University of Iowa
Iowa Research Online
Theses and Dissertations
Spring 2015
Supervisee cognitive complexity
Fred AlDean Washburn
University of Iowa
Copyright 2015 Fred AlDean Washburn
This dissertation is available at Iowa Research Online: http://ir.uiowa.edu/etd/1791
Recommended Citation
Washburn, Fred AlDean. "Supervisee cognitive complexity." PhD (Doctor of Philosophy) thesis, University of Iowa, 2015.
http://ir.uiowa.edu/etd/1791.
Follow this and additional works at: http://ir.uiowa.edu/etd
Part of the Vocational Rehabilitation Counseling Commons
SUPERVISEE COGNITIVE COMPLEXITY
by
Fred AlDean Washburn
A thesis submitted in partial fulfillment
of the requirements for the Doctor of
Philosophy degree in Rehabilitation and
Counselor Education (Counselor
Education and Supervision)
in the Graduate College of
The University of Iowa
May 2015
Thesis Supervisor: Associate Professor David K. Duys
Copyright by
FRED AlDean WASHBURN
2015
All Rights Reserved
Graduate College
The University of Iowa
Iowa City, Iowa
CERTIFICATE OF APPROVAL
PH.D THESIS
This is to certify that the Ph.D thesis of
Fred AlDean Washburn
has been approved by the Examining Committee for the
thesis requirement for the Doctor of Philosophy degree
in Rehabilitation and Counselor Education (Counselor
Education and Supervision) at the May 2015 graduation.
Thesis Committee:
David K. Duys, Thesis Supervisor
Carol K. Smith
Timothy N. Ansley
John S. Wadsworth
Stewart W. Ehly
To Evan, Jillian & Anderson.
This dissertation and doctorate degree is considered to be the apex of my
career and life as a student. While I am proud of this accomplishment it pales
in comparison to the joy, pride, and love I have being your father. You three
were my motivation to earn a doctorate degree, the best part of coming home
after a long day, and the best kids I could have ever hoped for. I am eternally
excited to be your father–– I love you all with all my all.
ii
ACKNOWLEDGMENTS
While writing a dissertation can at times feel lonely, it is a task that is not done in
solitude. I would like to acknowledge all those who played an important role in the
construction of this manuscript. First on this list is my wife, Kristina, who has had to sacrifice
more than anyone else while I went from degree to degree and paper to paper with few breaks
in between. She stood by my side and supported me through all of this. In many ways she
shares this degree with me.
I could not have written this dissertation without the help and support of my advisor, Dr.
David Duys. I thank him for his willingness to help throughout this process. His timely and
helpful feedback provided the balance of support and challenge I needed to motivate me to
finish this work. I would not have survived the unexpected challenges that came to my study
without him. I thank him for being a great advisor and even better thesis chair.
I acknowledge my committee for their willingness to help a doctoral student enter into
the profession. Much like in raising a child it takes a village (or a committee) to raise a doctor.
Your feedback and insights into this study were indelible.
iii
ABSTRACT
Supervision literature has indicated the importance of the supervisory working alliance
in the development of effective supervision (Landy, Ellis, & Friedlander, 1999). While
there has been a wealth of research on the role of the supervisory working alliance within
supervision, there is a dearth of information on how this alliance is formed (Cooper & Ng,
2009). The purpose of this study is to examine if supervision cognitive complexity is a
unique aspect of cognitive complexity within counseling and to better understand its role in
the formation of the supervisory working alliance.
Forty-two participants were selected from CACREP accredited programs located in
the North Central region of the Association of Counselor Educators and Supervisors
(NCACES). Cognitive complexity was measured via two different measures: the
Counselor Cognitions Questionnaire (CCQ) and Supervision Cognitive Complexity
Questionnaire (SCCQ). The supervisory working alliance was measured by the
Supervisory Working Alliance Inventory––Trainee (SWAI-T) which measures the
supervisory working alliance from the perspective of the trainee.
Results indicated a strong correlation between counseling cognitive complexity and
supervision cognitive complexity. Further, the supervision working alliance was not
significantly correlated with either measure of cognitive complexity. Supervision
cognitive complexity did provide a significant contribution to the variance accounted for in
the subscale of client focus in the SWAI-T. Implications for counselor educators and
supervisors are discussed.
iv
PUBLIC ABSTRACT
Student’s cognitive development has been linked to greater success in the
workforce (Harvey, Hunt, & Schroder, 1961; Hunt, 1966; Perry, 1970; Stoltenberg,
McNeill, & Delworth, 1998). The purpose of this study was to examine the impact of
counseling students’ cognitive development on their ability to form a strong working
relationship with their supervisor. It was hypothesized that students who have higher
levels of complex cognitions would form stronger relationships with their supervisors.
Forty-two students were used as participants in this study. Results indicated that students’
cognitive complexity did not have a significant contribution to the overall relationship
between student and supervisor. However, results did show that students who had higher
levels of supervision cognitive complexity were able to see more task specific (client focus)
functions of supervision than those who had lower levels of complexity.
v
TABLE OF CONTENTS
List OF TABLES……………………………………………………………………………...viii
LIST OF FIGUES………………………………………………………………………………ix
CHAPTER I INTRODUCTION. ............................................................................................. 1
Preparing Students for Supervision .......................................................................................... 3
Cognitive Complexity .............................................................................................................. 5
Statement of Problem .............................................................................................................. 6
Purpose of the Study ................................................................................................................ 7
Research Questions.................................................................................................................. 8
Definition of Terms ................................................................................................................. 9
CHAPTER II LITERATURE REVIEW .................................................................................11
Cognitive Complexity .............................................................................................................11
Kelly’s Personal Constructs Theory ........................................................................................15
Crockett’s Peer Studies ...........................................................................................................16
Cognitive Complexity in Counselor Education .......................................................................18
IDM........................................................................................................................................19
Cognitive Complexity in Supervision .....................................................................................24
Measures of Cognitive Complexity .........................................................................................28
Supervisory Working Alliance ................................................................................................32
Summary ................................................................................................................................33
Research Questions.................................................................................................................34
CHAPTER III METHODOLOGY ..........................................................................................37
Research Questions.................................................................................................................37
Participants .............................................................................................................................38
Measures ................................................................................................................................47
Summary ................................................................................................................................51
CHAPTER IV RESULTS .......................................................................................................53
Analyses for research question 1 .............................................................................................54
Analyses for research question 2a ...........................................................................................55
vi
Analyses for research question 2b ...........................................................................................59
Analyses for research question 2c ...........................................................................................62
Summary ................................................................................................................................65
CHAPTER V DICUSSION ....................................................................................................67
Discussion of the Findings ......................................................................................................67
Discussion of Research Question 2c .......................................................................................73
Limitations .............................................................................................................................75
Suggestions for Future Research .............................................................................................76
Conclusion .............................................................................................................................79
APPENDIX A Demogtaphic Questionnaire ............................................................................80
APPENDIX B Email Invitation ..............................................................................................81
APPENDIX C CCQ................................................................................................................82
APPENDIX D SCCQ .............................................................................................................83
Appendix E Supervisory Working Alliance Inventory: Trainee Form .....................................84
References ..............................................................................................................................86
vii
0
LIST OF TABLES
Table 1 Sex
39
Table 2 Age
39
Table 3 Program of study
40
Table 4 Semester in school
41
Table 5 Time in supervision
42
Table 6 Number of supervisors
43
Table 7 Supervision course
44
Table 8 Supervisory experience
44
Table 9 Demographic comparison: This study & CACREP
54
Table 10 Descriptive Statics for CCQ, SCCQ, & SWAI-T
56
Table 11 Correlation between CCQ, SCCQ, & SWAI-T
57
Table 12 Regression Model Summary CCQ, SCCQ, & SWAI-T
58
Table 13 Descriptive Statics for CCQ, SCCW, & Rapport
60
Table 14 Correlation between CCQ, SCCQ, & Rapport
61
Table 15 Regression Model Summary CCQ, SCCQ, & Rapport
62
Table 16 Descriptive Statics for CCQ, SCCW, & Client Focus
63
Table 17 Correlation between CCQ, SCCQ, & Client Focus
64
Table 18 Regression Model Summary CCQ, SCCQ, & Client Focus
65
viii
1
LIST OF FIGURES
Figure 1 Graph of Correlation between CCQ & SCCQ
ix
55
1
CHAPTER I.
INTRODUCTION
Supervision plays a singular role in the development of counselors. It is the
primary means where a counselor’s work is reviewed and counseling skills with clients
increased (Kaufman & Kaufman, 2006). Coupled with supervision’s importance to
counselor development is the potential for harm due to the unique difficulties that occur
in supervision (Grant, Schofield, & Crawford, 2012). A major factor that differentiates
effective and potentially harmful supervision is the supervisory working alliance (Sterner,
2009). A strong supervisory working alliance has been identified as potentially the most
important factor that promotes supervisee change (Landy et al., 1999). The strength of
the supervisory working alliance has been correlated to higher levels of counselor selfefficacy (Efstation, Patton, & Kardash, 1990) and less difficulty in addressing difficulties
in supervision (Grant et al., 2012). Weak supervisory working alliances have been linked
to poor coping with adverse client-related events (Kozowska, Nunn, & Cousens, 1997a)
and higher levels of despair and psychological distress among supervisees (Kozowska
Nunn, & Cousens, 1997b). The ability for supervisors and supervisees to form a strong
working alliance impacts a supervisee’s development and their work with clients.
Due to the importance of supervision, for both supervisee development and client
welfare, supervision is a mandatory component of The Council for Accreditation of
Counseling and Related Education Programs’ (CACREP) accredited programs (CACREP,
2009). Not only does CACREP mandate supervision, it also sets a relatively low ratio of
six supervisees to one supervisor; promoting the potential for a strong working alliance
2
between supervisors and supervisees. CACREP (2009) also requires supervisors to be
trained in supervision to promote student development and client welfare. The focus on
supervision in clinical training has led to supervision becoming a specialty in its own
right (Bernard & Goodyear 2009). Understanding the nuances of effective supervision
has yet to be established (Bennett, Mohr, Deal, & Hwang, 2013). Further, Pearson (2004)
argues that even with training and ideal conditions, supervision remains a challenging
enterprise. Pearson (2004) promotes supervision education for supervisees as a method
to mitigate the difficulties inherent in supervision.
One component of supervision education includes understanding how supervisees
conceptualize their supervisors. The relationship between supervisees’ conceptualization
of supervision and supervision effectiveness is still an emerging theme in supervision
competency research (Watkins, 2013a). Much like research on counselor’s cognitive
development (Duys & Hedstrom, 2000; Granello, 2000, 2001, 2002, 2010),
understanding the relationship between supervisee conceptualization of supervision and
supervision effectiveness would provide a useful tool for counselor educators and
supervisors to meet the supervisory developmental needs of their supervisees.
Additionally, understanding how supervisees develop cognitively in supervision could be
useful in understanding difficulties in supervision and how to address those difficulties.
For example, a supervisee who is struggling to show vulnerability in supervision may
have a limited conceptualization of their supervisor and cannot move past the evaluative
nature of supervision and thus protects himself through guardedness. The ability of the
supervisor to understand their guardedness through a developmental cognitive lens would
allow them to address this as a learning and growth opportunity for their supervisee as
3
opposed to a character flaw or intentional efforts on the supervisee’s part to manipulate
the supervisor. Chapter 1 of this dissertation will present a brief historical overview on
approaches to prepare students for supervision. Next, cognitive complexity, specific for
supervision, will be presented as a means of preparing students for supervision. Lastly,
the purpose and research questions of this study will be presented.
Preparing Students for Supervision
Historically, little effort has been made to prepare master's students for the
supervisory relationship prior to practicum (Pearson, 2004). Supervision remained
relatively obscure until the 1980s, when greater attention was given to supervision and
developmental models of how students develop into counselors (Watkins, 1995). Of
those models which came out in the 1980s, only Bordin (1983) purposed a model of
supervision which examined the relationship between supervisor and supervisee.
Bordin’s model looked exclusively at the power of the supervisory alliance in promoting
growth among supervisees. This model purported that a stronger alliance would produce
greater and more meaningful change among supervisees. Bordin’s model, much like
those of his predecessors, did little to address how to prepare students for supervision
(Berger & Buchholz, 1993; Pearson, 2004).
In an effort to address the preparation gap in Bordin’s model, Bahrick, Russell
and Salmi (1991) purposed the use of role induction for counseling supervisees. Their
results indicated that role induction may provide an increase in supervisee’s
understanding of supervision expectations and supervisor conceptualization. However,
these findings are limited due to a small sample size of three groups; one with five and
4
two with nine participants. Also, there was no control group from which to compare the
results of this study. Current preparation for supervision focuses more on preparing the
supervisor to interact with students, than the inverse. Kaufman and Kaufman (2006)
promote the use of the first supervisory meeting to address supervisee’s expectations,
goals, and potential problems which occur in supervision in an effort to lower supervision
anxiety. Pearson (2004) notes this approach may be too late for some supervisees and
points to supervision education before supervision occurs as a method of mitigating some
of the anxiety and challenges found in supervision. Pearson (2004) presented a practical
and theoretical model of supervision preparation which focuses on empowering the
supervisee before they enter into supervision. What is lacking is empirical support to
base Pearson’s (2004) claim that such preparation would be useful in promoting better
supervision.
What has been correlated to lower levels of supervisee anxiety and better
supervision outcomes is the working relationship between the supervisee and supervisor
(Efstation, et al., 1990; Grant et al., 2012; Lehrman-Waterman & Ladany, 2001).
Preparing supervisees to form stronger and more meaningful relationships with their
supervisors should then create lower levels of anxiety and stronger self-efficacy within
supervision. Hess, Hess, and Hess (2008) note the intimate connection between the
counseling relationship and the supervisory relationship. These relationships are based
on the same cognitive, emotional, and social factors. If counseling and supervisory
relationships are based on the same factors, then using methods which have been proven
to help counselors develop meaningful relationships with clients would also be effective
5
in creating meaningful and effective relationships with supervisors. One such method is
through the development of cognitive complexity.
