Profiling the Personality Traits of University Undergraduate and Postgraduate Students at a Research University in Malaysia

See Ching Mey, Melissa Ng Lee Yen Abdullah, Chuah Joe Yin

Research universities in Malaysia are striving to transform into world-class institutions. These universities have the capacity to attract the best students to achieve excellence in education and research. It is important to monitor the psychological well-being of students during the transformation process so that proactive intervention can help students cope with the learning and research demands. This study profiled and monitored the personality traits of postgraduate and undergraduate students in a selected Malaysian research university using a quantitative research method. The researchers profiled personality traits using an online assessment, the Behavioral Management Information System (BeMIS), and tracked real and preferred personality traits and positive changes during rapid institutional transition.


Keywords: personality traits, BeMIS, undergraduate students, research universities, psychological well-being



Malaysia is advancing toward a knowledge-based economy and relies heavily on its universities to educate and train the much-needed human capital for the country (Fernandez, 2010). Research universities have the capacity to attract the best students and have the autonomy to select students who excel in education and research. Various measures are being implemented to transform universities into world-class institutions (Wan, 2008). The institutional transformation at Malaysian universities focuses on critical areas such as governance, leadership, academia, teaching and learning, as well as research and development (Ministry of Higher Education, 2011). Educational institutions must monitor the psychological profile and well-being of their students, especially those who are potentially at risk of mental health issues such as anxiety and depression, as well as substance abuse, in order to promote optimum human capital development (Wynaden, Wichmann, & Murray, 2013). Moreover, during the institutional transformation process, all levels of the university community, including students, may experience changes driven by higher standards and demands in teaching and learning as well as research performance (Schraeder, Swamidass, & Morrison, 2006) that might result in stress (Becker et al., 2004; Gladstone & Reynolds, 1997; Smollan & Sayers, 2009). Certain personality traits may build the community’s resilience in coping with psychological stress (Lievens, Ones, & Dilchert, 2009; Nelson, Cooper, & Jackson, 1995). A detailed personality profile of university students can help research institutions put in place necessary support systems to strengthen students’ well-being during institutional transformation.


The Impact of Institutional Transformation


Institutional transformation at research universities in Malaysia can result in stress, anxiety and uncertainty for students at both undergraduate and postgraduate levels. Successful coping with new demands is integral to the process of transformation. Failure to cope with stressors may lead to fatigue and depressive mood. Such physical and psychological symptoms may impair daily living, work and school performance, and learning ability (Goretti, Portaccio, Zipoli, Razzolini, & Amato, 2010; See, Abdullah, Teoh, & Yaacob, 2011). Organizational change may affect personality changes in students and impact academic performance (Horng, Hu, Hong, & Lin, 2011; Nelson et al., 1995; Oreg & Sverdlik, 2011; See et al., 2011). Ongoing research including profiling and monitoring the personality traits and psychosocial behavior of students can assist students in adapting successfully (Marshall, 2010). Counselors and psychologists at the university can help students develop positive coping strategies during stressful transitional periods.


Personality Characteristics


Connor-Smith and Flachsbart (2007) have defined personality as characteristic patterns of thoughts, feelings and behaviors over time and across situations. Some theorists have described coping as a process of the personality responding to stress (Connor-Smith & Flachsbart, 2007). For example, individuals with the personality trait of extraversion may seek social support during life crisis, while someone with the trait of neuroticism may respond with avoidance or denial. Thus, personality traits may influence university students’ responses and coping skills in stressful situations. Individuals with an extraverted personality tend toward optimistic assessment of accessible coping resources and react less intensely to stress, while those with a neurotic personality often experience high rates of stress and intense emotional and physiological reactivity to stress (Connor-Smith & Flachsbart, 2007). Personality predispositions can predict an individual’s ability to adapt to change. Resilient traits enable stress management in reaction to institutional transformation (Nelson et al., 1995; Oreg, Vakola, & Armenakis, 2011; See et al., 2011). The goal of this study was to analyze the personality profile of undergraduate and postgraduate students at a research university in Malaysia during institutional transformation, and to propose proactive interventions to help the student community cope with change.


Overview of the Study


The selected research university in this study was awarded the status of Accelerated Program for Excellence (APEX) in 2008, making it the first and only APEX university in Malaysia. APEX is a fast-track development program that aims to enable a selected university to transform and seek world-class status (Razak, 2009). The APEX program has been identified as a critical initiative to increase the level of excellence of higher education in Malaysia (Razak, 2009). An APEX university has the autonomy to select students based on academic merit and other criteria that the university deems essential. For this study, the researchers randomly selected from among postgraduate and undergraduate students who had volunteered to participate, and used the Behavioral Management Information System (BeMIS) to investigate the students’ personality profile and well-being. The research objectives included the following: (a) profile the real and preferred personality traits of the university students during institutional transformation, and (b) explore personality changes over different phases during the university’s transitional period.


Participants and Design

This longitudinal study gathered data relating to personality traits and psychosocial behaviors of postgraduate and undergraduate students over three phases. Seventy-eight students (34 undergraduate students and 44 postgraduate students) participated in phase 1; 142 students (80 undergraduate students and 62 postgraduate students) participated in phase 2; and 169 students (72 undergraduate students and 97 postgraduate students) participated in phase 3.



The BeMIS is an online assessment and reporting tool used to measure personality. The BeMIS was developed using the Adjective Check List (ACL), a standardized personality trait measure comprised of 300 adjectives commonly used to describe personality traits (Gough & Heilbrun, 1983). The ACL is capable of effectively measuring 37 personality traits under five main categories of traits: (a) responsiveness, (b) psychological needs, (c) specific responses, (d) interpersonal behavior and (e) cognitive orientation (Gough & Heilbrun, n.d.; Center for Credentialing and Education, 2009). The 37 personality traits are enthusiasm, optimism, negativity, communality, achievement, dominance, endurance, order, exhibition, psychologically perceptive, nurturance, affiliation, social energy, autonomy, aggression, change, support seeking, self-blaming, deference, counseling readiness, self-control, self-confidence, personal adjustment, self-satisfaction, creativity, structure valuing, masculinity, femininity, fault finding, respectful, work centered, playful, security seeking, affected, intellectualistic, pragmatic and scientific. The behavior for each scale is presented in percentile ranks, and grouped into real and preferred personality traits. The real-self personality traits are the existing traits, and the preferred-self traits are the desired traits. The mean for each measured behavior is 50, with a standard deviation of 10. On average, scores range between 40 and 60. A score of 60 is considered high and indicates a strong expression of the trait. A score of less than 40 is considered low and suggests suppression of the trait. Any extreme score (exceeding 70 or less than 30) may reveal stress and dissatisfaction with life (Gough & Heilbrun, n.d.). The BeMIS was translated into Bahasa Malaysia and the reliability of the Bahasa Malaysia version was tested (See et al., 2011). The reliability and validity of the BeMIS and ACL have been adequately substantiated (See et al., 2011; Center for Credentialing and Education, 2009; Gough & Heilbrun, n.d.).



The researchers conducted the first phase of the study 1 year after the start of the university transformation process. They carried out phase 2 of the study 18 months after the university embarked on the transformation agenda, and carried out the third phase two and a half years after the start of the transformation process. The researchers sent questionnaires to all 26 schools in the university, requesting for each school to randomly select five postgraduate students and five undergraduate students to participate in the study. Participants were required to respond to BeMIS twice during each phase. The first time participants chose adjectives that they thought described them as they really were, while the second time they chose adjectives that they would prefer to describe them. In addition to the questionnaire, participants received a participant information and consent form that served to protect the confidentiality of student information.




Postgraduate Students’ Personality Profile

Figure 1 shows the real-self and the preferred-self traits of the postgraduate students in phase 1 of the study. The postgraduate students did not indicate any extreme low scores (less than 30) or extreme high scores (more than 70) during this phase of the study. The researchers performed a t test, and found significant differences (p < 0.05) in 17 of the 37 traits between the real self and the preferred self of postgraduate students. Among these 17 traits, four traits were significantly higher in the real self, compared to the preferred self: negativity, support seeking, self-blaming and security seeking. In contrast, 13 traits were significantly higher in the preferred self than the real self (optimism, achievement, dominance, endurance, order, affiliation, self-confidence, personal adjustment, self-satisfaction, structure valuing, masculinity, respectful and work centered), indicating that the postgraduate students desired to be stronger in these traits.

Figure 1. Postgraduate students’ personality traits (real/preferred) in phase 1.* p < 0.05. ** p < 0.01.




The real-self and the preferred-self traits of the postgraduate students in phase 2 of the study appear in Figure 2. The postgraduate students did not indicate any extreme low scores (less than 30) or extreme high scores (more than 70) during this phase of the study. The researchers found 24 traits to be significantly different (p < 0.05) between the real self and the preferred self of postgraduate students. Among the 24 traits, the researchers found five traits to be significantly higher in the real self than the preferred self: negativity, support seeking, self-blaming, security seeking and intellectualistic. The researchers found 19 of the 24 traits to be significantly higher in the preferred self than the real self (optimistic, achievement, dominance, endurance, order, psychologically perceptive, affiliation, exhibition, self-confidence, personal adjustment, self-satisfaction, creativity, structure valuing, masculinity, respectful, work centered, playful, affected, and scientific), indicating that the postgraduate students desired to be stronger in these traits.


