It attempts to extract, realize, and convey concepts that may be verging on the brink of the person’s awareness

(Truax & Carkhuff, 1967). Sometimes referred to as interpersonal empathy, it is putting a client’s personal experiences into words (Clark, 2007).

Empathy was popularized as a point of interest by Rogers in the 1940s and 1950s.

He introduced his non-directive therapy and called it client-centered therapy. It was divergent from many accepted and practiced philosophies of the time and was met with criticism and apprehension by the current field. Clinicians of the time questioned how

18 this construct fit into their existing world of psychotherapy (Elliott et al., 2011). It was dramatically over simplified and many clinicians misunderstood its concept (Watson,

2002). Rogers’s use of empathic reflection was one component that was highly criticized.

Disheartened by the poor response and misunderstandings of his initial attempts to introduce empathy, Rogers waited until his 1975 paper to revisit these concepts. He attempted to clarify his concept of empathy and hoped to promote more understanding of its complex nature. One of his main objectives was to tackle the many misconceptions surrounding the reflecting process. Rogers wanted to substantiate that reflection was not just repeating the words of the patient right back to them but rather a process that requires a secure, patient, strong, but gentle therapist (Rogers, 1975). Rogers attempted to substantiate that the use of empathic reflection is a much more complex and intimate attunement that requires “full attention and emotional connection” and was not simply parroting (Grogan, 2013). Watson posited that empathy is linked to the use of higher levels of cognitive complexity and listening to clients intensely is a “sophisticated exercise of critical deconstruction that requires great concentration” (Watson, 2002, p.

447).

Another concern of the time could have been that if clients could be trusted as their own guide to an empowered state (Bozarth, 1997; Rogers, 1975), then the client could promote and act as catalyst for growth and therapeutic development (Rogers,

1975). This would have greatly circumvented and minimized the inflated importance of therapists at the time and dethroned them as the experts of the lives of their clients.

Rogers (1975) believed that people possessed great power and using empathy, in conjunction with unconditional positive regard and warmth, could empower clients to assume responsibility for their own change. This continues to speak to holistic and layered understanding of empathy embraced in more contemporary times. Is empathy who a person is, what they do, or a combination of both? This becomes a quandary for researchers, as there is still dispute amongst the field today about how empathy is to be defined and measured. Despite this deficit there are still common threads that transcend both the current research and across counseling theories (Feller & Cottone, 2003).

One conceptualization describes empathy to be a “complex structure” that is utilized by therapists in multiple ways (Gladstein et al., 1987; Watson, 2002). Furthering this complexity, Duan and Hill (1996) encouraged a “diversity” of thinking to promote multiple conceptualizations, with the hope that empathy could more clearly defined and more accurate measurements could be found. To further convolute the issue, empathy expresses differently across theoretical orientations (Watson, 2002) and vacillates within therapists (Decker, Nich, Carroll, & Martino, 2013).

Truax and Carkhuff (1967) said the empathic therapist is able to “be inside” the client and experience the world as he does without ever sacrificing the sanctity of the therapeutic relationship. The goal is to become aware of how the client is feeling whilst being able to maintain the therapeutic direction and remain in the therapist role.

For the purpose of this paper, empathy is best described as being comprised of both affective and cognitive components (Davis, 1983; Rogers, 1975; Watson, 2002). It is important to reiterate that empathy is a two-way street with it both being projected and

20 received. The attitudinal component of empathy is expressed by an individual to another and is essentially what a therapist does. Rogers described empathy as what a counselor is doing, as he conceptualized empathy as a process not a destination. Recent adaptations include both subcategories of this construct and multiple ways of expressing it.

Therapists demonstrate empathy in several ways:

1. Communicating with interested, concerned, expressive tone of voice

2. Demonstrating a level of emotional intensity similar to the client’s

3. Reflecting client’s statements, nuances in meaning, or unsaid but implied

meanings back to them (Watson 2002).

The field of social neuroscience has traced the evolutionary origin of empathy and has succeeded in identifying the use of empathy back to over a million years ago (Carter et al., 2009). With recent studies detecting the precursors of empathy emerging from birth (Tousignant, Eugene & Jackson, 2017), but what makes Rogerian empathy different is the integration of the concept of empathy with unconditional positive regard (Bozarth,

2007). It becomes not only the ability to experience the emotions of others and have a cognitive understanding of how it affects the client, but also requires the therapist to regulate his or her own emotional responses (P. L. Jackson & Decety, 2004). It is the process of detecting what is on the brink of awareness (Rogers, 1975; Watson, 2002). It is attempting to internalize the covert messages to empower an individual to understand himself or herself more accurately than he or she would be able to communicate overtly

(Linehan, 1997). This again prompts the importance of understanding both the verbal and non-verbal communication expressed by clients (Truax & Carkhuff, 1967).

Detecting and measuring empathy. Many concerns and issues are presented when discussing the varying kinds of empathy scales. Similar to concerns with the investigation of cognitive complexity, research on empathy is disjointed and varies greatly across perceived conceptualizations, with each of these conceptualizations utilizing different scales to ascertain information. Inter-correlations calculated between the utilized scales are low, predominately attributed to each of these measurements intending to measure different aspects of empathy (Bohart et al., 2002). There is distinct disconnect and confusion amongst the operational definition of empathy with clear delineation occurring between those who recognize only the affective, cognitive, or combined components of empathy (Duan & Hill, 1996).

Four types of scales measure empathy; they are as follows:

1. Observer-rater empathy

2. Client ratings: Barrett-Lennard Relationship Inventory, Barrett-Lennard

(1981) and Gurman (1977) both concluded that the Client perceived empathy

predicted outcome better than observer or therapist rated empathy

3. Therapist ratings: Therapist Empathy Scale

4. Predictive Empathy (Bohart et al., 2002).

Research in empathy. Truax and Mitchell concluded from their review of research in empathy in 1971, that therapists who expressed accurate empathy were effective across theoretical orientations and with a wide range of clients at varying levels of clinical concern (Truax & Mitchell, 1971). Patterson (1984) examined the studies that have been conducted utilizing empathy, warmth, and genuineness and their effectiveness

22 in psychotherapy. Despite noted issues in methodology in the massive available research on empathy of the time, Patterson stated that the “evidence was nothing short of amazing” (Patterson, 1984, p. 437) for the importance of empathy. He even stated that his conclusion is that the therapeutic conditions presented by Rogers, including empathy, are necessary and potentially sufficient (Patterson, 1984).

Over the course of three studies, Benack (1988) investigated if epistemological thought affects empathic understanding. It was concluded that a more relativistic epistemological thought paradigm was associated with more accurate empathic understanding. This contributed to the underlying belief in the field that cognitive growth is a basis for advanced forms of empathy. Benack urged that closer attention should be paid by supervisors and educators to what epistemological assumptions their students are taking as this may reveal their ability to take another’s perspective. It was cautioned that students should not be labeled as these assumptions and thoughts may vary over time and across environments (Benack, 1988).

Sharing many of the methodological concerns with previous reviewers, Duan and

Hill (1996) investigated and posited potential solutions for the perceived issues in the research on empathy. They continued to state that this inconsistency in construct definition and issues with measurement mentioned earlier coupled with the inability to replicate research findings have all contributed to weak contributions to the overall body of work. Duan and Hill urged investigators to continue research in this area because more information is warranted to promote the therapist’s ability to contribute to the therapeutic relationship.

Bohart et al. (2002) conducted a meta-analysis of empathy research examining the potential relationship between empathy and psychotherapy outcomes. Researchers stated that they were “surprised” by the level of association between these two factors and that the therapists’ understanding of their clients’ perceptions is related to positive outcome

(Bohart et al., 2002). Empathy is not only a process, but a two-way street in which empathy is both expressed and received.

In their investigation of the available empathy research, Feller and Cottone (2003) reported strong evidence that despite theoretical orientation, empathy was a definitive contributor to the effectiveness of the therapeutic outcomes. They also voiced concerns about the issues in defining and measuring the construct of empathy, re-iterating the importance of not knowing exactly what empathy is but that it is important.

A cross-sectional study conducted by Lyons and Hazler (2002) attempted to uncover whether the year in program affected trait-based empathy and cognitive-skill based empathy. Although they were able to confidently report that empathy did increase from the participant’s first to second year, they stated “mixed” results in concerns to the relationship between cognitive development and empathy levels (Lyons & Hazler, 2002).

Elliott et al. (2011) attempted to define empathy as neuroanatomical sub-process that consisted of three concepts. These processes are emotional simulation, perspective- taking, and emotion-regulation. An updated meta-analysis was used to explore the relationship between empathy and psychotherapy outcome. With the group determining that empathy is a moderately strong predictor of outcome, they reinforced the previous

24 findings that the more understood the client feels, the better the outcome (Elliott et al.,

2011).

In their meta-analysis of empathy research, Wampold and Imel (2015) stated that empathy was a valuable therapeutic variable. They concluded that empathy along with congruence, genuineness, and therapist/client relationship are the most powerful variables that contribute to the effectiveness of counseling (Wampold & Imel, 2015).

Novice counselors are frequently told in training to follow the emotion because it is within these emotional responses that clients reveal what needs to be addressed in the therapeutic relationship. Therapists are able to unfurl the trapped and barely known realities of importance and the significance or meaning of events for them (Goleman,

1996; Greenberg, Rice, & Elliott, 1993; Orlinsky & Howard, 1986; Rogers, 1965; Taylor,

1990; Watson, 2002). Through empathy, counselors are able to facilitate a deeper understanding of events and life experiences that are lingering on the hearts and minds of the clientele we serve. Empathy empowers the user to be able to bring this understanding to fruition but also to have the ability to discern between what should be said and what should not be said (Watson, 2002), inciting a level of discretion that becomes necessary for safeguarding and protecting clients.

Empathy is a major predictive factor in the success of the therapeutic relationship

(Watson, 2002). It is very important that therapists are instilled and educated on ways of being empathic and expressing this empathy with their clients. Although many developmental theorists posit that the ability to express empathy is present from birth

(Watson, 2002), and that evolutionary biologists have traced its existence to over a

25 million years ago (Carter et al., 2009), novice therapists have been shown to lack the ability to emotionally understand their clients (Fiedler, 1950). Rogers himself stated that empathy can be taught and that it was not a characteristic that needs to be inherent in individuals from birth (Rogers, 1975). It is with the modeling of empathy that individuals can learn to be more empathic. When therapists listen empathically they are modeling empathy and clients are taught how to be more nurturing of themselves and others (Rogers, 1975; Watson, 2002).

Research supports that therapists who have more experience, have more empathy

(Lafferty, Beutler, & Crago, 1989; Marangoni, Garcia, Ickes, & Teng, 1995; Muller &

Abeles, 1971; Watson & Prosser, 2002). Novice counselors can be taught by individuals with empathy (Rogers, 1975) with most of the existing empathy coming from the therapists’ own experiences in their lives (Truax & Carkhuff, 1967).

Although the empathy has been substantiated to be a definitive predictor of client progress, it is still a necessity to articulate the importance of empathy to therapists and encourage them to challenge themselves to portray empathy with their clients (Watson,

2002). It is also important to ensure that current curriculum is aimed at cultivating and fostering the growth of empathy in counselors in training. Empathy is both who we are and what we do. It takes a mature, secure, and caring therapist to successfully delve into the world of another person without losing himself or herself. Keeping it about the client empowers the counselor to keep the focus on the therapeutic direction and use discretion to bring helpful thoughts into awareness.

Intelligence

Intelligence also plays an imperative role in the interpersonal interaction between counselors and clients. Counselors must be able to accurately and quickly process and discern information presented by their clientele, but the role of intelligence in counseling is complicated. If counseling itself is viewed as an exchange of information, then it is imperative that the counselor is aware and able to identify the difficulties faced by their clients. This includes difficulties perpetuated by both the outside world and those that could be potentially created by the counseling relationship itself (Heppner & Fitzgerald,

1987).

Sternberg (1982) defined intelligence as a way of responding to the world as goal- directed and adaptive to both cues from internal and external demands. If a counselor is able to use his or her intelligence to sort through the difficulties of misinformation processing in what their clients are engaging, they may be able to identify what needs to be addressed in counseling. With problem-solving and adaptive behavior being one of the major goals of counseling (Fretz, 1982; Heppner, 1978), counselors could use their intelligence to promote intelligence (Heppner & Fitzgerald, 1987).

History of intelligence. From its inception, the use of intelligence tests has been shrouded in controversy and ethical quandary. Many state that IQ tests have a long history of oppressing certain populations and believe that it is a science rooted in practices that succumbed to pressures of political agendas and oppressive ideologies

(Kamin, 1974a, 1974b). Mülberger (2014) also posited that there is a very real debate

27 regarding the reliability of empirical data with much of the methodological data stemming from problems with utilizing appropriate sample populations (Carson, 2014).

Despite the potential oppressive nature, intelligence testing has endured due to the perceived benefits. When used with children, it can identify cognitive limitations and provide both parents and teachers with the ability to allocate additional resources.

Providing gifted children with more challenging coursework and researchers with the ability to test large groups of people and discern large amounts of information. Some of these were the ability to diagnose intellectual disabilities, organic brain damage, and other mental pathology (Carson, 2014).

Detecting and measuring intelligence. The origin of intelligence tests as we know them today was developed in the early 1900s. Alfred Binet and Theodore Simon developed the first modern intelligence test in 1904 (Binet & Simon, 1905). They were commissioned by the French ministry of intelligence to establish an assessment to discern children with disabilities from children without disabilities. Their aim was to assess the children’s intellectual functioning and provide informative direction for establishing more effective pedagogy. Utilizing words such as “normals, idiocy, imbecility, and feeblemindedness” (Binet & Simon, 1905), attempts were made by the pair to quantize intellectual deficits more scientifically and accurately (Carson, 2007).

In 1908, Henry Herbert Goddard brought the latest version of the time to the

United States (Benjamin, 2009). He was the superintendent of the New Jersey training school for feeble-minded girls and boys in Vineland, New Jersey. He relied on intelligence tests to aid him to better assess and serve this population. The use of the

28 intelligence test prompted a heated debate and unearthed a dangerous implication for the potential congenital nature of intellectual deficiencies. Goddard has a particularly controversial and interesting history and gained much support with eugenicists of the time (Benjamin, 2009). This theory of hereditarianism prompted Kamin many years later to claim that there continues to be no support for this ideology historically or presently

(Kamin, 1974a, 1974b).

In 1916, Lewis Termin furthered the existing version and dubbed it the

Stanford-Binet (Termin, 1916). It included both old items and the inclusion of new items. Later, in 1937, revision of the scale offered improvements in allowances made for a wider range, a two-parallel form that allotted for re-testing, and most importantly was more adequately standardized. It was re-normed in both 1960 and 1972 with a completely revised version emerging in 1986.

During World War I, the United States set out to create a way to assess the massive amounts of recruits. Social changes and global economic climates greatly affected and careened the development of intelligence testing. The modern world was forced to contend with social issues and unrest on a larger scale than had been seen before. This prompted the best minds of the day to make adjustments to manage and adjust to the quickly changing world. The United States’ participation in World War I prompted the emergence of educational psychology as a legitimate field of science (R.

Jackson, 2007), rallying support and credibility for further development of quick, accessible, and widely applicable tests and measures. Lewis Termin and Robert Yerkes created the Alpha Beta test (Termin, 1916). It was easy to administer to large groups and

29 the performance of the candidate on the test determined the level of advanced training they would receive.

In order to alleviate a perceived deficit with the Stanford-Binet, David Wechsler began designing an instrument that was not intended for use with children (Wechsler,

1939). His instrument was the Wechsler-Bellevue and it was introduced in 1939. It has gone through many revisions since its inception. Revised in 1947, re-standardized in

1955, and renamed the WAIS, followed by the WAIS-R in 1981. There is also a version intended for children age 6–17. The 1949 version was entitled the WISC and the 1991 version was entitled the WISC-R.

Many other intelligence tests have gained popularity, but the Kaufman Brief Test of Intelligence 2 was selected for the purpose of this study. Further explanation for the selection of this test is provided in Chapter 2 with an overview of the characteristics and description of the test provided.

Despite the existence of perceived marginalization, the damaging labeling of children into winners and losers (Ayers, 1993), intelligence still exists in the world of education. The moniker has changed from intelligence testing to more user-friendly labels like standardized, aptitude, or skill testing and remains prevalent in class rooms from kindergarten to graduate school (Bledstein, 1994).

History of this realm of testing can be traced back to 1926. Carl Brigham utilized the Alpha Beta test to create a scholastic admission test. It was administered at Princeton

University and prompted the long-standing tradition that performance on standardized test could predict success in college. It was the test that we now know as the SAT. It is

30 broadly used for the admission into undergraduate programs and is taken by junior and seniors in high school. In 1947, the creation of Educational Testing Services prompted advancement in the accessibility of standardized testing. Since its inception, ETS offers the Graduate Record Examination (GRE), a high school equivalency test, Praxis, and has expanded to include two English language tests.

Research in intelligence. Most of the research conducted on the predictive value of standardized tests is done in conjunction with other variables including the participant’s previous grade point average. In one such study of 827 undergraduate institutions, it was posited that a combination of SAT scores and previous GPA correlated .58 with the grades of undergraduates (Manning & Jackson, 1984), raising important inquiry into the implication of only considering the right-tail applicants.

Mirroring this criticism, much of the existing body of work that has been conducted has been with the students admitted into college (Dawes, 1975; Linn, 1982).

Although there has been research done on the application process of counseling students, no consensus has been met on which of the utilized variables are the best predictors of success in a counseling program (De La Garza, 2013). However, there has been emphasis on not only assessing varying forms of intelligence but also in non- cognitive variables (Sedlacek, 2003). An example of this is the use of interviews, which have become an important part of the counseling admissions. This can be attributed to the need for the ability of counselors to connect and maintain interpersonal relationships.

In his study, De La Garza (2013) utilized a multiple regression analysis to investigate whether the factors of GRE scores, previous GPA, and faculty interview

31 ratings could be predictive of scores on the Counselor Cognitions Questionnaire. The study included 182 applicants and determined, despite small effect size, that the aforementioned variables were statistically significant of cognitive complexity scores.

Admission criteria predicted integration (p = .003), accounting for 9.8% of variance and predicted differentiation (p = .033) accounting for 6.6% of variance (De La Garza, 2013).

Intelligence testing, including the more contemporary application of standardized, aptitude, and skill testing, has a long-standing history of marginalizing ethnic minorities

(Mülberger, 2014). Despite this tragic legacy, these tests remain a constant and utilized professional tool for those in academia, raising the question of how academics can promote excellence whilst maintaining equality (R. Jackson, 2007).

Chapter Summary

This chapter has provided an overview of cognitive complexity, empathy, and intelligence. It included a brief history, issues, and concerns surrounding the detection and measuring of each variable, and the significant research available. Chapter 2 describes in detail the research question and plan for the application of the study.

