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Counselor cognitive complexity: correlating and comparing the Myers-Briggs type indicator with the role category questionnaire.

This study examined personality types and cognitive complexity levels in counseling trainees. Data from graduate-level counseling students were collected (N = 74). Cognitive complexity levels were measured using the Role Category Questionnaire (RCQ) and personality types were measured using the Myers-Briggs Type Indicator. Results showed that perceiving types tended to perform better on the RCQ than judging types, and that those who were highly differentiated in all preferences overall tended to score higher on the RCQ. These findings have implications for employment and career counselors and their clients who are, or aspire to be, in the helping profession.

Keywords: counselor cognitive complexity, Myers-Briggs Type Indicator, Role Category Questionnaire, counseling students


Career and employment counselors use the Myers-Briggs Type Indicator (MBTI; Briggs & Briggs Myers, 1998) to help clients better understand their preferences and strengths (Kennedy & Kennedy, 2004). The MBTI gauges an individual's "preferences with regard to perception and judgment, according to the concepts of C. G. Jung" (Goodyear, 1989, p. 435). Jung believed that people interact with the world by taking in information (perceiving) and then by organizing that information (judging). When perceiving, individuals may naturally prefer to use their senses or their intuition. When judging, individuals may prefer to think or to feel. As an added dimension, individuals may prefer to engage in the inner world (introversion) or the outer world (extraversion) when perceiving and judging (Myers & Kirby, 1994).

Accordingly, the MBTI is based on four dichotomous scales. The Extraversion-Introversion (EI) scale is an indicator of whether a person prefers the inner world (concepts and ideas) or outer world (people and objects) when judging and perceiving (Briggs & Briggs Myers, 1998; Goodyear, 1989), for example, whether one prefers to discuss career exploration with others or prefers to engage in self-reflection when considering career choices (Hammer, 1993). The Thinking--Feeling (TF) scale assesses whether one prefers to make judgments based on thoughts (engaging in a fact-based analysis) or on value-based feelings (Briggs & Briggs Myers, 1998; Goodyear, 1989), for example, whether one prefers to engage in an analysis of career choices or to make choices based on personal values and the effects on significant others (Hammer, 1993). The Sensing-Intuition (SN) scale gauges whether an individual would rather perceive the world through their senses (acquiring hard facts) or by using their intuition (Briggs & Briggs Myers, 1998; Goodyear, 1989), for example, whether one prefers to consider concrete aspects of a job (e.g., salary, benefits, hours) or career potentials (e.g., promotion, future career prospects) when engaging in career decisions (Hammer, 1993). Whereas the TF scale concerns how the individual would like to make judgments and the SN scale concerns how a person would like to make perceptions, the Judgment--Perception (JP) scale gauges whether an individual prefers to judge (by engaging in planning and organizing) or perceive (by being flexible and open) when interacting with the world (Briggs & Briggs Myers, 1998; Goodyear, 1989), for example, whether one prefers to focus on achieving specific, measurable career goals, or on starting on a career path where the future can lead to unknown possibilities (Hammer, 1993). All four preferences are combined into an MBTI type (Schaubhut, Herk, & Thompson, 2009). For example, people who have an ESTP type (i.e., extraverted sensing thinking perceiving) prefer to interact with the outer world by using their senses and to use their thoughts when making decisions.

MBTI preferences are just that--preferences; they are not absolutes (Kennedy & Kennedy, 2004). An individual can have preferences ranging from slight to very clear (Briggs & Briggs Myers, 1998). For example, one can be very clearly introverted (i.e., almost exclusively preferring to engage the inner world), moderately introverted (i.e., generally preferring the inner world, but also at times seeking to engage with others), or slightly introverted (i.e., often seeking to engage others, but being somewhat more inclined toward the inner world). Therefore, it is important to know the level of differentiation for each preference because this will dictate how consistently a person follows his or her natural preferences (Briggs & Briggs Myers, 1998).

Clients of career counselors include those who themselves are counselors, as well as counseling students who are just beginning their career development. The career development and exploration of such clients may include the assessment and development of those specific cognitive abilities that are conducive to the effective practice of counseling. One cognitive ability critical to those in the counseling profession is cognitive complexity (Duys & Hedstrom, 2000).