Figure 2. Postgraduate students’ personality traits (real/preferred) in phase 2. * p < 0.05. ** p < 0.01.



In phase 3, as revealed in Figure 3, the institutional transformation produced strong preferred-self traits (scores of more than 60), including optimism, self-satisfaction, creativity, playful, self-confidence and dominance. The postgraduate students indicated scores below 40 for two preferred-self traits—support seeking and security seeking—indicating a suppression of the traits. The postgraduate students did not indicate any extreme low scores (less than 30) or extreme high scores (more than 70) for either the real-self or the preferred-self traits in phase 3. The incongruence between the real-self and the preferred-self traits was most exaggerated in phase 3 (Figure 3), in which the researchers found 25 traits to be significantly different (p < 0.05). The five traits found to be significantly higher in the real self were the same as in phase 2 (negativity, support seeking, self-blaming, security seeking and intellectualistic). The 20 traits found to be significantly higher in the preferred self were similar to the ones in phase 2 (optimistic, achievement, dominance, endurance, order, psychologically perceptive, affiliation, exhibition, self-confidence, personal adjustment, self-satisfaction, creativity, structure valuing, masculinity, respectful, work centered, playful, affected and scientific), with the addition of the nurturance trait.


Figure 3. Postgraduate students’ personality traits (real/preferred) in phase 3. * p < 0.05. ** p < 0.01.


Undergraduate Students’ Personality Profile

The real-self and the preferred-self traits of undergraduate students in phase 1 of the study appear in Figure 4. The undergraduate students did not indicate any extreme low scores (less than 30) or extreme high scores (more than 70) during the first phase of the study. The researchers performed a t test on the real-self and the preferred-self traits of the undergraduate students and found significant differences (p < 0.05). In phase 1, 26 traits of the real self and the preferred self of the undergraduate students had significant differences. Six traits—negativity, support seeking, self-blaming, fault finding, security seeking and intellectualistic—were found to be significantly higher in the real self compared to the preferred self. The other 20 traits were significantly higher in the preferred self than the real self, indicating that the undergraduate students desired to be stronger in the following 20 traits: optimistic, achievement, dominance, endurance, order, psychologically perceptive, nurturance, affiliation, social energy, aggression, self-confidence, personal adjustment, self-satisfaction, creativity, structure valuing, masculinity, respectful, work centered, playful and scientific.

Figure 4. Undergraduate students’ personality traits (real/preferred) in phase 1. * p < 0.05. ** p < 0.01.



Figure 5 shows the real-self and the preferred-self personality traits of the undergraduate students in phase 2. The undergraduate students did not indicate any extreme low scores (less than 30) or extreme high scores (more than 70) in phase 2. In this phase, the researchers found 27 traits to be significantly different (p < 0.05) between the real self and the preferred self. Five of the 27 traits (negativity, support seeking, self-blaming, security seeking and intellectualistic) were found to be significantly higher in the real self than the preferred self. The following 22 of the 27 traits were found to be significantly higher in the preferred self than the real self: optimistic, achievement, dominance, endurance, order, psychologically perceptive, nurturance, affiliation, social energy, exhibition, self-confidence, personal adjustment, self-satisfaction, creativity, structure valuing, masculinity, respectful, work centered, playful, affected, pragmatic and scientific.

Figure 5. Undergraduate students’ personality traits (real/preferred) in phase 2. * p < 0.05. ** p < 0.01.



As found in the real self and the preferred self of the postgraduate students, the incongruence in personality traits of the undergraduate students was most obvious in phase 3. Figure 6 exhibits eight strong preferred-self traits (scores of more than 60) including optimism, achievement, dominance, self-confidence, self-satisfaction, creativity, work centered and playful. In contrast, the undergraduate students indicated scores below 40 for two preferred-self traits—support seeking and security seeking—indicating a suppression of the traits. The undergraduate students did not indicate any extreme low scores (less than 30) or extreme high scores (more than 70) in either the real-self or the preferred-self traits in phase 3. The researchers found 26 traits to be significantly different (p < 0.05). The five traits that were found to be significantly higher in the real self were the same as in phase 2 (negativity, support seeking, self-blaming, security seeking and intellectualistic). Twenty-one traits were found to be significantly higher in the preferred self: optimistic, achievement, dominance, endurance, order, psychologically perceptive, nurturance, affiliation, social energy, exhibition, self-confidence, personal adjustment, self-satisfaction, creativity, structure valuing, masculinity, respectful, work centered, playful, affected and scientific.


Figure 6. Undergraduate students’ personality traits (real/preferred) in phase 3. * p < 0.05. ** p < 0.01.


Personality Changes over Phases of the Transitional Period

     Real-self personality traits. Postgraduate and undergraduate students did not exhibit extreme real-self personality traits (scores of less than 30 or more than 70) throughout the process of the university’s transformation. The researchers performed nonparametric tests to examine changes within the real-self traits of the postgraduate and undergraduate students throughout the three phases of the study (see Figures 8 and 9). As shown in Figures 7 and 8, the researchers found more significant changes within the real-self traits of the postgraduate students compared to those of the undergraduate students. Sixteen real-self traits of the postgraduate students experienced significant changes over the three phases. Among the 16 real-self traits, eight traits (optimism, dominance, social energy, exhibition, self-confidence, structure valuing, masculinity and work centered) increased significantly over the three phases, while two traits (support seeking and self-blaming) decreased significantly over the three phases. Five traits decreased in phase 2, but increased significantly again in phase 3: enthusiasm, change, personal adjustment, creativity and playful. The negativity trait increased during phase 2 but decreased in phase 3. Despite the significant fluctuation of the postgraduate students’ traits, in general, positive traits increased while negative traits decreased. Conversely, the real-self traits of undergraduate students appeared more stable compared to the real-self traits of the postgraduate students (Figure 8). Four real-self traits of undergraduate students experienced significant changes: nurturance, affiliation, playful and intellectualistic. The nurturance and affiliation traits increased significantly over the three phases, whereas the playful and intellectualistic traits decreased significantly during phase 2, but increased again in phase 3.

Figure 7. Changes in postgraduate students’ real-self personality traits across phases 1, 2 and 3.* p < 0.05. ** p < 0.01.

Figure 8. Changes in undergraduate students’ real-self personality traits across phases 1, 2 and 3.* p < 0.05. ** p < 0.01.



     Preferred-self personality traits. As seen in Figures 9 and 10, the preferred-self personality traits of postgraduate and undergraduate students did not fluctuate radically throughout the three phases of the study. However, a greater number of the preferred-self traits of the postgraduate and undergraduate students experienced significant changes than the number of their real-self traits. Figure 9 depicts the comparison of the postgraduate students’ preferred-self traits across the three phases. The result of the nonparametric test showed that 27 of the preferred-self traits of the postgraduate students experienced significant changes over the three phases. Among the 27 traits, 13 traits significantly increased over the three phases (optimism, achievement, dominance, endurance, order, social energy, exhibition, self-confidence, self-satisfaction, creativity, masculinity, respectful and work centered), indicating students’ desire to be stronger in these traits. Four preferred traits (support seeking, self-blaming, self-control and security seeking) decreased significantly over the three phases. The constant decreases in support seeking and self-control indicate that postgraduate students prefer not to seek advice and emotional support and prefer to be less self-controlled and restrained, and the university ought to pay attention to this finding. In addition, eight preferred-self traits (enthusiasm, psychologically perceptive, nurturance, affiliation, personal adjustment, structure valuing, playful and pragmatic) decreased during phase 2, but increased again in phase 3; two preferred-self traits (negativity and counseling readiness) increased during phase 2, but dropped significantly in phase 3. The drop in counseling readiness in phase 3, which is congruent with the constant decrease in support seeking, requires attention from the university, because this finding indicates that the postgraduate students prefer not to accept counseling or professional advice to help them cope with their personal problems and psychological difficulties.

Figure 9. Changes in postgraduate students’ preferred-self personality traits across phases 1, 2 and 3.

* p < 0.05. ** p < 0.01.




As for undergraduate students, 27 preferred-self traits experienced significant changes over the three phases of the study. Fourteen preferred-self traits increased significantly over the three phases of the study: optimism, achievement, dominance, endurance, order, nurturance, affiliation, social energy, exhibition, self-confidence, self-satisfaction, creativity, respectful and work centered, indicating that undergraduate students had a constant desire to be stronger in these traits. On the other hand, four preferred-self traits decreased over the three phases: support seeking, self-blaming, security seeking and intellectualistic. As mentioned before, the constant decrease in support seeking is concerning because it indicates that students prefer not to seek support and advice when they encounter problems or issues. Undergraduate students showed less desire to be more intellectualistic, suggesting that they prefer not to emphasize versatility, unconventionality and individuality. In addition, eight preferred-self traits decreased during phase 2, but increased again in phase 3 (enthusiasm, communality, psychologically perceptive, change, personal adjustment, structure valuing, playful and scientific), while the negativity trait increased during phase 2 but decreased again in phase 3.

Figure 10. Changes in undergraduate students’ preferred-self personality traits across phases 1, 2 and 3.

* p < 0.05. ** p < 0.01.