CHAPTER II

METHODOLOGY

The purpose of this study is to identify which combinations of factors including empathy, intelligence, and number of client hours predict the level of cognitive complexity. Cognitive complexity has been linked to a counselor’s ability to provide better counseling. Higher levels of cognitive complexity empower counselors to be more flexible in session (Holloway & Wampold, 1986; Stoppard & Miller, 1985), and conceptualize and describe clients more effectively (Borders, 1989; Choate & Granello,

2006; Duys & Hedstrom, 2000; Fong et al., 1997; Martin, Slemon, Hiebert, Hallberg, &

Cummings, 1989). Cognitive complexity promotes more successful and efficient counselors (Welfare & Borders, 2010a). Explorations into the detection of cognitive complexity in counselors have implication for supervision, student retention, admissions, advisement, and program development.

This study investigates levels of intelligence as measured by the Kaufman Brief

Intelligence Test-2 (KBIT: Kaufman & Kaufman, 2004), perceived expressions of empathy as measured by the Hayes Scale of Empathy for Supervisors, and number of client hours, as predictors of cognitive complexity as measured by the Counselor’s

Cognitions Questionnaire (CCQ: Welfare, 2007b), in counseling students. A multiple regression analysis is utilized to determine the predictive ability of the independent variables (intelligence, empathy, and number of client hours) on the dependent variable

(cognitive complexity). The research question for this study is the following:

1. How accurately can empathy, intelligence, and number of client hours predict

the level of cognitive complexity (differentiation) in counseling students?

2. How accurately can empathy, intelligence, and number of client hours predict

the level of cognitive complexity (integration) in counseling students?

Participants

Counseling students and Supervisors from two Midwest, CACREP accredited programs, enrolled in practicum and internship were approached to participate in the study. Students and supervisors were approached face-to-face. At this time, the purpose of the study was explained, they were asked for participation, and written consent was obtained from the counselors-in-training and supervisors. The students had to verbalize that they had worked with two clients, one with whom they felt effective and one with whom they felt less effective working in the counseling relationship. This experience ensured that they were able to complete the CCQ with confidence. All participation was completely voluntary. Participants had to agree to complete all aspects of the study, be

18 years or older, feel comfortable with the English language, and complete the informed consent sheet. Demographic data were gathered from all student participants enrolled in clinical mental health counseling. No other inclusion criteria were established. Based on a high effect size, alpha at .05 and power at .80, 50 participants were utilized in this study.

Procedures

This chapter is intended to provide a detailed description of the methods engaged to perform the subsequent study. The procedures are outlined including the process for

34 the selection of participants. Next, a clarification of the choice to utilize multiple regression methods is discussed. Following this, the reader is provided with the rationale for the selection of each instrument including: the norming data, validity, reliability, and the scholarly critique of these instruments.

The demographic sheet, written copies of the CCQ, Hayes Scale of Empathy for

Supervisors, and the KBTI were prepared for dissemination. The KBTI-2 was purchased for use and the author of the CCQ granted permission for use. Copies of the CCQ are available by request from the author as listed in the Appendices. The author of the CCQ asked that any interested parties contact them directly for use of their assessments. The

Institutional Review Board at the university where the study was conducted reviewed and accepted the proposal.

In week one and two of the Fall semester, the researcher was present in the

University Counseling Center. Supervisors and students were introduced to the study and invited to voluntarily participate. They were given the description of the pending research. The requirements and time constraints were discussed. Prospective student participants were told that they would complete the CCQ during class time and schedule a time with the researcher outside of class to complete the KBTI-2. Prospective

Supervisor participants were informed that approximately 30 minutes of class time around the sixth week of the semester would be needed to administer the CCQ during class time. They were also given a copy of the Hayes Empathy Scale for Supervisors with which to become familiar and were asked to complete this scale for each student participant after the midterm evaluations. This would be around the week nine mark.

Once consent was obtained, the student participants were given the demographic sheet to complete and the researcher scheduled times with the students to administer the

IQ portion of study in weeks three, four, and five. One week before the scheduled date, the student participants were sent a reminder e-mail. In weeks three, four, and five the researcher met with the students and administered the KBTI-2. The administration of the test occurred in private spaces, as they were made available. Private spaces were defined as a room or office that was able to provide for a shut door that was not opened during the test, with no other persons in the room except for the researcher and participant.

The researcher then worked with the instructor to schedule a 30-minute time block to administer the CCQ around the sixth week of the semester. Students who were not interested in participating were given the option to be excused for the administration of the test or remain present. The CCQ was then administered. The participants were asked to list two clients: one with whom they believed they were effective and one with whom they believed they were less effective. The respondents were given 15 minutes per client and were asked to compare and contrast the defining characteristics of each client and write words or phrases that explain them. The participants were then asked to rank the importance of each characteristic and to categorize these characteristics into perceived groups. Time was kept on an iPhone 6 to ensure the time constraints. At the end of the administration, all completed paperwork was collected and stored in a locked file cabinet drawer.

Before the midterm evaluations, the supervisors were sent an e-mail reminding them to complete the Hayes Scale of Empathy for Supervisors. They were asked to

36 complete the assessment following the midterm evaluations but before the end of the semester for each of the student participants.

Upon completion of all aspects of the survey, the results were entered into the

Statistical Package for the Social Science (SPSS) for MAC IOS. This program was selected to conduct the analysis for the ascertained data. Demographic data, scores on the

KBTI-2, the CCQ, and the empathy scale were all entered into SPSS. All personal information was kept in a data-protected document in order to protect the anonymity of the participants. No identifiable information was shared with anyone or entered into

SPSS. All documentation and responses were safeguarded and maintained by the researcher.

Research Methodology

The main function of this study was to test whether it is possible to predict the level of cognitive complexity of students when other information was known. The appropriate method for study was a multiple regression because this allowed for the multiple independent variables of empathy, intelligence, and the demographic data to be understood in terms of their ability to predict the dependent variable of cognitive complexity. This allowed for the independent variables of age, client hours, intelligence and empathy to predict the dependent variable of cognitive complexity.

Multiple Regression

The standard regression model was utilized because the size of the overall relationship was desired, as well as how much each independent variable aided in the prediction of this relationship. In standard multiple regression, the predictor variables of

37 empathy, intelligence, and number of client hours could all be entered into the equation at the same time. This provided an overall picture of the relationship and gave pertinent information to move forward with more focused analysis such as the stepwise regression method.

Instrumentation

Multiple instruments were utilized in this study as well as a demographic sheet to ascertain information about the participants in the study. The instruments were the

Counselor Cognition Questionnaire (CCQ), the Hayes Scale of Empathy for Supervisors

(HSES), and the Kaufman Brief Intelligence Test (KBTI-2). The CCQ was utilized to ascertain a score for cognitive complexity. The Hayes Scale of Empathy for Supervisors provided scores for empathy and the KBTI-2 was for measuring intelligence. Each instrument is described in the subsequent paragraphs.

Demographic sheet. The demographic sheet given to participants was created to gain selected background information on all participants. The participants were asked to complete the demographic sheet. These components were included to identify which variables, if any, aid in the prediction of scores on the other assessments.

The Counselor Cognition Questionnaire. The CCQ was developed to measure the level of cognitive complexity in counselors of all levels of development and experience. It is intended to aid in the education and development of counselors, in supervision and research, and was created to fill a perceived gap in the research tools available (Welfare & Borders, 2010b). The CCQ measures the level of complexity of neophyte counselors’ conceptualizations about their clientele and have implications for

38 both supervision and curriculum (Welfare & Borders, 2010b). All inquiries involving the

CCQ should be directed to the author of the CCQ, Laura Welfare. This instrument should not be used without permission. All contact information is available in the

Appendix I.

The CCQ was developed from a four-phase process in which modifications were made to the Role Category Questionnaire (RCQ; Crockett, 1965). The RCQ was selected based on its perceived flexibility, proven validity, and reliability and became an appropriate template to incorporate a more client-focused orientation (Welfare &

Borders, 2010b). The general format and scoring protocol were utilized with the most significant modification being the inclusion of more counselor specific language. This was deemed necessary because complexity is domain specific (Crockett, 1965, 1982) and it is important that research conducted on cognitive complexity in counselors collect information of cognitive complexity in counseling (Welfare & Borders, 2010a).

Two scores are provided upon completion of the CCQ coinciding with both components of cognitive complexity: differentiation and integration (Crockett, 1965;

Kelly, 1955). Differentiation scores provide a score for the number of constructs a counselor has available to understand his or her client with scores of one or two points given for responses. These are essentially descriptions of the client provided for the counselor. Duplicate responses for both clients resulted in no points. Differentiation scores range from 0–75, with previous samples yielding scores in the 0–50 range. Most counselors in training score between 10–20, and a score of 25 or above is suggestive of a

39 complex system (Welfare & Borders, 2010b). Please see Table 1 for detailed descriptions of integration and differentiation.

Table 1

Counselor Cognitions Questionnaire Items, Descriptions, and Score Range (Welfare & Borders, 2007)

Item Item description Score Range

Differentiation Total number of descriptors of 0–75 (total number of the client provided by the descriptors) counselor

Integration The way the counselor organizes 0–more than 30 (how the and prioritizes the descriptors of descriptors are connected) the client

Integration scores provide a score for how these constructs are activated and connected to form an overall understanding and conceptualization of the client (Welfare

& Borders, 2010a). This is how the counselor organizes and is able to make connections between the provided descriptors. For integration scores the higher the number of categories the more complex the individual is and the lower the score the lower the level of complexity among the constructs. In previous samples, counselors in training scored below a score of 10 and doctoral or practicing counselors ranged between 12–18.

Integrations scores provide insight into how respondents create cognitive systems and map connections.

Norming, reliability and validity of the CCQ. Norming sample participants were both counselors (N = 36), and counselors-in-training (N = 77). All students attended one of 7 CACREP accredited degree programs across the country that were either public or private. The age range was from 22–59 years of age (M = 30.58, SD = 8.35), with 88% being female and 12% being male. Participants were predominantly White with 92 of the

113 participants identifying themselves from this demographic group. Twelve identified as Black or African American, 2 as American Indian or Alaska Native, 2 as Asian, 1 as

Native American or Other Pacific, 3 as Hispanic or Latino, and 3 as Other. All counseling specialties were represented. Means and standard deviations were calculated for both integration and differentiation scores. Differentiation scores ranged from 6–72 and integration scores ranged from 2–22. Means with standard deviations in parentheses were 21.68 (10.35) and 9.94 (3.82), respectively.

The CCQ is self-instructional, and prospective scorers are expected to complete all practice exercises, review response examples, and achieve an inter-rater reliability score of .90 before utilizing the CCQ in research. During the pilot study, the responses were scored and an inter-rater reliability score of .99 was achieved from differentiation and a score of .95 for integration (Pearson product moment correlation, r [differentiation]

=.99, sig .00, r [integration]=.95, sig=.00; Welfare & Borders, 2006). These findings suggest an appropriate consistency that promotes the CCQ as a reliable instrument.

Deeply rooted in the psychometric stability of the Role Category Questionnaire

(Crockett, 1965), and comparisons between the CCQ and the RCQ yielded evidence for the external aspect of construct validity (Welfare & Borders, 2010b). The developers of

41 the CCQ expounded on this by engaging in four phases of development to provide preliminary data of more extensive validity. Messick’s (1995) model influenced and shaped the validation questions and was utilized as a baseline to provide evidence for external, structural, and construct validity. The initial phase yielded support for a positive relationship between the two indices, integration and differentiation, in two independent samples. Phase four furnished a positive correlation (r[117] = .64, p = .00) again suggesting that although they are related they cannot sufficiently be explained by only one score. This suggests the importance of assessing both constructs when measuring cognitive complexity (Welfare & Borders, 2010b).

Construct validity was also generated by the mean differentiation and integration scores amongst the respondents. ANOVA were conducted across the differing specializations of counseling, integration, and differentiation scores. The ANOVA for integration was significant at the p = .05 level (F[4,113] = 2.78, p = .03). Mental health counselors were deemed to produce significantly higher scores. Researchers did, however, offer that there may be too many confounding variables when assessing across differing specializations and that further investigation may be warranted (Welfare &

Borders, 2010b).

Scholarly critique of the CCQ. Although evidence was suggested to substantiate external, structural, and construct validity, there are some cautions expressed by the creators of the CCQ. They encouraged rigorous questioning and ongoing verification of any instrument including the CCQ (Welfare & Borders, 2010b). The CCQ should not be used with individuals who are not English speakers, and there is some concern over the

42 attention and focus that is required to complete the desired tasks in the allotted time

(Welfare & Borders, 2010b).

The CCQ is one of the first measures to be both domain specific and to incorporate differentiation and integration. It is also intended to be used in the training and supervision of both emerging and established counselors. It has significant implication for both curriculum and supervision and was chosen for incorporation into this study as a measure of cognitive complexity.

The Hayes Scale of Empathy for Supervisors. The HSES is an observer rated, global measure of empathy that measures specific components of expressed empathy (see

Table 2). The theoretically underpinnings of the assessment are rooted in the work of

Rogers and conceptualizes empathy as both an “emotional and cognitive process”

(Rogers, 1959; Watson & Prosser, 2002). The researcher created this instrument to provide an over-all score for empathy. The HSES was completed by the supervisor for each student participant, following the mid-term evaluation to ensure that they will be able to accurately access the students’ ability to express empathy with clients. They completed the assessment utilizing the student participants’ overall work with clients and did not include any specific client data.

Each item was selected based on their ability to encompass the affective and cognitive components of empathy. In order to assess the student participant’s ability to express the affective features of understanding the feelings of their clients and respond appropriately (Bohart et al., 2002), items one and two were added. These two items require the supervisor to assess the non-verbal and verbal communication of the student

Table 2

Hayes Scale of Empathy for Supervisors Based on Watson (2002)

Item Item description Score Range

1 The counselor-in training utilizes non- Likert scale: Strongly Agree, Agree, Slightly verbal communication to convey Agree, Disagree, Slightly Disagree, Strongly empathy Disagree

2 The counselor-in training utilizes verbal Likert scale: Strongly Agree, Agree, Slightly communication to convey empathy Agree, Disagree, Slightly Disagree, Strongly Disagree

3 The counselor-in training expresses an Likert scale: Strongly Agree, Agree, Slightly understanding of the non-verbal Agree, Disagree, Slightly Disagree, Strongly communication expressed by clients Disagree

4 The counselor-in training expresses an Likert scale: Strongly Agree, Agree, Slightly understanding of the verbal Agree, Disagree, Slightly Disagree, Strongly communication expressed by clients Disagree

5 The counselor-in training is able to use Likert scale: Strongly Agree, Agree, Slightly empathic reflections to put the client’s Agree, Disagree, Slightly Disagree, Strongly experiences into words Disagree

6 The counselor-in training matches the Likert scale: Strongly Agree, Agree, Slightly emotional intensity of clients Agree, Disagree, Slightly Disagree, Strongly Disagree

7 The counselor-in training recognizes Likert scale: Strongly Agree, Agree, Slightly their own emotional responses Agree, Disagree, Slightly Disagree, Strongly Disagree

participant. Item six also speaks to the affective features of empathy. It is intended to rank how well the student participant can respond to clients with similar emotional intensity. Watson (2002) stated that empathy included that the counselor respond to the client’s emotions with similar emotions.

With so much of this accepted conceptualization of empathy utilized in this paper encapsulating a two-way street, it was deemed important to include items three and four.

These items are intended to determine how well the student participant is able to receive messages sent from the client in both non-verbal and verbal communications. The accurate expression of empathy requires that it is both a message sent and a message received.

Item five was included that the cognitive aspects of empathy be included in the overall assessment. As stated in the earlier chapter, the understanding of the feelings of another human being is imperative but professionals should always be striving for more significant meaning attribution. It is when a client’s experiences are put into words that deeper meaning attribution occurs (Clark, 2007). This item is intended to assess the student participant’s ability to do this.

The last item was included to assess how well the student participant was able to stay in the role of counselor and preserve the therapeutic relationship. The item asks the supervisor to rank how well the counselor-in-training recognizes his or her own emotional response. Truax and Carkhuff (1967) referred to this as recognizing the feelings and the underlying meanings of these feelings without being consumed by the world of the client.

Norming, reliability and validity of the HSES. The HSES was created for the purpose of this study and at this point there is no available norming or reliability information available at this time. Face validity was utilized during the development of this assessment. Four colleagues were given the assessment and feedback was elicited and integrated. This scale was developed to provide a global score of empathy so that this variable could be included in the multiple regression. The supervisors were selected to

45 complete the assessment as they could potentially bring an insight to the assessment that could not be ascertained from the observation of an outside party. The supervisory relationship would provide information of how the student participants conduct themselves with multiple clients, their ability to express empathy across these clients, how effectively they were able to provide meaning attribution, and how well they were able to become aware and regulate their own emotional responses.

Because clients are categorized as a vulnerable population, viable empathy measures that utilized outside observers viewing student participants with actual clients were ruled out for this study in order to preserve the sanctity of the client’s confidentiality. The supervisor utilized no specific client data and they utilized the HSES to provide a global assessment of empathy.

Scholarly critique of the HSES. One criticism is that there is no available inter-rater reliability because there are multiple supervisors completing the HSES. Future endeavors should include a way to control for this variable and at this point the data should be viewed with caution. Norming and reliability for this assessment could later be accounted for by multiple actions. The first step is to give this assessment to diverse populations and provide norming information for the assessments later use. The second would be to find a similar assessment more deeply rooted in psychometric stability and compare the scores.

The Kaufman Brief Intelligence Test of Empathy. The Kaufman Brief

Intelligence Test Second Edition was designed to provide individuals with limited psychometric experience, with a short, valid, reliable assessment that can be used for a

46 myriad of instances in which a brief assessment is more practical (Kaufman & Kaufman,

2004). The KBTI-2 covers a wide age range and can be used with individuals from age 4 through 90 years. Three scores are yielded including: a score for verbal, non-verbal, and

IQ composite.

The KBTI-2 is modified from the K-BTI. There is now a continuity of content across the entire age range with new items included to assess across all difficulty levels.

A new verbal score was added including two subtests that provides for pointing to responses not requiring the examinee to read. Other updates include an updated matrices subtest and full-color stimuli for all three subtests.

Norming, reliability and validity of the KBTI-2. The KBTI-2 was normed using

2,120 children and adults ranging from 4–90 years of age. The participants were recruited by site coordinators at 113 sites across 34 states and including Washington DC.

The standardization sample was stratified by sex, education level, race/ethnicity, and geographic region to ensure a representative sample. All participants spoke English, were un-institutionalized, and had no physical or cognitive impairments that would prevent them from performing the required tasks of the assessment.