Cognitive Complexity
Cognitive complexity in social behavior refers to how a person interprets events
and behaviors of others (Blaas & Heck, 1978). The more cognitively complex a person is
the more complex she will interpret events. Within counseling, cognitive complexity
refers to two specific meaning making domains. The first domain is differentiation, as
noted by Vannoy in 1965, or the ability to process behaviors of others in multiple and
complex ways (Welfare & Borders, 2010a). The second aspect of cognitive complexity
is the ability to integrate and make use of those differentiated behaviors (Granello, 2010).
Within the counseling profession, cognitive complexity is seen as the ability of a
counselor to be able to take multiple and increasingly complex views of a client and then
integrate those views into a whole (i.e. case conceptualization [Welfare & Borders,
2010a]).
In counselor education, cognitive complexity has been used to measure the
growth of students, develop methods that enhance growth, and is related to counseling
outcomes (Granello, 2010). Jennings and Skovholt (1999), in a qualitative study, found
that master counselors showed evidence of more complex meaning making
(differentiation) and a better ability to integrate seeming discrepant parts into a whole
(integration) when compared to novice counselors. Duys and Hedstrom (2000) found a
relationship between counselor skills training and the development of higher levels of
cognitive complexity. However, Fong, Borders, Ethington, and Pitts (1997) found no
6
growth in cognitive complexity. The discrepant findings of Duys and Hedstrom and
Fong et al. can be explained in the different instruments that were used to measure
cognitive complexity. A more counselor specific measure was used in the Duys and
Hedstrom study with a more general measure being used in the Fong et al. study (Welfare
& Borders, 2010a). While cognitive complexity increases during training, it is more
domain specific to the training than general, in effect (Welfare & Borders, 2010b).
A more specific measure of cognitive complexity to the counseling profession
provides a better understanding of counseling specific cognitive complexity. Such a
measure was created by Welfare and Borders (2010b), known as the Counselor
Cognitions Questionnaire (CCQ). Welfare and Borders (2010b) found that a specific
measure of counselor cognitive complexity provided a different measure of cognitive
complexity than other assessments used to that point. Swank (2010) then postulated that
cognitive complexity is domain specific, thus an increase in cognitive complexity in one
domain does not indicate an increase across all domains. If Swank (2010) is correct, then
increased counseling cognitive complexity may not be related to supervision cognitive
complexity. This indicates a need to prepare students not just for their clinical experience
but also for their supervisory experience. One of the aims of this study is to understand
the relationship between counseling cognitive complexity and supervision cognitive
complexity.
Statement of Problem
Understanding how to best prepare students for supervision is an important step in
counselor development. To date, the literature has provided little empirical evidence on
7
preparation for supervision. Students are left on their own to prepare for an educational
relationship which is unlike any other (Berger & Buchholz, 1993). By preparing students
for supervision there may be a decrease in student anxiety and better supervision
outcomes in the working relationship between the supervisee and supervisor (Efstation et
al., 1990; Grant et al., 2012; Lehrman-Waterman & Ladany, 2001). Understanding the
relationship between counseling cognitive complexity, supervision cognitive complexity
and the supervisory working alliance would allow counselor educators and supervisors to
better provide more empirical sound supervision. The use of cognitive complexity would
also provide a framework for counselor educators and supervisors to help prepare
students for supervision by following a similar pedagogy in preparing students for
clinical practice (Duys & Hedstrom, 2000). Developing a measure which accounts for
supervision cognitive complexity could then provide empirical support for understanding
one factor which promotes a stronger supervisory relationship.
Purpose of the Study
The purpose of this study is to understand the relationship between the
supervisory working alliance and cognitive complexity. Specifically, is supervision a
domain specific function of cognitive complexity and how is it related to counseling
cognitive complexity? The researcher hypothesized that a non-significant relationship
would exist between supervision cognitive complexity and counseling cognitive
complexity. It is also hypothesized that there would be a non-significant relationship
between levels of counseling cognitive complexity and the supervisory working alliance
and its subscales rapport and client focus. Further, there would be a positive significant
8
correlation between levels of supervision cognitive complexity and the supervisory
working alliance and its subscales rapport and client focus.
Research Questions
The specific research questions of this study are as follows:
1. Is supervision cognitive complexity specific to the supervision or is it related to
counseling cognitive complexity?
a. What is the relationship between supervision cognitive complexity and
counseling cognitive complexity?
2. What is the role of supervisee cognitive complexity in the supervisory working
alliance?
a. Do higher levels of supervision cognitive complexity or counseling
cognitive complexity correlate to higher levels of the total supervisory
working alliance?
b. Do higher levels of supervision cognitive complexity or counseling
cognitive complexity correlate to higher levels of the subscale rapport
in the supervisory working alliance?
c. Do higher levels of supervision cognitive complexity or counseling
cognitive complexity correlate to higher levels of the subscale client
focus in the supervisory working alliance?
9
Definition of Terms
Supervision
Supervision is defined by Bernard and Goodyear (2009) as:
An intervention provided by a more senior member of a profession to a more
junior member or members of that same profession. The relationship is
evaluative and hierarchical, extends over time, and has the simultaneous purposes
of enhancing the professional functioning of the more junior person(s),
monitoring the quality of professional services offered to the clients that she, he,
or they see, and serving as a gatekeeper for those who are to enter the particular
profession. (p. 7)
Supervisory Working Alliance
Bordin (1983) identifies three parts of the supervisory working alliance: a)
supervisor and supervisee have a mutual understanding and agreement of the goals
sought in the supervisory process; b) both parties also share an understanding and
agreement of the tasks in the supervisory relationship; and c) the bond between
supervisor and supervisee is necessary to sustain the goals and tasks set forth by the
supervisor and supervisee.
ACA Code of Ethics
A set of ethical guidelines developed by the American Counseling Association
(2014) designed for guiding the ethical decision-making process of counselors, counselor
educators, counselors-in-training, and researchers within the counseling profession.
CACREP Standards
10
A set of guidelines developed by the Council for Accreditation of Counseling and
Related Educational Programs (CACREP, 2009) used for accrediting counseling and
related educational programs.
Cognitive Complexity
Cognitive complexity in social behavior refers to how a person interprets events
and behaviors of others (Blaas & Heck, 1978). The more cognitively complex a person is
the more complex they will interpret events. Within counseling, cognitive complexity
refers to two specific meaning making domains. The First is differentiation, as noted by
Vannoy in 1965, or the ability to process behaviors of others in multiple and complex
ways (Welfare & Borders, 2010a). The second aspect of cognitive complexity is the
ability to integrate and make use of those differentiated behaviors (Granello, 2010).
Within the counseling profession, cognitive complexity is seen as the ability of a
counselor to be able to take multiple and increasingly complex views of a client and then
integrate those views into a whole (Welfare & Borders, 2010a). For supervision, it is the
ability of the supervisee to take multiple and increasingly complex views of their
supervisor and integrate those views into a whole.
11
CHAPTER II
LITERATURE REVIEW
Chapter II presents a literature review on cognitive complexity and the
supervisory working alliance. The purpose of this study was to determine if counseling
cognitive complexity and supervision cognitive complexity are unique cognitive
constructs. In addition, this study aims to examine the relationship of cognitive
complexity in the supervisory working alliance. This chapter presents a framework that
delineates the history and role of cognitive complexity and the supervisory alliance.
Cognitive complexity is examined both historically and within the counselor education
and supervision domain. The supervisory working alliance is also examined within
counselor education.
Cognitive Complexity
Cognitive complexity has been defined in social behavior as how a person
interprets events and behaviors of others (Blaas & Heck, 1978). The more cognitively
complex a person is the more varied she will interpret events. Within counselor
education research, cognitive complexity refers to two specific meaning making domains.
The first is differentiation as noted by Vannoy in 1965, or the ability to process behaviors
of others in multiple and complex ways (Welfare & Borders, 2010a). The second aspect
of cognitive complexity is the ability to integrate and make use of those differentiated
observations (Granello, 2010). Within the counseling profession, cognitive complexity is
seen as the ability of a counselor to be able to take multiple and increasingly complex
views of a client and then integrate those views into a whole (Welfare & Borders, 2010a).
12
The roles of differentiation and integration have played an important role in the history of
talk therapy and education.
Historical Overview
Kelly’s (1955) personal construct theory is often credited as providing the
foundation for cognitive complexity within the counseling profession (Duys & Hedstrom,
2000; Owen & Lindley, 2010; Welfare & Borders, 2010a). However, the basic ideas
behind cognitive complexity have existed since the early days of talk therapy. Kelly
(1955) himself, notes that his theory has existed “throughout the centuries” and gives
credit to William James, Sigmund Freud, and John Locke for the formation of personal
construct theory (p.3). The core components of cognitive complexity, differentiation and
integration were first used in a therapeutic setting by Austrian psychoanalyst Otto Rank
(Rank, 1931). Rank proposed that client problems often occurred through selfdifferentiation; clients would split their psyche causing distress to the whole. The role of
the analyst, according to Rank, is to promote the integration of the client. Rank (1931)
described the integration process as an attempt to become whole or achieving individual
unity.
Fritz Perls (1965) furthered Rank’s work in the development of Gestalt therapy,
which viewed individuals as constantly going through a process of self-differentiation
with the goal of the counselor being to help them achieve total integration. What is
implicit in the work of Rank and Perls, is that it is the role of the counselor to understand
the client’s differentiated self while seeing the client as an integrated whole. This
approach is very similar to the current understanding of cognitive complexity, where a
counselor is asked to identify the multiple and complex factors which frame the client’s
13
presenting problems while being able to integrate those traits into a meaningful
conceptualization of the client. The main difference between Rank, Perls, and current
approaches to differentiation and integration is that differentiation is no longer viewed as
a pathological aspect of the psyche nor is it always the goal of the counselor to promote
the development of an integrated-self to the client. Despite these differences, the idea of
a counselor’s or analyst’s ability to understand the complexity of their client
(differentiation) and develop a meaningful conceptualization of the client as a whole
(integration) is related.
Erik Erikson also used the ideas of self-differentiation to create his theory of
psychosocial development. Erikson (1950) viewed the first task of a human as
differentiating herself from the world around her. According to Erikson, most infants
live in a state of ego-centrism, in which there is no differentiation from the self and the
external world. Once an infant is able to differentiate herself from her world, she is then
able to begin to organize her affective life into objects. The ability for an infant to
differentiate herself from her world is the first step in developing an identity. Failure to
differentiate creates an identity which is completely reliant on the world around the
individual. For Erikson (1950) differentiation is a needed primary step in identity
development.
Like Erikson, Bowen (1978) conceptualized differentiation as a positive
developmental attribute. Bowen (1978) defined differentiation as the ability to “maintain
emotional objectivity while in the midst of an emotional system in turmoil, yet at the
same time actively relate to key people in the system” (p.485). In Bowen’s (1978) view a
differentiated person would be able to maintain the balance between autonomy and
14
connection in significant and emotionally charged relationships. This is done as an
individual is able to differentiate the self, stress, and well-being within relationships. The
more differentiated a person is the greater their ability to handle stressful events, while
less differentiated people more easily experience stress.
Rank and Perls developed the ideas of differentiation and integration within a
therapeutic setting. Erikson and Bowen developed ideas of differentiation on identity.
Lewin (1933) used differentiation to promote a theory of intelligence. Lewin (1933)
wrote, “Retardation may be related especially to a smaller degree of differentiation of the
total systems of the physical person, be it in all parts or in certain important spheres” (p.
225). Lewin’s focus on differentiation was geared toward understanding those who
lacked the mental capacities to have ‘normal’ relationships. Tolman (1948) used these
same ideas to explain the opposite end of the intelligence spectrum. Tolman’s (1948)
work examined the cognitive maps rats created through a series of mazes. In Tolman’s
experiments rats that were able to develop more complex cognitive maps were more
successful in obtaining rewards. A key element of Tolman’s (1948) work is the ability of
rats to develop increasingly complex maps under the right conditions. These conditions
are: a) an absence of brain damage, b) adequate environmental support (mostly through
visual cues), c) introduction of new stimuli and d) the right physiological and cognitive
support (if a rat is too hungry or the maze too difficult the rat will not find their goal). As
a forerunner to experimental psychology Tolman then compared the rats’ capabilities to
the capabilities of humans to understand complex situations.
Tolman’s work opens the door for educators to help develop cognitive complexity
within their students. Duys and Hedstrom (2000) were able to demonstrate the ability of
15
counseling students to increase in their cognitive complexity through taking a microskills course. The connection between the works of Rank, Perls, Lewin, and Tolman
provides a strong rationale for counselor educators to seek to develop, assess, and
monitor the cognitive complexity of students and practitioners (Granello, 2000, 2001,
2002; Duys & Hedstrom, 2000).
Kelly’s Personal Constructs Theory
Current models of cognitive complexity are based on Kelly’s personal constructs
theory. Kelly’s (1955) personal constructs theory was developed during the heyday of
behaviorism in psychology, and as such, his theory is linked to basic behaviorist elements
(i.e. prediction and control). Constructs are formed by people to anticipate events and
thus allow a sense of control over their environment. Within a clinical setting, the
counselor is to understand the constructs of her client and help shape new constructs
which are unique to the client in an effort to create an environment which will be more
conducive for the growth of the client (Kelly, 1955). Counselor and client enter into a
relationship built on understanding existing constructs and the continual process of reconstructing the client’s environment. Counselors who have a greater ability to
understand their client’s constructs will be more successful in building a collaborative
relationship with their client and be more successful in the development of new
constructs which promote the client’s growth (Kelly, 1955).