Granello (2010) stated that cognitive complexity "is the ability to absorb, integrate, and make use of multiple perspectives" (p. 92). Granello elaborated on this definition by stating that cognitively complex individuals are reflective, analytical, interrogative, tolerant, investigative, and accommodating. Welfare and Borders (2010) specifically defined counselor cognitive complexity as the ability of counselors to differentiate and integrate--in other words, to differentiate among the various characteristics of a client and the ability to integrate those various characteristics to better understand the client and the client's underlying issues.

Duys and Hedstrom (2000) stated that cognitive complexity has been defined "as the degree of cognitive differentiation or the number of interpersonal constructs a person can use to define social reality" (p.11). This definition has also been used by Little, Packman, Smaby, and Maddux (2005). Duys and Hedstrom further stated that interpersonal constructs have been defined "as templates that are used to describe life experiences" and that "highly developed systems of interpersonal constructs consist of many integrated elements that focus on enduring psychological, motivational, and dispositional qualities in others" (p. 11).

For example, an individual who has low cognitive complexity might consider client issues in fairly concrete and categorical terms. These issues are more likely to be identified quickly, with less tolerance for ambiguity or conflicting information. A counselor operating at a more complex level is likely to consider a wider range of related concerns, the presenting problem's context, developmental dynamics, or systemic variables influencing the presentation and interpretation of the client's concerns. Thus, as the level of cognitive complexity increases, the ability to see different points of view, to process and manage information, and to formulate clinical hypotheses is enhanced (Duys & Hedstrom, 2000).

A high level of cognitive complexity also facilitates the review of research and the writing of literature reviews. Cognitively complex individuals are able to organize and integrate the literature according to themes, then analyze and synthesize such themes, especially noting implications and limitations in the literature, and finally to propose areas for future research (Granello, 2001). Thus, counseling students benefit from developing higher levels of cognitive complexity throughout their program when they engage in research and writing. Furthermore, as consumers of research, counseling students who have developed cognitive complexity are more proficient in critiquing journal articles (Granello, 2001).

There have been several suggestions for increasing the cognitive complexity of counselors and counselors-in-training. For example, Granello (2001) focused on using Bloom's taxonomy as a means of developing the cognitive

complexity of students when they engage in academic writing. Granello (2000) also suggested using Bloom's taxonomy in supervisory relationships. Choate and Granello (2006) proposed reconceptualizing the role of the faculty advisor, who can be in a position to promote the cognitive development of his or her advisees. Such suggestions can also be used by career and employment counselors when counseling their own clients.


Case conceptualization is an inherent aspect of counseling (Falvey, Bray, & Hebert, 2005), which "involves the integration of cognitive, behavioral, emotional, and interpersonal aspects of the client" (Loganbill & Stoltenberg, 1983, p. 235). In other words, case conceptualization requires cognitive complexity. Given that professional counselors and counseling students will vary in their information processing styles (i.e., MBTI preferences), an issue arises as to whether they can sufficiently screen and attend to these aspects of their clients. For example, given particular preferences for intuition or sensing, or judging or perceiving the world, counselors may score differently on cognitive complexity instruments. Thus, there is a need to ascertain whether these processing styles affect cognitive complexity as assessed through cognitive complexity instruments.

To determine whether processing styles affect cognitive complexity, we correlated and compared results from the MBTI Form M (Briggs & Briggs Myers, 1998) with cognitive complexity scores obtained from the Role Category Questionnaire (RCQ; Burleson & Waltman, 1988). Specifically, we wished to determine whether any of the four MBTI preferences predicted RCQ scores. Furthermore, we wished to examine whether the magnitude of differentiation for all four MBTI dichotomies influenced RCQ scores (in other words, whether those who strongly prefer all four of their preferences do better on the RCQ than those who only have mild preferences).


We used a single-test, correlational research design. The university institutional review board gave approval to engage in the research. Program coordinators and instructors from three programs approved by the Council for Accreditation of Counseling and Related Educational Programs (CACREP) were contacted, and they gave us approval to present the study to their students. Potential subjects were provided with a consent information sheet and were informed of the nature of the study through a presentation given to the class by the first researcher. Participation was voluntary. Those who wished to participate were given a packet containing a basic questionnaire, the RCQ, and the MBTI Form M. Other than age and gender, no identifying information was obtained.