Reliability was substantiated by engaging internal consistency reliability and test-retest practices. Internal consistency reliability was calculated for the standardized norming group by using the split half method. Internal consistency reliabilities for the verbal score ranged from .86 to .96 (M = .91). Score reliabilities for children and adolescents ages 4-18 yielded high scores (M = .90). For nonverbal, reliabilities ranged from .78 to .93 (M = .86), and for children (M = .86). According to Kaufman and

Kaufman (2004), IQ composite scores yielded very high, ranging from .89-.96 (M = .93) across the entire age range.

Test-retest reliability was also utilized to substantiate reliabilities. The KBTI-2 was administered to the 271 examinees twice. The interval range averaged about 4 weeks with the participants taking the test for a second time between 6–56 days.

Test-retest reliabilities for the verbal score ranged from .88 to .93 (M = .91). For the nonverbal, scores of .76 to .89 (M = .86) were yielded. IQ composite scores ranged from .88 to .92 (M = .90).

Trends of the subset mean raw scores with age were calculated across the verbal and non-verbal standard scores. Correlations between the verbal and nonverbal standard scores were calculated across the 23 age groups. The correlations ranged from .40-.71

(M = .53), suggesting that the correlation is low enough to support the two constructs measuring different phenomena but enough connection to justify combing them for the

IQ composite score (Kaufman & Kaufman, 2004). Validity evidence provides trends of the subset mean raw scores with age correlate with other tests and score profiles of clinical samples.

Scholarly critique of the KBTI-2. The KBTI-2 provides intelligence scores for verbal, non-verbal, and IQ composite (Kaufman & Kaufman, 2004). The KBTI-2 is appropriate for a variety of settings including clinical, in schools, and in research. The

KBTI-2 covers a wide age range and can be used with individuals from age 4 through 90 years. The psychometric properties provide for an adequate level of acceptance and is relatively easily administered and scored. The KBTI-2 should be administered by

48 individuals who have some experience and knowledge with psychometric testing and should not be utilized in diagnostic endeavors.

Data Analysis

The Statistical Package for the Social Sciences (SPSS) for MAC OS was utilized for the data analysis. Multiple regression analysis was calculated using an alpha level of .05 and the beta level was maintained to balance the likelihood between making a

Type I and a Type II error. Normality was tested by using a histogram.

Descriptive statistics were calculated for empathy (including subscales of tone of voice, emotional intensity, and empathic reflection), intelligence (including subscales of verbal, non-verbal and composite IQ), and cognitive complexity (including subscales of differentiation, integration). Descriptive statistics were also calculated for the demographic information of age and number of client hours. Relative frequencies were determined for all subscales that possessed nominal data. This included gender and area of study. Table 3 provides information on the variables used in the study.

Table 3

Univariate Analysis

Variables Data Analysis

Tone of Voice Mean, SD

Emotional Intensity Mean, SD

Empathic Reflection Mean, SD

Verbal Mean, SD

Non-Verbal Mean, SD

Gender Relative Frequency

Number of Client Hours Mean, SD

Differentiation Mean, SD

Integration Mean, SD

Multiple regression analysis was utilized to analyze the ability of the predictor variables to predict the criterion variables. Two standard regression models were used to determine which combination of the predictor variables of empathy, intelligence

(subscales verbal and non-verbal), age, and number of client hours were the best predictor of the two components cognitive complexity. Cognitive complexity was the criterion variable and was measured through the subscales of differentiation and integration. Table 4 reiterates the criterion and predictor variables.

Table 4

Multiple Regression Analysis

Predictor Variables Criterion Variables

Empathy - Summative Cognitive Complexity - Differentiation

Intelligence Cognitive Complexity - Integration

Intelligence -Verbal

Intelligence - Non-Verbal

Intelligence - Composite IQ

Age

Client hours

A sample size of 50 participants was selected for this investigation. It was estimated based on the necessity of obtaining a .80 power at alpha < .05 (Lenth, 2001).

There were also 5 predictor variables of empathy, intelligence, gender, and number of client hours.

Delimitations

The intent of this research study was to explore which factors of empathy and intelligence can aid in the prediction of levels of cognitive complexity. Although all three constructs have been measured by a myriad of alternate instruments, the HSES, the

KBTI-2, and the CCQ have all been selected. Scrutiny was utilized in the selection process and rationale has been explained in the above paragraphs. Limitations of resources, validity, reliability, and ease of use were all contributing factors to selection.

Cognitive Complexity research spans across the entire population of counselors with significant data being found among many of the differing demographics. Counseling students within their internship was selected in order to gain a deeper understanding of the prediction value of empathy and intelligence of cognitive complexity on that specific population.

There are a number of questions within this quadrant of speculation that would be beneficial to the existing body of work, some of which would engage different types of research methods and approaches. The scope of this research had to be limited to the established research question. Due to the focus and intent of the given research, it is best answered by the utilization of the quantitative approach of multiple regression.

Chapter Summary

This chapter was intended to provide an overall description and outline of the current study. The purpose and rationale were re-stated and the procedure was described.

This included the inclusion criteria for participants, the data analysis, and the delimitations. Following is the analysis of the results of the study.

CHAPTER III

RESULTS

The following chapter is an in-depth explanation of the results from this study.

First, a summary of the utilized sampling procedure is outlined followed by a review of the purpose of the study and the research hypothesis. Next, a breakdown of the data analysis that includes demographic data, descriptive statistics, and the multiple linear regressions with result explanation. The purpose of this study was to investigate empathy, intelligence, and other demographic information as a predictor of cognitive complexity in Master level counseling students. Linear regression models were created to determine if any of the predictive variables would show a relationship with the dependent variable. The predictive variables were age, client hours, intelligence and empathy. The dependent variable was cognitive complexity (differentiation and integration). The research question are as follows:

Research Hypothesis #1: How accurately can empathy, intelligence, and number of client hours predict the level of cognitive complexity (differentiation) in Master level counseling students?

Null Hypothesis: Empathy, intelligence, and number of client hours would not predict the level of cognitive complexity (differentiation) in Master level counseling students.

Alternative Hypothesis: Empathy, intelligence, and number of client hours would predict the level of cognitive complexity (differentiation) in Master level counseling students.

Research Hypothesis #2: How accurately can empathy, intelligence, and number of client hours predict the level of cognitive complexity (integration) in Master level counseling students?

Null Hypothesis: Empathy, intelligence, and number of client hours would not predict the level of cognitive complexity (integration) in Master level counseling students.

Alternative Hypothesis: Empathy, intelligence, and number of client hours would predict the level of cognitive complexity (integration) in Master level counseling students.

Sampling

Master level counseling students at two CACREP accredited programs, enrolled in practicum and internship and their supervisors were approached to participate in this study. Each participant completed the Counselor Cognition Questionnaire and the

Kaufmann Brief Intelligence Test-2 during his or her time enrolled in coursework. The supervisors of the participants, identified as research participants, completed the Hayes

Scale of Empathy for supervisors for each consented student enrolled in their course between the mid-term point and the end of the semester. A total of 55 participants completed all aspects of the study and data was collected from the Fall 2017 semester to the Fall 2018 semester.

Data Analysis

Data collected for this study included demographic data which included: sex assigned at birth, current gender identity, total number of client hours, age, race /

54 ethnicity, and course enrolled, yes or no responses to questions concerning their comfortability with the English language and their ability to identify two client (one with who they felt more effective and one with whom they felt less effective with), course currently enrolled, previous master degree, and undergraduate major. Results were also collected from the three instruments utilized in the study: Counselor Cognitions

Questionnaire (CCQ), the Kaufmann Brief Test of Intelligence-2 (KBIT-2), and the

Hayes Scale of Empathy for Supervisors (HSES).

Demographic Data

In order to gain a better understanding of the sample population that was utilized in this study, descriptive statistics were utilized. The following section is dedicated to truncating the demographic data in the following categories: sex assigned at birth/ current gender identity, client hours, age, race/ ethnicity, course enrolled, education / previous

Master.

Sex assigned at birth / current gender identity. Sex assigned at birth and current gender identity information was collected to ensure inclusivity to all participants.

Of the 55 participants all currently identified as their sex assigned at birth. The majority of participants were female (n = 43, 78.2%). The male participants represented (n = 12,

21.8%).

Client hours. Client hours was entered as a dichotomous variable. It was categorized into two groups: less than or equal to 30 hours and more than 30 hours.

Participants with less than or equal to 30 hours was (n = 36, 65.5%). Participants who

55 reported more than 30 hours was much lower (n = 14, 25.5%). There were participants that left this question blank (n = 5, 9.1%). This resulted in the total n = 55.

Age. The age range spanned from 21-50. Most of the participants (n = 50, 90%), could be classified as Millennials. This includes the group of individuals whose birth year is 1981–2002 (Elam et al., 2002). The mean age of the participants was 27.07 years old with a standard deviation of 5.931. The most frequently occurring age of the participants was 23 years old with a frequency of 13.

Race / Ethnicity. The majority of participants were White. More participants identified as White (n = 51, 92.7%). Two participants identified as African American and Other/Multi-race respectively constituting (n = 1,1.9%) each of the sample. Two participants declined to answer equaling (n = 2, 3.6%) of the sample.

Course enrolled. Most of the participants were enrolled in their Practicum, with the highest frequency being Practicum I students (n = 42, 76.4%). The next highest frequency was Practicum II (n = 7, 12.7%). Only n = 6 (10.9%) of participants stated that they were enrolled in Internship.

Education previous Master. Participants were asked whether they had received a previous Master degree. Results showed that n = 30 (54.5%) reported that they had no previous Master degree and n = 7 (12.7%) stated that they had acquired a previous Master degree. There were quite a few participants that left this question blank (n = 18, 32%).

Table 5

Demographic Data Including Sex Assigned at Birth, Current Gender Identity, Client Hours, Age, and Race Ethnicity

N % of Sample

Sex assigned at Birth (N = 55) Male 12 21.8 Female 43 78.2

Current Gender Identity Male 12 21.8 Female 43 78.2

Client Hours Less than or Equal to 30 36 65.5 More than 30 14 25.5

Age 21 1 1.8 22 1 1.8 23 13 23.6 24 8 14.5 25 8 14.5 26 5 9.1 27 3 5.5 28 5 9.1 29 2 3.6 30 1 1.8 31 1 1.8 32 1 1.8 33 1 1.8 34 1 1.8 35 1 1.8 38 1 1.8 39 1 1.8 41 1 1.8 46 1 1.8 50 1 1.8

Race / Ethnicity White 51 92.7 African American 1 1.8 Other / Multi-race 1 96.4

Missing 2 3.6

Testing Instruments

Three instruments were utilized to provide continuous variables for the data of this study. The first is the CCQ, and scores for differentiation and integration were collected. Next, is the KBIT-2, which provided intelligence scores for verbal, non- verbal, and composite. The composite score was later removed from the regression with consideration given to the concept of multicollinearity. Last, is the Hayes Scale of

Empathy for Supervisors, which provided Likert scores for different facets of empathy.

This is a Rogerian-based empathy (Rogers, 1975) and rooted on the work of Watson in

2002. These scores were added together to form an empathy summative score.

Descriptive Statistics

There were multiple assessments utilized in this study. The CCQ, KBIT-2, and

HSES will be discussed and their descriptive statistics will be presented. Preliminary data implications will also be presented.

CCQ. Differentiation scores range from 0–75, with previous samples yielding scores from 0-50. The norming differentiation score for graduate students is between

10-20 (Welfare, 2010), compared to this samples reported M = 21.26. In previous samples, counselors-in-training scored at or below a score of 10 and doctoral or practicing counselors ranged between 12–18 for integration. The reported mean for integration in this sample is M = 10.04.

Table 6

Descriptive Statistics for Cognitive Complexity: Differentiation and Integration

N Minimum Maximum Mean Std. Deviation

Cognitive Complexity 55 8 48 21.14 6.695 (Differentiation)

Cognitive Complexity 55 6 15 10.09 2.084 (Integration)

KBIT-2. The norming average for intelligence is M=100 with the standard deviation being SD=15. Average means for intelligence for this sample was slightly above average at 104.81 for verbal and 103.79 for non-verbal. Although these mean scores are slightly elevated from the normed means they are not statistically significant.

HSES. All of the research participants, participant’s supervisors, completed the

HSES for the participants included in this study. Overall data suggests that the research participants Agree that the participants are utilizing the differing aspects of empathy. The empathy summative scores range from 28-42, M = 36.45 and SD = 4.022. The items with the highest frequencies were “the counselor-in-training utilizes non-verbal communication to convey empathy” and the “the counselor-in-training expresses an understanding of the verbal communication expressed by clients.” The items with the lowest frequencies were “counselor-in-training matches the emotional intensity of clients” and “the counselor-in-training recognizes their own emotional responses.”

Preliminary data implications. The utilized sample were predominantly White, cisgender females. More than half were enrolled in their first semester of Practicum and had accrued 30 hours or less at the time of assessment. Ninety percent were millennials

59 and a little more than half had no previous Master degree. Five participants were dropped from the linear regressions as they failed to report how many client hours they had accrued at the time of the data collection.

Effect size. Using G* Power software (Version 3.1; Faul, Erdfelder, Buchner, &

Lang, 2009), two linear regression analyses were conducted with a large effect size, a power of .80, and an alpha level of .05. 50 total participants were utilized for this study.

The results yielded particularly low correlations. At the preliminary running of data, evidence suggested that even with the desired effect size achieved being achieved, there would be no linear relationship between the hypothesized variables. It was decided that this was not a predictive linear regression and current sample size was deemed appropriate. There was, however, important data achieved that provided interesting information for discussion that will be covered more extensively in Chapter 4.

Modifications. After the two multiple regressions, discussed below, were run, no significance was calibrated. In a collinearity diagnostic, IQ composite violated the issues of multicollinearity. This meaning that multiple predictor variables explained the same thing in the dependent variable. IQ composite was removed as a variable in the linear regression.

Table 7 below is entitled Hayes Scale of Empathy for Supervisors as Completed by Research Participants. It includes all of the items included on the Scale and the reported frequencies and their respective percentages are reported.

Table 7

Hayes Scale of Empathy for Supervisors as Completed by Research Participants

Item Likert Scale Score Frequency Percentage

The counselor-in-training utilizes Strongly Agree 23 41.8 non-verbal communication to Agree 21 38.2 convey empathy Slightly Agree 10 18.2 Slightly Disagree 1 1.8

The counselor-in-training utilizes Strongly Agree 29 52.7 verbal communication to convey Agree 19 34.5 empathy Slightly Agree 7 12.7

The counselor-in-training Strongly Agree 22 40 expresses an understanding of the Agree 24 43.6 non-verbal communication Slightly Agree 8 14.5 expressed by clients Slightly Disagree 1 1.8

The counselor-in-training Strongly Agree 31 56.4 expresses an understanding of the Agree 21 38.2 verbal communication expressed Slightly Agree 3 5.5 by clients Strongly Agree 18 32.7 The counselor-in-training is able Agree 24 43.6 to empathic reflections to put the Slightly Agree 13 23.6 client’s experiences into words

The counselor-in-training Strongly Agree 12 21.8 matches the emotional intensity Agree 30 54.5 of clients Slightly Agree 12 21.8 Slightly Disagree 1 1.8

The counselor-in-training Strongly Agree 15 27.3 recognizes their own emotional Agree 29 52.7 responses Slightly Agree 11 20

Linear Regression for Cognitive Complexity—Differentiation Score

Table 8, entitled Descriptive Statistics for Linear Regression Differentiation, reports all the means and standard deviations for variables included in this research design. These variables are cognitive complexity (differentiation), age, client hours

61 dichotomous, intelligence-verbal, intelligence-non-verbal, and the empathy summative score.

Table 8

Descriptive Statistics for Linear Regression Differentiation

Mean Std. Deviation N

Cognitive Complexity 21.14 6.695 50 (differentiation)

Age 27.44 6.102 50

Client hours .28 .454 50 dichotomous

Intelligence-verbal 104.28 12.532 50

Intelligence- non-verbal 103.72 10.037 50

Empathy- sum score 36.50 3.851 50

Table 9 includes the Model Summary for differentiation. This provides information about the regression line’s ability to account for the total variation in the dependent variable. For this example, it is cognitive complexity (differentiation). R square = .087, which means that 8.7% of the variance, in differentiation, can be explained by the variables provided.

Table 9

Model Summary Differentiation

Std. Error or Model R R Square Adjusted R Square Estimate

1 .294 .0087 -.017 6.751

Table 10 includes the F-test that was conducted. The ANOVA results are reported as follows: F (5,44) = .836, p = .532. A p value = .532 is greater than the .05 which means that we accept the null and the regression is not statistically significant. For research question one: How accurately can empathy, intelligence, and number of client hours predict the level of cognitive complexity (differentiation) in Master level counseling students, the null is accepted stating that empathy, intelligence, and number of client hours would not predict the level of cognitive complexity (differentiation) in

Master level counseling students. This regression is not predictive.

Table 10

ANOVA Differentiation

Sum of Model Squares df Mean Square F Sig.

1 Regression 190.431 5 38.086 .836 .532

Residual 2005.589 44 45.582

Total 2196.020 49

Table 11, entitled Correlations for Linear Regression Differentiation, reports all the correlations for variables included in this research design. These variables are cognitive complexity (differentiation), age, client hours dichotomous, intelligence-verbal, intelligence-non-verbal, and the empathy summative score. It is important to note that sig. 1-tailed for differentiation and empathy sum score is reported as .032 less than the p value of .05, which is significant and positively correlated. This stating, that as empathy sum score went up, so did differentiation scores.

Table 11

Correlations for Linear Regression Differentiation

Cognitive Empathy complexity- Client hours Intelligence Intelligence - sum differentiation Age dichotomous -Verbal Non-Verbal score

Sig. Cognitive (1- Complexity- tailed) differentiation .101 .462 .248 .391 .032

Age .101 .257 .001 .287 .004

Client hours .462 .257 .221 .118 .013 dichotomous

Intelligence- .248 .001 .221 .014 .243 Verbal

Intelligence- .391 .287 .118 .014 .243 Non-Verbal

Empathy Sum- .032 .004 .013 .202 .243 Score

In Table 12, Coefficients Differentiation, the values in column B represent the extent to which the value of the independent variable contribute to the value of the

64 dependent variable. Looking at the column providing the Sig. values, all reported values are above .05. This means that all values are not significant.

Table 12

Coefficients Differentiation

Standardized Unstandardized Coefficients Coefficients Model B Std. Error Beta t Sig.

1 (Constant) -1.956 14.749 -0.133 0.895

Age 0.113 0.191 0.103 0.589 0.559

Client hours -0.961 2.282 -0.065 -0.421 0.676 dichotomous

Intelligence- -0.001 0.093 -0.003 -0.014 0.989 Verbal

Intelligence- 0.042 0.105 0.064 0.403 0.689 Non-Verbal

Empathy Sum- 0.439 0.283 0.252 1.550 0.128 Score

Linear Regression for Cognitive Complexity-Integration Score

Table 13 includes the Descriptive Statistics for Linear Regression for Integration.