Hunt (1966) builds upon Kelly’s (1955) personal constructs theory and expands
the role of developing complexity to teachers, parents, social workers and others involved
in the helping profession. Hunt’s (1966) focus was on developing the complexity of
helpers so that they can better influence and teach those they work with. Cognitively
16
complex helpers would be better able to select more efficacious methods of helping than
those with low levels of complexity (Hunt, 1966). Kelly (1955) and Hunt (1966)
examined cognitive complexity from a top down perspective, highlighting the importance
of cognitive complexity among those in power. Currently, the literature is lacking an
understanding on how cognitive complexity functions with those holding less power in
relationships.
Crockett’s Peer Studies
The first research which examined cognitive complexity outside of the top down
approach was conducted by Crockett and colleagues who examined cognitive complexity
across peers (Delia, Crockett, Press, & O’Keefe, 1975; Mayo & Crockett, 1964; Meltzer,
Crockett, & Rosenkrantz, 1966; Nidorf & Crockett, 1965; Press, Crockett, Delia, 1975;
Rosenbach, Crockett, & Wapner, 1973; Rosenkrantz & Crockett, 1965). These peer
related findings offer some insight into the role cognitive complexity may play within
supervision. Mayo and Crockett (1964) found that the order in which information is
given influences how impressions are formed. Those with high levels of complexity are
better able to integrate information given at different times and are more capable of
holding discrepant information about a peer (Meltzer et al., 1966; Nidorf & Crockett,
1965). Those with low levels of complexity are less able to integrate this information and
are more likely to form a single impression of their peer.
Cognitive construct formations are also dependent on the orientation of the
receiver. Press et al. (1975) found that an evaluative orientation causes a decrease in
construct formation among those who are high and low in complexity, while an
understanding orientation causes an increase in construct formation. Rosenbach et al.
17
(1973) found that developmental levels and emotional involvement also influence
construct formation. Rosenbach et al. (1973) studied young men at three different
developmental ages: 6-7, 12-13 and 18-19 and found that the older groups were able to
differentiate more constructs and integrate those constructs more effectively when
compared to those at earlier stages of development. These results are linked with Kelly’s
(1955) theory in that they demonstrate that those with more experiences in life (i.e. are
older) are able to develop more constructs and then integrate those constructs into
cognitions which better predict and control environments. However, emotional
involvement with another person negated the effects of age on cognitive complexity.
Those in the oldest age group showed the starkest decrease in differentiation and
integration when they had developed an emotional attachment (positive or negative) to
another person (Rosenbach et al., 1973). Emotion was then shown to dampen cognitive
complexity. Rosenbach et al. (1973) did not provide a rationale for this dampening in
complexity. Their research spawned a new line of study which examines cognitiveaffective complexity.
Labouvie-Vief (1992, 1998, & 2003) has been a leader in the field of cognitiveaffective complexity in adults. In her theory, Labouive-Vief (1992; 1998) postulates that
emotions interfere with the ability of a person to retain their level of cognitive
complexity. The ability to integrate emotion into cognitive processes is a skill developed
in adulthood. Cognitive-affective complexity is developed through three primary means:
a) understanding that emotion and cognition are not at odds with each other (LabouvieVief, Hakim-Larson, & Hobart, 1987; Lobouvie-Vief & Medler, 2002), b) the ability to
differentiate personal emotional standards from societal standards (e.g. I am male and it
18
is okay for me to feel sad when I am hit, even if society states otherwise; LabouvieVief,Hakim-Larson, De Voe & Schoeberlein, 1989), and c) the ability to integrate
multiple emotional perspective to form a conceptualization based on cognitive-affective
complexity of a given situation (Labouvie-Vief & Medler, 2002). Cognitive-affective
complexity has been shown to develop in adults in their mid-twenties and peak in forties
and fifties before tapering off in the sixties (Labouvie-Vief, 2003). Rosenbach et al.’s
(1973) study capped at the age 19. Participants then would not have developed their
ability to be cognitive-affectively complex which would explain the dampened effect of
emotion on cognitive complexity.
Cognitive Complexity in Counselor Education
Cognitive complexity has played an important role in understanding the academic
(Granello, 2000; 2001; 2002, 2010) and clinical (Blass & Heck, 1978; Duys & Hedstrom,
2000; Little, Packman, Smaby, & Maddux, 2005; Welfare & Borders, 2010) development
of counseling students and practicing counselors. Cognitive complexity within counselor
education is based on Kelly’s (1955) personal construct theory, but its role has been
expanded by Harvey et al., (1961) and Perry (1970). Harvey et al. (1961) conceptualized
cognitive complexity as the ability to function more abstractly and be less likely to be
reliant on others for social cues regarding appropriate cognitions and behaviors. Perry
(1970) built upon Harvey et al.’s (1961) work and developed his scheme of cognitive and
intellectual development. Perry (1970) looked exclusively at college students and
developed nine conceptual categories or “positions” which students move across in their
development. Student development is seen as moving from a dualist authoritarian based
19
scheme to gradually accepting multiple perspectives and moving towards a more
relativistic stance on where guidance from an authority is no longer sought. Finally,
students make a commitment to a particular epistemology and/or philosophy where they
vacillate between doubt and reaffirmation. Perry (1981) further refined this theory of
student development to include four categories: dualism, multiplicity, relativism, and
committed relativism.
A dualistic developmental orientation would be students who see the world in a
simplistic black or white view. Students at this level rely heavily on authority figures to
guide them in knowing what is right and what is wrong. Once a student moves past this
orientation they begin to see the world as being composed of multiple and varying ideals.
Relying on authorities becomes less apparent as students begin to understand that
different authorities have different views on the world. This then allows students to take
a more relativistic view of the world where they believe there is no one correct way, but
the environment creates an unlimited number of possibilities. Ways of knowing are then
relative to the perspective of the person. Finally, students enter into an orientation where
they have committed to a set of beliefs or principals but allow others that same privilege
and are also willing to examine and reconsider their views. These levels of development
have been applied to counseling students in the integrated developmental model (IDM;
Stoltenberg et al., 1998).
IDM
Stoltenberg et al., (1998) based the IDM on cognitive (Anderson, 1985; Gagné,
Yekovich & Yekovich, 1993), developmental (Harvey et al. 1961; Perry, 1970), and
20
interpersonal influence (Dixon & Claiborn, 1987) models. As counselors develop so do
their cognitive processing (Anderson, 1985) and schema development and refinement
(Gagné et al., 1993) as well as their motivation to meet clients’ needs and develop as a
counselor (Dixon & Claiborn, 1987). The IDM outlines three different developmental
changes which counselors undergo (Stoltenberg et al., 1998).
Level 1 counselors have high levels of motivation along with high levels of
anxiety and are focused on skill acquisition. These counselors are more dependent on
their supervisors. As such they often seek to please their supervisors and have a simple
schema for what constitutes good counseling which is often based on the feedback they
receive from their supervisor. Their awareness in session is mainly focused on the self
and their ability to demonstrate the use appropriate counseling skills in session. Due to
their high levels of dependence on their supervisor they are often non-confrontational in
supervision. Given enough support from their supervisor, and time meeting with clients,
level 1 counselors begin to transition from the level 1 stage. This transition is marked by
an increase in their comfort with a specific approach to counseling. Counselors in
transition will also begin to require more autonomy from their supervisor as they begin to
identify more with their clients.
This transition leads counselors to level 2. Level 2 counselors fluctuate in their
motivation, autonomy, and awareness. These counselors vary in their motivation and
confidence in working with clients. At times they feel very confident in their ability but
as their cognitive complexity increases they may also feel overwhelmed with their ability
to manage the complexity of their clients. These feelings of confidence and being
overwhelmed lead to a conflict in dependency and autonomy. When confident, level 2
21
counselors can be assertive in pursing what they think is the correct course of action with
a client. However, when diffident, level 2 counselors can return to dependence on their
supervisors–– much like a level 1 counselor–– and even perhaps become evasive in
supervision as they are embraced at their dependency. Finally, with their new found
complex schemas of clients level 2 counselors can often over identify with their clients
feeling that they alone truly understand the complexities of the client. Much like the
transition from level 1 to level 2 if counselors are given enough support and challenge
from their supervisor and adequate time in session with clients they will begin to
transition out of their current developmental stage.
Counselors transitioning to level 3 are marked with an increased and more stable
motivation to work with clients. They begin to understand their limits and show levels of
conditional autonomy. Level 3 counselors also begin to separate themselves from their
clients and include self-reactions to clients in session. A level 3 counselor will still have
doubts about their ability to work with all clients but these doubts will not dissuade the
counselor from their work. A professional identity develops as level 3 counselors
understand their limits and know when to consult with other counselors when they are
unsure of how to work with a specific client. Through the process of consultation a level
3 counselor will retain their responsibility for their client. Level 3 counselors are able to
balance an awareness of their own strengths and weakness with a complex and empathic
understanding of their clients. Finally, these counselors will continue to refine and hone
their skills in counseling and enter into a subset level of level 3 known as level 3i. In
level 3i counselors are on an unending journey to improve their craft and increase in
complexity.
22
Cognitive complexity plays an important role in development of counselors
within the IDM. The speeds at which counselors are able to move through these stages in
the IDM have been correlated to their level of cognitive complexity (Stoltenberg &
Delworth, 1987). Counselors with higher levels of cognitive complexity move more
quickly through these stages while those with lower levels move more slowly. High
levels of cognitive complexity are also linked to higher levels of empathy with and
greater depth in case conceptualization of clients (Granello, 2010).
Clinically, counselors with high levels of cognitive complexity are better able to
meet the needs and address the issues of their clients (Welfare & Borders, 2010). High
cognitive complexity especially enhances case conceptualizations skills, which enable
counselors to sensitively match interview data with an evolving understanding of clients’
issues (Welfare & Borders, 2010; Ladany et al., 2001). Counselors who are tolerant of
ambiguity and are patient with this process develop a more complete understanding of
client concerns, and consequently, make better decisions about appropriate interventions
to use to address these identified issues. These case conceptualization skills are
dependent not only on counselors’ theoretical framework and general knowledge about
clients’ concerns, but also on the cognitive processing that yields the conceptualizations
in the first place (Blaas & Heck, 1978).
However, counselors who operate at a less cognitively complex level will be
limited in the number of constructs and explanations they can consider while developing
a case conceptualization. Their level of cognitive development will also affect how
simply and how complexly they view variables of concern. Further, those with low
cognitive complexity are less able to “comprehend scholarly material” (Granello, 2001, p.
23
300), are less able to “be objective with clients, are less skilled in implementing
counseling techniques” (Welfare & Borders, 2010a, p. 163), and are “less empathic”
(Brendel, Kolbert, & Foster, 2002, p. 218; Lovell, 1999, p. 198). For these reasons,
several studies have examined factors associated with counselors’ cognitive processes.
Duys and Hedstrom (2000) were able to show that counselor training in a microcounseling skills course is associated with higher levels of cognitive complexity. The
implication of Duys and Hedstrom’s work is that cognitive complexity can be developed
within counseling students through their course work. Little et al., (2005) compared
cognitive complexity’s development in a similar manner to Duys and Hedstrom (2000).
Little et al. (2005) demonstrated that students who were given specialized skills training,
with an emphasis on role-play and feedback from classmates and instructor, developed
higher levels of cognitive complexity than those who did not receive such training. To
date, the majority of literature on cognitive complexity in counselor education has
focused exclusively on clinical work with clients. A paucity of research has examined
the role of cognitive complexity in the supervisory relationship.
Stoltenberg and Delworth’s (1987) use of cognitive complexity in counselor
development attends to general cognitive complexity. This general approach has been
shown to be an insufficient measure of cognitive complexity within the counseling
context (Welfare & Borders, 2010a). It appears that cognitive complexity has both
general and domain specific effects, indicating that high levels of general cognitive
complexity do not correlate to domain specific levels of cognitive complexity. Just as
general levels of cognitive complexity do not correlate to domain specific levels, so then
would counseling specific levels of cognitive complexity not be significantly correlated
24
with supervision cognitive complexity? Only Blocher (1983) has proposed a theory of
supervision based on a developmental cognitive approach.
Blocher’s (1983) theory on a cognitive developmental approach to supervision
spans across three dimensions: purpose, process and environment, and specific
characteristics. Within each dimension, the supervisee must be able to process the many
different, and at times divergent, aspects of supervision. A supervisee is required to track
their supervisor as they change their function within supervision. A supervisor plays
many roles within supervision. Blocher (1983) notes these roles as: lecturer, teacher,
case reviewer, collegial-peer supporter, monitor, and therapist. In effective supervision,
both the supervisor and supervisee need to know and understand their role and the role of
their counter-part (Landy, Friedlander, & Nelson, 2005). While a supervisee is
processing the role of their supervisor they are also processing their work with clients in
supervision, while still attempting to gain a feel of the supervisory environment. Finally,
Blocher’s model (1987) brings attention to the specific characteristics of supervision,
which postulates that this type of educational relationship is most often new to
supervisees. These differences require the supervisee to be able to process, develop, and
accept new constructs regarding education. Understanding how supervisees are able to
process these variables may shed light on understanding what makes an effective
supervision session.
Cognitive Complexity in Supervision
The implications of cognitive complexity within supervision are meaningful.
Kelly (1955) explains the importance of the counselor in developing high levels of
25
cognitive complexity for the benefit of one’s client. Connecting counselor and supervisor
appeals in an intuitive sense; supervisors with high levels of cognitive complexity would
be considered to be more effective within this theory than those with lower levels.