Clearly, postgraduate and undergraduate students shared similar trends in their preferred-self traits (Figures 9 and 10). Both the postgraduate and undergraduate students recorded constant increases in the same 12 preferred traits (optimism, achievement, dominance, endurance, order, social energy, exhibition, self-confidence, self-satisfaction, creativity, respectful and work centered) and constant decreases in three of the preferred-self traits (support seeking, self-blaming and security seeking).




Findings on the personality profile of undergraduate and postgraduate students at this research university in Malaysia are promising. The results suggest that students are coping well with the institutional transformation. In fact, personality traits such as optimism, endurance, dominance, order, exhibition, self-confidence and creativity were highly expressed and developed, as profiled in phase 3 of the study. These highly expressed and developed traits indicate that students are dignified, flexible, hopeful and unyielding in their desire to excel. They also value cognitive activity and insight. However, their profile shows some concerns in traits such as support seeking and security seeking, which dropped continuously during the study. Such findings suggest that students may not be ready for counseling and prefer not to seek help and support when they encounter problems.


Because change in an organization may cause strain and uncertainty (Nelson et al., 1995), Marshall (2010) proposed that early assessment and intervention be implemented accordingly. Assessment of students’ perceptions of the transformation initiatives, particularly on teaching, learning and research activities, would help to evaluate the impact of institutional transformation on the psychological well-being of the students (Loretto, Platt, & Popham, 2010). Preparing and guiding students through the transformation process helps them to adapt and thrive (Marshall, 2010; Tosevski, Milovancevic, & Gajic, 2010). Loretto et al. (2010) found that preparation for change and timely training with open communication may build trust and minimize uncertainty by increasing control.


Gradual and orderly structural policy changes may facilitate adjustment and minimize needless stressors. Secrecy and poor communication may result in poor morale and low self-satisfaction (Becker et al., 2004; Nelson et al., 1995; Smollan & Sayers, 2009). In contrast, promoting transparency and coordination in the learning environment may encourage attitudes of independence, objectivity, industriousness, respectfulness, confidence, assertiveness, initiative and enthusiasm. These interventions may help ensure the mental well-being of students, which in turn affects their academic achievement positively and contributes toward the success of the university transformation process. Tosevski et al. (2010) have suggested building trust in instructor-student relationships to promote autonomy and clarify role expectations. Practicing a student-driven learning approach may inspire creativity and leadership, bringing forth greater self-satisfaction among students.


As the university moves toward becoming a world-class institution, students fit themselves into the vision and mission of the university. In this study, the differences between the real-self and the preferred-self traits were most exaggerated in the third phase. When the preferred-self traits are much higher than the real-self traits, students may feel frustrated. According to Rogers (2007), incongruence between real and preferred value in personality traits may increase one’s vulnerability to stress or anxiety. Mild anxiety brings forth self-awareness in response to the incongruence in personality and may result in therapeutic change and the learning of new coping skills (Rogers, 2007). The university can provide counseling services to assist those students who need help.




The APEX initiative is transforming the selected research university to embrace excellence, innovation and dynamism in moving toward the goal of becoming a world-class institution. The results of this study suggest that university students are coping well with the institutional transformation. In fact, many desired personality traits became more strongly expressed and developed during the transformation phases. It is crucial to continually monitor the personality profile and psychological well-being of students. The institution also can implement proactive interventions to support the mental health and development of human capital in all students.





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See Ching Mey is Deputy Vice-Chancellor of the Division of Industry and Community Network at the Universiti Sains Malaysia. Melissa Ng Lee Yen Abdullah is a senior lecturer in the School of Educational Studies at the Universiti Sains Malaysia. Chuah Joe Yin is the assistant registrar in the Division of Industry and Community Network at the Universiti Sains Malaysia. Correspondence can be addressed to See Ching Mey, Division of Industry and Community Network, 6th Floor, Chancellory Building, Universiti Sains Malaysia, 11800, Penang, Malaysia,


The Relationship Between Socioeconomic Status and Counseling Outcomes

Lisa D. Hawley, Todd W. Leibert, Joel A. Lane

In this study, we examined the relationship between various indices of socioeconomic status (SES) and counseling outcomes among clients at a university counseling center. We also explored links between SES and three factors that are generally regarded as facilitative of client change in counseling: motivation, treatment expectancy and social support. Regression analyses showed that, overall, SES predicted positive changes in symptom checklists over the course of treatment. Individual SES variables predicting positive change were educational attainment and whether the client had health insurance. SES was not associated with motivation, treatment expectancy or social support. Implications for SES research and counseling are discussed.

Keywords: socioeconomic status, counseling outcomes, social support, motivation, treatment expectancy, university counseling center

There is a robust relationship between socioeconomic status (SES) and mental health (Goodman & Huang, 2001; Strohschein, 2005), a finding that researchers have consistently replicated (Adler, Epel, Castellazzo, & Ickovics, 2000; Kraus, Adler, & Chen, 2012; Muntaner, Eaton, Miech, & O’Campo, 2004; von Soest, Bramness, Pedersen, & Wichstrøm, 2012). Furthermore, researchers have linked SES to important outcomes in a number of domains, including academic achievement and employability (Blustein et al., 2002) and health service utilization (Goodman & Huang, 2001). Pope-Davis and Coleman (2001) argued that SES is an important cultural variable that is closely aligned with race and gender. Despite the risk factor that SES poses for mental health and well-being, the current literature does not empirically represent SES as much as other cultural variables, especially with regard to counseling outcome research (Falconnier, 2009; Liu, 2011). To respond to this shortcoming, we investigated potential links between SES and counseling outcome.


SES and Mental Health


SES as a Variable of Study

In the last 20 years, two content analyses have reviewed cultural variables and SES within counseling (Liu, Soleck, Hopps, Dunston, & Pickett, 2004; Pope-Davis, Ligiero, Liang, & Codrington, 2001). Liu et al. (2004) reviewed three journals from 1981–2000 and concluded that SES was mainly studied post hoc, and used primarily to account for unexplained variance. Similarly, focusing on the Journal of Multicultural Counseling between the years of 1985 and 1999, Pope-Davis et al. (2001) analyzed the content of articles for prominent multicultural variables and found that SES was underexamined as a primary variable of study. Taken together, both content analyses pointed to an overall lack of attention to SES in mental health counseling literature.


There is agreement regarding the multicultural and social justice relevance of economic empowerment and SES in the field of counseling (Ratts, Toporek, & Lewis, 2010); however, available SES counseling literature is predominantly conceptual and not empirical. There are several possibilities for the overall lack of empirical investigations into SES and counseling outcomes. First, only recently have mental health counselors made a concerted effort to empirically demonstrate counseling outcomes (Hays, 2010). In addition, Smith, Chambers, and Bratini (2009) opined that, while research on the pathogenic impact of poverty on emotional well-being is robust and logical, the development of practitioner-based interventions has been limited. The counseling profession has not been a leader in empirically studying this complex variable, which further limits the profession’s contributions to research-based interventions. Moreover, SES is complex (Liu et al., 2004); its etiology is often interconnected with mental health risk factors. One challenge of SES research, then, is effectively conceptualizing which aspect of the variable to address first. This challenge is best expressed in the old adage “Which came first, the chicken or the egg?” In other words, do lower SES levels lead to higher rates of mental health disorders or do higher rates of mental health disorders lead to lower SES levels? Eaton, Muntaner, Bovasso, and Smith (2001) identified four possible answers: (a) Lower SES raises the risk of developing a mental health disorder, (b) lower SES prolongs the duration of a mental health disorder episode, (c) mental health disorders lead to downward social mobility or (d) mental health disorders hinder attainment of upward SES status. It also is plausible that these answers are not mutually exclusive, further complicating the role of SES in mental health.


Objective Versus Subjective Indicators of SES

Another possible reason for the limited pursuit of SES research is the difficulty in operationalizing SES. As a construct, SES is multifaceted, impeding the use of discrete variables (Liu et al., 2004). Frequently it is measured using objective, actuarial data such as household income, occupation, zip code and healthcare coverage. However, Braveman et al. (2005) demonstrated that objective indicators of SES, such as education and income, are inadequate because they are not interchangeable with other SES indicators of wealth, education and neighborhood (e.g., zip code clusters). Braveman et al. (2005) concluded that better measures were needed, especially subjective SES measures, such as perceptions of financial security and broad, culturally driven definitions such as lower-, middle- and upper-class SES levels (Adler et al., 2000; Dennis et al., 2012). Other researchers have reached similar conclusions after using both subjective and objective markers of SES (Adler et al., 2000; Hillerbrand, 1988). Even formal measures of SES, including the Hollingshead’s SES indicator (Hollingshead, 2011) and the Duncan Socioeconomic Index (Duncan, 1961), make limited use of subjective measurement strategies. Liu, a leading advocate for the study of SES in counseling, emphasized the need for a multidimensional approach for data collection to best capture contemporary client experiences (Liu, 2011; Liu et al., 2004). In this article, we integrate subjective and objective variables and examine their impact on clinical outcomes.