The included variables and their reported means and standard deviations for cognitive complexity (differentiation), age, client hours dichotomous, intelligence-verbal, intelligence-non-verbal, and the empathy summative score. The results showed that the mean and standard deviation for integration was M = 10.04 and SD = 2.099.

Table 13

Descriptive Statistics for Linear Regression for Integration

Mean Std. Deviation N

Cognitive Complexity (integration) 10.04 2.099 50

Age 27.44 6.102 50

Client hours dichotomous .28 .454 50

Intelligence-Verbal 104.28 12.532 50

Intelligence- Non-Verbal 103.72 10.037 50

Empathy- Sum Score 36.50 3.851 50

Table 14 is the Model Summary Integration. R square = .107. This means that

10.7% of the variance, in integration, can be explained by the variables provided.

Table 14

Model Summary Integration

Std. Error or Model R R Square Adjusted R Square Estimate

1 .327 .107 .0006 2.093

Table 15 includes the F-test conducted for the second linear regression. The

ANOVA results are reported as follows: F(5,44) = 1.054, p =.398, which provides no evidence that this model predicts any better than zero. A p value of .398 > .05 so it is not statistically significant. For research question two: How accurately can empathy,

66 intelligence, and number of client hours predict the level of cognitive complexity

(integration) in Master level counseling students, the null is accepted stating that empathy, intelligence, and number of client hours would not predict the level of cognitive complexity (integration) in Master level counseling students.

Table 15

ANOVA Integration

Sum of Model Squares df Mean Square F Sig.

1 Regression 23.102 5 4.620 1.054 .398

Residual 192.818 44 4.382

Total 215.920 49

Table 16, entitled Correlations for Linear Regression Integration, reports all the correlations for variables included in this research design. These variables are cognitive complexity (integration), age, client hours dichotomous, intelligence-verbal, intelligence-non-verbal, and the empathy summative score. The sig. (1-tailed) for integration was significant and positively correlated with empathy scores. The correlation for integration and the summative empathy score was reported as 0.025. This is less than reported p value at .05, so this result is significant, suggesting that the higher the score for cognitive complexity (integration) the higher the summative empathy score.

Table 16

Correlations for Linear Regression Integration

Cognitive Empathy complexity Client hours Intelligence- Intelligence - Sum (integrations) Age dichotomous Verbal Non-Verbal Score

Sig. Cognitive .152 .134 .351 .477 .025 (1- Complexity- tailed) (integration)

Age .152 .257 .001 .287 .004

Client hours .134 .257 .221 .118 .013 dichotomous

Intelligence- .351 .001 .221 .014 .202 Verbal

Intelligence- .477 .287 .118 .014 .243 Non-Verbal

Empathy .025 .004 .013 .202 .243 Sum-Score

In Table 17, Coefficients Integration, the values in column B represent the extent to which the value of the independent variable contribute to the value of the dependent variable. Looking at the column providing the Sig. values, all reported values are above .05. This means that all values are not significant.

Table 17

Coefficients Integration

Unstandardized Coefficients Standardized t Sig. Model B Std. Error Coefficients Beta 4.561 4.570 0.998 .324 1 (Constant) Age 0.046 0.059 0.133 0.771 0.445 Client hours 0.334 0.708 0.072 0.472 0.639 dichotomous

Intelligence-Verbal -0.028 0.029 -0.166 - 0.339 0.967 Intelligence-Non- 0.022 0.033 0.107 0.687 0.496 Verbal

Empathy Sum-Score 0.129 0.088 0.236 1.468 0.149

Conclusion

Above was a detailed description of the results that were compiled in this study.

First, was an explanation for the sampling procedures, followed by an overview of the purpose and review of the research hypothesis, which concluded with a breakdown of the data analysis. This included the demographic data, descriptive statistics, model summaries, ANOVAs, correlations, and coefficients for both of the linear regressions. In conclusion, the predictive variables are not showing linear relationship with the dependent variable. This is not an effective regression model and the null hypothesis was accepted for both the research questions. There is, however, a slight correlation between the empathy summative score and cognitive complexity.

CHAPTER IV

DISCUSSION

This final chapter goes into further detail of the findings and implications of this study. A summary of the findings and discussion are provided and there are anecdotal observations followed by an overview of the limitations. In addition, there are recommendations for future research and implications for counseling, supervision and counselor education.

Discussion of Findings

In running of the univariate research design, it can be concluded that this was not a predictive linear regression. The variables of age, number of client hours, empathy, and intelligence do not accurately predict cognitive complexity. The research questions are as follows:

Research Hypothesis #1: How accurately can empathy, intelligence, and number of client hours predict the level of cognitive complexity (differentiation) in Master level counseling students? The null hypothesis was accepted and states that empathy, intelligence, and number of client hours would not predict the level of cognitive complexity (differentiation) in Master level counseling students.

Research Hypothesis #2: How accurately can empathy, intelligence, and number of client hours predict the level of cognitive complexity (integration) in Master level counseling students? The null hypothesis was accepted and reads as follows: Empathy, intelligence, and number of client hours would not predict the level of cognitive complexity (integration) in Master level counseling students.

However, it was concluded that there is a slight correlation between how this study defines and measures empathy and cognitive complexity. When the empathy score increased, so did the integration score of cognitive complexity.

Sample Characteristics

The majority of participants shared common characteristics. This included them identifying as female (n = 43, 78.2%), White (n = 51, 92.7%), millennials (n = 50, 90%), having less than or equal to 30 client hours (n = 36, 65.5%), and being enrolled in their practicum (n = 49, 76.4%).

Discussion of Statistical Analysis

The initial part of the analysis began with addressing why these regressions were not predictive. The constructs of cognitive complexity, intelligence, and empathy seem to be related both from a visceral and research-based perspective, and yet a linear relationship was not established through the methodology. The non-predictive outcome of the regression may illustrate issues with the scales utilized to capture these concepts.

There are some definitive issues with the empathy scale that will be more thoroughly discussed in the limitations section. Despite concerns with the Hayes Scale of Empathy, there was a slight correlation between empathy and both constructs of cognitive complexity.

A second concerning result is the coefficient of determination being low for both regressions. The coefficient of determination is the proportion of the variance in the dependent variable that is predicted from the independent variables. For differentiation r2 = 8.7% and for integration r2 = 10.7% for integration. These are low and even though

71 they are seemingly related, something random such as a tossing a coin would have provided a better predictive model. Questions around the unforeseeable gap between all these variables and whether there are multiple variables better explaining this relationship will be provided in the following sections.

Cognitive Complexity, Empathy, and Intelligence

The clinical importance of cognitive complexity and empathy has been well substantiated throughout this document. The overall take away is that these two constructs promote the ability of individuals to be better counselors and yet, these concepts still remains somewhat elusive in application. Further discussion on how to best promote and facilitate the growth of cognitive complexity and empathy in counselors-in-training and beyond are included below.

Unsurprisingly, the concepts of empathy and cognitive complexity improve the quality of counseling. Counselors are better able to provide effective counseling to their clients and take care of themselves at the same time. One of the main contributing factors being that if they are more comfortable with ambiguity (Holloway & Wampold,

1986; McAuliffe & Lovell, 2006), counselors with higher levels of empathy and cognitive complexity are able to think critically and problem-solve difficult and complex situations, including ones that may occur internally.

Clients approach counseling with the hope that they will be seen, understood, and gain something from their time with the counselor. It is a hope they will be accepted and appreciated for who they are (Rogers, 1975). This is powerful concepts that is a direct application of empathy and cognitive complexity because it requires that the counselor

72 understand themselves, is able to distinguish that from who they are in the counseling relationship and engage in the client’s world without truly judging them (Rogers, 1975).

Results from this study suggests that this group of participants were very comfortable and able to convey empathy through their own non-verbal behaviors and express an understanding of the verbal communication expressed by their clients. There were deficits in their ability to convey empathy and in self-evaluating, monitoring and regulating how they are feeling. There were appropriate levels of cognitive complexity detected and correlations found between the two concepts mirroring similar sentiments in previous studies.

Cognitive Complexity. Cognitive Complexity has been improved through the supervisory relationship (Dodge, 1982; Granello & Underfer-Babalis, 2004; Holloway &

Neugeldt, 1995; Holloway & Wolleat, 1981; Kindsvatter, Granello & Duba, 2008), in utilizing constructs as Bloom’s Taxonomy (Granello, 2001), through faculty advising

(Choate & Granello, 2006), skills models (Eriksen & McAuliffe; 2003; Little; Packman,

Smaby, & Maddox, 2005; Morran, Kurpius, Brack, & Brack, 2001), assessments

(Welfare & Borders, 2010a, 2010b), and in counseling practice (Granello, 2010; Welfare

& Borders, 2010b).

Multiple studies have concluded that counseling experience, supervisory experience, counselor education experience and highest degree completed all contribute to higher levels of cognitive complexity (Granello, 2010; Welfare & Borders, 2010b).

Introducing the concept of cognitive complexity to counselor trainees may be able to prompt cognitive development before extensive counseling experience leading to the

73 development of more efficacious counselors sooner. Facilitating conversations surrounding the actual concept of cognitive complexity may improve this construct and an individual’s ability to apply it to counseling. Talking about how we think about our thinking can be the catalyst to more quickly developing cognitive complexity.

Foreseeable ways to facilitate this comes from two concepts discussed in this paper. The first is utilizing Bloom’s taxonomy as it was discussed in Granello (2001). Although it was proposed that this ideology could be utilized as a way to improve literature reviews, it is hypothesized that it could also be used to promote better understanding of thinking.

The other proposed way of improving cognitive complexity is to utilize the Counselors

Cognition Questionnaire for its intended purposes in supervision, counseling, and counselor education (Welfare, 2007).

Empathy. Empathy is a major part of effective counseling (Gutierrez, Mullen, &

Fox, 2017) and is detectable from birth with more complex applications emerging during infancy before verbal communication (Tousignant, Eugene, & Jackson, 2017). Higher orders of empathy include perspective taking and emotional regulation (Tousignant,

Eugene, & Jackson, 2017), imperative to effective counseling, and encapsulating

Rogerian empathy. It is the integration of empathy with unconditional positive regard

(Bozarth, 2007) that describes Rogerian empathy more effectively.

Empathy improves counseling outcomes (Clark, 2010) and counselors who have more experience have more empathy (Lafferty, Beutler, & Crago, 1989; Marangoni,

Garcia, Ickes, & Teng, 1995; Muller & Abeles, 1971; Watson & Prosser, 2002).

Wampold and Imel (2015) stated that empathy was one of the most powerful variables that contribute to the effectiveness of counseling.

Intelligence. Intelligence is the ability to take experience and previously learned information to adapt to novel situations and manipulate and interact with the current environment. Intelligence was selected as variable in this study for multiple reasons and the most popular way of quantifying intelligence is from a verbal perspective. Language, and more importantly, the words we choose and how they are defined, is exceedingly important for counselors at all stages of development. Meaning is derived from our own personal experiences and rooted in that word choice. The hypothesis was that the more verbal intelligence an individual possessed the broader the vocabulary they would have at their disposable to holistically describe the clients that they are counseling. The more words available to them the more equipped they would be to describe their clients. Based on the results of this study, there was no correlation between verbal intelligence and the participants ability to describe their clients.

The second reason for selecting intelligence was to analyze the non-verbal portion of intelligence. The hypothesis was that if individuals were effective at constructing the provided matrices in the KBIT-2, they would be able to put together puzzles more effectively, resulting in more accurate case conceptualizations. Similar to the verbal outcomes, there is no suggestion that non-verbal intelligence had anything to do with cognitive complexity.

Based on research methodology utilized in this study, intelligence has no correlation with either cognitive complexity or empathy. One possible explanation for

75 this could be that there are different kinds of intelligence and although the KBIT-2 is sufficiently rooted in psychometric stability, it is not encapsulating the intelligence needed to explain counselor intelligence. It might even be comparable to the difference between street smarts and book smarts. In theory, counselors would need to express both in order to be successful. Perhaps, traditional marks of intelligence are only representing a limited facet of counselor intelligence. Emotional intelligence could be an explanation for counselor intelligence.

Emotional Intelligence

Emotional intelligence can be defined as how one understands, manages, and uses emotions (Mayer, Salvoney, & Caruso, 2008; Petrides & Furnham, 2001). This is applicable to emotional responses in both the self and in others. Although the exact relationship between empathy and emotional intelligence is unclear (Gutierrez & Mullen,

2016), recent evidence has suggested that emotional intelligence has a significant relationship with empathy and stress management (Gutierrez et al., 2017). In their study of 307 counselor trainees, higher levels of emotional intelligence were associated with higher affective and cognitive empathy. Lower stress and distress were also reported.

Further supporting previous claims that emotional intelligence can act as a buffer and protective factor to prevent counselor burnout (Gutierrez & Mullen, 2016) and that more emotional intelligence suggests higher empathy. This study recommended more research on student counselors and hypothesized that integrating more emotional intelligence concepts into existing curriculum and programmatic emotional intelligence check-ins

76 could further safeguard counselors-in-training from emotional burn-out (Gutierrez et al.,

2017).

Millennials

With such a large group of the population being categorized as millennials born in the years 1981–2002 (Elam et al., 2002), special consideration needs to be paid into the implications of having such a large percentage of this cohort being represented in this sample. According to Koltz, Smith, Tarabocha, and Wathen in 2017, millennials first appeared on college campuses in 2000. Since that time there have been many attempts to identify and articulate the overall defining characteristics of this group. With the mean age being 23 years old, it can be hypothesized that this cohort will continue to be represented in counselor education for years to come.

Although dedicated and highly motivated (Smith & Koltz, 2012), millennials frequently express low stress tolerance, have academic struggles (Koltz et al., 2017), and have difficulty with work / life balance. The have less social skills, less self-motivation, and less emotional intelligence and empathy (Scott, Valley, & Simmecka, 2017).

Millennials frequently experience high connectivity which is the use of cellphones, internet, and all social media applications. In their review of the literature,

Li, Lepp, and Barkley (2015) listed an extensive list of negative outcomes of cell phone use including poor sleep quality (Fossum, Nordness, Storemark, Bjorvatz, & Palleson,

2014; Lanaj, Johnson, & Barnes, 2014; Lemola, Perkinson-Gloor, Brand, Dewald-

Kaufmann, & Grob, 2015; Munezawa et al., 2011; Murdock, 2013; Thomee, Harenstam,

& Hagberg, 2011) and decreased mental health (Beranuy, Oberst, Carbonell, &

Chamarro, 2009; Harwood, Dooley, Scott, & Joiner, 2014; Jenaro, Flores, Gomez-Vela,

Gonzalez-Gil, & Caballo, 2007; Lepp et al., 2014; Li, Lepp & Barkley, 2015; Rosen et al., 2014). Furthering these studies, Morteza, Saeed, and Abdulrahib found that cell phone overuse can cause significant changes in sleep quality, anxiety, and depression among students enrolled at University (Morteza, Saeed, & Abdulrahib, 2015).

Cell phone overuse is disrupting sleep habits, negatively impacting mental health, and complicating an individual’s ability to create and maintain work / life boundaries

(Scott et al., 2017). There is also a very high propensity of this cohort to use technology compulsively (Rosen, 2012). Millennials are, however, reporting significant emotional gains from their technological obsession. When using their cell phones, millennials are experiencing enjoyment and positive emotions as well as reporting an overall reduction of negative emotions (Zhitomirsky-Geffet & Blau, 2016). They are feeling happier and less stressed while using their phones but are limited in their ability to actualize positive emotional responses without them.

Increased anxiety from this high connectivity can exacerbate worry about missing out on something (Scott et al., 2017), and not being seen or making a difference in the world (Sinek, 2016). Pressure to conform is enforced by cultural norms and marketing.

They cannot apply for a job without using the Internet, and many functions of everyday life are becoming limited to ability to download and utilize an app on a smart phone.

The detrimental side effects of adults who experience continual connectivity include: lowered social skills and self-motivation, diminished emotional intelligence, and empathy (Scott et al., 2017). Staying present with clients and not being hyper-vigilant of

78 their own emotional and physical reactions to the counseling process become predictable boundaries for this generation.

Exacerbated need for meaning in their life and to “make a difference” signifies that this generation is thinking beyond themselves and do have a deeper need to give back and help others but lack the ability to help themselves. CACREP requires that current counselor education curriculum provides self-care strategies appropriate for counselors (CACREP, 2016; Standard 1.l.). This may be very different in application but should provide for adequate space and time for deep personal reflection and heightened self-awareness. If millennials are already having difficulty with a work / life balance

(Scott et al., 2017), this inability to identify and articulate emotions could result in unethical practice. The ACA code of ethics require that “counselors are alert to the signs to impairment from their own physical, mental, or emotional problems and refrain from offering or providing professional services when such impairment is likely to harm a client or others” (ACA, 2014). Current self-care strategies prove efficacious but only if plans are personalized and individuals take responsibility for their own well-being.

Further investigation is needed into the effectiveness of implanting self-care strategies, self-care contracts, and programmatic wellness checks.

Millennials have lower emotional intelligence, lower empathy, and lower stress tolerance (Scott et al., 2017). They are willing and able to express empathy with clients but lack the ability self-evaluate, monitor and regulate their own emotional responses.

The onset of millennial population could be the prompting event to challenge the current norms of counselor education pedagogy (Arrendondo & Arciniega, 2001), and make

79 efforts to incorporate more empirically supported strategies for promoting cognitive development in the form of cognitive complexity. This millennial quandary presents an opportunity to make slight course corrections to combat many of the generational boundaries affecting this group.

Spirituality

It was also observed that there was very little recognition of spirituality and religiosity in client descriptions of the CCQ. Of all of the 1000+ descriptors that were awarded points and provided by all the participants, only 3 of these descriptors were categorized in the spiritual category. This represents .003% of the total responses.

Although a staggering low percentage, this aligns with the frequency of the occurrence of the differing categories as described by the CCQ rating manual (Welfare, 2007). The manual states that amongst the five categories: cognitive, spiritual, emotional, contextual, and behavioral, the most utilized at this developmental level would be behavioral and emotional characteristics (Welfare & Borders, 2007). More complex cognitive structure would elaborate to include cognitive, contextual / relational, and spiritual characteristics

(Welfare & Borders, 2007). One possible explanation can be that the participants are not cognitive complex enough to be thinking about spirituality and thus would not include them into their list of client descriptions. Another explanation is that neither clients or counselors-in-training are talking about spirituality or religiosity and are overall uncomfortable with the topic. Or, is the best explanation simply that this group as a whole puts less emphasis and importance on spiritual and religious beliefs?

The theme of Spirituality and Religiosity not occurring more readily in the differentiation portion of the cognitive complexity assessment may or may not be representative or generalizable in counselors-in-training across population, but more information is needed on this topic. There have been some inferences in other fields of study that millennials in general are bucking the traditional ideas of religiosity and preferring spirituality (Morrison & Morgan, 2010). There may be a gap in counselor education programs and there is insufficient programming to support a deficit in one of these areas. Counselor education programs focus on how to work with clients on their beliefs and little attention is being paid to aiding counselor trainees in how to integrate their own beliefs (Morrison & Morgan, 2010).