However, in this study the researcher is examining the role of cognitive complexity in the
supervisee who holds less power in the relationship. In essence, this present study
explores that which was ignored by Kelly. What do high levels of cognitive complexity
mean for the person holding less power in a clinical relationship? If constructs are
formed in an effort to anticipate events, then supervisees who have higher levels of
cognitive complexity should then be able to better anticipate critical events in supervision
and thus feel less anxious about those events. The ability for supervisees to experience
less anxiety in supervision has been correlated to higher levels of a working alliance and
better supervision outcomes (Efstation et al., 1990; Gnilka, Chang, & Dew, 2011; Grant
et al., 2012; Lehrman-Waterman & Ladany, 2001). Promoting the development of
supervisee cognitive complexity may then create a more predictable environment for the
supervisee and thus reduce anxiety. Reduction in supervisee anxiety could be one
possible benefit of increasing supervisee cognitive complexity; however, there could be
another less efficacious outcome.
While anticipation may reduce anxiety in supervisees, it could also lead to a
supervisee attempting to control or manipulate their supervisor. A foundational aspect of
Kelly’s (1955) theory is the ability to predict and then control one’s environment. Given
this implication, a supervisee with higher levels of cognitive complexity could then
attempt to control their supervisor within their session. Grant et al. (2012), in their
examination of difficulties within supervision, reported that the most common difficulty
26
supervisors report is having a supervisee who attempts to manipulate the supervision
session through relational, confrontational, and avoidant interventions. By increasing a
supervisee’s cognitive complexity they could then be more likely to attempt to
manipulate their supervisor as a method of reducing their anxiety within session. Due to
the paucity of literature in the preparation of supervisees, there is no empirical evidence
suggesting which result is most likely to ensue.
Moving beyond Kelly’s (1955) model, Press’ et al.’s (1975) findings indicate that
previous exposure of the supervisor –– acting as an instructor or in some other capacity –
– influences the ability of the supervisee to accept this new role of their supervisor. A
supervisee with low levels of cognitive complexity may have already formed an
impression of their supervisor outside of supervision and it would then become difficult
for the supervisee to accept this new role of their supervisor. As role acceptance and
function is a key aspect of the supervisory working alliance the inability of a supervisee
to accept their supervisor in that position would then lessen the strength of the working
alliance between them (Bordin, 1983).
Rosenbach et al.’s (1973) findings would indicate that providing a supervisee with
motivational information on the part of the supervisor –– on why they acted in a certain
manner –– may not influence the complexity of the supervisee’s perceptions of the
supervisor. However, asking the supervisee to understand the supervisors’ actions on
their own would create an increase in complexity. This complexity is most likely related
to the ability of the supervisee to develop a level of reflexivity in understanding the
actions of others (Crockett, Manhood, & Press, 1975). Reflectivity may then be
dependent on orientation. An evaluative orientation has been linked to dampened
27
reflectivity and cognitive complexity (Press, et al., 1975), while an understanding
orientation is linked to an increase in reflectivity and cognitive complexity (Crockett at
al. 1975). Within counseling and supervision, the role of supervisee can be both
evaluative and understanding; as supervisees have to conceptualize and diagnose
(evaluate) as well as be empathic and caring of their clients (understanding). In
supervision, supervisees are evaluating the effectiveness of their supervision and are
being trained to increase their understanding of their clients. However, the how a
supervisee balances these orientations in session and in supervision have yet to be
explored directly.
Welfare and Border’s (2010a) study of counseling specific cognitive complexity
indirectly examines reflectivity in counseling students. The evaluative stance of
beginning counselors, both of their clients and of their own work, may have been one
reason why Welfare and Borders found lower complexity scores for the majority of their
participants. Dampened complexity in an evaluative orientation may have been one
reason why Welfare and Border (2010a) found differences in counseling specific
cognitive complexity and general cognitive complexity. As counselors are in an
evaluative position of their client, their ability to form numerous constructs of their
clients may be dampened as they attempt to evaluate clients according to pre-existing
criteria (e.g. diagnostic and statistics manual). Inferences can also be made to
supervision. As supervisors are tasked with evaluating their supervisees, supervisors
could then have lower levels of complexity as they attempt to evaluate. Further,
supervisees may also be evaluating their supervisor’s effectiveness in supervision––
especially, if the supervisee has had multiple supervisors. Understanding the factors
28
which influence a supervisee’s ability to form complex cognitions about their supervisor
is important due to the relationship between successful supervision and effective
counseling (Landy et al., 1999).
Rosenbach et al.’s (1973) findings could also be important in supervision as
supervision has been characterized as a highly emotional relationship (Cooper & Ng,
2009). Does the emotional nature of the supervisory relationship then impact the level of
cognitive complexity among its members? Also, could the age of the supervisee
influence their level of cognitive complexity and allow them to be more susceptible to the
influences of an emotionally charged relationship? These factors could help explain
some of the issues encountered in supervision: sexual behaviors (Landy, et al., 2005),
poor termination (Dawson & Akhurst, 2013), and high levels of anxiety (Efstation et al.,
1990; Gnilka, et al., 2011; Grant et al., 2012; Lehrman-Waterman & Ladany, 2001). The
ability for supervisees to develop high levels of cognitive complexity before entering into
the supervisory relationship may help to diminish the effects of an emotional relationship
on cognitive complexity.
Measures of Cognitive Complexity
Measures of cognitive complexity did not come into effect until Kelly’s (1955)
Role Construct Repertory Test (RCRT). Rank and Gestalt did not develop formal
methods of measuring differentiation and integration for their clients. Neither did
Tolman’s work on differentiation and integration expand beyond putting rats into
different mazes. Lewin (1935) viewed Binet’s intelligence test as a form of measuring
differentiation but he never developed his own measure of differentiation. Thus Kelly’s
29
RCRT is the first to measure how people construe other people. The RCRT is based on
Kelly’s personal constructs theory and requires participants to sort people into groups via
construct formation. The person administering the RCRT provides a list of constructs to
the participants and has the participant sort people they know according to those
constructs. Once the initial sorting is complete, the administrator asks the participant to
look at a group of people who have been placed in the same construct grouping and note
similarities and differences among those in the grouping. Once the groupings are
complete the participant is then asked to select one of their groupings. The participant is
to then imagine a situation where they are with the chosen grouping and explain where
they would be, what they would be doing, how they would be feeling, and how the other
members of the group would be feeling. While they are going through this imaginary
situation the administrator is noting all the constructs the participant is providing. Once
the participant has finished answering the administrator’s questions, the RCRT is scored
by counting the number of constructs the participant was able to give and examining how
many constructs overlapped different people. The more unique constructs a person is
able to provide the higher their score. Kelly (1955) noted that training in the RCRT
would be necessary. The psychometric properties of the RCRT are somewhat limited due
to the era in which it was created. The test-retest for the RCRT among hospital patients
was a mean of 69 constructs with a standard deviation of six. Among college students
the test-retest had a mean of 70 constructs with a standard deviation of “a little below
eight” (Kelly, 1955, p.232).
Bieri, Atkins, Briar, Leaman, Miller, and Tripodi (1966) developed their own
method of measuring cognitive complexity which is based on Kelly’s RCRT. Bieri et
30
al.’s (1966) measure uses a series of role descriptions and the completion of a 10 x 10
construct-by-person grid. Unlike the RCRT, participants need to supply their own
constructs to the subject and the participants do not indicate which construct applies to a
given subject. Cognitive complexity is measured by a comparison of ratings between
rows of constructs. Identical ratings are given a score of one, while dissimilar ratings are
scored as zero. Once all possible comparisons between constructs have been made,
scores are summed and a total score is produced. Higher scores represent higher levels
of cognitive complexity. The one-week test-retest reliability for adults ranged from .46
to .85 (Kapp, 1971), while the two-week test–retest was only .26 (Ohbuchi & Horike,
1978), and Bavelas, Chan, and Guthrie (1976) reported a .67 reliability among adults
over a one to three weeks span. Kelly’s (1955) and Bieri’s et al.’s, (1966) measures
provided the base for other measures of differentiation in cognitive complexity and are
compiled by the number of constructs a participant can produce (O’Keefe & Sypher,
1981).
Used by Duys and Hedstrom (2000) and Little et al. (2005), the Role Category
Questionnaire (RCQ) was originally developed by Crockett, Press, Delia, and Kenny at
the University of Kansas (Burleson & Waltman, 1988). Participants are given five
minutes to write as many constructs as they can about a liked and a dis-liked peer.
Cognitive complexity is scored by the number of constructs participants are able to
produce, the more constructs the higher the level of cognitive complexity. O’Keefe and
Sypher (1981) refer to the RCQ as having the most merit of all the measures of cognitive
complexity in their review of cognitive complexity measures in communications.
However, O’Keefe and Delia (1982) challenge the RCQ as a measure of cognitive
31
complexity; saying that the measure falls victim to assimilation bias. Thus stating that,
the RCQ is unable to measure if participants are viewing others as they are or if they are
assimilating people into already existing schemas. Leichty (1997) points to the multiple
interpretations among constructivists of the RCQ and calls to a new interpretation stating
that the RCQ measures how often participants engage in “deep processing” of other
people; indicating that the RCQ is more about motivation than it is about construct
structuring. Thus, educational affordances which provide students with an opportunity to
process deep aspects of other people would then be related to an increase in scores on the
RCQ. Another critique of the RCQ in counselor education is brought forth by Welfare
and Borders (2010), who bring attention to the general and specific domains of cognitive
complexity. They argue that cognitive complexity is a domain specific form of
complexity and does not translate across cognitive schemas. It is possible someone may
hold a level of complexity in one area but not in another.
To help gain a better understanding of cognitive complexity in counseling,
Welfare and Borders (2010b) developed the Counselor Cognitive Questionnaire (CCQ)
which measures counseling domain specific cognitive complexity. The CCQ is based on
the RCQ and gives participants five minutes to write down as many constructs as possible
about a client in a session that they felt was effective and about a client in a session that
they felt was not effective. Differentiation scores are related to the number of different
constructs a participant is able to produce. The more constructs produced the higher level
of cognitive complexity. While the CCQ is based on the RCQ, it is considered to be a
unique measure of cognitive complexity (Welfare & Borders, 2010b). The work of
32
Welfare and Borders (2010b) provides a strong rational for a unique measure of cognitive
complexity to examine its role within the working alliance in the supervisory relationship.
Supervisory Working Alliance
The supervisor-supervisee relationship is central to effective supervision
(Kaufman & Kaufman, 2006). This relationship includes a number of important factors,
but as Watkins (2014a) notes, none of the other factors in the supervisory relationship
“exert more power and influence on supervisor and supervisee than their jointly-forged
supervisory alliance.” (p. 20). This literature review will examine the impact of the
supervisory working alliance on counselor development. While the supervisory working
alliance has taken center stage in effective supervision, research continues to explore
what conditions create this alliance. One purpose of this literature review is to provide a
rationale for studying the role of supervision specific cognitive complexity among
supervisees in understanding an element in the creation of the supervisory alliance.
Current definitions of the supervisory working alliance are based on the work of
Bordin (1983). Bordin’s (1983) definition of the supervisory working alliance grew from
his work and understanding what creates change in therapy. Bordin (1983) attributed
change in therapy to two factors: a) the strength of the alliance between the counselor and
client and b) the power of the tasks that are combined within that alliance. Bordin (1983)
saw value in expanding the role and power of alliance in creating change to move beyond
counseling. Specifically, Bordin (1983) thought that a strong alliance was needed in
educational, parental-filial, and supervisory relationships to create meaningful change.
Within supervision, Bordin (1983) identified three parts of the supervisory working
33
alliance: a) supervisor and supervisee have a mutual understanding and agreement of the
goals sought in the supervisory process, b) both parties also share an understanding and
agreement of the tasks in the supervisory relationship, and c) the bond between
supervisor and supervisee is necessary to sustain the goals and tasks set forth by the
supervisor and supervisee. It is with Bordin’s (1983) definition of the supervisory
working alliance in mind that the role of attachment is analyzed in supervision.
Summary
Supervision plays a singular role in the development of counselors. It is the
primary means where a counselor’s work is reviewed and counseling skills with clients
increased (Kaufman & Kaufman, 2006). Coupled with supervision’s importance to
counselor development is the potential for harm due to the unique difficulties that occur
in supervision (Grant et al., 2012). A major factor that differentiates effective and
potential harmful supervision is the supervisory working alliance (Sterner, 2009). A
strong supervisory working alliance has been identified as potentially the most important
factor that promotes supervisee change (Landy, et al., 1999). The strength of the
supervisory working alliance has been correlated to higher levels of counselor selfefficacy (Efstation,et al., 1990) and less difficulty in addressing difficulties in supervision
(Grant et al., 2012). Weak supervisory working alliances have been linked to poor
coping with adverse client-related events (Kozowska et al., 1997a) and higher levels of
despair and psychological distress among supervisees (Kozowska et al. 1997b). The
ability for supervisors and supervisees to form a strong working alliance impacts a
supervisee’s development and their work with clients.
34
One component of supervision education includes understanding how supervisees
conceptualize their supervisors. The relationship between supervisees’ conceptualization
of supervision and supervision effectiveness is still an emerging theme in supervision
competency research (Watkins, 2013a). Much like research on counselor’s cognitive
development (Duys & Hedstrom, 2000; Granello, 2000, 2001, 2002, 2010),
understanding the relationship between supervisee conceptualization of supervision and
supervision effectiveness would provide a useful tool for counselor educators and
supervisors to meet the supervisory developmental needs of their supervisees. Further,
understanding how supervisees develop cognitively in supervision could be useful in
understanding difficulties in supervision and how to address those difficulties.
The purpose of this study was to understand the relationship between the
supervisory working alliance and cognitive complexity. Specifically, is supervision a
domain specific function of cognitive complexity or is it related to counseling cognitive
complexity? The researcher hypothesized that a non-significant relationship would exist
between supervision cognitive complexity and counseling cognitive complexity; thus
indicating two distinct cognitive domains. Further, there would be a positive significant
correlation between levels of supervision cognitive complexity and the supervisory
working alliance; while there would be a non-significant relationship between levels of
counseling cognitive complexity and the supervisory working alliance.