SES and Clinical Outcomes

In general, psychotherapy reviews show that higher SES is associated with greater therapy retention (Clarkin & Levy, 2004; Petry, Tennen, & Affleck, 2000). However, SES is not consistently related to symptom reduction (Petry et al., 2000). On the other hand, SES does relate to counselor perceptions of the client. For example, in one study at a university counseling center, 163 case files were randomly selected to evaluate the association between the Hollingshead SES rating scale and therapy outcome (Hillerbrand, 1988). According to the results, counselors rated clients with lower SES levels as having greater dysfunction, greater goal disagreement about treatment and less successful counseling outcomes. Mental health practitioners have perceived clients as less motivated when they have lower SES levels (Leeder, 1996) and lack similar social support (Beatty, Kamarck, Matthews, & Shiffman, 2011). In another study, counselors and counselor trainees rated case vignettes and videos of presenting problems featuring clients from either lower or higher SES (Dougall & Schwartz, 2011). Again, counselors rated lower-SES clients as having more severe problems than higher-SES clients. These results reflect other research investigating perceptions and attitudes about lower-SES populations. Historically, clinicians have tended to view poorer clients as lacking in effort (Feagin, 1975; Kluegel & Smith, 1986) and motivation (Seccombe, James, & Walters, 1998), and as being apathetic and passive (Leeder, 1996). Although these studies provide some useful information regarding the present line of inquiry, studies related to clinical outcome and SES as a main variable of study are sparse (Liu, 2011). There is a need to better refine and understand the relationship between SES and mental health.


Present Study

To address the dearth of counseling outcome studies examining SES, the primary purpose of the present study was to prospectively explore the relationship between SES indicators and counseling outcome. In light of the aforementioned SES literature (e.g., Braveman et al., 2005; Adler et al., 2000), we conceptualized SES as including a combination of objective data and subjective self-perceptions regarding class. Thus, in operationalizing SES as a variable of study, we collected commonly researched objective indices—namely educational attainment, household income and health insurance status, as well as subjective data including client perceptions of financial security and class level.


In the present study, we also examined potential links between SES and three psychological variables thought to facilitate positive change through counseling: client motivation, treatment expectancy and social support. Also of interest was the degree to which the expectation of positive outcome through therapy was linked to SES and counseling outcome. If lower-SES clients indeed fit the perception of increased apathy, we conjectured that these clients would report lower levels of expectation for improvement. Lastly, social support was relevant to this study because it can minimize the impact of lower SES on mental health (Beatty et al., 2011). For example, in a recent study of homeless individuals, social support mediated everyday stressors (Irwin, LaGory, Richey, & Fitzpatrick, 2008). Additionally, Beatty et al. (2011) showed that lower childhood SES was related to less perceived social support. In summary, lower SES level is potentially related to reduced client motivation, treatment expectancy and social support.


Thus, we tested two main hypotheses. First, we hypothesized that lower SES levels were linked to lower levels of client motivation, treatment expectancy and subjective social support. Second, we hypothesized that objective SES variables (e.g., education level, income, health insurance status) and subjective SES variables (e.g., perceived financial security, perceived SES) predicted counseling outcome. Because results have been inconclusive about the primacy of objective versus subjective SES variables, as well as the most predictive combination of SES variables, we entered both sets of predictors into one block of a regression analysis to explore which variables uniquely accounted for variance in outcome. Finally, we tested whether psychological variables (e.g., client motivation, treatment expectancy, social support) explained outcome variance beyond that accounted for by SES variables.




Participants and Procedure

Study participants were adult clients starting counseling at an on-campus university training center. The center, located in a Midwestern suburban area, serves both university students and individuals from surrounding communities at no cost, and is staffed by students enrolled in a CACREP-accredited counseling program.


Between January and April 2010, front desk staff at the training center provided new adult clients with the consent form and study measures, which included the Outcome Questionnaire-45.2 (OQ; Lambert et al., 2003), one item from the Social Adjustment Scale-Self Report (SAS-SR; Weissman & Bothwell, 1976), the Subjective Social Support (SSS) subscale of the Duke Social Support Index (DSSI; Blazer, Hybels, & Hughes, 1990), the Treatment Expectancy Scale (TES; Sotsky et al., 1991), and numerous demographic questions including gender, race, age, relationship status, reasons for entering counseling, income, educational attainment and health insurance status. Clients who consented to participate completed all forms and returned them to the front desk before beginning their initial counseling session. Participants completed the OQ prior to each subsequent counseling session. The method of asking participants to complete OQs prior to each session offers at least two advantages for outcome researchers (Ogles, Lambert, & Fields, 2002): (a) It reduces confusion over when to administer outcome measures, and (b) it reduces potential data loss from unexpected dropout because the last available measure serves as the posttest (Ogles et al., 2002). In the current study, 54 clients consented to participate and completed an initial OQ, at least one additional OQ (posttest) and the other study measures.


The clients reported coming to counseling to address various personal and career-related issues such as relationship difficulties, anxiety, depression, job loss and career transition. The majority estimated that their presenting concern had lasted on and off for the last few years (38.8%). The ages of the participating clients ranged from 19–79 years old (M = 38.76, SD = 12.41) and most (61.2%) were female. The majority of the sample described themselves as Caucasian (91.8%) and married/partnered (30.6%). Others reported being unmarried (24.5%), divorced/widowed/separated (22.4%) or dating (22.4%). The majority of the sample reported being employed (65.3%), with 16.3% indicating no job and 18.4% leaving the response blank. One participant was a university student.



Outcome Questionnaire-45.2. The OQ is a standardized, 45-item self-report instrument that is commonly used as a general “index of mental health” (Lambert et al., 2003, p. 10). The items utilize 5-point Likert scale responses ranging from 0 (never) to 4 (almost always) to determine the severity of various symptoms and psychosocial stressors, resulting in a score ranging from 0–180. Concurrent validity has been established between the OQ Total Score and various other measures of symptomology (e.g., Behavior and Symptom Identification Scale [BASIS-32] Depression and Anxiety subscale; Doerfler, Addis, & Moran, 2002). Construct validity is demonstrated by the OQ’s sensitivity to client change and ability to discriminate between clinical and non-clinical populations (Lambert et al., 2003). The manual (Lambert et al., 2003) reports high internal consistency (a = .93) and 10-week test-retest reliability (.66–.86).


Objective SES. Objective SES was operationalized using three indicators: education level, income and health insurance. For education level, participants indicated their educational attainment, with answer choices ranging from 1 (some high school) to 8 (Ph.D. or equivalent). Income level was assessed by asking participants to indicate their yearly household income, with a continuum of choices ranging from 1 (under $10,000) to 8 (over $100,000) in $10,000–$20,000 increments. Health insurance was dichotomously assessed by asking participants to indicate whether they were receiving health insurance benefits—either through an employer, Medicaid or other source—or were uninsured (see Table 1 for descriptive statistics regarding the SES variables).


     Subjective SES. Subjective SES was operationalized using two indicators: perceived financial security and perceived SES. Perceived financial security was measured using one item from the SAS-SR (Weissman & Bothwell, 1976). Participants were asked if they had had enough money for their financial needs in the past 2 weeks. The item was rated on a 5-point scale ranging from 1 (I had great financial difficulty) to 5 (I had enough money for needs). Regarding perceived SES, participants were asked to choose “the economic class that best describes you” on a three-point scale corresponding to either 1 (lower), 2 (middle) or 3 (upper economic class). With each subjective variable, we did not analyze differences between financially independent versus dependent clients since only one participant was a university student.


Table 1


Frequencies of Participant Responses for SES Variables (N = 49)



M  (SD)



Education level

1.80 (1.08)

  1. Did not finish high school



  1. High school diploma or equivalent



  1. Some college



  1. Undergraduate degree



  1. In master’s program



  1. Master’s degree



  1. In doctoral program



  1. Doctoral degree



Income level

4.04 (1.99)

  1. $0–$10,000



  1. $10,000–$20,000



  1. $20,000–$30,000



  1. $30,000–$40,000



  1. $40,000–$60,000



  1. $60,000–$80,000



  1. $80,000–$100,000



  1. > $100,000



Health insurance status
  1. Uninsured



  1. Insured



Perceived financial security

3.45 (1.57)

  1. Great financial difficulty



  1. Usually not enough money



  1. Enough money half the time



  1. Usually enough money



  1. Enough money for needs



Perceived SES

1.73 (0.49)

  1. Lower economic class



  1. Middle economic class



  1. Upper economic class





     Subjective Social Support. Social support was measured using the SSS subscale of the DSSI (Blazer et al., 1990). The SSS consists of 10 items rated on a 3-point scale; for this study, however, a 5-point Likert-type scale was used, resulting in a possible range of 10–50. Prior studies incorporating the 5-point scale have demonstrated enhanced internal consistency compared to the 3-point scale of the original version, and comparable scale correlations indicative of concurrent validity (Leibert, 2010). Items pertain either to the perceived frequency of positive, fulfilling family and peer interactions (1 = none of the time, 5 = all of the time) or to the degree of satisfaction with family and peer relationships (1 = extremely dissatisfied, 5 = extremely satisfied). Internal consistency was good in the present study (a = .82).