In his work with pastoral counseling students, Baard (2017) found that students were predominantly struggling with how to live according to their beliefs, not just think about them. How this applies to counseling is that trainees are prompted to think about counseling, how they conceptualize the counseling relationship, their role as counselor, how change happens in the knowledge and beginning portion of their education. When the skills portion is applied, they have to start thinking about how they are thinking and what that may look like practical application. This is reminiscent of previous research done by Granello (2010) that Bloom’s taxonomy is a helpful tool in promoting epistemology.

Counselor empathy has also been positively correlated with multicultural competency (Fuertes & Brobst, 2002). One important aspect for counselor trainees is their own multicultural identity, religion, and spirituality, and there has been concerns

81 that it is being overlooked (Morrison & Borgen, 2010) bringing into question whether current counselor education programs are making sufficient adjustments in the current upheaval in current political climate. Consideration paid to the psychological safety and appreciation of difference (Garvin et al., 2008) of counseling trainees has been investigated. Counselors who identified as more conservative political ideologies and intrinsically spiritual reported perceiving their environment as less safe and less appreciating of their differences (Giordano et al., 2017). Bringing to light the possibility that students who think and feel differently from those around them in their counselor programs, are more likely to modify, at least extrinsically, their ideologies in order to fit in. With the underlying message of curriculum focusing on personal reflection and ability to be vulnerable, do students with particular spirituality and religiosity beliefs feel safe to have these difficult conversations? Especially, when they are occurring with individuals in position of power who they know do not share their personal beliefs and, if when they do share these beliefs they are met with pejorative tones and punitive remediations? Is there a cultural in counselor education that strong religious beliefs are deficits and impede the ability of counselor trainees to express empathy?

In the field of pastoral counseling there have been multiple studies that have explored this link between spirituality / religiosity and empathy. Positive correlations have been found between religiosity and empathy in Catholic undergrads (Luyten,

Corveleyn, & Fontaine, 1988), in empathy and religious integration in Christian graduate counseling programs (Muse, 1992), and in spiritual well-being and empathy in Christian students in graduate school (Maciak, 2002).

Limitations

Measuring the constructs outlined in this study is worthwhile and important, but there are some definitive boundaries. Below is an outline of the limitations that should be considered when interpreting the presented data.

Cognitive complexity. Issues of construct validity should be mentioned when considering the identification and measuring of cognitive complexity. There are many instruments that are utilized throughout the cognitive complexity literature and little consensus is established on which one most accurately captures this concept (Castillo,

2018). Although the Counselor Cognition Questionnaire does provide a domain-specific measure of cognitive complexity in counselors, there are a few factors that should be taken into consideration, one of which comes from the author of the assessment.

Concerns surrounding attention and focus that is required to complete the assessment in the 15-minute time requirement (Welfare & Borders, 2010b). Confounding variables might include processing speed and any previous diagnosis of ADHD or anxiety.

Another consideration could include that there is never a check for accuracy for either of the two concepts of differentiation and integration. For differentiation, participants are just instructed to list as many characteristics that describe the client as possible. There is never a time that anyone goes back through the characteristics and discerns if they accurately describe the client. The can also be applied to the test for integration. Participants are asked to group the characteristics into categories but no consideration is paid to whether those groupings make any discernable sense. The paper

83 and pencil formatting of the assessment may have lacked the experiential strategies that have showed promise in maximizing the potential of these age group.

Empathy. There are many flaws and areas of concern surrounding the scale that are utilized for empathy, the first being that there is no inter-rater reliability for the empathy score. Multiple research participants were utilized to obtain scores for the counselor trainees on the Hayes Scale of Empathy for Supervisors. The main issue is that in order to get a holistic picture of empathy, only individuals who had permission and vested interest in the client could observe and evaluate the participants in session.

Bringing in an unbiased, trained outside researcher to observe all the research participants with clients could resolve this issue but could potentially put client care in jeopardy and disrupt ethical practice. A solution for this issue is unknown to the researcher and warrants deeper reflection.

Another perceived issue is that the assessment, although deeply rooted in

Rogerian underpinnings and based strongly in the work of Watson (2002), has not been normed in any way. Although some attention was paid to face validity, the HSES would benefit from being more rooted in psychometric stability. It was intended to identify concrete and identifiable characteristics and behaviors that could be easily recognized by observers. It provided some insight into behavior that are more frequently expressed by counselors-in-training and some that are not expressed as frequently.

A concern about the Likert scale being too close to the assessment used by supervisors at this training facility should also be mentioned. This may have influenced supervisors from marking below a certain level, because based on the other assessment it

84 would have resulted in a recommendation for the individual to repeat their current practicum.

Client hours / practicum enrollment. In alignment with previous cognitive complexity studies, would this study have been more effective in finding higher levels of cognitive complexity if more individuals were utilized at later stages of their master level clinical experience? Most of the population (89.1%), were enrolled in Practicum I or II and seeing clients in the University run counseling center. This may have limited their ability to see more diverse clientele.

Population. The population utilized is this study was one of convenience and was predominantly White, cisgender females. This is not generalizable to the population of master-level counselors-in-training but provides a snapshot of this phenomena.

Recommendations for Future Research

Evidence to suggest empathy can be influenced by cognitive complexity has been substantiated multiple times over the years but many of the existing studies have samples consisting of either social workers or undergraduate students (Castillo, 2018). It is necessary to replicate these studies and utilize graduate level counseling students and analyze the results. But it would be difficult to alleviate many of the concerns with studying the development of empathy or cognitive complexity if a consensus is not found on how to measure and identify these two constructs. Construct validity in measuring cognitive complexity, continues to impede progress and too many instruments are still actively being used in the varying studies available (Castillo, 2018).

Despite the inherent flaws and concerns surrounding the empathy scale, there was a slight correlation found between empathy and cognitive complexity. This means that as empathy scores increased, so did cognitive complexity scores. If there is a better understanding of the client’s world view (empathy) there is a better ability to case conceptualize (cognitive complexity). Resolution needs to be found for measuring empathy in counselors-in-training. More clearly defining how to identify and measure empathy can aid counselor educators and supervisors in more quickly identifying problematic behaviors in counselors-in-training, getting at-risk counselor trainees the support and guidance they need, especially for the population of counseling student who are millennials and crave structure, supervision, and feedback as well as praise for their counseling work (Furr & Carroll, 2003; Howe & Strauss, 2003; Koltz et al., 2017).

Remediation plans provide more concrete and clear expectations for students at a deficit to learn and express empathy with the therapeutic environment.

Utilizing Bloom’s taxonomy, similar to what was proposed by Granello (2001) could prompt a conversation that promotes a counselor trainee’s ability to think about their thinking. This would also facilitate the development of cognitive complexity. This should be done in both counselor education curriculum (knowledge) and in supervision

(skills).

There are also implications provided that some of these behaviors can be more frequently expressed by counselors in training and are more representative of a developmental perspective. In analyzing the results of the Hayes Scale of Empathy for

Supervisors, counselor trainees were better able to convey empathy through their own

86 verbal behavior and recognize non-verbal behaviors in clients. They had difficulty evaluating, monitoring, and regulating their own emotional responses and matching the emotional intensity of clients. In this specific sample, this trend in behavior expression was observed. It would be beneficial to investigate if similar results were achieved in more generalizable samples.

Traditional intelligence as measured by the KBIT-2 may have nothing to do with cognitive complexity. It was hypothesized that if participants had higher verbal scores they would describe their clients more effectively and if they had higher non-verbal scores they would be able to take these descriptions and piece them together more effectively in case conceptualizations. This was disproven. There is a different kind of intelligence that better encompasses the rigors of ambiguity that counselors endure in practical application. Further investigation into a domain-specific intelligence test may prove interesting and fruitful. Emotional intelligence has shown promise as a potential explanation and description of counselor intelligence.

Cognitive complexity development in the on-line counseling student needs to be further investigated because there is limited information available (Palmer, 2016).

Stigma exist that on-line counseling programs are less rigorous and respected

(Blackmore, Tantam, & VanDeurzen, 2008; Dobbs et al., 2009; Duncan, Range, &

Hvidston, 2013; Palmer, 2017) and little evidence has been presented to negate or accept this notion. Despite this, more and more counselors are graduating with on-line counseling degrees and entering the field as practicing counselors. Further investigation

87 is needed into how this is impacting our counselor professional identities and if course corrections need to be made.

Implications for Counseling, Supervision and Counselor Education

The results of this study have many implications for counseling. Below will be a brief description of the implications for counseling, supervision and counselor education.

Counseling. The prospect of truly being seen and accepted is precious and should always be the ultimate goal of counseling, but it is difficult. It requires that counselors do the work on getting to know themselves and have difficult and sometimes complicated conversations with themselves about who they are and why they want to help. This is why there is such a focus on self-reflection and processing in counselor education programs. The importance of continuing this self-reflection into practice would continue to develop that level of empathy as counseling practice experience increases the level of cognitive complexity.

Growing levels of empathy could improve a work-life balance and fight compassion fatigue and burnout. Counselors should continue to challenge themselves to think critically and work methodically through complex situations to conscientiously promote cognitive growth. It is a way of conceptualizing that clinicians can optimize their personal cognitive complexity growth and not simply the role they take as counselors (Granello, 2010). Counselors could also consider teaching experiential courses in higher education such as internship or practicum because counselor education experience has correlated with higher cognitive complexity (Granello, 2010).

Supervision. Utilizing the CCQ for supervision purposes (Welfare, 2010) can change the way that counselors and counselors-in-training will conceptualize and work with clients by improving their cognitive complexity. If the counselors are able to draw the connection between how effective they feel with clients to their ability to understand them, supervision could prove to be more efficient. The CCQ also provides structure and clearly identifies expectations that most millennials need to be successful (Koltz et al.,

2017), but could also open the opportunity to get comfortable with ambiguity in the confines and safety of the supervisory relationship.

From an anecdotal perspective, increased awareness of participants was observed by the researcher. Upon completion of CCQ multiple participants would make comments such as: “It was so much harder to describe the client I was less effective with” or “I had very few positive characteristics for the client I was less effective with.” As this was not the focus or scope of this research, no efforts were made by the researcher to elicit these comments or encourage them.

Although with so many students making these comments, it did very informally support previous research efforts made by Welfare et al. in 2013. They investigated and posited that participants would, in fact, establish more positive than negative characteristics for clients they felt more effective with (Welfare et al., 2013). It also supported the hypothesis and side benefit of completing the CCQ, of cognitive complexity being improved by completing the CCQ (Welfare, 2007) and processed effectively in supervision.

Counselor education. Cognitive complexity can also be promoted in counseling education programs by focusing more on reflective practices (Davidson & Schmidt,

2014). It is not just in simply talking through how they are feeling about certain experiences but rather providing students with an opportunity to bring what they think they know and apply it to what they do or will be doing as counselors. This includes defining any potential problems and concocting solutions (Whitaker & VanGardern,

2009), and problem solving. It is both what we are doing and how we are doing it as counselor educators. Davidson and Schmidt (2014) stated that there are specific classroom interventions that accomplish this but that the culture that is established is just as important. Optimal environments would include students interacting with each other and the instructor in a way that promotes the challenging of previously held or potentially unknown biases. Structure can be provided by progressing through the Socratic method

(Overhauser, 1991), or more counseling friendly strategies that outline themes and guidelines in their work (King & Kittener, 2002).

Reflective practice has standing tradition in counselor education program but may not be implemented as readily and frequently as previously thought (Davidson &

Schmidt, 2014). Reflective practices improve a counselor’s ability to express empathy, be multiculturally competent, and be in tune and committed to the needs of their clients

(Belenky et al., 1986; King & Kichener, 1994; Magolda, 1992; Perry, 1981). One boundary that explains why this is not as prevalent could be counselor educators are not comfortable with making their students uncomfortable and saving them from experiencing ambiguity or cognitive dissonance (Davidson & Schmidt, 2014). Not

90 allowing their students to struggle in an academic setting will impede their ability to effectively contend with these phenomena in practice.

Do the same rules apply of acceptance without prejudice and judgment (Rogers,

1975) that are awarded to clients apply for counseling students? Brings into question our counselor professional identity or solely propagated by a fear of legal ramifications

(Cases of Keeton V. Anderson; Wiley, Ward V. Polite; Cash V. Hofherr). Or is it always difficult to interact and influence with those who have differing beliefs, especially if those beliefs are intertwined and deeply imbedded with whom we identify as a human being and as professional counselors?

Inability to create work / life balance and low stress tolerance can be lessened by a further incorporation of emotional intelligence education. Emotional intelligence has been shown to improve empathy and lower stress distress (Guiterrez, Mullen, & Fox,

2017). A solution can be that personal counseling be incorporated into curriculum. The practice of mandating counseling for remediation is common practice and the requiring of counseling as a response to perceived deficits in the gatekeeping process has proven to be problematic (Forrest et al., 2018; Gilfoyle, 2008; Homrich, 2009). There is currently no evidence to support that required / mandated counseling is effective in resolving these deficits (Henderson & Dufrene, 2011; Homrich, 2009; Russell et al., 2007; Roberts &

Franzo, 2013). It is not effective to require counseling but encouraging it may prove more fruitful. Homrich (2009) posited that there are gate-keeping checkpoints that could be utilized in monitoring the academic and practical performance of counseling students.

These checkpoints provide intervention points to create a structure and support system for

91 students. It would be at each of these points that the benefits and availability of personal counseling could be discussed with students by their advisors.

In the classroom a culture of openness and flexibility could be supported by introducing personal counseling as an effective self-care strategy. It remains an ongoing concern that students in counseling attempt to counsel (self-heal) themselves as not only a way to familiarize themselves with new concepts but manage new concerns as they present. Changing the conversations that are had at multiple interjection points can shift the connotation that personal counseling is a pejorative and punitive response. This could destigmatize the helper that seeks out help in the form of personal counseling. Utilizing an approach that involves multiple check-ins (Homrich, 2009), conversations with advisors and counselor educators, and alternate course work considerations, personal counseling can become more approachable and desirable for counseling students.

Counselor education curricula, although promoting opportunity for personal growth, is not personal counseling. Counseling programs are ethically obligated to uphold The ACA code of Ethics (Standard A.4.b.), which states that “Counselors are aware of—and avoid imposing—their own values, attitudes, beliefs and behaviors”

(ACA, 2014). Counselor educators are like optometrists switching the varying lenses and asking when their vision is becoming more clear or blurry. The responsibility is on the counselor trainee to articulate what they are experiencing so that modifications and support can be utilized. The counselor educator focus is to promote a safe and accepting environment where students feel safe to disclose their internal dialogue. It is exceedingly important that the personal biases and stigma surrounding counselors receiving

92 counseling be mitigated in years to come. Given the inclination for the millennial population to have difficulty regulating their emotion, creating and maintaining a work / life balance, and managing stress, personal counseling for counselor trainees can aid in developing instincts for identifying and tolerating ambiguity and generating self-efficacy for navigating through the difficulties of counseling.

Conclusion

There have been many ideals and concepts discussed surrounding the constructs of empathy, cognitive complexity, and intelligence. How to define and promote their development in counseling trainees have been discussed. Counseling is difficult which implies that teaching counseling can be even more challenging. It is a continual process of balancing the art and science, the ethics and the tenderness, the aspirational and the practical, the kind with the stern. And all aspects are necessary. We cannot be afraid to have difficult conversations both with ourselves as practitioners and academics, and with others because the work we do as counselors is important and can change lives.

Summary

The purpose of this study was to identify if age, client hours, empathy, and intelligence could predict level of cognitive complexity in counseling students. Results from this study suggested that although this was not a predictive linear regression, there were many recommendations for future research and implications for counseling, counselor education, and supervision. Various limitations were discussed and caution should be utilized in generalizing these results to more diverse populations. One finding from this study did reveal that there was a slight correlation between empathy and the

93 scores for cognitive complexity. Cognitive development promotes a comfortability with ambiguous situations (Perry, 1970) and the ability to continually self-reflect on self and others (Skovolt & Ronnestad, 2003). It is hypothesized that novice counselors can be taught empathy (Rogers, 1975), and that most existing empathy comes from personal experiences (Truax & Carkhuff, 1967). More investigation is needed to more clearly define and measure the constructs of cognitive complexity and empathy and research should be conducted in identifying the domain-specific intelligence that is utilized by efficient and effective counselor.

APPENDICES

APPENDIX A

SCRIPT FOR IN-PERSON RECRUITMENT TO SUPERVISORS

Appendix A

Script for In-Person Recruitment to Supervisors

Hello CHDS supervisor! I am conducting a research study in which you have been invited to participate. The purpose of this study is to identify which combination of factors including empathy, intelligence, and number of client hours predict level of cognitive complexity.

Your participation will include: (1) Approximately 30 minutes of your class time to administer the Counselor Cognition Questionnaire around week 6 of the semester; (2) Approximately 10 minutes of your time to complete the Hayes Scale of Empathy for Supervisors per student participant to be completed following midterm evaluations

If you are interested, please feel free to complete the informed consent paperwork or contact me so we can schedule a time to introduce this opportunity to your students. My email address is [email protected]. I look forward to hearing from you.

This project is being supervised by Dr. Jason McGlothlin ([email protected]) and Dr. Betsy Page ([email protected]), in the Counseling and Human Development Services program at Kent State University, and will serve as my dissertation research. The title of this research study is: Empathy and Intelligence as Predictors of Cognitive Complexity in Counseling Student.

APPENDIX B

SCRIPT FOR IN-PERSON RECRUITMENT TO STUDENT PARTICIPANTS

Appendix B

Script for In-Person Recruitment to Student Participants

Hello CHDS student! I am conducting a research study in which you have been invited to participate. The purpose of this study is to identify which combination of factors including empathy, intelligence, and number of client hours predict level of cognitive complexity.

Your participation will include: (1) Approximately 30 minutes of your class time to complete the Counselor Cognition Questionnaire around week 6 of the semester; (2) Approximately 10 minutes of your time to complete the demographic questionnaire; (3) Approximately 30 minutes outside of class time to complete the Kaufmann Brief Test of Intelligence-2.

If you are interested, please feel free to fill out the informed consent paperwork or feel free to contact me. My email address is [email protected]. I look forward to hearing from you.

APPENDIX C

E-MAIL RECRUITMENT FOR INTERNSHIP SUPERVISORS (KSU)

100

Appendix C

E-Mail Recruitment for Internship Supervisors (KSU)

Study Title: Empathy and Intelligence as Predictors of Cognitive Complexity in Counseling Students

Principal Investigators: Jason McGlothlin and Co-Investigators: Staci Hayes and Betsy Page

Hello CHDS supervisor! My name is Staci Hayes and I am conducting a research study in which you have been invited to participate. The purpose of this study is to identify which combination of factors including empathy, intelligence, and number of client hours predict level of cognitive complexity in counseling students.