Research Questions
The specific research questions of this study are as follows:
1. Is supervision cognitive complexity specific to the supervision or is it related to
counseling cognitive complexity?
35
a. What is the relationship between supervision cognitive complexity and
counseling cognitive complexity?
2. What is the role of supervisee cognitive complexity in the supervisory working
alliance?
a.
Do higher levels of supervision cognitive complexity or counseling
cognitive complexity correlate to higher levels of the total supervisory
working alliance?
b. Do higher levels of supervision cognitive complexity or counseling
cognitive complexity correlate to higher levels of the subscale rapport
in the supervisory working alliance?
c. Do higher levels of supervision cognitive complexity or counseling
cognitive complexity correlate to higher levels of the subscale client
focus in the supervisory working alliance?
This study is a quasi-experimental cross-sectional research design as it seeks to
measure cognitive complexities attributes in pre-formed groups. Participants were
selected from the NCACES region and from CACREP accredited programs. Forty-two
participants were self-selected to participate in this study. To test for the uniqueness of
the supervision cognitive complexity questionnaire a Pearson product moment correlation
was used with a two tailed alpha level of .05.
To test to for the relationship between cognitive complexity and the supervisory
working alliance a hierarchical regression was run where semester in school, cognitive
complexity as measured by the CCQ and cognitive complexity as measured by the
36
supervision cognitive complexity questionnaire will account the variance in supervisory
working alliance. The researcher decided to input semester in school as the first variable
entered into the regression equation so that cognitive complexity can be measured as a
unique effect and control for the growth of cognitive complexity which has been shown
to increase with time in clinical settings (Granello, 2010), age (Rosenbach et al., 2000),
and exposure to counseling courses (Duys& Hedstrom, 2000).
37
CHAPTER III
METHODOLOGY
The purpose of this study is to understand the relationship between the
supervisory working alliance and cognitive complexity. Specifically, the study examines
whether supervision is a domain specific function of cognitive complexity or if it is
related to counseling cognitive complexity. The researcher hypothesized a nonsignificant relationship would exist between supervision cognitive complexity and
counseling cognitive complexity. It is also hypothesized that there would be a nonsignificant relationship between levels of counseling cognitive complexity and the
supervisory working alliance and its subscales rapport and client focus. Further, there
would be a positive significant correlation between levels of supervision cognitive
complexity and the supervisory working alliance and its subscales rapport and client
focus. This chapter presents an overview of the research questions, participants, research
procedure, instruments, designs, and proposed data analysis.
Research Questions
The specific research questions of this study are as follows:
1. Is supervision cognitive complexity specific to the supervision or is it related to
counseling cognitive complexity?
a. What is the relationship between supervision cognitive complexity and
counseling cognitive complexity?
38
2. What is the role of supervisee cognitive complexity in the supervisory working
alliance?
a. Do higher levels of supervision cognitive complexity or counseling
cognitive complexity correlate to higher levels of the total supervisory
working alliance?
b. Do higher levels of supervision cognitive complexity or counseling
cognitive complexity correlate to higher levels of the subscale
rapport in the supervisory working alliance?
c. Do higher levels of supervision cognitive complexity or counseling
cognitive complexity correlate to higher levels of the subscale
client focus in the supervisory working alliance?
Participants
All 42 participants were selected from CACREP accredited programs in school,
clinical counseling, and counselor educational and supervision programs located in the
North Central region of the Association of Counselor Educators and Supervisors
(NCACES). The NCACES region contains 13 states: Illinois, Indiana, Iowa, Kansas,
Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, Oklahoma, South Dakota,
and Wisconsin. Participants came from seven of the 13 states in the NCACES region:
Illinois, Iowa, Kansas, Michigan, Minnesota, Missouri, and Ohio.
Sex
Of the 42 participants 33 identified as female (78.6%), seven identified as male
(16.7%), while two (4.8%) declined to identify their sex. These results are consistent
39
with CACREP enrollment across the United States where 83.04% identify as female and
16.96% as male. Table 1 presents the summary of demographic characteristics of the
participants’ sex.
Table 1 Sex
N
Percent
Male
7
16.7
Female
33
78.6
Missing
2
4.8
Total
42
100
Age
The ages of the participants ranged from 23 to 51 years of age; with a mean age of
30, a median age of 28, and a mode age of 24 years. Table 2 presents the summary of
demographic characteristics of the participants’ ages.
Table 2 Age
Age
N
Minimum
Maximum
Mean
SD
36
23
51
30.36
8.68
Program
Forty (95.2%) participants were enrolled in counseling programs at the masters’
level, one (2.4%) was enrolled at the doctoral level, and one (2.4%) did not indicate their
level of education. These results are also consistent with CACREP enrollment across the
United States, where 94.4% of students are in Master’s programs and 5.2% are in
40
doctoral programs. Table 3 presents the summary of demographic characteristics of the
participants’ program.
Table 3 Program of study
Frequency
Percent
Master’s
40
95.2
Ph.D
1
2.4
Total
42
100
Semester
Two (4.8%) participants were in their second semester, nine (21.4%) were in their
third semester, 17 (40.5%) were in their fourth semester, six (14.3%) were in their fifth
semester, two (4.8%) were in their sixth semester, with one (2.4%) participant each in
their seventh, eighth, and ninth semesters, respectively. Finally, two (4.8%) participants
were in their tenth plus semester and one (2.4%) participant did not state which semester
they were in. Table 4 presents the summary of demographic characteristics of the
participants’ semester in school.
41
Table 4 Semester in School
Semester in school
N
Percent
Second
2
4.8
Third
9
21.4
Fourth
17
40.5
Fifth
6
14.3
Sixth
2
4.8
Seventh
1
2.4
Eighth
1
2.4
Ninth
1
2.4
Tenth plus
2
4.8
Missing
1
2.4
Total
42
100
Time in Supervision
Twelve (28.6%) participants indicated that they were in their first semester of
supervision, ten (23.8%) participants were in their second semester of supervision, 16
(38.1%) participants were in their third semester, two (4.8%) participants were in their
fourth semester, one (2.4%) participant was in their fifth semester and one (2.4%)
participant did not indicate which semester they are currently enrolled in. Table 5
presents the summary of demographic characteristics of the participants’ time in
supervision.
42
Table 5 Time in Supervision
Time in supervision
N
Percent
First
12
28.6
Second
10
23.8
Third
16
38.1
Fourth
2
4.8
Fifth
1
2.4
Missing
1
2.4
Total
42
100
Number of Supervisors
Ten (23.8%) participants stated that they have had just one supervisor, nine
(21.4%) participants indicated they have had two supervisors, 14 (33.3%) participants
indicated they have had three supervisors, five (11.9%) participants stated they have had
four supervisors, while one (2.4%) participant indicated that they have had five and nine
supervisors, respectively. Two (4.8%) participants did not indicate how many
supervisors they have had. Table 6 presents the summary of demographic characteristics
of the participants’ number of total supervisors.
43
Table 6 Number of Supervisors
Number of supervisors
N
Percent
1
10
23.8
2
9
21.4
3
14
33.3
4
5
11.9
5
1
2.4
6
0
0
7
0
0
8
0
0
9
1
2.4
Missing
2
4.8
Total
42
100
Supervision Course
Three (7.1%) participants indicated that they have had a course on supervision, 38
(90.5%) participants indicated they have not received a course on supervision, with one
(2.4%) participant who did not indicate how many supervision courses they had. Table 7
presents the summary of how many participants have taken a supervision course.
44
Table 7 Supervision Course
Taken supervision course
N
Percent
Yes
3
7.1
No
38
92.7
Missing
1
2.4
Total
42
100
Previous Supervisory Experience
Three (7.1%) participants indicated they had pervious supervisory experience, 38
(90.5%) participants indicated they have not had any pervious supervisory experience,
with one (2.4%) participant who declined to answer that question. Of those who have
had supervisory experience, two (4.8%) participants indicated they have had one
semester of said experience while one (2.4%) participant indicated they have had two
semesters of supervisory experience. Table 8 presents the summary of how many
participants have had previous experience has a supervisor.
Table 8 Previous Supervisory Experience
Yes
No
Missing
Total
N
3
38
1
42
Percent
7.1
90.5
2.4
100
Procedure
All research was conducted after Institutional Review Board approval. To be
recruited, participants needed to be students who were enrolled in individual supervision
in practicum or internship at the time of study. Participants were recruited via email.
45
The researcher contacted the clinical coordinators of CACREP accredited programs
across the NCACES region and invited them to either take the survey in person or send
out the link for the survey to doctoral and master’s level students currently enrolled in
their practicum or internship courses. There are currently 164 CACREP accredited
programs within the NCACES region which met the recruitment criteria for this study.
Recruitment
Recruitment occurred in three waves. The first wave of recruitment was an email
sent to all 164 programs. Five programs replied stating they would be willing to
participate in the study, allowing for a response rate of 3.0%. The second wave was an
additional email sent to remaining the 159 programs who did not respond to the first
wave of emails. In the second wave, four programs responded that they would participate
in the study, a response rate of 2.5%. An additional program responded that they would
not participate in the study. For the third and final wave of recruitment, an additional
email was sent to the 154 programs who had not responded to the first and second email
sent. In the third wave two additional programs responded that they would participate in
the study for a response rate of 1.2%. With another program which stated it would not
participate and a final program which stated it might participate did not contact the
researcher after follow-up emails. The total response rate for the three waves was 6.7%,
11 programs out of 164.
Assessment Administration
Programs were given the option to administer the assessment online or have the
researcher come and administer the assessment in person. Nine of the 11 programs chose
to administer the assessment on-line. Thirty-eight participants began the online
46
assessment, one elected not to participate in the study and 11 did not finish the study.
The attrition rate among online participates was at 32%. A total of 26 students completed
the online assessment. Due to the anonymity provided to the students, the researcher is
unaware of how many students were eligible to participate within those sites which stated
they were willing to participate.
Two programs allowed the researcher an opportunity of an in-person
administration of the assessment at their institution. The first in-person site provided
three sections with a total of 18 students who were eligible to participate. Seventeen of
these participants elected to take the study and one elected not to participate. All 17
participants who began the in-person assessment finished the assessment. Data were
collected in the participants’ classroom. The second site did not allow the researcher to
collect data in a classroom so collection was attempted in a common space outside the
classroom during the participants’ free time. There were a total of 12 students who were
eligible to participate in the study. None of the students who were eligible elected to
participate in the study. Out of a possible 30 in-person participate 17 elected to
participate (56%) while 13 (43%) elected not to participate.
Participants were asked to complete a demographic questionnaire (See Appendix
A) and three different measures: the Counselors Cognition Questionnaire (CCQ; See
Appendix C), the Supervision Cognitive Complexity Questionnaire (SCCQ; See
Appendix D), and the supervisory working alliance inventory-trainee (SWAI-T; See
Appendix E) to measure the perceived strength of the supervisory working alliance.
Upon completion, participants were taken to a concluding screen where they were
47
thanked for their efforts in this project and given a final opportunity to not have their data
collected.
Measures
The CCQ and SCCQ were scored by two independent raters who were unaware of
the researcher’s hypothesis. Raters were given instructions on scoring as outlined by
Welfare and Borders’ (2007) CCQ rater training manual. Raters were given the scoring
instructions manual and were also given verbal instructions as the primary reviewer read
the scoring instructions to them. Both the CCQ and SCCQ were scored using the
methods outlined in the training manual. Scores on the CCQ and SCCQ are generated as
the rater counts each construct given by the participant in the successful and unsuccessful
counseling or supervision session. After individual scores are given, raters then remove
one point for redundant constructs given in either the successful or unsuccessful
counseling or supervision session. A total score is created for counseling and supervision
with the sum of constructs created in the successful and unsuccessful session and like
constructs being removed. This total score represents the participants’ differentiation
cognitive complexity score. The raters had an inter-rater reliability score of .98 on the
CCQ and an inter-rater reliability score of .96 on the SCCQ. A third independent rater
was used to make final decisions on number of constructs when the first two reviewers
were in disagreement.
CCQ
The counselor cognitions questionnaire (CCQ) was developed by Welfare and
Borders (2010b) to test the specificity or generality of cognitive complexity within the
48
profession of counseling. Like other cognitive complexity measures, the CCQ is based
on Kelly’s (1955) personal construct theory and Crockett’s et al. (1975) application of
Kelly’s theory. Kelly’s theory postulates that people develop templates or constructs to
make meaning and understand other people, experiences, and objects. The more
constructs a person is able to create the more highly complex their cognitions are
considered to be. Participants are given 15 minutes to write down as many traits as
possible about a client in a successful counseling session and 15 minutes to write down as
many traits as possible about a client in an unsuccessful counseling session.
The CCQ was validated using a sample of 120 master’s students and postmaster’s counselors; 14 participants were male, 105 were female, and one did not
respond. The majority of the participants were Caucasian, numbering 98; while 12
identified as African American, 3 as Hispanic or Latino/a, 2 as American Indian or
Alaska Native, 2 as Asian, 1 identified as Native Hawaiian or Other Pacific, 1 as other,
and 1 did not respond. Finally, the CCQ was found to be a unique measure of cognitive
complexity when compared to a more general measure (e.g. the Role Category
Questionnaire; Sentence Completion Test of Ego Development). A Pearson product
moment correlation showed no significant correlation between the CCQ and the other
measures of cognitive complexity.
Participants’ scores in this study on the CCQ ranged from 0 to 32 with an
arithmetic mean of 12.5. Welfare and Borders’ (2010b) study produced similar results
with the majority of their participants scoring between 10 and 20. Scores of 10 or below
are representative of low construct differentiation. Meaning, participants who score in
this range have fewer constructs with which they can describe clients. Scores of 25 or
49
above represent participants with complex cognitive systems. Meaning, participants are
able to differentiate many constructs when working with a client.