     Client Motivation for Therapy Scale. Motivation, conceptualized using self-determination theory (Ryan & Deci, 2000), postulates six types of motivation along a continuum from intrinsic to external to no motivation (i.e., amotivation). The 24-item Client Motivation for Therapy Scale (CMOTS; Pelletier, Tuson, & Haddad, 1997) has six 4-item subscales that measure each type of motivation while one is receiving therapy. We were interested in two CMOTS subscales that could be used before counseling began in order to assess pretreatment motivation levels potentially associated with SES variables. Those subscales included identified motivation (e.g., attending counseling “because I would like to make changes to my current situation”) and external motivation (e.g., attending counseling “because other people think that it’s a good idea for me to be in therapy”). Participants rated their reasons for participating in counseling on a 7-point scale (1 = does not correspond at all, 7 = corresponds exactly). A summary score for each subscale was created using its arithmetic mean. The CMOTS was validated on 138 inpatient and outpatient clients seeking help for a variety of mental health concerns (e.g., self-esteem, interpersonal problems; Pelletier et al., 1997). Internal reliability coefficients in the present study were acceptable for identified motivation (a = .76) and external motivation (a = .80).


Treatment Expectancy Scale. Client expectation for positive treatment outcome was measured using the TES (Sotsky et al., 1991). The TES consists of a single item: “Which of the following best describes your expectations about what is likely to happen as a result of your treatment?”, with responses ranging from “I don’t expect to feel any different” (1) to “I expect to feel completely better” (5). Although reliability data was not reported, the TES was one of the strongest client predictors of outcome in the National Institute of Mental Health Treatment of Depression Collaborative Research Program, a large randomized control trial (Meyer et al., 2002; Sotsky et al., 1991).



Data analyses followed the guidelines for outcome research that Ogles et al. (2002) outlined. Primary analyses included correlation and multiple regression techniques, beginning with tests of the assumptions of regression (Cohen, Cohen, West, & Aiken, 2003). A repeated measures t test was used to evaluate pre-post change, and ANCOVAs were used to test the need to include various covariates as control variables in the regression analyses. For each participant, the initial OQ total score was considered the pretest score and the last OQ completed was used as the posttest. Because computing a simple difference score between pretest and posttest is subject to regression to the mean (i.e., highest initial scores change the most), we analyzed outcome by partialing out the OQ pretest scores from OQ posttest scores in the first step of the hierarchical multiple regression analysis (Hill & Lambert, 2004). Before conducting hypothesis tests, we inspected data for potential violations of univariate and multivariate assumptions in multiple regression analyses, including outliers, atypical scores, multicollinearity and assumptions of linearity, normality and homoscedasticity (Cohen et al., 2003). Five cases showed highly atypical scores according to recommended cutoff guidelines (Cohen et al., 2003) in small data sets (i.e., DFFITS > 1) and were removed before hypothesis testing. No further problems were evident.


Initial analyses were conducted to determine whether any demographic variables should be included as covariates in the regression model. Aside from age and length of time in counseling, demographic variables were categorical: gender, marital status (unmarried versus married) and employment status (unemployed versus employed). These variables were dummy coded for the analysis. Separate ANCOVAs were run for the three categorical variables with OQ pretest scores entered as the covariate. The three categorical variables were not significantly related to outcome (ps ranged from .29 to .84). A simple regression evaluating age on outcome with OQ pretest scores partialed out showed no significance (p = .77). Because the amount of time in counseling may have affected how much change had occurred at posttest, we regressed OQ posttest scores on length of time in counseling, controlling for OQ pretest scores. The regression showed no effect of length of time in counseling on amount of change (p = .12). Therefore, no demographic variables were included in the hierarchical multiple regression.



A repeated measures t test showed that client OQ’s significantly improved from pretreatment (M = 72.6, SD = 19.1) to the final session of counseling (M = 64.0, SD = 20.0), t(48) = 5.42, p < .001. To test our first hypothesis that lower SES levels would be linked to lower levels of client motivation, treatment expectancy and subjective social support, we conducted zero-order correlations for continuous variables. Table 2 displays the results, starting with objective SES variables (e.g., education level, income) and subjective SES variables (e.g., perceived financial security, perceived SES), followed by the two indicators of motivation (identified and external), as well as treatment expectancy and social support. For the dichotomously coded objective SES variable, health insurance status, independent samples t tests were conducted on the four dependent variables of identified motivation, external motivation, treatment expectancy and subjective social support. Reported effect sizes adhered to Cohen’s (1992) conventions for correlations, with small, medium and large effect sizes corresponding to r = .10, r= .30, and r= .50, respectively.


Table 2


Summary of Intercorrelations for Continuous SES Indicators with Social Support, Treatment Expectancy and Motivation Scores











1. Education level

2. Income level


3. Financial security



4. Perceived SES




5. Identified motivation





6. External motivation






7. Treatment expectancy







8. Social support








Note. N = 49; financial security = perceived financial security; social support = Subjective Social Support; treatment expectancy = Treatment Expectancy Scale. Health insurance status is a categorical variable and is not included in this table.

* p < .05. ** p < .01.


As shown in Table 2, neither of the continuous objective SES variables (e.g., educational attainment, income level) significantly related to identified motivation, external motivation, treatment expectancy or subjective social support. The independent samples t tests indicated no significant effect regarding insurance status (p > .05). The subjective SES variable, perceived financial security, significantly and positively correlated with subjective social support (r = .40, p < .01), with a medium to large effect size. Consistent with our hypothesis, clients who reported feeling more secure financially also felt more supported by their social network; conversely, clients feeling less supported by their social network felt less secure financially. The other subjective SES variable, perceived SES, did not significantly correlate with motivation, treatment expectancy or subjective social support. Therefore, the overall pattern of findings did not support the first hypothesis.

Hierarchical Multiple Regression Analysis

We used hierarchical multiple regression to test the second hypothesis that objective SES variables (e.g., education level, income, health insurance status) and subjective SES variables (e.g., perceived financial security, perceived SES) predicted counseling outcome. In the first step of the hierarchy, we entered OQ pretest scores to control for initial differences in symptoms. In the second step, we entered objective and subjective SES variables. In the third step, we entered psychological variables (subjective social support, treatment expectancy and client motivation) to test whether these variables accounted for additional outcome variance beyond that which SES variables explained. Because we did not have hypotheses about the primacy of specific individual variables’ effects on counseling outcome, we examined semipartial correlations (sr) to identify which predictors within each step had the greatest impact on outcome.


Results of the hierarchical regression analysis appear in Table 3. Controlling for OQ pretest scores in the first step, results supported the hypothesis that SES variables significantly predicted counseling outcome, ΔR2 = .05, F(5, 42) = 2.93, p < .05, a small to medium size effect. Taking into account the other predictors, the following two of the six SES variables significantly predicted outcome: education level and health insurance status. The semipartial correlations indicated that education level and health insurance status accounted for 3% and 4% of outcome variance, respectively, small to medium effect sizes. The beta coefficient for education indicated that for every unit increase in education, clients had, on average, a 3.6-point reduction in their final OQ scores relative to their initial level (t = -2.49, p < .05). Similarly, clients who had health insurance reported an average 8.7 OQ points greater positive change than those who did not have insurance (t = –2.60, p < .05). In the third step of the regression, after controlling for both OQ pretest scores and SES variables, the psychological variables (subjective social support, treatment expectancy and client motivation) did not predict significantly more variance in outcome, ΔR2 = .02, F(5, 37) = 0.90, p > .05.


Table 3


Hierarchical Multiple Regression Analyses Predicting OQ Posttest Score











Baseline – OQ pretest







1, 47

Model 1





5, 42
   Education level –.18* –.63 1.46 –.20
   Income .12 1.54 0.93 .05
   Health insurance –.19* –8.67 3.34 –.22
   Financial security –.01 –0.12 1.05 –.01
   Perceived SES –.01 –0.36 3.58 –.01
Model 2



5, 37

   Social support .01 0.71 4.22 .02
   Treatment expectancy –.10 –3.30 2.47 –.12
   Identified regulation –.06 –3.22 3.84 –.07
   External motivation .13 4.06 2.28 .16
   Amotivation –.09 –3.42 2.79 –.13

Note. rsp = semipartial correlation coefficient.Initial covariate in the first step was Outcome Questionnaire-45 pretest score. Negative signs indicate lower posttreatment symptoms. OQ = Outcome Questionnaire-45; financial security = perceived financial security; perceived SES = perceived socioeconomic status; social support = Subjective Social Support; treatment expectancy = Treatment Expectancy Scale; health insurance = Health Insurance Status; coding: no = 0, yes = 1.

*p < .05. **p < .01.





Overall, SES variables significantly predicted counseling outcome. In particular, two of the objective SES variables—education level and health insurance status—each individually predicted greater improvement in counseling, explaining 3% and 4% of the outcome variance, respectively. Contrary to expectations, income level and the subjective SES variables did not predict outcome. Overall, our hypothesis that SES variables would relate to social support, treatment expectancy and motivation was not supported. However, the subjective SES variable—perceived financial security—significantly and positively related to subjective social support.


Surprisingly, as a whole, SES variables did not correlate with clients’ subjective sense of social support. The only exception was a significant positive link between subjective social support and perceived financial security. It may be that the perception of having sufficient funds to meet recent individual or family needs aligns with the perception of having a supportive social network. However, the finding that income level did not correlate with social support was interesting given the common perception among mental health workers that low-income clients lack social support (Krause & Borawski-Clark, 1995). In this study, from the perspectives of lower-income clients, there were no perceptions of support system deficits. The degree and frequency with which one experiences positive interactions with peers is the basis of the SSS instrument. Within SES research, social support measures may include community social support, as well as family and peers. The definition of social support may differ from participant to participant. One of the challenges of social support within SES is that lower-SES individuals often experience similar increased economic stressors to others in their social support network (Mickelson & Kubzansky, 2003). Therefore, a more limited study using multiple social support measures is a possible direction for future research.