One (or more) of your supervisees has (have) consented to participate. The supervisee(s) name is (are) (Insert name)(s).

Your participation will include: Reading and electronically signing the consent form and approximately 5-10 minutes of your time to complete the Hayes Scale of Empathy for Supervisors per student participant to be completed following midterm evaluations. You may have more than one participant. You will be made aware of the participation level of the student participants and will be asked to keep participation and all scores on the empathy scale confidential.

If you are interested, please log onto to Qualtrics software link below. You will be prompted to complete the informed consent paperwork and the empathy scale. My email address is [email protected]. Please do not hesitate to contact me with any further questions. I look forward to working with you.

Thank you so much in advance for your time and commitment!

(https://kent.qualtrics.com/jfe/form/SV_dhGcPFlA9W8lPJr)

This project is being supervised by Dr. Jason McGlothlin ([email protected]) 330-972- 0716 and Dr. Betsy Page ([email protected])330-672-0696, in the Counseling and Human Development Services program at Kent State University. If you have any questions about your rights as a research participant or complaints about the research, you may call the IRB at 330-672-2704. The title of this research study is: Empathy and Intelligence as predictors of Cognitive Complexity in Counseling Student.

APPENDIX D

E-MAIL RECUITMENT FOR INTERNSHIP SUPERVISORS (YSU)

102

Appendix D

E-Mail Recruitment for Internship Supervisors (YSU)

Study Title: Empathy and Intelligence as Predictors of Cognitive Complexity in Counseling Students

Principal Investigators: Jason McGlothlin and Co-Investigators: Staci Hayes and Betsy Page

Hello Youngstown State University supervisor! My name is Staci Hayes and I am conducting a research study in which you have been invited to participate. The purpose of this study is to identify which combination of factors including empathy, intelligence, and number of client hours predict level of cognitive complexity in counseling students. My contact person for your University is Dr. Jacob Protivnak ([email protected]) 330- 941-1936.

One (or more) of your supervisees has (have) consented to participate. The supervisee(s) name is (are) (Insert name)(s).

Thank you so much in advance for your time and commitment!

(https://kent.qualtrics.com/jfe/form/SV_dhGcPFlA9W8lPJr)

This project is being supervised by Dr. Jason McGlothlin ([email protected]) 330.972.0716 and Dr. Betsy Page ([email protected]) 330.672.0696, in the Counseling and Human Development Services program at Kent State University. If you have any questions about your rights as a research participant or complaints about the research, you may call the IRB at 330-672-2704. The title of this research study is: Empathy and Intelligence as predictors of Cognitive Complexity in Counseling Student.

APPENDIX E

E-MAIL RECRUITMENT FOR INTERNSHIP STUDENTS (YSU)

104

Appendix E

E-Mail Recruitment for Internship Students (YSU)

Study Title: Empathy and Intelligence as predictors of Cognitive Complexity in Counseling Students

Principal Investigator: Jason McGlothlin and Co Investigators: Staci Hayes and Betsy Page

Hello Youngstown State University student! My name is Staci Hayes and I am conducting a research study in which you have been invited to participate. The purpose of this study is to identify which combination of factors including empathy, intelligence, and number of client hours predict level of cognitive complexity. My contact person for Youngstown State University is Dr. Jacob Protivnak, 330.941.1936 ([email protected]).

Your participation will include: 1) Approximately 30 minutes of your class time to complete the Counselor Cognition Questionnaire 2) Approximately 10 minutes of your time to complete the demographic questionnaire 3) Approximately 30 minutes outside of class time to complete the Kaufmann Brief Test of Intelligence-2.

Your supervisor will complete Hayes Scale for Supervisors, which asks questions about your ability to express empathy. These empathy scores will be linked to your scores. Please be aware that your participation will not be confidential to your instructor as a result of this procedure. Other data collected will not be shared with your supervisor. In order to participate in in this study you must be willing to permit your supervisor to complete this survey. Participation will not have a positive or negative impact on your class standings / grade. You may at anytime discontinue participation or ask your supervisor to not complete the empathy scale.

If you are interested, please feel free to read through the informed consent paperwork which I have attached or feel free to contact me. I will be visiting your practicum class on (Fill in date). At this time, I will address any concerns and answer any further questions. I will also bring more copies of the consent form and will be acquiring consent from interested individual.

My email address is [email protected]. I look forward to working with you.

This project is being supervised by Dr. Jason McGlothlin, 330.672.0716 ([email protected]) and Dr. Betsy Page, 330.672.0696 ([email protected]), in the Counseling and Human Development Services program at Kent State University, and will serve as my dissertation research. The title of this research study is: Empathy and

105

Intelligence as predictors of Cognitive Complexity in Counseling Student. If you have any questions about your rights as a research participant or complaints about the research you may call the IRB at 330.672-2704.

APPENDIX F

INFORMED CONSENT DOCUMENT PRACTICUM SUPERVISORS (KSU)

107

Appendix F

Informed Consent Document Practicum Supervisors (KSU)

Study Title: Empathy and Intelligence as Predictors of Cognitive Complexity in Counseling Students

Principal Investigator: Jason McGlothlin and Co-Investigators: Staci Hayes and Betsy Page

You are being invited to participate in a research study. This consent form will provide you with information on the research project, what you will need to do, and the associated risks and benefits of the research. Your participation is voluntary. Please read this form carefully. It is important that you ask questions and fully understand the research in order to make an informed decision. You will receive a copy of this document to take with you.

Purpose The purpose of this study is to learn if the variables of empathy and intelligence have the ability to predict the level of cognitive complexity in counseling students.

Procedures Participation in the study will involve the researcher gaining access to your practicum class to administer the Counselor Cognitions Questionnaire. This will take approximately 30 minutes and occur around the 6th week of the semester. You will also be responsible for completing the Hayes Scale of Empathy for Supervisors for each of the student participants following the midterm evaluations. This will take approximately 10 minutes per participant and will provided to you following the signing of this consent form. Weeks 2,3,4 of the semester: Reminder e-mails will be sent to student participants on their designated times to take the KBTI-2. Weeks 3,4,5 of the semester: Researcher will meet with the student participants and complete the KBTI-2. Reminder e-mails will be sent to the supervisors about the date and time of the administration of the CCQ in their practicum classes. Week 6 of the semester: Researcher will administer the CCQ in the Practicum classes Week 8 of the semester: Researcher will send out reminder e-mails to supervisor to complete the HSES following midterm evaluations for every student participant. The HSES will be collected and stored by Staci Hayes, Dr. Jason McGlothlin, or Dr. Jason Miller upon completion.

Benefits

108

This research may benefit the participants by providing for a growth in their ability to conceptualize and describe clients. This will include their ability to identify positive characteristics. Your participation in this study will help us to better understand how the variable of empathy, intelligence, and cognitive complexity interact. This could have implications for supervision, counselor education, and curriculum and program development.

Risks and Discomforts Although the scores for the intelligence and cognitive complexity portions of the assessment will remain confidential, you will be providing the empathy score for each student participants. In completing the Hayes Scale of Empathy of Supervisors, you will be made aware of the student who have consented for participation. You are asked to be sensitive to the privacy and participation level of the students and not allow participation or lack of participation affect their assessment of each individual and will have no impact on their grade. Please keep all scores and level of participation confidential.

Privacy and Confidentiality Your study related information will be kept confidential within the limits of the law. Any identifying information will be kept in a secure location and only the researchers will have access to the data. Research participants will not be identified in any publication or presentation of research results; only aggregate data will be used. Your research information may, in certain circumstances, be disclosed to the Institutional Review Board (IRB), which oversees research at Kent State University, or to certain federal agencies. Confidentiality may not be maintained if you indicate that you may do harm to yourself or others.

Voluntary Participation Taking part in this research study is entirely up to you. You may choose not to participate or you may discontinue your participation at any time without penalty or loss of benefits to which you are otherwise entitled. You will be informed of any new, relevant information that may affect your health, welfare, or willingness to continue your study participation. Participating or not participating will not affect the course grade.

Contact Information If you have any questions or concerns about this research, you may contact Jason McGlothlin at 330.672.0716 or Betsy Page at 330.672.0696. This project has been approved by the Kent State University Institutional Review Board. If you have any questions about your rights as a research participant or complaints about the research, you may call the IRB at 330.672.2704.

109

Consent Statement and Signature I have read this consent form and have had the opportunity to have my questions answered to my satisfaction. I voluntarily agree to participate in this study. I understand that a copy of this consent will be provided to me for future reference.

______Participant Signature Date

______Witness Signature Date

APPENDIX G

INFORMED CONSENT DOCUMENT PRACTICUM STUDENTS (KSU)

111

Appendix G

Informed Consent Document Practicum Students (KSU)

Study Title: Empathy and Intelligence as Predictors of Cognitive Complexity in Counseling Students

Principal Investigator: Jason McGlothlin and Co-Investigators: Staci Hayes and Betsy Page

Purpose The purpose of this study is to learn if the variables of empathy and intelligence have the ability to predict the level of cognitive complexity in counseling students.

Procedures Participation in the study will take approximately 30 minutes of class time, approximately 40 minutes of out of class time. You will complete a demographic questionnaire and two assessments. The first of the two assessments in the Counselor Cognitions Questionnaire and will be administered during class time around the 6th week of class. The CCQ will be used to assess cognitive complexity. Instructions will be provided before administration and will take approximately 30 minutes. The second assessment is the Kaufman Brief Intelligence Test, second edition and will be administered the 3rd, 4th or 5th week of the semester. Email correspondence will be utilized to coordinate a time. At the completion of this consent form you will be given the demographic sheet. Your scores on the intelligence and cognitive complexity portions of the assessment will remain confidential. Upon consenting, your supervisor will be made aware of your participation as they will be responsible for providing the score for empathy. Weeks 3,4,5 of the semester: Researcher will meet with the student participants and complete the KBTI-2. Reminder e-mails will be sent to the supervisors about the date and time of the administration of the CCQ in their Internship classes. Week 6 of the semester: Researcher will administer the CCQ in the Internship classes. Week 8 of the semester: Researcher will send out reminder e-mails to supervisor to complete the HSES following midterm evaluations for every student participant.

112

Benefits You may be benefited by a growth in your ability to conceptualize and describe clients including the ability to identify positive characteristics. Your participation in this study will help us to better understand how the variable of empathy, intelligence, and cognitive complexity interact. This could have implications for supervision, counselor education, and curriculum and program development.

Risks and Discomforts Your supervisor will complete Hayes Scale for Supervisors, which asks questions about your ability to express empathy. These empathy scores will be linked to your scores. Please be aware that your participation will not be confidential to your instructor as a result of this procedure. Other data collected will not be shared with your supervisor. In order to participate in in this study you must be willing to permit your supervisor to complete this survey. Participation will not have a positive or negative impact on your class standings / grade. You may at anytime discontinue participation or ask your supervisor to not complete the empathy scale. If you are a student in Dr. McGlothlin’s class, Dr. Miller will be completing the empathy scale to avoid any potential bias.

Privacy and Confidentiality Your cognitive complexity and intelligence scores will be kept confidential. Supervisors will be asked to keep both your participation and scores on the empathy scale confidential. Other related information will be kept confidential within the limits of the law. Any identifying information will be kept in a secure location and only the researchers will have access to the data. Research participants will not be identified in any publication or presentation of research results; only aggregate data will be used. Your research information may, in certain circumstances, be disclosed to the Institutional Review Board (IRB), which oversees research at Kent State University, or to certain federal agencies. Confidentiality may not be maintained if you indicate that you may do harm to yourself or others.

Voluntary Participation Taking part in this research study is entirely up to you. You may choose not to participate or you may discontinue your participation at any time without penalty or loss of benefits to which you are otherwise entitled. You will be informed of any new, relevant information that may affect your health, welfare, or willingness to continue your study participation. You should not feel any pressure to participate as participating or not participating will not have positive or negative impact on standing grades.

Contact Information If you have any questions or concerns about this research, you may contact Jason McGlothlin at 330.672.0716 or Betsy Page at 330.672.0696. This project has been

113 approved by the Kent State University Institutional Review Board. If you have any questions about your rights as a research participant or complaints about the research, you may call the IRB at 330.672.2704.

______Participant Signature Date

______Witness Signature Date

APPENDIX H

INFORMED CONSENT DOCUMENT INTERNSHIP STUDENT (KSU)

115

Appendix H

Informed Consent Document Internship Student (KSU)

Study Title: Empathy and Intelligence as predictors of Cognitive Complexity in Counseling Students

Principal Investigator: Jason McGlothlin and Co-Investigators: Staci Hayes and Betsy Page

Purpose The purpose of this study is to learn if the variables of empathy and intelligence have the ability to predict the level of cognitive complexity in counseling students.

116

Voluntary Participation Taking part in this research study is entirely up to you. You may choose not to participate or you may discontinue your participation at any time without penalty or loss of benefits to which you are otherwise entitled. You will be informed of any new, relevant information that may affect your health, welfare, or willingness to continue your study participation. You should not feel any pressure to participate as participating or not participating will not have positive or negative impact on standing grades.

Contact Information If you have any questions or concerns about this research, you may contact Jason McGlothlin at 330.672.0716 or Betsy Page at 330.672.0696. This project has been

117 approved by the Kent State University Institutional Review Board. If you have any questions about your rights as a research participant or complaints about the research, you may call the IRB at 330.672.2704.

______Participant Signature Date

______Witness Signature Date

APPENDIX I

INFORMED CONSENT DOCUMENT INTERNSHIP STUDENT (YSU)

119

Appendix I

Informed Consent Document Internship Student (YSU)

Study Title: Empathy and Intelligence as Predictors of Cognitive Complexity in Counseling Students

Principal Investigator: Jason McGlothlin and Co-Investigators: Staci Hayes and Betsy Page

Youngstown State Contact Person: Jacob Protivnak

Purpose The purpose of this study is to learn if the variables of empathy and intelligence have the ability to predict the level of cognitive complexity in counseling students.

120

Week 8 of the semester: Researcher will send out reminder e-mails to supervisor to complete the HSES following midterm evaluations for every student participant.

Risks and Discomforts Your supervisor will complete Hayes Scale for Supervisors, which asks questions about your ability to express empathy. These empathy scores will be linked to your scores. Please be aware that your participation will not be confidential to your instructor as a result of this procedure. Other data collected will not be shared with your supervisor. In order to participate in in this study, you must be willing to permit your supervisor to complete this survey. Participation will not have a positive or negative impact on your class standings / grade. You may at anytime discontinue participation or ask your supervisor to not complete the empathy scale.

Voluntary Participation Taking part in this research study is entirely up to you. You may choose not to participate or you may discontinue your participation at any time without penalty or loss of benefits to which you are otherwise entitled. You will be informed of any new, relevant information that may affect your health, welfare, or willingness to continue your study participation. You should not feel any pressure to participate as participating or not participating will not have positive or negative impact on standing grades.

Contact Information If you have any questions or concerns about this research, you may contact Jason McGlothlin at 330.672.0716 ([email protected]) or Betsy Page at 330.672.0696

121

([email protected]). The Youngstown State University contact person is Jacob Protivnak at 330.941-1936 ([email protected]). This project has been approved by the Kent State University Institutional Review Board. If you have any questions about your rights as a research participant or complaints about the research, you may call the IRB at 330.672.2704.

______Participant Signature Date

______Witness Signature Date

APPENDIX J

QUALTRICS RECRUITMENT E-MAIL FOR SUPERVISORS (KSU)

123

Appendix J

Qualtrics Recruitment E-mail for Supervisors (KSU)

Study Title: Empathy and Intelligence as Predictors of Cognitive Complexity in Counseling Students

Principal Investigators: Jason McGlothlin and Co-Investigators: Staci Hayes and Betsy Page

One (or more) of your supervisees has (have) consented to participate. The supervisee(s) name is (are) (Insert name)(s).

Thank you so much in advance for your time and commitment!

(insert link to Qualtrics Survey Software)

This project is being supervised by Dr. Jason McGlothlin ([email protected]) 330-972- 0716 and Dr. Betsy Page ([email protected]) 330-672-0696, in the Counseling and Human Development Services program at Kent State University. If you have any questions about your rights as a research participant or complaints about the research, you may call the IRB at 330-672-2704. The title of this research study is: Empathy and Intelligence as predictors of Cognitive Complexity in Counseling Student.

APPENDIX K

QUALTRICS RECRUITMENT E-MAIL FOR SUPERVISORS (YSU)

125

Appendix K

Qualtrics Recruitment E-mail for Supervisors (YSU)

Study Title: Empathy and Intelligence as predictors of Cognitive Complexity in Counseling Students

Principal Investigators: Jason McGlothlin and Co-Investigators: Staci Hayes and Betsy Page

Hello YSU supervisor! My name is Staci Hayes and I am conducting a research study in which you have been invited to participate. The purpose of this study is to identify which combination of factors including empathy, intelligence, and number of client hours predict level of cognitive complexity in counseling students.

One of your supervisees has consented to participate. The supervisee's name is (insert student name).

Thank you so much in advance for your time and commitment! Please click on the link below.

(Insert Qualtrics link).

This project is being supervised by Dr. Jason McGlothlin ([email protected]) 330-972-0716 and Dr. Betsy Page ([email protected]) 330-672-0696, in the Counseling and Human Development Services program at Kent State University. If you have any questions about your rights as a research participant or complaints about the research, you may call the IRB at 330- 672-2704. The title of this research study is: Empathy and Intelligence as predictors of Cognitive Complexity in Counseling Student.

APPENDIX L

REMINDER E-MAIL TO SUPERVISOR (KSU)

127

Appendix L

Reminder E-mail to Supervisor (KSU)

Hello CHDS Supervisor! This serves as a friendly reminder about the research study in which you have been invited to participate.

I will be attending your class on (insert date) at (insert time), to administer the Counselors Cognition Questionnaire. Thank you so much and see you then!

Staci Hayes, MEd, PC Doctoral Candidate

Kent State University

Counseling & Human Development Services

APPENDIX M

REMINDER E-MAIL TO STUDENT PARTICIPANTS (KSU)

129

Appendix M

Reminder E-mail to Student Participants (KSU)

Hello CHDS student participant! This serves as a friendly reminder about a research study in which you have been invited to participate.

Your participation will include: (1) Approximately 30 minutes of your class time; (2) Approximately 30 minutes of your time outside of class; (3) Completion of the demographic questionnaire.

You are scheduled on ______to complete the intelligence portion of my research. My email address is [email protected]

Staci Hayes, MEd, PC Doctoral Candidate

Kent State University Counseling & Human Development Services

APPENDIX N

REMINDER E-MAIL TO STUDENTS (YSU)

131

Appendix N

Reminder E-mail to Students (YSU)

Hello YSU student participant! This serves as a friendly reminder about a research study in which you have been invited to participate.