The total sample for this study consisted of 74 master's-level counseling students from CACREP-approved programs at three universities in the Midwest. Twelve students identified as men, with the remaining identifying as women. The average age was 29.66 (SD = 8.28), with the youngest participant being 21 and the oldest being 55. The average number of counseling course hours completed was 26.61 (SD = 17.25), where the minimum number of hours was 0 and the maximum was 60. The average number of years of supervised counseling experience was 0.86 (SD = 1.46), where the minimum number of years was 0 and the maximum was 6. Nineteen students reported never having taken a microskills course, one student did not answer, and the remaining 54 students had taken (he class. Fifty-seven students reported having previously taken some form of the MBTI (five of whom had taken it more than once). Five students reported having previously taken the RCQ.


RCQ. For purposes of this study, we used the RCQ to assess cognitive complexity in counselors-in-training. The RCQ has a history of use in assessing the cognitive complexity of those in the helping profession and has been found to be sensitive enough to detect changes in cognitive complexity (e.g., Duys & Hedstrom, 2000; Little et al., 2005). With the RCQ, as used in counselor cognitive complexity research, participants answer two open-ended questions about a peer that the counselor likes and a peer that the counselor dislikes. Following standard RCQ instructions (Burleson & Waltman, 1988), participants have 5 minutes to write their answers for each peer and are instructed to provide descriptions of "personal characteristics, mannerisms, and habits" of the peer (Little et al., 2005, p. 193). In particular, the participant is to describe the peer as completely as possible, "so that a stranger might be able to determine the kind of person he/she is" (Burleson & Waltman, 1988, p. 24). Arguably, if a participant is to describe a liked or disliked peer to another, such a description would show why the peer was or was not liked. These descriptions are then manually scored according to the number of qualities described. The greater the number of articulated qualities and descriptions provided, the higher the RCQ score.

O'Keefe, Shepherd, and Streeter (1982) reported a test-retest reliability of the RCQ at .84 and .86, given a 4-week interval between tests. Validity studies have indicated that the higher the RCQ score, the higher the level of cognitive complexity (Duys & Hedstrom, 2000). For example, Meyer (1996) found support for the claim that increasing levels of conceptual psychological knowledge are associated with higher RCQ scores. Furthermore, it has been found that RCQ scores "are not confounded with measures of cognitive performance" (Allen, Mabry, & Preiss, 1997, p. 129).

MBTI Form M. The MBTI Form M was used to assess participants' personality-type preferences. The instrument comprises 93 forced-choice items and takes approximately 15-25 minutes to complete. The 93 items are divided among the four dichotomies as follows: 21 EI, 26 SN, 24 TF, and 22 JP. A particular preference is assigned to the one with more points. For example, if 11 choices correspond with an introversion preference and 10 for extraversion, then the participant is assigned an introversion preference. In the event of a tie, an introversion, intuition, feeling, or perceiving preference is assigned (Briggs & Briggs Myers, 1998).

Test-retest reliability coefficients ranged from .53 to .93. Internal consistencies of MBTI dichotomies ranged from .80 to .92. (Schaubhut et al., 2009). The MBTI has also shown correlations with other personality measures, such as the Strong Interest Inventory (Schaubhut et al., 2009).


Data were collected in April, July, and August 2011. Participants completed a short questionnaire, which sought the following information: age, gender, whether a microskills class was taken, number of counseling course hours completed, years of supervised experience, and whether the participant had ever taken the RCQ or the MBTI. Participants then turned to the RCQ. The standard RCQ protocol was followed (Burleson & Waltman, 1988). The first researcher read the first portion of the RCQ and students had 5 minutes to complete it, with a 1-minute warning provided. The first researcher then read the second portion of the RCQ and participants had 5 minutes to complete the second portion, with a 1-minute warning provided. Participants then completed the MBTI Form M, which was not timed. The first researcher rated the MBTI, and the second researcher separately scored the RCQ.


Table 1 displays the means, standard deviations, and sample sizes for each of the eight MBTI preferences, as well as RCQ correlations for each type. In addition, a Pearson correlation of RCQ scores and course hours taken was calculated (r = .267, p = .021). Out of the four dyads, only the judging-perceiving scores were significantly correlated when compared with RCQ results ([r.sup.2] = .07). From these findings, one can state that those who prefer perceiving tend to perform better on the RCQ than those who prefer judging.