Though the first hypothesis was not supported, the results indicate a trend in the hypothesized direction, with higher perceived financial security being marginally related to treatment expectancy, accounting for 7% of the variance, a medium-sized effect. In other words, before counseling began, clients who reported a greater sense of financial security also had greater expectation of a positive treatment outcome. There was, however, no significant relationship between all other SES indicators and either motivation type. Given that this hypothesis was based on studies of perceptions among mental health professionals working with low-income clients (e.g., Dougall & Schwartz, 2011; Hillerbrand, 1988; Krause & Borawski-Clark, 1995; Leeder, 1996; Seccombe et al., 1998), it is possible that the findings are indicative of SES-related biases in the helping professions. That is, the overall findings of the present study did not reveal significant relationships between SES and social support, treatment expectancy or client motivation, even though clinicians have frequently reported beliefs that such relationships exist.


Of the three objective SES variables, education level and health insurance status each predicted greater improvement in counseling. Education level is commonly used in poverty research, which shows that lower education is associated with decreased physical and mental health. For example, Goodman, Slap, and Huang (2003) found that lower household income and parental education were associated with depression and obesity. Similarly, SES studies using neighborhood indices such as zip code or concentrated populations with similar income levels often find lower-income communities facing challenges such as lack of quality education, lower education levels and fewer employment opportunities, with these chronic stressors impacting depressive symptoms (Groh, 2007).


The second finding of health insurance status contributing to improvements through counseling is particularly intriguing given that counseling services in the present study were offered at no cost. Arguably, access to health insurance provided a safety net, a positive external resource that allowed low- and high-income clients alike to focus on the internal work of change in counseling. That is, health insurance fulfilled a basic need, which in turn seemed to aid clients in benefiting from counseling. This finding is important given the recent attempts to obtain mental health parity. The Paul Wellstone and Pete Domenici Mental Health Parity and Addiction Equity Act (2008) was passed in an effort to reduce costs of mental health services by offering treatment continuously. Recent research highlights the political and societal complexity of mental health parity (Hernandez & Uggen, 2012). Within counseling, there is a lack of research focused on client outcomes and perceptions of healthcare. And in the present study, the finding of a relationship between perceptions of healthcare and outcomes was unexpected. Outside the counseling literature, recent studies focused on parity at the macro level have found disconnects between providers and consumers related to education. In a 2009 study in California, many consumers stated a need for increased education about parity (Rosenbach, Lake, Williams, & Buck, 2009). The current research direction focuses more on utilization and access issues and less on the impact on outcomes. The implications for counselors lie in the ability to provide individuals with easy access to mental healthcare and to reduce or remove the stigmatization often associated with receiving mental health services. Furthermore, current research suggests the need for service providers to educate clients on mental healthcare options. The myriad of choices, rules and requirements can be overwhelming for clients already experiencing elevated distress. In conclusion, counselors benefit the profession by advocating for clients and not being silent stakeholders. Further research is necessary to understand this finding and its implications for policy and service provisions.


The present results show that subjective and objective measures collectively predicted outcomes. Within the counseling literature, there are few studies that both empirically study subjective and objective measures, as well as examine SES measures with clinical outcomes in counseling. The results also support the premise that SES is a complex variable warranting further empirical inquiry in counseling research (Liu, 2011). If SES is predictive of client outcomes in a counseling training program, then further research to investigate discrete variables and causal relationships is necessary. Current trends in SES health research involve the inclusion of subjective measures. Studies have shown that subjective low SES is linked to poorer health outcomes (Adler et al., 2000). Professional counselors can both emulate the current health research already using both subjective and objective measures in clinical outcomes and forge their own SES research agenda.



Several methodological limitations warrant attention. First, the small sample size, comprised mostly of Caucasian and female clients, limits the generalizability of this study. Given that SES is linked with race and gender (Pope-Davis & Coleman, 2001), a heterogeneous sample would enrich the study’s findings. Along those lines, it is conceivable that the health insurance–outcome link in this study was a spurious correlation that might be accounted for by a third unmeasured variable. In short, the sample of convenience and the naturalistic correlational design reduces internal validity. Though each counselor had similar coursework prior to practicum, counselor trainees were not the same. We made no attempt to control variables such as counseling approach, counselor competence or client diagnosis; each of these variables may have changed the results of this study. Finally, a possible confounding contextual factor was that this study occurred within a time of significant economic challenge. Similar to mandated healthcare and parity, the economic contexts in which SES studies occur are important areas for further study. Despite these limitations, the study provides important contributions and has implications for further research.


Implications and Future Research

The results of the present study are consistent with the work of researchers who have argued that SES variables have complex relationships with one another and with mental health (Liu, 2011). When measured together, subjective and objective SES measures impacted clinical outcomes. As individual variables, however, only educational level and health insurance status predicted improved outcome. Indices of SES have not evolved to the point that they can be measured with discrete variables. Counseling SES research would benefit from further development of SES indices, as well as comprehensive studies using measures as a whole within broader contextual issues to fully understand the utility in mental health counseling research.


Results also show that clients who had access to health insurance experienced greater amelioration of symptoms even though counseling services in the present study were provided at no cost. This result was unexpected and must be studied further. Future research might examine whether access to insurance satisfies a basic need of security, which, in turn, improves counseling outcomes. Increasingly, states are incorporating mental health parity (Paul Wellstone and Pete Domenici Mental Health Parity and Addiction Equity Act, 2008); therefore, studies must review the long-term effects associated with clinical outcomes and cost-effectiveness. Regarding short-term findings, Lang (2013) found that suicide rates were significantly reduced when states required parity between physical and mental health benefits. Also, studies controlling for counselor and client differences are needed. For example, an experimental design might examine counselor countertransference regarding lower-SES clients. Results might show how much counselor perceptions could be altered on the one hand, and biased on the other. This study also indicates a further need for counselors to understand the contextual influences of SES with regard to counseling outcome. It is important for counselors to embody the full characteristics of their professional identity—including that of mental health advocate—to address SES issues involving both misconceptions and gaps in SES research.




The present study contributes to the body of knowledge regarding the effect of client SES on counseling outcome. Results show that higher education and access to health insurance—even at a free counseling clinic—may improve counseling outcome. For all clients, possession of health insurance augmented the amount of improvement. Although these findings should be regarded as tentative, SES appears to be an important client variable affecting the success of counseling and meriting further research. The results also underscore the need for a comprehensive SES measure to gain a more complete picture of how SES influences counseling outcome. Finally, we found no links between lower SES levels and motivation, treatment expectancy and perceived social support. An important implication for the practicing counselor is to value the nuances of SES as potential influences on client outcome. Counselors would benefit from exploring potential SES stressors with clients and accessible resources to minimize mental health stressors and improve counseling outcomes.





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Lisa D. Hawley and Todd W. Leibert, NCC, are associate professors at Oakland University. Joel A. Lane, NCC, is an assistant professor at Portland State University. Correspondence can be addressed to Lisa D. Hawley, 435F Pawley Hall, Oakland University, Rochester, MI 48309-4401,


Small but Mighty: Perspectives of Rural Mental Health Counselors

Anastasia Imig

Limited research is available on the experiences of rural mental health counselors. The following is a phenomenological study grounded in critical theory. Four practicing licensed professional counselors currently working in rural settings in the Midwest region of the United States were interviewed to elicit stories regarding rural counseling, supervision and professional development experiences. The participants’ responses included the following themes: (a) need for flexibility, (b) resource availability, (c) isolation, (d) ethical dilemmas and (e) finding meaning in one’s work. The results contribute to a small but growing body of research about rural counselors, who are often misunderstood in the context of mental health.


Keywords: rural counseling, rural mental health, ethical dilemmas, phenomenological, professional development



No common culture for the rural United States is absolute. Rural communities range according to geographic location, diversity of inhabitants, social and economic factors, problems and resources available (Bushy & Carty, 1994). As such, counselors-in-training often lack critical background information to competently and ethically serve traditionally underserved clientele (Smalley et al., 2010); in addition, counselors often lack the professionalism necessary for dealing with the profusion of unique issues in rural settings. Authors have documented studies related to rural school counseling, rural marriage and family therapy, rural mental health practitioners, rural clinical psychology, and rural healthcare and education (Bambling et al., 2007; Boyd et al., 2007; Curtin & Hargrove, 2010; Curtis, Waters, & Brindis, 2011; Ellis, Konrad, Thomas, & Morrissey, 2009; Endacott et al., 2006; Hartley, Loux, Gale, Lambert, & Yousefian, 2010; Lockhart, 2006; McCord et al., 2011; Morris, 2006; Murry, Heflinger, Suiter, & Brody, 2011; Owens, Richerson, Murphy, Jageleweski, & Rossi, 2007; Smalley et al., 2010). There is a noticeable gap in the literature, however, related to rural mental health counseling experiences.