SCCQ
The supervision cognitive complexity questionnaire is a modified form of the
CCQ. The measure was modified by the primary researcher. The modification made was
changing the word from ‘client’ to ‘supervisor’. No other modifications were made from
the CCQ to the SCCQ. Cognitive complexity is measured by the amount of constructs a
supervisee is able to produce about their supervisor. Participants were given 15 minutes
to write down as many traits as possible about a supervisor in a successful supervision
session and 15 minutes to write down as many traits as possible about a supervisor in an
unsuccessful supervision session. Participants’ scores on the SCCQ ranged from 0 to 34
with an arithmetic mean of 14.31. The majority of participants’ scores were between 10
and 20. The SCCQ was significantly correlated to CCQ (r = .595, p < .01). As such, the
rationale used for the scoring method of the CCQ is applicable for the SCCQ. Thus,
indicating that scores of 10 or below represent low levels of supervision cognitive
complexity. Participants with low scores then have few constructs with which to describe
their supervisor. Scores of 25 or above are indicative of highly complex cognitions on
supervision. Participants with scores of 25 or above are able to differentiate many
constructs of their supervisor. While little psychometric data are available, the work of
Welfare and Borders (2010b) provide a strong rationale in modifying existing measures
of cognitive complexity to measure more domain specific aspects of cognitive
complexity.
50
SWAI-T
Efstation,et al., (1990) developed the Supervisory working alliance inventorytrainee (SWAI-T) based on Bordin’s (1983) model of the supervisory working alliance.
The SWAI-T was designed to measure the supervisory working alliance from the
perspective of the trainee. Scoring for the SWAIT-T is based on 19 items which are
ranked on a seven-point Likert scale ranging from almost never (1) to almost always (7).
Higher scores on the SWAI-T are indicative of higher levels of perceived working
alliance. Items for the SWAI-T were based on input from ten experienced supervisors
within the American Psychological Association. A factor analysis was completed and
two factors were found to be significant in developing a strong supervisory working
alliance from the trainee perspective: rapport and client focus. Questions one to 12
comprise the rapport scale while questions 13 to 19 comprise the client focus scale.
Alpha coefficients were .90 for rapport and .77 for client focus. The SWAI-T was
validated using a national sample with participants from 42 different states, Washington
D.C., and Canada (Efstation et al., 1990). Data were obtained from 178 trainees in
professional psychology internship programs in counseling and clinical psychology. Of
the 178, 103 identified as women, 72 as men, and 2 did not specify their gender
(Efstation et al., 1990).
Participants’ total scores for the SWAI-T ranged from 54 to 133, with an
arithmetic mean of 109.52. The majority of scores feel between 105 and 118. The
highest possible score for SWAI-T is 133 with a low of 19. For the subscales on the
SWAI-T, scores ranged from 28 to 91 with an arithmetic mean of 70.29 for rapport. The
51
highest possible score for rapport is 91 with a low of 12. Scores for client focus ranged
from 26 to 42 with an arithmetic mean of 39.34. The highest possible score for the client
focus is 42 with a low of seven. Higher scores at all levels on the SWAI-T represent
higher levels of perceived working alliance.
Summary
The purpose of this study is to understand the relationship between the
supervisory working alliance and cognitive complexity. Specifically, is supervision a
domain specific function of cognitive complexity or is it related to counseling cognitive
complexity? The researcher hypothesizes that a non-significant relationship exists
between supervision cognitive complexity and counseling cognitive complexity; thus
indicating two distinct cognitive domains. Further, there would be a positive significant
correlation between levels of supervision cognitive complexity and the supervisory
working alliance; while there would be a non-significant relationship between levels of
counseling cognitive complexity and the supervisory working alliance.
This study is a quasi-experimental cross-sectional research design as it seeks to
measure cognitive complexities attributes in pre-formed groups. All statistics were
calculated using the Statistical Package for the Social Sciences (SPSS; Release 21, 2012).
Inter-rater reliabilities for the CCQ and SCCQ were calculated using a Pearson product
moment correlation. The normality of each distribution was assessed using a histogram.
Based on the work of Welfare and Boarders (2010) a Pearson product moment
correlation was used with an alpha level of .05 was used to test for the uniqueness of the
SCCQ when compared to the CCQ.
52
To test for the relationship between cognitive complexity and the supervisory
working alliance a hierarchical regression was run where semester in school, cognitive
complexity as measured by the CCQ and cognitive complexity as measured by the SCCQ
will account the variance in supervisory working alliance. Semester in school was
entered as the first variable in the regression model as it is demographic variable. The
CCQ and SCCQ they were entered in that order after semester in school. Aguinis (2004)
notes that predictor variables should have less than perfect collinearity, which was
achieved in this study. Semester in school was used as the control variable as it
combines many factors which have been linked to increases in cognitive complexity.
These factors include: time in a clinical settings (Granello, 2010), age (Rosenbach et al.,
2000), and exposure to counseling courses (Duys& Hedstrom, 2000). With the control
variable in place, the variance explained by the CCQ and SCCQ would account for a
unique contribution to the supervision working alliance. The results of the analysis will
be presented in chapter IV.
53
CHAPTER IV
RESULTS
Chapter 4 presents the findings for the research questions. These findings include
a power analysis for the study, a correlation between the CCQ and SCCQ, and a
hierarchal regression where semester in school, cognitive complexity as measured by the
CCQ and cognitive complexity as measured by the supervision cognitive complexity
questionnaire will account the variance in supervisory working alliance. To place these
findings in relation to the whole of counselor education, some demographic comparisons
can be made. Table 9 presents a summary of the demographic findings between the
participants of this study and students enrolled in CACREP accredited programs. The
2012 CACREP annual report provides some basic demographic statistics from which
general comparisons can be made to the participants in this study (CACREP, 2012).
The comparison demonstrates that the participants of this study are similar to the
general CACREP student enrollment. The majority of participants are female (78.6%)
along with the majority of students enrolled in CACREP accredited programs (83.04%).
Also, the majority of participants are at the masters’ level (95.2%) which coincides with
the student enrollment in CACREP programs (94.8%). These data show that the results
of this study are representative to the general enrollment in CACREP accredited
programs. Other demographic information was not provided by CACREP for
comparisons to be made.
54
Table 9 Demographic comparison: This study’s participants and
CACREP enrollment
Sex
Program
Female
Male
Masters’
Doctoral
This Study
78.6 %
16.7%
95.2%
2.4%
CACREP
83.04%
16.96%
94.8%
5.2%
Enrollment
Analyses for research question 1
The researcher hypothesized that a non-significant relationship would exists
between supervision cognitive complexity and counseling cognitive complexity; thus
indicating two distinct cognitive domains. The researcher’s hypothesis was no supported.
There was a significant relationship between supervision and counseling cognitive
complexity domains (r = .595; n = 42, p < .01). The Pearson’s correlation of .595
indicates that the ability to provide traits for a supervisor is positively correlated with the
ability to provide traits for a client. The data indicate that counseling and supervision
cognitive complexity are related domains of cognitive complexity. Table 10 shows the
correlation between the CCQ and SCCQ. Figure 1 is an illustration of the relationship
shown in table 10.
Figure 1 Correlation between CCQ & SCCQ
55
Analyses for research question 2a
The researcher hypothesized that there would be a positive significant correlation
between levels of supervision cognitive complexity and the supervisory working alliance;
while there will be a non-significant relationship between levels of counseling cognitive
complexity and the supervisory working alliance. The researcher’s hypothesis was
unsupported. While a significant correlation between counseling cognitive complexity
and overall score on the SWAI-T did not exist (r = -.095, n = 41, p = .278), there was a
non-significant relationship between supervision cognitive complexity and the SWAI-T
56
(r = -240, n = 41, p =.220). The regression analysis indicated that the semester the
student was enrolled in their program was the best predictor of a positive working
alliance in supervision. While supervision cognitive complexity contributed a higher R2
value than counseling cognitive complexities (.064 to .001), neither were significant
predictors of overall supervisory working alliance from the supervisee’s perspective.
Table 10 shows the descriptive statistics for the regression analysis of total score
on the SWAI-T and semester in school, CCQ, and SCCQ. The mean for the total score
on the SWAI-T was 109.68 with a standard deviation of 15. Mean scores for the SWAIT were typical when compared to other studies which also assessed counseling students
in CACREP accredited programs with other studies (Sterner, 2009; Wester, Vogel, &
Archer, 2004). The mean score for semester in school was 5.8 with a standard deviation
of 2.66, indicating that most of the participants were in their fifth semester of their
program. The mean for the CCQ was 12.49 with a standard deviation of 6.6. The
majority of participants had moderate counseling cognitive complexity, which is typical
for counseling students (Welfare & Boarders, 2010b). The mean for the SCCQ was
13.93 with a standard deviation of 7.7. Once again the majority of participants had
moderate supervision cognitive complexity.
Table 10 Descriptive Statistics for SWAI-T, Semester in school, CCQ, & SCCQ
Mean
Standard Deviation
N
SWAI-T Total
109.68
15.28
41
Semester in school
5.88
2.66
41
CCQ
12.49
6.60
41
SCCQ
13.93
7.69
41
57
Table 11 shows the correlations between the variables of interest. Semester in
school and total score on the SWAI-T had a significant positive of correlation of r = .378
(n = 41, p = .007). The longer a participant is in school the better their relationship is
with their supervisor. The correlation between total score on the SWAI-T and CCQ was
non-significant and negative, r = -.095 (n = 41, p = .278). Lastly, SCCQ had a nonsignificant (p = .220) positive relationship (r = .124, n = 41) with SWAI-T total score.
For this sample, a higher SCCQ score was indicative of a higher perceived working
alliance.
Table 11 Correlation between SWAI-T, Semester in school, CCQ, & SCCQ
Correlation
Significance
Total
Semester
CCQ
SCCQ
Total
1.00
.378
-.095
.124
Semester
.378
1.00
-.322
-.240
CCQ
-.095
-.322
1.00
.615
SCCQ
.124
-.240
.615
1.00
Total
.
.007
.278
.220
Semester
.007
.
.020
.065
CCQ
.278
.020
.
.000
SCCQ
.220
.065
.000
.
N
41
41
41
41
Table 12 shows the model summary for the regression analysis of total SWAI-T
score, semester in school, CCQ, and SCCQ. Semester in school accounted for 14.3% of
58
the total variance in the perceived working alliance. The semester’s contribution to
variance is significant (F1, 39 change = 6.514, p = .015). Participants’ counselor
cognitive complexity had a unique contribution of .1% of the total variance in the
perceived working alliance. This contribution to total variance is not significant (F1, 38
change = .036, p = .851). Finally, participant supervision cognitive complexity uniquely
contributed 6.4% of the total variance in the perceived working alliance. Supervision
cognitive complexity contribution is also non-significant (F1, 37 change = 2.98, p = .093).
The variance accounted for by all variables (semester in school, CCQ, and SCCQ)
was .208. All independent variables accounted of 20.8% of the total variance in the
perceived working alliance.
Table 12 Regression model summary: SWAI-T, Semester in school, CCQ, & SCCQ
Change Statistics
SWAI-T
Semester
Beta
R
R2
Adjusted
R2
F
R2
Change
Change
df1
df2
.414
.378
.143
.121
.143
6.514*
1
39
CCQ
.074
.379
.144
.099
.001
.036
1
38
SCCQ
.246
.456
.208
.143
.064
2.98
1
37
in School
*Significance p < .05
59
Analyses for research question 2b
Examining the supervisory working alliance on the subscale level: Rapport
Rapport did not have a significant correlation between counseling cognitive
complexity (r = -.05, n = 41, p = .379) and the rapport score on the SWAI-T, which was
also a non-significant relationship between supervision cognitive complexity and the
SWAI-T (r =.066, n = 41, p=.341). The regression analysis indicated that the semester
the student is enrolled in their program is the best predictor of a stronger rapport between
the supervisee and their supervisor. In this instance, supervision cognitive complexity
contributed a higher R2 value than counseling cognitive complexity (.005 to .021).
Neither were significant predictors of overall supervisory working alliance from the
supervisee’s perspective. Also, of interest within these results, is the direction of the
relationship between counselor cognitive complexity and rapport. There was no
relationship among participants who had higher levels of counseling cognitive
complexity levels of rapport.
Table 13 shows the descriptive statistics for the subscale rapport on the SWAI-T,
semester in school, CCQ, and SCCQ scores. The statistics for semester in school, CCQ,
and SCCQ are the same as they were in table 11. The mean score for the subscale
rapport was 70.39 with a standard deviation of 10.28. A score of 91 is the highest score
which can be achieved on the SWAI-T. The scores represented in this study indicate
moderate levels of rapport as perceived by the supervisees.
60
Table 13 Descriptive statistics: Rapport, Semester in school, CCQ, & SCCQ
Mean
Std. Deviation
N
Rapport
70.39
10.28
41
Semester in school
5.87
2.66
41
CCQ
12.49
6.60
41
SCCQ
13.93
7.69
41
Table 14 shows the correlation between the subscale rapport, semester in school,
CCQ, and SCCQ scores. The correlation between rapport and semester in school was
positive (r = .370) and significant (p = .009). Thus indicating, that the longer a
participant is in school the better rapport they have with their supervisor. The correlation
between rapport and counselor cognitive complexity was negative (r = -.050) and nonsignificant (p = .379). In this sample, participants who had higher levels of counselor
cognitive complexity were correlated to lower perceived rapport. Finally, the
relationship between rapport and supervision cognitive complexity was positive (r = .066)
and non-significant (p = .341). For this sample, higher levels of perceived rapport were
slightly correlated with higher levels of supervision cognitive complexity.