Differentiation levels among the individual MBTI dichotomies did not yield additional statistically significant differences when compared with RCQ scores. However, when the cumulative differentiation score was calculated, a Pearson correlation of r = .309 (p = .007) resulted. This cumulative differentiation was calculated by summing the absolute values of the differences between each pair. In other words, the stronger the preferences in each of the four scales, the higher the RCQ score (e.g., those who had strong preferences for, say, extraversion, sensing, feeling, and judging, would tend to have higher RCQ scores than those who had mild preferences in each of the four dichotomies.


The results explore the relationships between counselors' cognitive complexity and information processing styles. We were motivated to explore these variables because it is poorly understood how the selective filtering of MBTI styles affect the consideration of information and cognitive complexity (RCQ scores).

The analysis showed that there was a statistically significant predictive model considering judging, perceiving, and RCQ scores, wherein those who prefer perceiving tend to have better scores on the RCQ. We speculate that this may be because of the inherent tendency of those who prefer perceiving to take in more information, thus enabling them to differentiate and articulate such information on cognitive complexity assessments.

Also, as previously stated, higher overall information processing differentiation is correlated with stronger RCQ scores. This may be because those who are highly differentiated in their information processing and management styles capitalize on their strengths when engaging in cognitively complex tasks, whereas those with low differentiation are not proficient enough with a single trait to use it to the greatest advantage.

Implications for Career and Employment Counselors

Given that those who prefer perceiving tend to perform better on the RCQ than those who prefer judging, employment and career counselors working with aspiring helping professionals should consider empowering those who prefer perceiving to utilize their cognitive complexity strengths, while helping those who prefer judging to work on developing their cognitive complexity. For example, career and employment counselors can help clients: (a) to consider others from multiple perspectives (e.g., the client's perspective and then the other person's perspective); (b) to identify the psychological, motivational, and dispositional qualities of others, and then to integrate those qualities into a unified understanding of the person; and (c) to understand and move through the stages of Bloom's taxonomy (i.e., knowledge, comprehension, application, analysis, synthesis, and evaluation) and to write papers and read journal articles with an eye toward mastery at each taxonomic level. Also, per Granello (2000), employers and supervisors may consider working with their supervisees to become more cognitively complex through helping them progress through the stages of Bloom's taxonomy (e.g., when dealing with difficult clients, or when working through countertransference issues).

Furthermore, as those who are highly differentiated tend to do better on the RCQ, employment and career counselors should work with clients on further developing their primary traits. For example, if the individual has a mild preference for sensing, career and employment counselors can consider advising that the individual intentionally integrate sensory data to a stronger degree when obtaining information. For example, master's-level counseling students who prefer sensing could focus on observing body posture, facial expressions, verbal intonation, and the like to enhance their case conceptualization.

Future Research

More research in this area needs to be undertaken. A replication study with a larger sample size might detect additional differences. Additional combinations of MBTI codes could be considered together, as observed in MBTI temperament studies. For example, two-preference combinations, such as comparing those who prefer extraverted sensing with those who prefer introverted intuition, may demonstrate even more marked differences in cognitive complexity scores.

Additional information about the nature of training conditions (not merely completed course hours) would be helpful to know when considering these comparisons. Advanced practitioners with additional years of experience could also be studied to explore how experience over time affects the development of cognitive complexity and the use of different information-processing styles. Replication studies could also substitute the RCQ with cognitive development (construct organization) measures to determine if the relationships between processing styles are different, and perhaps inverse. For example, it may be that perceivers, as compared with judgers, are more adept at cognitive differentiation; however, the obverse may be true in that judgers may be better at cognitive integration and organization. A future study comparing cognitive organization with MBTI results may show such a relationship.

We suggest that additional studies be undertaken to compare personality preferences with cognitive complexity measures--possibly the RCQ--and to continue efforts to develop counseling context-specific measures to track counselor cognitive complexity.

Differences in speed of information processing (SIP) between those who prefer introversion and those who prefer extraversion (e.g., Socan & Bucik, 1998) may have an effect on time-limited cognitive complexity assessments, such as the RCQ. Given that we did not find a difference in RCQ scores between those who prefer introversion and those who prefer extraversion, the issue of potential SIP influences is not relevant to this study. However, an area for future research may be to take into account this additional variable when engaging in similar research.