Your participation will include: (1) Approximately 30 minutes of your class time; (2) Approximately 30 minutes of your time outside of class; (3) Completion of the demographic questionnaire.

You are scheduled on ______to complete the intelligence portion of my research. My email address is [email protected]

Staci Hayes, MEd, PC Doctoral Candidate

Kent State University

Counseling & Human Development Services

APPENDIX O

DEMOGRAPHIC QUESTIONNAIRE

133

Appendix O

Demographic Questionnaire

Please complete the following questionnaire. Please write in response if applicable.

1. I am interested in participating in this study Yes No

2. Sex assigned at birth on birth certificate Male Female Decline

3. Current gender identity Male Female Female-to-Male (FTM) / Transgender Male/ Trans Man Male-to-Female (MTF) / Transgender Female/ Trans Woman Genderqueer, neither exclusively male nor female Additional Gender Category/ (or Other), ______Decline

4. Total # of client hours 0-15 16-30 31-45 46 and over

5. Age ______

6. Race / Ethnicity White African American Hispanic / Latino Asian Native Hawaiian or Other Pacific Islander (HNPI) American Indian or Alaskan Native Other / Multi-race

7. Supervisor name ______

8. Course enrolled Practicum I Practicum II

134

9. Education Undergraduate ______Previous Masters? Yes No If Yes, please list ______

10. I am comfortable with the English Language Yes No

11. I have seen at least two clients: one of whom I feel more effective and one of whom I feel less effective Yes No

Please enter your email so that you may be contacted by the researcher to schedule a time to administer the intelligence portion of the study.

Email Address ______

APPENDIX P

REMINDER EMAIL FOR SUPERVISORS TO COMPLETE HSES

136

Appendix P

Reminder E-mail to Supervisors to Complete HSES

Hello CHDS Supervisor! This serves as a friendly reminder about the research study in which you have been invited to participate.

Please complete the Hayes Scale of Empathy for Supervisors for each of the student participants. This should be completed after midterm evaluations (around week 9 of the semester). Please put all completed documents in a sealed envelope and give to the counseling staff personnel to be put in a designated locked drawer for retrieval.

I have attached the scale for your use.

Staci Hayes, MEd, PC Doctoral Candidate

Kent State University

Counseling & Human Development Services

APPENDIX Q

COUNSELOR COGNITIONS QUESTIONNAIRE CONTACT INFORMATION

FOR INQUIRIES

138

Appendix Q

Counselor Cognitions Questionnaire Contact Information for Inquiries

All inquiries concerning the CCQ should be directed to the author of the assessment, Laura Welfare. This assessment is not to be used without permission.

Laura E. Welfare, Phd, LPC, NCC, ACS Associate Professor of Counselor Education Virginia Tech 1750 Kraft Drive, Suite 2001, Mail Code 0302 Blacksburg VA 24060 [email protected]

APPENDIX R

HAYES SCALE OF EMPATHY FOR SUPERVISORS

140

Appendix R

Hayes Scale of Empathy for Supervisors

Strongly Agree Slightly Slightly Disagree Strongly Items Agree Agree Disagree Disagree 1. The counselor-in training utilizes non-verbal communication to convey empathy 2. The counselor-in training utilizes verbal communication to convey empathy 3. The counselor-in training expresses an understanding of the non-verbal communication expressed by clients 4. The counselor-in training expresses an understanding of the verbal communication expressed by clients 5. The counselor-in training is able to use empathic reflections to put the client’s experiences into words 6. The counselor-in training matches the emotional intensity of clients 7. The counselor-in training recognizes their own emotional responses

REFERENCES

142

REFERENCES

Ayers, W. (1993). To teach: The journey of a teacher. New York, NY: Teachers College

Press.

Bain, S. K., & Jaspers, K. E. (2010). Review of Kaufman Brief Intelligence Test, Second

Edition. Journal of Psychoeducational Assessment, 28, 167-174.

Barrett-Lennard, G. T. (1962). Dimensions of therapy response as causal factors in

therapeutic change. Psychological Monographs, 76, 1-33.

Barrett-Lennard, G. T. (1981). The empathy cycle: Refinement of a nuclear concept.

Journal of Counseling Psychology, 28, 91-100.

Benack, S. (1988). Relativistic thought: A cognitive basis for empathy in counseling.

Counseling Education and Supervision, 27, 216-232.

Benjamin, L. T. (2009, January). The birth of American intelligence testing. Monitor on

Psychology, 40(1), 20-21.

Beranuy, M., Oberst, U., Carbonell, X & Chamarro, A. (2009). Problematic Internet and

mobile phone use and clinical symptoms in college students: The role of

emotional intelligence. Computers in Human Behavior, 25, 1182-1187.

Bieri, J. (1955). Cognitive complexity-simplicity and predictive behavior. Journal of

Abnormal and Social Psychology, 51, 263-268.

Bieri, J., Atkins, A. L., Briar, S., Leaman, R. L., Miller, H., & Tripodi, T. (1966). Clinical

and social judgment. New York, NY: Wiley.

Binet, A., & Simon, T. (1905). Méthodes nouvelles pour diagnostiquer l’idiotie,

l’imbécillité et la débilité mentale [New methods for diagnosing idiocy,

143

imbecility, and mental weakness]. In S. de Sanctis (Ed.), Atti del V Congresso

internazionale di psicologia tenuto in Roma dal 26 al 30 aprile 1905 sotto la

presidenza del Prof. Giuseppi Sergi (pp. 507–510). Rome, Italy: Foranzi.

Birk, J. M., & Mahalik, J. R. (1996). The influence of trainee conceptual level, trainee

anxiety, and supervision evaluation on counselor developmental level. The

Clinical Supervisor, 14, 123-137.

Blackmore, C., Tantam, D., & van Deurzen, E. (2008). Evaluation of e-earning

outcomes: Experience from an online psychotherapy education programme. Open

Learning, 23, 185-201.

Bledstein, B. J. (1994). Terminology counts, “Termin”ly stupid. Reviews in American

History, 22, 113-118.

Blocher, D. H. (1983). Supervision in counseling: II. Contemporary models of

supervision: Toward a cognitive developmental approach to counseling

supervision. The Counseling Psychologist, 11, 27-34. doi:10.1177/0011000083

111006

Bohart, A., & Greenberg, L. S. (1997). Empathy reconsidered. Washington, DC:

American Psychological Association.

Bohart, A., Elliott, R., Greenberg, L. S., & Watson, J. C. (2002). Empathy. In J. C.

Norcross (Ed.), Psychotherapy relationships that work: Therapist contributions

and responsiveness to patients (pp. 89-108). New York, NY: Oxford University

Press.

144

Boland, D. L. (2004). Program evaluation and public accountability. In M. H. Oermann

& K. T. Heinrich (Eds.), Annual review of nursing education (Vol. 2, pp. 47-69).

New York, NY: Springer.

Borders, L. D. (1989). Ego development and counseling ability during training.

Counselor Education and Supervision, 29, 71-83.

Bordin, E. (1979). The generalizability of the psychoanalytic concept of the working

alliance. Psychotherapy: Theory, Research, and Practice, 16, 252-260.

Bozarth, J. (2007). Unconditional positive regard. In M. Cooper, M. O’Hara, P. F.

Schmid, & G. Wyatt (Eds.), The handbook of person-centered psychotherapy and

counseling (pp. 182-193). New York, NY: Palgrave Macmillan.

Bozarth, J. D. (1997). Empathy from the framework of client-centered theory and the

Rogerian hypothesis. In A. Bohart & L. S. Greenberg (Eds.), Empathy

reconsidered (pp. 81-102). Washington, DC: American Psychological

Association.

Brendel, J. M., Kolbert, J. B., & Foster, V. A. (2002). Promoting student cognitive

development. Journal of Adult Development, 9, 217-227.

doi:10.1023/A.1016056227344

Calvin, A. (2000). Intelligence testing in historical perspective: Use of standardized tests

in admissions in post-secondary institutions of higher education. Psychology,

Public Policy and Law, 6, 20-25.

145

Carson, J. (2007). The measure of merit: Talents, intelligence and inequality in the

French and American republics, 1750-1940. Princeton, NJ: Princeton University

Press.

Carson, J. (2014). Mental testing in the early twentieth century internalizing the mental

health testing story. History of Psychology, 3, 249-255.

Carter, C. S., Harris, J., & Porges, S. W. (2009). Neural and evolutionary perspectives on

empathy. In J. Decety & W. Ickes (Eds.), The social neuroscience of empathy (pp.

169-182). Cambridge, MA: MIT Press.

Castillo, J. H. (2018). Cognitive complexity in counseling and counselor education: A

systematic and critical review. The Journal of Counselor Preparation and

Supervision, 11(1). Retrieved from http://wcsu.edu/jcps/vol11/iss1/3

Choate, L. H., & Granello, D. H. (2006). Promoting student cognitive development in

counselor preparation: A proposed expanded role for faculty advisors. Counselor

Education and Supervision, 46, 116-130.

Clark, A. J. (2007). Empathy in counseling and psychotherapy: Perspectives and

practices. Mahwah, NJ: Lawrence Erlbaum Associates.

Clark, A. J. (2010). Empathy and sympathy: Therapeutic distinctions in counseling.

Journal of Mental Health Counseling, 32, 95-101.

Clark, A. J. (2016). Empathy and Alfred Adler: An integral perspective. Journal of

Individual Psychotherapy, 72, 237-253.

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd edition).

Hillsdale, NJ: Lawrence Erlbaum Associates.

146

Council for Accreditation of Counseling and Related Educational Programs [CACREP].

(2016). CACREP Accreditation manual: 2016 standards. Alexandria, VA:

Author.

Crockett, W. H. (1965). Cognitive complexity and impression formation. In B. A. Maher

(Ed.), Progress in experimental personality research (pp. 47-90). New York, NY:

Academic Press.

Crockett, W. H. (1982). The organization of construct systems: The organization

corollary. In J. C. Mancuso & J. R. Adams-Webber (Eds.), The construing person

(pp. 62-95). New York, NY: Praeger.

Davis, M. H. (1983). Measuring individual differences in empathy: Evidence of multi-

dimensional approach. Journal of Personality and Social Psychology, 44(1), 113.

Dawes, R. M. (1975). Graduate admission variables and future success. Science, 187,

721-723.

De La Garza, M. A. (2013). The relationship of counselor education program applicants’

cognitive complexity to other admission criteria (doctoral dissertation). University

of Texas, TX.

Decety, J., & Ickes, W. (Eds.). (2009). The social neuroscience of empathy. Cambridge,

MA: MIT Press.

Decker, S. E., Nich, C., Carroll, K. M., & Martino, S. (2013). Development of the

Therapist Empathy Scale. Behavioural and Cognitive Psychotherapy, 42, 339-

354.

147

Dodge, J. (1982). Reducing supervisee anxiety: A cognitive-behavioral approach.

Counselor Education and Supervision, 22, 55-60.

Duan, C., & Hill, C. E. (1996). A critical review of empathy research. Journal of

Counseling Psychology, 43, 261-274.

Dufrene, R. L., & Henderson, K. L. (2009). A framework for remediation plans for

counselor trainees. In G. R. Walz, J. C. Bleur, & R. K. Yep (Eds.), Compelling

counseling interventions: Vistas 2009 (pp. 149-159). Alexandria, VA: American

Counseling Association.

Duncan, H. E., Range, B., & Hvidston, D. (2013). Exploring student perceptions of rigor

online: Toward a definition of rigorous learning. Journal of Excellence in College

Teaching, 24(4), 5-29.

Duys, D., & Hedstrom, S. (2000). Basic counselor skills training and counselor cognitive

complexity. Counselor Education and Supervision, 40, 8-19.

Elam, C., Stratton, T., & Gibson, D. (2007). Welcoming a new generation to college: The

millennial students. Journal of College Admission, 195, 20-25.

Elliott, R., Bohart, A. C., Watson, J. C., & Greenberg, L. S. (2011). Empathy.

Psychotherapy, 48, 43-49.

Elliott, R., Filopovich, H., Harrigan, L., Gaynor, J., Reinmschuessel, C., & Zapadka, J. K.

(1982). Measuring response empathy: The development of multicomponent rating

scale. Journal of Counseling Psychology, 29, 379-387.

Eriksen, K. P., & McAuliffe, G. J. (2006). Constructive development and counselor

competence. Counselor Education and Supervision, 45, 180-192.

148

Faul, F., Erdfelder, E., Buchner, A., & Lang, A.G. (2009). Statistical power analyses

using G*Power 3.1: Tests for Correlation and Regression Analyses. Behavioral

Research Methods, 21, 1149-1160.

Feller, C. P., & Cottone, R. R. (2003). The importance of empathy in the therapeutic

alliance. Journal of Humanistic Counseling, Education and Development, 42, 53-

61.

Fiedler, F. E. (1950). A comparison of therapeutic relationships in psychoanalytic,

nondirective, and Adlerian theory. Journal of Consulting Psychology, 14, 239-

245.

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 and Supervision, 37, 100-115.

Forrest, L., Elman, N. S., & Miller, D. S. (2008). Psychology trainees with competence

problems: From individual to ecological conceptualizations. Training and

Education in Professional Psychology, 27, 627-686.

Fossum, I. N., Nordness, L. T., Storemark, S. S., Bjorvatz, B., & Palleson, S. (2014). The

association between use of electronic media in bed before going to sleep and

insomnia symptoms, daytime sleepiness, morningness, and chronotype.

Behavioral Sleep Medicine, 12(5), 343-357.

Fretz, B. R. (1982). Perspective and definitions. Counseling Psychologist, 10, 15-19.

Fuertes, J. N., & Brobst, K. (2002). Clients’ ratings of counselor multicultural

competency. Cultural Diversity and Ethnic Minority Psychology, 8, 214-223.

149

Furr, S., & Carroll, J. (2003). Critical incidents in student counselor development.

Journal of Counseling and Development, 81(4), 483-489.

Gilfoyle, N. (2008). The legal exosystem: Risk management in addressing student

competence problems in profession psychology trainings. Training and Education

in Profession Psychology, 2, 202-209.

Gladstein, G. A., Brennan, J., Feldstein, J., Ham, M. A., Kreiser, J., & McKrell, S.

(1987). Empathy and counseling: Explorations in theory and research. New

York, NY: Springer-Verlag.

Goldberg, A. D. (1974). Conceptual system as a predisposition toward therapeutic

communication. Journal of Counseling Psychology, 21, 364-368.

Goleman, D. (1996). Emotional intelligence. New York, NY: Bantam Books.

Granello, D. H. (2002). Assessing the cognitive development of counseling students:

Changes in epistemological assumptions. Counselor Education and Supervision,

41, 279-293.

Granello, D. H. (2010). Cognitive complexity among practicing counselors: How

thinking changes with experience. Journal of Counseling and Development, 88,

92-100.

Granello, D. H., & Hazler, R. J. (1998). A developmental rationale for curriculum order

and teaching styles in counselor education programs. Counselor Education and

Supervision, 38, 89-105.

150

Granello, D. H., & Underfer-Babalis, J. (2004). Supervision of group work: A model to

increase supervisee cognitive complexity. Journal of Specialists in Group Work,

29, 159-173.

Greenberg, L. S., & Watson, J. (1998). Experiential therapy of depression: Differential

effects of client-centered relationship conditions and process experiential

interventions. Psychotherapy Research, 8, 210-224.

Greenberg, L. S., Rice, L. N., & Elliott, R. (1993). Facilitating emotional change. New

York, NY: Guilford Press.

Grogan, J. (2013, March 11). It’s not enough to listen: Carl Rogers taught empathy as

attunement, not parroting. Retrieved from Psychologytoday.com

Gurman, A. S. (1977). The patient's perception of the therapeutic relationship. In A. S.

Gurman & A. M. Razin (Eds.), Effective psychotherapy: A handbook of research

(pp. 503-543). New York, NY: Pergamon Press.

Gutierrez, D., & Mullen, P. R. (2016). Emotional intelligence and the counselor:

Examining the contribution of train emotional intelligence to counselor burnout.

Journal of Mental Health Counseling, 38, 187-200.

Gutierrez, D., Mullen, P. R., & Fox, J. (2017). Exploring Emotional Intelligence Among

Master’s-Level Counseling Trainees. Counselor Education and Supervision, 56,

19-32.

Harvey, O. J., & Hoffmeister, J. K. (1967). The Conceptual Systems Test and criterion

factors (revised). Boulder, CO: University of Colorado.

151

Harvey, O., Hunt, D., & Schroeder, H. (1961). Conceptual systems and personality

organization. New York, NY: Wiley.

Harwood, J., Dooley, J. J, Scott, A. J., & Joiner, R. (2014). Constantly connected – The

effects of smart-devices on mental health. Computers in Human Behavior, 34,

267-272.

Hazler, R. J. (1999). Person-centered theory. In D. Capuzzi & D. R. Gross (Eds.),

Counseling and psychotherapy (2nd ed., pp. 179-202). Columbus, OH: Merrill.

Hazler, R. J. (2001). Humanistic theories of counseling. In D. C. Locke, J. E. Myers, & E.

L. Herr (Eds.), The handbook of counseling (pp. 151-158). Thousand Oaks, CA:

Sage.

Hazler, R. J., & Barwick, N. (2001). The therapeutic environment: Core conditions for

facilitating therapy. London: Open University Press.

Heppner, P. P. (1978). A review of the problem-solving literature and its relationship to

the counseling process. Journal of Counseling Psychology, 25, 366-375.

Heppner, P. P., & Fitzgerald, K. M. (1987). Human intelligence: Implications for

counseling. Journal of Counseling and Development, 65, 266-267.

Holloway, E. L., & Neugeldt, S. A. (1995). Supervision: Its contributions to treatment

efficacy. Journal of Consulting and Clinical Psychology, 63, 207-213.

Holloway, E. L., & Wampold, B. E. (1986). Relation between conceptual level and

counseling-related tasks: A meta-analysis. Journal of Counseling Psychology, 33,

310-319.

152

Holloway, E. L., & Wolleat, P. L. (1980). Relationship of counselor conceptual level to

clinical hypothesis formation. Journal of Counseling Psychology, 27, 539-545.

Homrich, A. M. (2009). Gatekeeping for personal and professional competence in

graduate counseling programs. Counseling and Human Development, 41, 1-23.

Howe, N., & Strauss, W. (2003). Millennials go to college. Great Falls, VA: American

Association of Registrars and Admissions Offices and Life Course Associates.

Hunt, D. E., Butler, L. E., Noy, J. E., & Rosser, M. E. (1977). Assessing conceptual level

by the Paragraph Completion Method. The Ontario Institute for Studies in

Education, Toronto, Canada.

Hy, L. X., & Loevinger, J. (1996). Measuring ego development (2nd ed.). Hillsdale, NJ:

Lawrence Erlbaum.

Ivey, A. (1991). Developmental strategies for helpers. Pacific Grove, CA: Brooks/Cole.