The many definitions of rural reflect the complexity and dynamism of this elusive concept. In one scenario, population density may be the focus of the definition, whereas in other cases, geographic isolation may take precedence. For example, the U.S. Census Bureau (2013) uses the urban-rural classification system to distinguish between two types of urban areas: (a) urban communities of 50,000 or more people and (b) urban clusters of between 2,500 and 50,000 people. Rural thereby encompasses all population, housing and territory not included within an urban area (U.S. Census Bureau, 2013). However, the U.S. Department of Agriculture uses a regional-economic concept as defined by the Office of Management and Budget, which distinguishes metropolitan areas as broad labor-market areas that include (a) central counties with one or more urbanized areas (densely populated areas with more than 50,000 people), and (b) outlying counties that are economically tied to the core counties as evidenced by labor-force commuting (U.S. Department of Agriculture, 2013). Nonmetropolitan areas are therefore those outside these metropolitan areas (U.S. Department of Agriculture, 2013). For the purposes of this article, rural is defined according to the Office of Management and Budget geographic isolation definition, with rural counties constituting those with fewer than 50,000 people as well as counties not economically tied to densely populated counties.

Bushy and Carty (1994) authored one of two articles specifically devoted to rural mental health counseling. The authors provided a solid foundation of rural mental health considerations, outlining the availability, accessibility and acceptability of services. The authors also described rural culture and its intersection with mental health, stating that utilization patterns are typically characterized by informal support systems versus social services. When rural residents do seek help, it is often because of crisis with higher associated incidents of depression, alcohol abuse, domestic violence, and child abuse and neglect.

Erickson (2001) defined the multiple relationships inherent in rural counseling. She explored various problems with regard to dual relationships in rural settings, as well as an ethical decision-making model for use in such instances. Through a case study, Erickson applied her decision-making model and promoted adherence to ethical guidelines in spite of multiple relationship occurrence.

The Council for Accreditation of Counseling and Related Educational Programs (CACREP) provides accreditation standards for licensed professional counselors. With CACREP as the industry hallmark for promoting competence through properly trained counselors, practitioners and leaders in the mental health field must take note of the lack of research and training for rural mental health counselors. The purpose of this qualitative phenomenological study was to identify the counseling experiences of licensed professional counselors working in rural settings in the Midwest region of the United States. From a critical theory perspective, this study asked the global question, “What is the experience of rural mental health counselors?” Three subquestions included the following: (a) How does the experience of working in a rural setting impact the counselor’s roles? (b) What are the contextual factors impacting counseling supervision in rural areas? and (c) What is the essence of the professional development of supervisors and supervisees providing counseling services in rural areas?





Participants were recruited via network selection or “snowballing” (Creswell, 2007), in which participants and other field contacts make referrals for participation. Participants were four women who met the following research criteria: They were licensed professional counselors (LPC) currently working in the counseling field in a rural setting as defined by the U.S. Department of Agriculture (2013). Participants lived in the Midwest region of the United States at the time of the interviews, recruited from Nebraska and South Dakota. All four women were Caucasian; three were in their mid-30s and one was in her mid-50s. Each participant’s amount of experience at her current work setting fell between 2 months and 10 years. Two participants had additional credentials in either art or equine-assisted therapy (see Table 1 for a demographic summarization of participants).



The author and other doctoral students participating in a graduate-level qualitative research course developed 13 interview questions. Interviews were based on research questions and semistructured, allowing for both interviewees and the interviewer to spontaneously elaborate and provide further questions and information when necessary. The author conducted the interviews face-to-face with three participants; she conducted the fourth interview over the phone. The author used two digital recorders to audiotape all four interviews, which ranged in length from 45 minutes to 1.5 hours. The author also conducted transcription of the audiotapes. She stored data in a locked drawer to ensure participant confidentiality, and used coding for participant identification to further protect anonymity. Prior to investigation, the author wrote an epoch (Moustakas, 1994) in which she identified her own experiences with rural culture in order to suspend previous understandings and to gain a fresh perspective. Such bracketing of the author’s experiences was used during and after the interviews and data analysis to further assess for and reduce potential bias.


Table 1


Participant Demographics







in field

Current setting

Years at current setting

Super Nanny


LPC, Equine-assisted therapy



Home-based, community health


The Pastor’s Wife





Children’s services, outpatient


Putting Out Fires



Art therapy



Outpatient, American Indian reservation


All Things Rural





Nonprofit, outpatient






The author first read all transcripts in order to become familiar with the data, and then read the transcripts a second time with a subsequent data analysis, following the phenomenological approach that Creswell prescribed (2007). The current author categorized individual statements into specific codes closely resembling the participants’ statements. She clustered the codes according to their subject, with similar codes combined into units of meaning in order to better manage the data, and then she labeled each unit of meaning in a theme. From key sentiments that each participant expressed, the author developed a pseudonym to reflect her unique perspectives. Using member checking (Creswell, 2007), the author restated and summarized information, and then questioned each participant to determine accuracy throughout the interview. The author emailed both transcripts of the interviews and analyzed data to the participants so each could either agree or disagree that her experiences, views and feelings were represented accurately and completely. The author additionally utilized peer and expert audit reviews (including her doctoral classmates and class instructor) to ensure credibility of the overall findings.




The author identified the following themes: (a) need for flexibility, (b) resource availability, (c) isolation, (d) ethical dilemmas and (e) finding meaning in one’s work.


Need for Flexibility

 One of the dominant themes of the interviews was the need for rural mental health counselors to be flexible. All participants noted having to be flexible in order to accommodate changing schedules, multiple roles and responsibilities, working in a variety of different settings, and driving long distances. For example, The Pastor’s Wife explained her struggle with flexibility when she is in a town only once a week: “Scheduling is hard. . . . And so if a kid can only be seen after school . . . at a certain time . . . I’m only in [town] one day a week. That’s a challenge.”


Super Nanny explained driving as part of rural life: “I mean, where I grew up, you just have to drive everywhere. To get groceries, to get a job, you have to drive at least half an hour.” For this participant, it was therefore not challenging to commute: “It’s about an hour drive from my home to visit with my supervisor.” Putting Out Fires, on the other hand, described struggles with driving: “The hardest part for me is the drive. I drive 45 minutes one way. I just hate that. For me, that’s the most frustrating.” The Pastor’s Wife had a different problem with driving: “The thing that gets me . . . is cost. . . . It costs a lot for travel. . . . With budget cuts, they cut back on that kind of stuff. So, to get creative, [I] carpool to different trainings.”


To help close the distance typically found in rural areas, participants met with clientele in a variety of untraditional settings to lessen the physical gap between counselors’ offices and clients’ homes. Settings included town libraries, churches, schools and funeral homes. Putting Out Fires candidly remarked, “I even go to their work. It’s approved by their boss that I meet them. I do that every week.” Even in her office space located in a church, All Things Rural must be flexible with the comings and goings of congregation members.


We’re very respectful of the church people and they are very respectful of us. If they know we have something going on, they stay away. Like [if] we have someone in the family room, they’ll go somewhere else. It’s wonderful.


In addition to juggling different settings, participants juggled many roles in their positions as well. Whether it was the role of teacher, case manager, secretary, grant writer, administrator, supervisor or advocate, all four participants acknowledged that an essential part of being a rural counselor entailed wearing many hats. All Things Rural commented, “So, we do everything: phones, insurance, make our own appointments, case notes. We make our own grants. It is very all encompassing.” Similarly, Putting Out Fires admitted, “I do trainings with the pre-natal classes. . . . I do a lot of community activities. I do a lot of prevention. [When] they have community activities, like National AIDS Awareness Day, we’ll have a booth.” Of course, Super Nanny described her teaching responsibilities accordingly: “I do a lot of Super Nanny type stuff . . . a lot of hands on, experiential, teaching type stuff. Like taking advantage of teachable moments. So I’m teaching.”


Flexibility also resulted in fewer people doing more jobs in the community as a whole. For example, doubling of other roles also occurred. All Things Rural explained, “The church secretary also double-times as our treasurer.” Putting Out Fires echoed a similar example: “They were without a social worker for a while. So they had a nurse trying.” And if roles are not filled, then it is the community that must go without.



Another dominant theme was resources in rural communities. All Things Rural described the affordability of counseling for community members: “We can see people who for any reason aren’t having their mental health or counseling needs met, we never refuse anyone for inability to pay.” Putting Out Fires mentioned the availability of transportation for clients: “I really do not have very many no-shows because we provide transportation. So we even go to the houses and pick them up.” For those individuals initiating services, culturally diverse staff is available, according to Super Nanny:


At the agency that I work, they have at least two licensed therapists that, one of them is actually from Somalia and the other one is kind of like an expert in that area . . . really knows a lot of the culture and all that stuff. . . . I’m impressed with that in where I’m working now. There’s also a large Hispanic culture and at least half of all the staff, the family service workers, as well as the therapists, are bilingual.


Despite being in rural locations, participants had access to other professionals and trainings. One of the benefits of working for an American Indian/Native Alaska tribe, confided Putting Out Fires, was that “there’s lots of funds. When it comes to CEUs [continuing education units], trainings, I am very spoiled. They pay for all of that. That’s a huge benefit. It’s huge.” Although the other participants did not have comparable financial backing for professional development, The Pastor’s Wife commented on local trainings: “I think that there are some local things that are available. There’s been some . . . workshops at the hospital . . . which has been nice. And they’re free. So that’s good.” All Things Rural similarly described a local conference: “Here in [town] there is an annual Mental Wellness Conference.”