Several limitations are noted. The participants were divided into two groups for each MBTI dichotomy. For example, out of the 74 participants, 60 were those who prefer thinking, but only 14 were those who prefer feeling. Thus, there may not have been enough power to detect a statistically significant difference in RCQ scores on certain dyad comparisons because of the smaller subgroups. Furthermore, given the number of correlations performed with the RCQ, the study would have benefited from a larger sample size. However, although a larger sample size would have increased the power of the study to detect differences, the actual sample size of 74, while small, was still sufficient to detect a statistically significant difference in RCQ scores. Nevertheless, if future studies are undertaken, a larger sample size may detect greater differences.

Second, there are some limitations using the RCQ to assess the cognitive complexity of counseling students. It is a general social differentiation measure comparing liked and disliked peers rather than client impressions. It may be that other cognitive complexity assessments that are being developed and tested, such as the Counselor Cognitions Questionnaire (Welfare & Borders, 2010), would show stronger differences in RCQ scores among the various MBTI types (as norms emerge and reliability studies are conducted for the instrument).

Third, the difference in RCQ scores among those who prefer judging and those who prefer perceiving may not be due to differences in cognitive ability, but rather differences in the ability to do well on the RCQ. It is possible that, overall, those who prefer judging have just as much capability to be cognitively complex as those who prefer perceiving in construct organization; however, those who prefer perceiving are better able to articulate more information about their experiences and differentiate more quickly. If this is the case, however, then career and employment counselors may be advised to assist those whose preference is judging in identifying and articulating the information they have about a client.

Fourth, this study was conducted among three different universities over the course of two semesters. A delay in time could have affected the results, as well as led to a cohort effect.

Fifth, there may be extraneous variables that could account for the difference. For example, another personality preference, not accounted for by the MBTI, could have caused the difference in RCQ scores. Performance anxiety (especially because of the time limits) also could have affected the results. Also, given that this was not a true experimental design, we cannot say that there is a cause-and-effect relationship between perceiving/judging and RCQ score differences.

Finally, because this study focused on master's-level counseling students, the findings herein may not necessarily be indicative of how practicing professional counselors may process information given their MBTI preferences.


We correlated the MBTI personality preference types with RCQ scores to determine if there was a relationship between cognitive complexity scores and the four personality dyads. The results showed that there is a positive relationship between perceiving types and RCQ scores (and, thus, a negative relationship between judging types and RCQ scores). Whereas those clients who prefer a perceiving style may naturally do better at cognitive differentiation, those who prefer judging should be encouraged to especially focus on client information (e.g., personal characteristics and behaviors).

Furthermore, as the data indicate that there is a relationship between the strength of overall personality differentiation and cognitive differentiation, employment and career counselors should help their clients to develop their preferred styles of information processing. For example, those who prefer sensing should be encouraged to enhance their intake of facts and supplemental data, whereas those who prefer intuition should focus on being even more pattern sensitive when considering client characteristics.

Although the above recommendations are based on the results and analyses gleaned from this study, further research on cognitive complexity and information processing preferences will yield more suggestions and implications for employment and career counselors. We encourage researchers to examine how cognitive complexity affects the interpretation of employee issues, counseling interventions, and assessment strategies to help clients deal with balancing life and work stressors.

DOI: 10.1002/joec.12006

Received 10/13/13

Revised 02/28/14

Accepted 03/02/14


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George J. Rashid and David K. Duys, Department of Rehabilitation and Counselor Education, The University of Iowa. This research was funded by a grant from the North Central Association for Counselor Education and Supervision. Correspondence concerning this article should be addressed to George J. Rashid, Department of Rehabilitation and Counselor Education, The University of Iowa, N338 Lindquist Center, Iowa City, IA 52240 (e-mail:
Role Category Questionnaire (RCQ) Scores Correlated With
Myers-Briggs Type Indicator (MBTI) Preferences


MBTI Preference     N        M       SD        r         p

Extraversion       45    23.64     8.37     .152    .197
Introversion       29    20.31     8.66    -.156    .184
Sensing            27    24.22     9.10     .110    .350
Intuition          47    21.26     8.18    -.108    .360
Thinking           14    22.71    11.39    -.094    .426
Feeling            60    22.25     7.91     .094    .426
Judging            47    21.40     5.87    -.270    .020 *
Perceiving         27    23.96    11.90     .268    .021 *

* p < .05.
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Author:Rashid, George J.; Duys, David K.
Publication:Journal of Employment Counseling
Article Type:Report
Date:Jun 1, 2015
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