Jackson, P. L., & Decety, J. (2004). Motor cognition. A new paradigm to study self other

interactions. Current Opinion in Neurobiology, 14, 259-263.

Jackson, R. (2007). Retooling education: Testing and the Liberal Arts. Academic

Questions, 20, 332-346.

Jenaro, C., Flores, N., Gomez-Vela, M., Gonzalez-Gil, F., & Caballo, C. (2007).

Problematic internet and cell-phone use: Psychological, behavioral, and health

correlates. Addiction Research & Theory, 15, 309-320.

Kamin, L. J. (1974a). The science and politics of IQ. Translation: Ciencia y política del

cociente intelectual, Madrid: Siglo XXI, 1983.

Kamin, L. J. (1974b). The science and politics of I.Q. Social Research, 41, 387–425.

153

Kaufman, A. S., & Kaufman, N. L. (2004). Kaufman Brief Intelligence Test. (2nd ed.).

Bloomington, MN: Pearson.

Kelly, G. (1955). The psychology of personal constructs. New York, NY: Norton.

Kelly, G. (1963). A theory of personality: The psychology of personal constructs. New

York, NY: W. Norton & Company.

Kendjelic, E. M., & Eells, T. D. (2007). Generic psychotherapy case formulation training

improves formulation quality. Psychotherapy: Theory, Research, Practice,

Training, 44, 66-77. doi:10.1037/0033-3204.44.1.66

Kindsvatter, A., & Desmond, K. J. (2013). A problem-based approach to skill acquisition

and cognitive complexity with pre-practicum supervisees. The Clinical

Supervisor, 32, 212-223.

Kindsvatter, A., Granello, D. H., & Duba, J. D. (2008). Cognitive techniques as a means

for facilitating supervisee development. Counselor Education & Supervision, 47,

179-192.

King, P. M., & Kitchener, K. S. (1994). Developing reflective judgment: Understanding

and promoting intellectual growth and critical thinking in adolescents and adults.

San Francisco, CA: Jossey Bass.

King, P. M., & Kitchener, K. S. (2002). The reflective judgment model: Twenty years of

research on epistemic cognition. In B. K. Hofer & P. R. Pintrich (Eds.), Personal

epistemology: The psychology of beliefs about knowledge and knowing (pp. 37-

61). Mahwah, NJ: Lawrence Erlbaum Associates.

154

Kohlberg, L. (1984). The philosophy of moral development: The nature and validity of

moral stages. San Francisco, CA: Harper & Row.

Kohut, H. (1971). The analysis of self. New York, NY: International University Press.

Kohut, H. (1977). The restoration of self. New York, NY: International University Press.

Koltz, R. L., Smith, A., Tarabochia, D. S., Wathen, C. (2017). A pedagogical framework

for counselor educators working with millennial students. Journal of Counselor

Preparation and Supervision, 9, 140-161.

Kottler, J. (1991). The complete therapist. San Francisco, CA: Jossey-Bass.

Lafferty, P., Beutler, L. E., & Crago, M. (1989). Differences between more and less

effective psychotherapists: A study of select therapist variables. Journal of

Consulting and Clinical Psychology, 57, 76-80.

Lambert, M. J., & Barley, D. E. (2002). Research summary on the therapeutic

relationship and psychotherapy outcome. In J. C. Norcross (Ed.), Psychotherapy

relationships that work: Therapist and responsiveness to patients (pp. 17-32).

New York, NY: Oxford-University Press.

Lanaj, K., Johnson, R. E., & Barnes, C. M. (2014). Beginning the workday yet already

depleted? Consequences of late-night smartphone use and sleep. Organizational

Behavior and Human Decision Processes, 124, 11-23.

Lemola, S., Perkinson-Gloor, N., Brand, S., Dewald-Kaufmann, J. F., & Grob, A. (2015).

Adolescents electronic media use at night, sleep disturbance, and depressive

symptoms in the smartphone age. Journal of Youth and Adolescence, 44, 405-418.

155

Lenth, R. V. (2001). Some practical guidelines for effective sample size determination.

The American Statistician, 55, 187-193.

Lepp, A., Barkley, J., & Karpinski, A. (2014). The relationship between cell phone use,

academic performance, anxiety, and satisfaction with life in college students.

Computers in Human Behavior, 31, 343-350.

Li, J., Lepp, A., & Barkley, J. (2015). Locus of control and cell phone use: Implications

for sleep quality, academic performance, and subjective well-being. Computers in

Human Behaviors, 52, 450-457.

Linehan, M. M. (1997). Validation and psychotherapy. In A. Bohart & L. S. Greenberg

(Eds.), Empathy reconsidered (pp. 353-392). Washington, DC: American

Psychological Association.

Linn, R. L. (1982). Admission testing on trial. American Psychologist, 37, 279-291.

Little, C., Packman, J., Smaby, M. H., & Maddux, C. D. (2005). The skilled counselor

training model: Skills acquisition, self-assessment, and cognitive complexity.

Counselor Education and Supervision, 44, 189-200.

Loevinger, J. (1976). Ego development: Conceptions and theories. San Francisco, CA:

Jossey-Bass.

Loevinger, J., & Wessler, R. (1970). Measuring ego development: Construction and use

of a sentence completion test (Vol. 1). San Francisco, CA: Jossey-Bass.

Lovell, C. W. (1999). Empathic-cognitive development in students of counseling.

Journal of Adult Development, 6, 195-203.

156

Lovell, C. W. (2002). Development and disequilibration: Predicting counselor trainee

gain and loss on the Supervisee Levels Questionnaire. Journal of Adult

Development, 9, 235-240.

Luyten, P., Corveleyn, J., & Fontaine, J. R. J. (1998). The relationship between religiosity

and mental health: Distinguishing between shame and guilt. Mental Health,

Religion and Culture, 1, 165-184.

Lyons, C., & Hazler, R. (2002). The influence of student development on empathy.

Counselor Education and Supervision, 42, 119-130.

Manning, W. H., & Jackson, R. (1984). College entrance examinations: Objective

selection or gatekeeping for the economically privileged. In C. R. Reynolds & R.

T. Brown (Eds.), Bias in mental testing (pp. 189-219). New York, NY: Plenum

Press.

Marangoni, C., Garcia, S., Ickes, W., & Teng, G. (1995). Empathic accuracy in clinically

relevant setting. Journal of Personality and Social Psychology, 68, 854-869.

Martin, J., Slemon, A. G., Hiebert, B., Hallberg, E. T., & Cummings, A. L. (1989).

Conceptualizations of novice and experienced counselors. Journal of Counseling

Psychology, 36, 395-400.

Mayer, J. D., Salvoney, P., & Caruso, D. R. (2008). Emotional intelligence: New ability

or eclectic traits? American Psychologist, 63, 503-517.

McAuliffe, G., & Lovell, C. (2006). The influence of counselor epistemology of on the

helping interview: A qualitative study. Journal of Counseling and Development,

84, 308-317.

157

McWhirter, J. J. (1973). Two measures of the facilitative conditions: A correlations

study. Journal of Counseling Psychology, 20, 317-320.

Messick, S. (1995). Validity of psychological assessment: Validation of inferences from

persons’ responses and performances as scientific inquiry into score meaning.

American Psychologist, 50, 741-749.

Moore, W. S. (1989). The learning environment preferences: Exploring the construct

validity of an objective measure of the Perry scheme of intellectual development.

Journal of College Student Development, 30, 504-514.

Morrison, M., & Borgen, W.A. (2010). How Christian spiritual and religious beliefs help

and hider counselors’ empathy towards clients. Counseling and Values, 55, 25-45.

Morteza, M., Saeed, P., & Abdulrahim, K. (2015). The role of cell phones over use in

anticipation of sleep quality, anxiety, and depression among university students.

Journal of Education and Community Health, 2, 38-46.

Mülberger, A. (2014). The need for contextual approaches to the history of mental

testing. History of Psychology, 17, 177-186.

Muller, J., & Abeles, N. (1971). Relationship of liking, empathy and therapists

experience of outcome and psychotherapy. Journal of Counselling Psychology,

18, 39-43.

Munezawa, T., Kaneita, Y., Osaki, Y., Kanda, H., Suzuki, K., & Higuchi, S. (2011). The

association between use of mobile phones after lights out and sleep disturbances

among Japanese adolescents: A nationwide cross-sectional survey. Journal of

Sleep and Sleep Disorders Research, 34(8), 1013-1020.

158

Murdock, K. K. (2013). Texting while stressed: Implications for students’ burnout, sleep,

and well-being. Psychology of Popular Media Culture, 2(4), 207-221.

Norcross, J. C. (2010). The therapeutic relationship. In B. L. Duncan, S. B. E. Miller, &

M. A. Hubble (Eds.), The heart and soul of change: Delivering what works in

therapy (2nd ed., pp. 113-141). Washington, DC: American Psychological

Association.

O’Keefe, D. J., Shepherd, G. J., & Streeter, T. (1982). Role category questionnaire

measure of cognitive complexity: Reliability and comparability of alternate forms.

Central States Journal, 33, 333-338.

Orlinsky, D. E., & Howard, K. I. (1986). Process and outcome in psychotherapy. In S.

Garfield & A. Bergin (Eds.), Handbook of psychotherapy and behavior change

(pp. 283-330). New York, NY: Wiley.

Overholser, J. C. (1991). The Socratic method as a technique in psychotherapy

supervision. Professional Psychology: Research and Practice, 22(1), 68-74.

Palmer, K. (2016). The development of cognitive complexity in online master’s

counseling students (doctoral dissertation). Regent University, Virginia.

Patterson, C. H. (1984). Empathy, warmth, and genuineness in psychotherapy: A review

of reviews. Psychotherapy, 21, 431-438.

Perry, W. G., Jr. (1970). Forms of intellectual and ethical development in the college

years. New York, NY: Holt, Rinehart & Winston.

159

Petrides, K. V., & Furnham, A. (2001). Trait emotional intelligence: Psychometric

investigation with reference to established trait taxonomies. European Journal of

Personality, 15, 425-448.

Roberts, L. C., & Franzo, T. (2013). Requiring personal therapy for counselors-in-

training: Application of an Ethical Decision-Making Model. Presented at the

North Carolina Counseling Association conference, Greensboro, NC.

Rogers, C. R. (1957). The necessary and sufficient conditions of therapeutic personality

change. Journal of Consulting Psychologist, 21, 95-103.

Rogers, C. R. (1959). A theory of therapy, personality, and interpersonal relationships as

developed in the client-centered framework. In S. Koch (Ed.), Psychology: A

study of science (Vol. 3, Formulations of the person and the social context). New

York, NY: McGraw Hill.

Rogers, C. R. (1961). On becoming a person. Boston, MA: Houghton Mifflin.

Rogers, C. R. (1965). Client-centered therapy: Its current practice, implications, and

theory. Boston, MA: Houghton-Mifflin.

Rogers, C. R. (1975). Empathic: An unappreciated way of being. The Counseling

Psychologist, 5, 2-10.

Rosen, L. D. (2012). iDisorder: Understanding our obsession with technology and

overcoming its hold on us (1st ed.). New York, NY: Palgrave Macmillan.

Rosen, L. D., Lima, A. F., Felt, J., Carrier, N. A., Lara-Ruiz, J. M., & Medoza, J. S.

(2014). Media and technology use predicts ill-being among children, preteens and

160

teenagers independent of negative health impacts of exercise and eating habits.

Computers in Human Behavior, 35, 354-375.

Russell, C. S., DuPree, W. J., Beggs, M. A., Peterson, C. M., & Anderson, M. P. (2007).

Responding to remediation and gatekeeping challenges in supervision. Journal of

Marital and Family Therapy, 33, 227-244.

Schlossberg, N. K., Lynch, A. W., & Chickering, A. W. (1989). Improving higher

education environments for adults. San Francisco, CA: Jossey-Bass.

Schmidt, E. A., Homeyer, L. E., & Walker, J. L. (2009). Predictors of success on the

Counselor Preparation Comprehensive Examination. Counselor Education and

Supervision, 48, 226-238.

Scott, D., Valley, B., & Simmecka, B. (2017). Mental health concerns in the digital age.

International Journal of Mental Health & Addiction, 15, 604-613.

Sedlacek, W. E. (2003). Alternative admissions and scholarship selection measures in

higher education. Measurement and Evaluation in Counseling and Development,

35, 263-272.

Sexton, T. L. (1998). Reconstructing counselor education: Supervision, teaching, and

clinical training revisited [Special Section]. Counselor Education and

Supervision, 38, 66-69.

Shrout, P. E., & Fleiss, J. L. (1979). Interclass correlations: Uses in assessing rater

reliability. Psychological Bulletin, 86, 420-428.

Sinek, S. (2016, October 29). Millennials in the workplace. [Video File]. Retrieved from

https://youtu.be/hER0Qp6QJNU

161

Skovolt, T. M., & Ronnestad, M. H. (1992). Themes in therapist and counselor

development. Journal of Counseling and Development, 70, 505-515.

Smaby, M. H., Maddux, C. D., Richmond, A. S., Lepkowski, W. J., & Packman, J.

(2005). Academic admission requirements as predictors of counseling knowledge,

personal development, and counseling skills. Counselor Education and

Supervision, 45, 43-57.

Smith, A., & Koltz, R. (2012). Supervision challenges: Where is the manual for the

millennial generation? Professional Issues in Counseling, 19(5). Retrieved from

http://www.shsu.edu/piic/Summer2012.htm

Spengler, P. M., & Strohmer, D. C. (1994). Clinical judgmental biases: The moderating

roles of counselor cognitive complexity and counselor client preferences. Journal

of Counseling Psychology, 41, 8-17.

Sternberg, R. J. (Eds.). (1982). Handbook of human intelligence. Cambridge, England:

Cambridge University Press.

Stoltenberg, C. (1981). Approaching supervision from a developmental perspective: The

Counselor Complexity Model. Journal of Counseling Psychology, 28, 59-65.

doi:10.1037/0022-0167.28.1.59

Stoppard, J. M., & Miller, A. (1985). Conceptual level matching in therapy: A review.

Current Psychological Research and Reviews, 4, 47-68.

Taylor, C. (1990). Human agency and language. New York, NY: Cambridge University

Press.

162

Tepper, D. T., & Haase, R. F. (1978). Verbal and nonverbal communication of

facilitative conditions. Journal of Counseling Psychology, 25, 35-44.

Termin, L. M. (1916). The measurement of intelligence; an explanation of and complete

guide for use of the Stanford Revision and extension of the Binet-Simon

intelligence scale. Boston, MA: Houghton Mifflin Company.

Thomee, S., Harenstam, A., & Hagberg, M. (2011). Mobile phone use and stress, sleep

disturbances, and symptoms of depression among young adults—A prospective

cohort study. BMC Public Health, 11, 66.

Tousignant, B., Eugene, F., & Jackson, P. L. (2017). A developmental perspective on the

neural bases of human empathy. Infant Behavior and Development, 48, 5-12.

Truax, C. B. (1966). Therapist empathy, warmth, and genuineness and patient personality

change in group psychotherapy: A comparison between interaction unit measures,

time sample measures, and patient perceptions measures. Journal of Consulting

and Clinical Psychology, 22, 225-229.

Truax, C. B., & Carkhuff, R. R. (1967). Toward effective counseling and psychotherapy:

Training and practice. Chicago, IL: Aldine.

Truax, C. B., & Mitchell, K. M. (1971). Research on certain therapist interpersonal skills

in relation to process and outcome. In A. E. Bergin & S. L. Garfield (Eds.),

Handbook of psychotherapy and behavior change (pp. 299-344). New York, NY:

John Wiley.

163

Vinkhuyzen, A. A. E., van der Sluis, S., de Gues, E. J. C., Boomsma, D. I., & Posthuma,

D. (2010). Genetic influences on ‘environmental’ factors. Genes, Brain and

Behavior, 9, 276-287.

Wampold, B. E., & Imel, Z. E. (2015). The great psychotherapy debate: The evidence for

what makes psychotherapy work (2nd ed.). New York, NY: Routledge.

Washburn, F. A. (2015). Supervisee cognitive complexity (doctoral dissertation),

University of Iowa, Iowa.

Watson, J. C. (1999). Measure of expressed empathy. Unpublished measure. Toronto:

OISE/UT.

Watson, J. C. (2002). Re-visioning empathy. In D. Cain & J. Seeman (Eds.), Humanistic

psychotherapies: Handbook of research and practice (pp. 445-471). Washington,

DC: American Psychological Association.

Watson, J. C., & Prosser, M. (2002). Development of an observer rated measure of

therapist empathy. In J. C. Watson, R. Goldman, & M. Warner (Eds.), Client

centered and experiential psychotherapy in the 21st century: Advances in theory,

research and practice (pp. 303-314). Ross on Wye, UK: PCCS Books.

Watts, R. E. (1996). Social interest and the core conditions: Could it be that Adler

influenced Rogers? Journal of Humanistic Education and Development, 34, 165-

170.

Wechsler, D. (1939). The measurement of adult intelligence. Baltimore, MD: Williams

and Witkins.

164

Welfare, L. E. (2007a). Counselor Cognitions Questionnaire. (Permission from Laura E.

Welfare, PhD, Virginia Tech, 309 Eggleston Hall [0302], Blacksburg VA 24061.

E-mail: [email protected]).

Welfare, L. E. (2007b). Counselor cognitive complexity: Instrument development and

validation (Doctoral dissertation). University of North Carolina at Greensboro,

Greensboro, NC.

Welfare, L. E., & Borders, L. D. (2006). Counselor Cognitions Questionnaire: Rater

training manual and scoring protocol. Unpublished manuscript, University of

North Carolina at Greensboro.

Welfare, L. E., & Borders, L. D. (2007). Counselor Cognitions Questionnaire: Rater

training manual. (Permission form Laura E. Welfare, PhD, Virginia Tech, 309

Eggleston Hall [0302], Blacksburg VA 24061. E-mail: [email protected]).

Welfare, L. E., & Borders, L. D. (2010a). Counselor cognitions: General domain-specific

complexity. Counselor Education and Supervision, 49, 162-178.

Welfare, L. E., & Borders, L. D. (2010b). The Counselor Cognitions Questionnaire:

Development and validation. The Clinical Supervisor, 29(2), 188-208.

Welfare, L. E., Farmer, L. B., & Lile, J. J. (2013). Empirical evidence for the importance

of conceptualizing client strengths. Journal of Humanistic Counseling, 52, 146-

163.

Whitaker, C., & Van Garderen, D. (2009). Using a metacognitive approach with case-

based instruction to enhance teacher reflection and promote effective educational

practices for diverse learners. Actions in Teacher Education, 31(2), 5-16.

165

Zajonc, R. B. (1960). The process of cognitive tuning in communication. Journal of

Abnormal Psychology, 61, 159-167.

Zhitomirsky-Geffet, M., & Blau, M. (2016). Cross-generational analysis of predictive

factors of addictive behavior in smartphone usage. Computers in Human

Behavior, 64, 682-694.