61
Table 14 Correlation between Rapport, Semester in school, CCQ, & SCCQ
Correlation
Significance
Rapport
Semester
CCQ
SCCQ
Rapport
1.00
.370
-.050
.066
Semester
.370
1.00
-.322
-.240
CCQ
-.050
-.322
1.00
.615
SCCQ
.066
-.240
.615
1.00
Rapport
.
.009
.379
.341
Semester
.009
.
.020
.065
CCQ
.379
.020
.
.000
SCCQ
.341
.065
.000
.
N
41
41
41
41
Table 15 shows the model summary for the regression analysis for rapport in the
SWAI-T and semester in school, CCQ, and SCCQ scores. Semester in school accounted
for 13.7% of the total variance for rapport in supervisory working alliance. This is a
significant contribution to the total variance (F1, 39 change = 6.178, p = .017). Counselor
cognitive complexity accounted for an additional .5% of the variance of rapport in the
supervisory working alliance, a non-significant contribution (F 1, 38 = .237, p = .629).
Finally, supervision cognitive complexity accounted for an additional 2.1% of the
variance of rapport in the supervisory working alliance, a non-significant contribution (F1,
37 change
= .919, p = .344). The regression analysis demonstrated that semester in school
is the best predictor for rapport in the supervisory working alliance. The longer a
62
participant was in the school the greater likelihood they would have better rapport with
their supervisor.
Table 15 Regression model summary: Rapport, Semester in school, CCQ, & SCCQ
Change Statistics
Rapport
Semester
Beta
R2
R
Adjusted
R2
F
R2
Change
Change
df1
df2
.461
.370
.137
.115
.137
6.178*
1
39
CCQ
-.026
.377
.142
.097
.005
.237
1
38
SCCQ
.101
.404
.163
.095
.021
.919
1
37
in School
*Significance p < .05
Analyses for research question 2c
Examining the supervisory working alliance on the subscale level: Client Focus
Lastly, semester in school was significantly correlated to the client focus subscale
on the SWAI-T (p = .027). Counseling cognitive complexity and supervision cognitive
complexity were non-significant. Once again, supervision cognitive complexity provided
a greater R2 change than did counseling cognitive complexity (.133 to .002).
Table 16 shows the descriptive statistics for the subscale client focus on the
SWAI-T, semester in school, CCQ, and SCCQ scores. The mean score for client focus
was 39.29 with a standard deviation of 6.53. These scores represent high levels of client
63
focus among the participants. The descriptive statistics for semester in school, CCQ, and
SCCQ are unchanged from table 11.
Table 16 Descriptive Statistics for Client focus, Semester in school, CCQ, & SCCQ
Mean
Standard Deviation
N
Client focus
39.30
6.53
41
Semester in school
5.88
2.66
41
CCQ
12.49
6.60
41
SCCQ
13.93
7.69
41
Table 17 shows the correlation between client focus, semester in school,
counselor cognitive complexity, and supervision cognitive complexity. Semester in
school had a positive (r = .303, n = 41) and significant (p = .027) correlation to client
focus in the supervisory working relationship. The longer students are in school the
greater the likelihood they will feel that their supervision session is focused on the clients
they are working with. Counselor cognitive complexity had a negative (r = .144, n = 41)
and non-significant relationship (p = .185) with client focus. Finally, supervision
cognitive complexity had a positive (r = .186, n = 41) and non-significant correlation (p
= .122) with client focus in the SWAI-T.
64
Table 17 Correlation between Client focus, Semester in school, CCQ, & SCCQ
Correlation
Significance
Client focus
Semester
CCQ
SCCQ
Client focus
1.00
.303
-.144
.186
Semester
.303
1.00
-.322
-.240
CCQ
-.144
-.322
1.00
.615
SCCQ
.186
-.240
.615
1.00
Client focus
.
.027
.185
.122
Semester
.027
.
.020
.065
CCQ
.185
.020
.
.000
SCCQ
.122
.065
.000
.
N
41
41
41
41
Table 18 shows the model summary for the regression analysis of client focus,
semester in school, counselor cognitive complexity, and supervision cognitive
complexity. Semester in school accounted for 9.2% of the total variance of client focus.
This contribution to variance was non-significant (F 1, 39 change = 3.948, p = .054).
Counselor cognitive complexity uniquely accounted for .2% of the variance in client
focus. This contribution was non-significant (F1, 38 change = -.100, p = .753). Finally,
supervision cognitive complexity uniquely accounted for 13.3% of variance in client
focus. This contribution was significant (F 1, 37 change = 6.341, p = .016). All the
variables accounted for 22.7% of the variance in client focus.
65
Table 18 Regression model summary: Client focus, Semester in school, CCQ, & SCCQ
Change Statistics
Client
Beta
R
R2
focus
Semester
Adjusted
R2
F
R2
Change
Change
df1
df2
.242
.303
.092
.069
.092
3.948
1
39
CCQ
.132
.307
.094
.047
.002
.100
1
38
SCCQ
.416
.476
.277
.164
.133
6.341*
1
37
in School
*Significance p < .05
Summary
The purpose of this study is to understand the relationship between the
supervisory working alliance and cognitive complexity. The participants in this study are
representative of students enrolled in CACREP accredited program (see table 9). A
power analysis revealed a moderate effect size for the study (d = .75). With the
generalizability of the results in mind, an analysis of the results revealed a statistically
significant positive relationship between the CCQ and SCCQ (r = .595; p < .001).
Further analysis showed a non-significant relationship between the CCQ and SCCQ for
the total SWAI-T scale (p = .278; p = .220) and its subscale rapport (p = .379; p = .341)
and client focus (p = .185; p = .122). A hierarchal regression demonstrated that CCQ did
not contribute a significant portion of variance to the SWAI-T (p = .851) or its subscale:
66
rapport (p = .629) and client focus (p = .753). The analysis also revealed that the SCCQ
did not contribute a significant portion of variance to the total SWAI-T score (p = .093)
or its subpart rapport (p = .344). However the SCCQ was shown to contribute a
significant portion of variance to the subpart client focus (p = .016). Discussions of these
results will follow.
67
CHAPTER V
DISCUSSION
The purpose of this study was to understand the relationship between the
supervisory working alliance and cognitive complexity. Specifically, is supervision a
domain specific function of cognitive complexity or is it related to counseling cognitive
complexity? The researcher hypothesized that a non-significant relationship would exist
between supervision cognitive complexity and counseling cognitive complexity (Welfare
& Borders, 2010a). The researcher’s hypothesis was not supported a by the results.
Additionally, the researcher hypothesized there would be a positive significant
correlation between levels of supervision cognitive complexity and the supervisory
working alliance; while there would be a non-significant relationship between levels of
counseling cognitive complexity and the supervisory working alliance. The data
provided partial support for these findings. Chapter five provides a discussion of what
these finding entail and their implications for counselor educators and supervisors.
Further the limitations of this study as well as suggestions for future research are
discussed.
Discussion of the Findings
Discussion of Research Question 1
The researcher hypothesized that a non-significant correlation would exist
between supervision cognitive complexity and counseling cognitive complexity; thus
indicating two distinct cognitive domains. The researcher hypothesis was not supported
68
by the data. There was a significant relationship between supervision and counseling
cognitive complexity domains. While the researcher’s hypothesis was not supported
there is evidence in the measurement literature of measures which are related and yet are
still considered to be unique.
Within the social sciences there exists a bevy of measures which are said to be
unique but still have a statistically significant correlation with each other; the most
prominent of these being intelligence measures. Jordan’s (1923) seminal work on the
validation of intelligence tests showed a strong correlation between seven different
intelligence measures: Terman, Otis, Miller, Otis self-administering, University of
Minnesota Tests, Haggerty––Delta 2, and Myers Mental Measures with the StanfordBinet mental age intelligence quotient (IQ). The strongest correlation between the tests
was r = .75 with the lowest correlation at r = .35 with an average correlation of r = .49.
The conclusion which Jordan drew from this study is that those tests which most strongly
correlated to the Stanford-Binet and with each other were the better measures of
intelligence. Those measures which had weaker correlations were found to be less valid
measures of intelligence.
Welfare and Borders (2010b) chose to interpret their results as being a unique
from general cognitive complexity because their measure was not significantly correlated
to existing general measures of cognitive complexity. Welfare and Borders (2010b)
conceptualized their non-significance by arguing that their measure of cognitive
complexity measured a domain specific form of cognitive complexity. Using the
intelligence measures analogy, this would be like comparing a general measure of
intelligence to domain specific measures of intelligence. Grigorenko and Sternberg
69
(2001) noted that intelligence is viewed as a construct which spans multiple domains.
Spearman (1904) referred to intelligence as some combination of specific and general
abilities which have varying levels of correlations. Grounded in this rationale Welfare
and Boarders (2010b) used their findings to promote the development of a specific
measure cognitive complexity which did not have a significant relationship to the more
general measures. In the present study the researcher used the same rationale as Welfare
and Borders (2010b) in framing his hypothesis and expecting a non-significant
relationship between the CCQ and SCCQ. However, what was not taken into account is
that both the CCQ and the SCCQ measure domain specific forms of cognitive complexity.
Thus, indicating that a statically significant relationship should exist between them
(Jordan, 1923). While the researcher’s hypothesis was not supported that does not mean
that the measures are not unique measures of cognitive complexity.
Having the SCCQ be significantly correlated to the CCQ indicates that the SCCQ
is able to measure cognitive complexity which can be generalized to counselors and
supervisees. However, the correlation between the two measures does not preclude the
SCCQ from being a unique measure of cognitive complexity. The correlation as shown
in table 10 was r = .595 which would equate to an r2 value of .354. In other words 35.4%
of the total variance in the SCCQ is accounted for by the CCQ; leaving 64.6% of the
variance in supervisee cognitive complexity left unaccounted. This unaccounted for
variance sustains the idea of the SCCQ as a related yet unique measure of cognitive
complexity.
Viewing supervision as a related but unique domain of counseling fits within the
current paradigm of supervision in the counselor education literature (Bernard &
70
Goodyear, 2009; Hess et al., 2008). The findings of this study provide further evidence
to the close yet distinct relationship between supervision and counseling. The
implications for counselor educators and supervisors are meaningful. While the
relationship between counseling and supervision cognitive complexity is strong, they are
not synonymous. Counseling students who have high levels of counseling cognitive
complexity may still struggle in their supervisory relationship. This struggle may be due
to the unique nature of supervision and require the supervisor to attend to supervisory
needs of their students. These results also imply that effective supervisors need to have
skills which go beyond a complex understanding of clients, and be able to attend to
supervision specific needs of their supervisees.
Discussion of Research Question 2a
The researcher hypothesized that there would be a positive significant correlation
between levels of supervision cognitive complexity and the supervisory working alliance;
while there would be a non-significant relationship between levels of counseling
cognitive complexity and the supervisory working alliance. The researcher’s hypothesis
was partially supported. While there did not exist a significant correlation between
counseling cognitive complexity and overall score on the SWAI-T there was also a nonsignificant relationship between supervision cognitive complexity and the SWAI-T. The
regression analysis indicated that the semester the student is enrolled in their program
was the strongest contributor of variance in the model. While supervision cognitive
complexity contributed a higher r2 value than counseling cognitive complexities neither
were significant predictors of overall supervisory working alliance from the supervisee’s
perspective.
71
Results from this analysis imply that time in a counseling program is the best
factor within this model which explains the variance in the supervisory working alliance.
As students spend more time in their program they will develop a better alliance with
their supervisors. These results are in line with the work of student developmental
theories (Harvey et al., 1961; Hunt, 1966; Perry, 1970; Stoltenberg et al., 1998). These
theories indicate that students’ schemas and relationships with instructors and supervisors
are dynamic and under normal circumstances student will begin to create more complex
and healthy relationship with their instructors or supervisors overtime. Counselor
educators and supervisors can then expect that even when supervisee’s experience a
growth in their cognitive schemas about counseling or supervision that affective change
within supervision requires time on the part of the supervisee. Further, the negative
correlation between cognitive complexity and working alliance is in line with
Stoltenberg’s et al., (1998) IDM model.
Within the IDM, students who begin to develop more complex schemas of their
clients they will being to pull away from their supervisors as they feel that their
supervisor does not understand the client as well as they do. This pulling away can result
in a weakening of the supervisory working alliance. The researcher hypothesized that an
increase in supervision cognitive complexity would mitigate the effects of this pulling
away on the working alliance. However, this hypothesis was not supported. The strong
correlation between counseling and supervision cognitive complexity (as explained
above) helps in part explain why this hypothesis was incorrect. With only 14% of the
total variance in the supervisory working alliance which is explained by time in school
72
and only 20% which is explained by the total model there is a need to understand which
factors significantly contribute to a strong supervisory working alliance.
Discussion of Research Question 2b
Examining the supervisory working alliance on the subscale level: Rapport
Once again the correlation between counseling cognitive complexity, and the
rapport score on the SWAI-T was non-significant. There was also a non-significant
relationship between supervision cognitive complexity and the SWAI-T. The regression
analysis indicated that the semester the student is enrolled in their program is the best
predictor of a stronger rapport between the supervisee and their supervisor. In this
instance supervision cognitive complexity contributed a higher r2 value than counseling
cognitive complexity while neither were statistically significant predictors of overall
supervisory working alliance from the supervisee’s perspective. Also, of interest within
these results is the direction of the relationship between counselor cognitive complexity
and rapport. Participants who had higher levels of counseling cognitive complexity
tended to have lower levels of rapport. These findings are in line with Stoltenberg’s et
al., (1998) IDM model and Bahrick et al., (1991) research on role induction in
supervision.