All four participants identified local availability for interacting with other rural mental health counselors. Whether through staff meetings, informal office drop-ins or contact with other area personnel, all have been able to find resources nearby. All Things Rural stated, “I always have people I can talk to.” Super Nanny described a similar experience: “And then . . . if I don’t know about something, I access the person that does within the agency.” Furthermore, the Internet has proven helpful for participants when asking questions over e-mail, finding information or materials online, or utilizing telesupervision. Putting Out Fires explained, “The big thing now is telesupervision. And even using Skype. I actually went to a seminar at the last art therapy conference, and it was all about telesupervision. Because I even had supervised somebody through Skype.”


On the other hand, participants also had experiences where wait lists formed due to high need and not enough local professional staff available. The Pastor’s Wife said, “I’m the only QMHP [qualified mental health professional] in [town] on Fridays.” Putting Out Fires also complained about the lack of professionally qualified area staff: “Their CPS [Child Protective Services] workers don’t have to have a college education. . . . I’m not sure what their requirements are. So they may not necessarily even be trained.”


Because there are few professionals serving a small population, there is often a lack of clinician anonymity. Super Nanny described the challenge of maintaining a private life while out in public: “Is it somebody I’m working with? Is it somebody I’m going to work with possibly in the future? . . . What are they seeing? What opinions are they forming?” Super Nanny expressed similar concern: “It’s just [that you’re] always having to represent yourself in a professional manner whether you’re at work or not at work.” All Things Rural summarized, “You run into your clients more in a rural setting than you would otherwise.”



Not surprisingly, another aspect of being a rural counselor involved experiences with wide, open spaces. The Pastor’s Wife elaborated on the complications as a result of unavailable cell signals:


If I have a question, or something, and I need to call back, at times there’s trouble with reception. . . . Like down in [town] . . . you have to go to . . . the top of this hill to get cell reception. . . . In somebody’s house, there’s no cell reception.


Super Nanny struggled with a different piece:


I miss the office interaction, though. That’s where you do a lot of the collaborating. A lot of consultation . . . a lot of ideas are generated. “I’m struggling with a client, what do you do?” Just, you know, passing in the hall. Or, when you have a 10-minute break and you’re in someone else’s office. “I’ve got a quick question for you. I’m struggling with . . . What advice do you have?” I miss that.


Ethical Dilemmas

Dual relationships abound in rural communities. Putting Out Fires explained, “It’s so small in the community, you become friends, then . . . you see their kid.” She further detailed, “And we don’t have an EAP [Employee Assistance] program. So we’ve seen co-workers. That’s really hard.” The Pastor’s Wife added, “And also I see a few of the kids of staff, of my co-workers. . . . I haven’t had any issues, but it’s . . . a whole different situation I guess. Because you’re coworkers and a client.” The Pastor’s Wife also explained the intersection of her personal and professional lives:


Well, I’ve run into some difficulties with my husband being a pastor at the church. . . . I’ve had some clients that have also been parishioners, and so with the confidentiality, I can’t talk to my husband about things. But he also has confidentiality about things, being a pastor. And he can’t talk to me about things. But there have been times that I’ve been on-call, and he has gotten a call from a parishioner, that he has had to encourage to call the crisis line, then I answer the crisis line. And it’s just . . . it hasn’t caused any problems, but the uncomfortableness [sic] is there. And, so that has been difficult at times.


In addition to dual relationships, participants cited concerns regarding other rural professionals’ multicultural competency. The Pastor’s Wife described several colleagues’ biases:


I know that there is natural stereotypes, of you know, this kid’s a Native American kid versus a White kid. So the Native American kid is gonna be, you know, have more problem behaviors. I think there’s stereotypes for sure.


Putting Out Fires had a similar experience: “The thing that is really frustrating . . . there are teachers in the schools that are really racist.” In addition to advocating for clients while on the job, she stresses the importance of doing so while interacting with family and friends. Putting Out Fires explains, “Oh, I say stuff” to combat stereotypes and injustices.


Finding Meaning in One’s Work

In spite of the obstacles of rural mental health counseling, all four participants identified sources of job satisfaction. Putting Out Fires remarked about her American Indian clients, “They’re so resilient. And you know, they’re strong. They adapt to the circumstances.” The Pastor’s Wife reflected on her multiple responsibilities by saying, “There’s some benefits in working in rural areas, too. I think . . . it can be more rewarding because you feel you’re doing more. You have to.” Super Nanny was proud to be giving back to her childhood community: “I feel that it’s very rewarding to work in the communities . . . that I grew up in and to be able to actually help the people I work with.” Super Nanny also added the following:


And within small communities, the chances that I’m going to run into them in the future are very high. . . . And I have had that experience where I do run into people from the past and I see them doing very well . . . to me [this] is very rewarding.


All Things Rural stated, “We have a wonderful staff and we’re very happy.” To reiterate, she said, “I love my work. I love my work.” All Things Rural summarized her rural mental health counseling experiences by saying, “We are small, but mighty.”




This study demonstrates many aspects of rural mental health counseling and answers the research question related to rural counselors’ various roles and supervision and professional development experiences. Given the extra roles that participants take on and the multiple settings in which they practice, the findings of this study are similar to those of Bushy and Carty (1994). This study further highlights the ambiguous nature of availability and accessibility of rural mental health resources. In some instances, participants described ample collegial accessibility. Putting Out Fires said, “I feel supervision-wise, I get a lot of good support.” All Things Rural concurred: “I know a lot of my colleagues in the area. And that’s helpful.” In other instances, participants bemoaned a lack of resources. Putting Out Fires replied, “You have to work your butt off. We have scraped. We have scraped.” Super Nanny responded, “They’re there. You just have to look for them.” With such contradictions occurring within the context of the four interviews, the complexity of rural mental health counseling is apparent. Hard work is expected. Putting Out Fires explained, “If you’re going to be successful, you’re going to have to work at it.”


By using a qualitative design, the author was able to gain insight into the nature of rural mental health counseling experiences that she could not study easily through quantitative methods. Allowing participants to speak candidly about their experiences in a semistructured interview format provided an increased understanding of rural mental health counseling experiences, supervision and professional development. The participants also represented a variety of service venues, including outpatient services on an American Indian reservation.


One limitation of the study relates to the questionable reliability of self-reports. Some participants may have felt political or internal pressure to portray their geographic location or job in a positive light. The author’s presence during data collection may similarly have impacted participants’ responses. An obvious limitation is the narrow demographic representation and sample size. Although the participants represented a variety of community mental health settings, all the participants were Caucasian females. Having more substantive demographic differences (e.g., age, race, gender, years in the field) and a larger sample size could have further enriched the findings.


Implications for Clinical Practice, Counselor Education and Future Research


It remains clear that certain personal qualities and professional skills can lead to increased rural mental health job satisfaction and success. For example, knowing how to adapt to ever-changing situations, be they role or setting related, is important. Whether being prepared to help a community sandbag for an approaching flood, anticipating loss of cell phone reception or writing one’s own grants, flexibility becomes key. As All Things Rural said, “You just have to be very versatile.”


The current study reinforced findings from previous rural mental health research. Working in isolation is a hard truth for rural mental health counselors (Curtin & Hargrove, 2010). All Things Rural said, “Smaller communities . . . don’t have services.” The Pastor’s Wife expanded on the dilemma: “And the resources out there are even . . . less than here, so it was really important to have those credentials.” Obtaining additional credentials may not only help advance one’s career goals, but in rural mental health counseling, it can become a function of survival.


Smalley et al. (2010) further suggested anticipation of ethical dilemmas. Participants in this study recognized the coping skills necessary for dealing with concerns surrounding confidentiality, dual relationships and discrimination. Super Nanny used deflection and planned ignoring. All Things Rural used humor. Putting Out Fires and The Pastor’s Wife used limit setting. While Curtin and Hargrove (2010) promoted overall administrative and supervisory support for rural mental health professionals, it is the current author’s belief that such encouragement can prove additionally important regarding ethical concerns. Furthermore, Endacott et al. (2006) advocated that licensing boards differentiate between acceptable and unacceptable boundary crossings for rural mental health counselors and develop corresponding guidelines for protection when such occurrences happen.


Bushy and Carty (1994) found limited training regarding rural mental health practice. Inevitably, counseling training programs have an urban orientation toward the counseling profession (Bushy & Carty, 1994). Ellis et al. (2009) recommend specialized training to meet the unique needs of rural mental health counselors. Training areas of particular importance include telesupervision, social justice advocacy, and managing inevitable dual relationships and breaches in confidentiality.


In light of this study’s findings, optimism remains for this growing area of mental health counseling. All four participants were able to glean meaning despite extra responsibilities, isolation, ethical hardships and unavailable resources. It is clear these four rural mental health counselors are able to transcend tremendous obstacles. Indeed, “small but mighty” is a fitting description for this specialized group of mental health professionals.







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Anastasia Imig is a doctoral candidate at the University of South Dakota. Correspondence can be addressed to University of South Dakota, Room #210 Delzell, 414 E. Clark St., Vermillion, SD 57069,