As explored in chapter two, within the IDM, students who begin to develop more
complex schemas of their clients they will being to pull away from their supervisors as
they feel that their supervisor does not understand the client as well as they do. Pulling
away results in a weakening of the supervisory working alliance (Stoltenberg et al.,
1998). Bahrick et al., (1991) found that supervisee’s who went through role induction
training did not form significantly better rapport with their supervisors than students who
73
had not gone through role induction. Bahrick et al. (1991) results indicate that a
supervisee’s understanding of supervision does not equate to a better rapport within
supervision. Theoretically, these results may indicate that the person who is in the
position of more power may have a larger influence over the rapport within the
relationship. This may be why counselors who have high levels of cognitive complexity
are able to form stronger therapeutic relationships with clients (Granello, 2010) and
supervisees with higher levels of complexity are unable to create a similar strength in
rapport.
For counselor educators and supervisors this implies that a supervisee may
understand the need for supervision and even the value of supervision and still not build a
strong rapport initially with their supervisor. While time in program was once again a
significant contributor to the model, indicating that overtime students will begin to
develop stronger rapport with their supervisors. This also falls in line with the IDM
model, as student move past that initial pulling away from their supervisor they then
begin to grow more comfortable with their own work and are able to use their supervisor
as a resource for improving their work (Stoltenberg et al., 1998). However, with only
14% of the variance in rapport accounted for by time in program there are still many
factors which are as yet unknown which contribute to the rapport process in supervision.
Discussion of Research Question 2c
Examining the supervisory working alliance on the subscale level: Client Focus
Semester in school was significantly correlated to the client focus subscale on the
SWAI-T. While counseling cognitive complexity and supervision cognitive complexity
74
had non-significant correlations. Unlike the other regression models within this study
supervision cognitive complexity provided the only significant contribution to variance
accounted for client focus within the model. These results indicate that supervision
cognitive complexity plays an important role in having the supervisee feel that the
supervision session is focused on their client(s).
These results are also in line with Bahrick et al. (1991) study which showed the
utility of role induction in increasing supervisee conceptualization of supervision and
understanding expectations of supervision. As the purpose of supervision is to ensure the
welfare of the clients (Bernard & Goodyear, 2009) it would then stand to reason that as
supervisee grow in supervision cognitive complexity they would be able to more clearly
see client focus in supervision. It is interesting to note that this increase in client focus is
not related to the counseling cognitive complexity. A supervisee may be able to
understand their client(s) in complex ways and not grasp that same level of complexity
within supervision. Rather, a supervisee who better understands the complexity of their
supervisor is then able to see how the supervisor focuses on the client within supervision.
The relationship between supervision cognitive complexity and client focus may
also be explained through the developmental lens of the IDM. Beginning counselors are
often self-focused and cannot determine much about their client because they are too
consumed with the self in session (Stoltenberg et al., 1998). There may be a parallel
process in supervision where a supervisee is so focused on themselves that they do not
understand that the true focus of the session is on the client. As the supervisee becomes
less self-focused in supervision and are able to see their supervisor through a more
complex schema they are also able to understand that they are not the focus of the
75
supervision session but rather their client(s) is/are. However, due to the limitations of
this study this theory of supervisee development can be considered incomplete at best.
Limitations
Specific to the results presented in this study is the significant contribution of
semester in school to variance in the SWAI-T and its subscale rapport. Semester in
school was used as the control variable as it combines many factors which have been
linked to increases in cognitive complexity. These factors include: time in a clinical
settings (Granello, 2010), age (Rosenbach et al., 2000), and exposure to counseling
courses (Duys& Hedstrom, 2000). The implications of semester in school as a significant
contribution to the supervisory working alliance should be seen in light of these other
varying contribution which have been shown to influence cognitive complexity.
Other more general limitations of this study include the measures used in this
study all had limited reliability and validity scores. Another limit of this study is that it
only examines the supervisee’s perceptions of the supervisory relationship and does not
include those of the supervisor. While this approach to understand the supervisory
relationship is common in supervision literature (Bennett, 2008; Gnilka et al., 2012;
Lehrman-Waterman & Landy, 2001; Riggs & Bretz, 2006). The findings are limited as
they only capture half of the supervisory relationship. Gathering the supervisors
perspective would allow for a more complete picture of the supervisory working alliance.
Another limitation of this study is it is not longitudinal and thus does not capture
the growth of cognitive complexity over the course of supervision. Instead, the findings
are based on a cross-sectional sample which only shows a snap-shot of the impact of
76
cognitive complexity within a specific time frame. Due to the design of this study all
findings are reliant on the attitudinal and cognitive positions of the participants at the
time they participated. Their results may have been very different if taken under different
circumstances.
Finally, the SWAI-T is a self-report measure which is open to errors due selfbasis (positive or negative). Also, the SWAI-T assumes that participants to have some
level of reflective ability which has already been shown to be related to cognitive
complexity (Blocher, 1983). Thus, participants with low levels of cognitive complexity
may also have greater difficulty in capturing the actual supervisory working alliance
when compared to those with higher levels of cognitive complexity. Finally, the sample
size of the study restricts for high levels of generalizability, and thus limits the scope of
the findings.
Suggestions for Future Research
The limitations of this study provide insight for future research. More data are
needed is needed to validate the SCCQ scale. Future studies could examine the use of the
scale through multiple groups of students to provide reliability and validity scores for the
scale. As stated earlier, without sufficient reliability and validity score the SCCQ is an
incomplete measure of supervisee cognitive complexity. By comparing the SCCQ to
other measures of supervision would provide a better foundation for the SCCQ. In
particular assessing the relationship between the SCCQ and the Supervisee Levels
Questionnaire (SLQ; McNeill, Stoltenberg, & Romans, 1992) –– which measures
77
supervisee developmental level and provides an IDM level–– would better show the
relationship between developmental level and supervision specific cognitive complexity.
Of interest to the primary researcher was the non-significant relationship between
cognitive complexity and rapport. These results are consistent with Bahrick et al. (1991)
study on role induction in supervision. The combination of these two studies indicate
that the role of rapport in supervision requires greater attention. In particular Bordin’s
(1983) assumption that rapport is a required component of successful supervision has
received mixed empirical support (Bahrick et al. 1991; Gunn & Pisotle, 2012). Greater
rapport has been shown to greater levels of self-disclosure (Gunn & Pistole, 2012) but it
is not related to supervisee’s understanding of supervision (Bahrick et al., 1991). Future
research could explore with greater depth the role of rapport in supervisee development.
Examining the supervisory relationship from the perspective of the both the
supervisor and supervisee would provide a more complete picture of the relationship
between cognitive complexity and the supervisory relationship. Does the cognitive
complexity of the supervisor impact the supervisee’s cognitive complexity? As shown by
Duys and Hedstrom (2000) educational courses in counselor education have a positive
influence on student cognitive complexity. However, the source of this influence is of
yet unknown. There are multiple sources of influence within a classroom (e.g. instructor,
reading materials, fellow classmates) within individual supervision these additional
influences are removed. An examination of how the interaction between supervisor and
supervisee cognitive complexity could help isolate the role of an instructor in the
development of cognitive complexity.
78
A longitudinal approach would allow for a more complete picture of the role
cognitive complexity in supervision. Supervisees’ cognitive complexity could be
measured across multiple sessions. While supervisor’s cognitive complexity could be
examined across supervisees. Currently there is no research which examines cognitive
complexity in this manner. It has been assumed that successful and unsuccessful sessions
are generalizable. These successful and unsuccessful sessions may be outliers and thus
standout allowing participants to generate more constructs. Examining cognitive
complexity among the unremarkable sessions as well as the successful and unsuccessful
sessions would provide a more complete picture of the role of cognitive complexity in
supervision.
This longitudinal methodology could also be used exclusively on supervisees.
Tracking supervision and counseling cognitive complexity for multiple supervisory
experiences could examine cognitive complexity attrition and returned growth.
Supervisees often experience a drop-off in counseling skills when they return from school
breaks and are not actively practicing. By examining supervisees throughout their course
of study would allow for a more accurate depiction of how cognitive complexity ebbs and
flows among students and how quickly complexity can regained after a drop-off.
Moving away from quantitative analysis a qualitative analysis of common traits
developed by students could show common themes and thought patterns developed by
students at their various levels of training. Understanding these themes would allow for
counselor educators and supervisors to better anticipate the cognitive schemas of their
supervisees. This understanding could then better allow supervisors to better meet their
supervisees at their developmental level while balancing their support and challenge.
79
Finally, the focus of supervision is to help ensure competent client care.
Examining the impact of cognitive complexity at both levels (counseling and supervision)
and the supervisory working alliance on client outcomes would be the end goal of this
line of research.
Conclusion
Supervision’s importance to counselor educators and supervisors is a wellestablished and long held belief of the profession (Kaufman & Kaufman, 2006). While
there has been an increase in supervision literature in the past 50 years (Watkins, 2014a).
The present study provided some insight into the role of supervisee cognitive complexity
in the supervisory working alliance. However, due to the non-significance of the results
there is still much to learn in which factors promote a strong supervisory alliance. The
supervisory working relationship is not related to supervisee’s ability to form complex
constructs about clients and supervisors. Rather, this relationship is based on other
factors outside of the supervisee’s cognitive complexity. More research would be helpful
in understanding what factors contribute to an effective supervision session and the
impact of effective supervision on supervisee development and client welfare.
80
APPENDIX A
Demographic Questionnaire
Gender:
Male Female Transgendered Male to Female Transgendered Female to Male
Age in years:
Under 18 18-22 22-24 25-27 28-30 31-33 34-36 37-39 Over 40
Program:
Masters in School Counseling
Masters in Clinical Counseling
Ph.D in Counselor Education and Supervision
Semester in school:
1st
2nd
1st summer
3rd
4th
2nd Summer
5th
6th
3rd Summer
7th
8th
9th
4th Summer
More than 10 semesters
Clinical Supervision experience: Circle your current amount of clinical supervision, if
you are no longer receiving clinical supervision mark how many experiences you have
received
1st clinical supervision experience
2nd clinical supervision experience
3rd clinical supervision experience
4th clinical supervision experience
5th Clinical supervision experience
6 or more clinical supervision experiences
How many clinical supervisors have you had during your current program and any
previous counseling programs? _________
Supervisor experience:
Have you ever provided clinical supervision? Yes/No
If so how many semesters how you provided clinical supervision? ___________
81
APPENDIX B
Email Invitation
We invite you to participate in a research study being conducted by investigators from
The University of Iowa. The purpose of the study is to understand the relationship
between supervisee cognitive complexity and the supervisory relationship.
If you agree to participate, we would like you to fill out a demographic sheet, take three
separate surveys (two on cognitive complexity and one on the supervisory working
alliance). You are free to skip any questions that you prefer not to answer. It will take
approximately 20 to 80 minutes to complete this study.
We will not collect your name. It will not be possible to link you to your responses on
the survey.
Taking part in this research study is completely voluntary. If you do not wish to
participate in this study, you may return the research packet or exit the website at any
time. You need not answer any or all of the questions.
If you have questions about the rights of research subjects, please contact the Human
Subjects Office, 105 Hardin Library for the Health Sciences, 600 Newton Rd, The
University of Iowa, Iowa City, IA 52242-1098, (319) 335-6564, or e-mail
[email protected].
Thank you very much for your consideration of this research study.
82
APPENDIX C
CCQ
Please take up to 15 minutes to write down as many unique traits as you can think of
about a client in a successful counseling session.
Please take up to 15 minutes to write down as many unique traits as you can think of
about a client in an unsuccessful counseling session.
83
APPENDIX D
SCCQ
Please take up to 15 minutes to write down as many unique traits as you can think of
about a supervisor in a successful supervision session.
Please take up to 15 minutes to write down as many unique traits as you can think of
about a supervisor in an unsuccessful supervision session.
84
Appendix E1
Supervisory Working Alliance Inventory: Trainee Form
Instructions: Please indicate the frequency with which the behavior described in each of
the following items seems characteristic of your work with your supervisee. After each
item, check (X) the space over the number corresponding to the appropriate point of the
following seven- point scale:
1
2
Almost
Never
3
4
5
6
7
Almost
Always
I feel comfortable working with my supervisor.
1
2
3
4
5
6
7
My supervisor welcomes my explanations about the client's behavior.
1
2
3
4
5
6
7
My supervisor makes the effort to understand me.
1
2
3
4
5
6
7
My supervisor encourages me to talk about my work with clients in ways that are
comfortable for me.
1
2
3
4
5
6
7
My supervisor is tactful when commenting about my performance.
1
2
3
4
5
6
7
My supervisor encourages me to formulate my own interventions with the client.
1
2
3
4
5
6
7
My supervisor helps me talk freely in our sessions.
1
2
3
4
5
6
7
My supervisor stays in tune with me during supervision.
1
2
3
4
5
6
7
I understand client behavior and treatment technique similar to the way my supervisor
does.
1
Supervisory Working Alliance from: Efstation, J. E, Patton, M. J., & Kardash, C. M.
(1990). Measuring the working alliance in counselor supervision. Journal of Counseling
Psychology, 37, 322 -329.
85
1
2
3
4
5
6
7
I feel free to mention to my supervisor any troublesome feelings I might have about
him/her.
1
2
3
4
5
6
7
My supervisor treats me like a colleague in our supervisory sessions.
1
2
3
4
5
6
7
In supervision, I am more curious than anxious when discussing my difficulties with
clients.
1
2
3
4
5
6
7
In supervision, my supervisor places a high priority on our understanding the client's
perspective.
1
2
3
4
5
6
7
My supervisor encourages me to take time to understand what the client is saying and
doing.
1
2
3
4
5
6
7
My supervisor's style is to carefully and systematically consider the material I bring to
supervision.
1
2
3
4
5
6
7
When correcting my errors with a client, my supervisor offers alternative ways of
intervening with that client.
1
2
3
4
5
6
7
My supervisor helps me work within a specific treatment plan with my clients.
1
2
3
4
5
6
7
My supervisor helps me stay on track during our meetings.
1
2
3
4
5
6
7
I work with my supervisor on specific goals in the supervisory session.
1
2
3
4
5
6
7
86
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