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Article 33

Professional Development and Personal Adjustment Predictors of Students’ Counseling Self-Efficacy

Sherri Ruggiero, Alani Rabaino, Danielle Richards, & William E. Martin, Jr.

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Information from this manuscript was presented at the Rocky Mountain Psychological Association Conference, March, 2013, Denver, CO.

Counselor training and supervision play an important role in supporting counselor success, and there have been a number of models proposed as well as applied with varying degrees of success (Daniels & Larson, 1998; Lent, Hill, & Hoffman, 2003; Melchert, Hays, Wiljanen, & Kolocek, 1996). Counseling self-efficacy models have wide appeal since they are grounded in Bandura’s social-cognitive theory which is one of the most widely used theories in applied psychology. This approach is based on self- reflective thought that affects one’s behavior (Bandura, 1989). Applied to a counselor training model, it helps to identify perceptions about characteristics and abilities that relate to successful behavior by establishing what a counselor attempts to achieve and how much effort the counselor will invest in her performance (Bandura, 1989). Studies have shown that as counselors develop self-efficacy, they increase their professional abilities (Larson, Suzuki, Gillespie, Potenza, Betchtel, & Toulouse, 1992). Moreover, client improvement outcomes have been shown to relate to counselor self-efficacy when client feedback is used during supervision (Reese et al., 2009). Little is known about how personal development behaviors of counselors-in-training relate to self-efficacy. Nugent and Jones (2009) state “Effective counselors are well-integrated individuals committed to their own continued growth” (p. 35). A unique focus of this study is to assess whether healthy personal development perceptions of graduate counseling students significantly predict higher counseling self-efficacy among the students. The ability to provide effective counseling requires clinical knowledge and skills obtained from training and supervised experience (Reese et al., 2009). It is also important for a professional counselor to have counseling self-efficacy, which is the belief that one has the ability to perform required counseling competencies and produce successful outcomes (Daniels & Larson, 1998). The development of counseling self-efficacy is an essential element in preparing graduate students to become professional counselors (Melchert et al., 1996). Professional training and supervised experience has been shown to improve counseling self-efficacy in graduate students (Melchert et al., 1996). It is expected that healthy personal development practices by graduate students will also increase their counseling self-efficacy. If so, a more focused emphasis on healthy personal development in counselor training programs may be warranted. Self-compassion, emotional regulation, and personal growth initiative were three variables chosen in this study to measure healthy personal development of graduate students preparing to be professional counselors. These three variables encompass six of the 14 personal characteristics of an effective counselor identified by Nystul (2006): (1) emotionally stability, (2) empathy and caring, (3) self-awareness, (4) self-acceptance, (5) positive self-esteem, and (6) self- realization.

Self-compassion is a personal development practice that is expected to increase counseling self-efficacy. Self-compassion is described as acknowledging one’s own feelings of caring and kindness towards oneself, understanding one’s inadequacies, and being aware that one’s experiences are a part of the human condition (Neff, 2003). Neff (2003) found that increased levels of self-compassion are linked to a more developed psychological well-being. Additionally, higher levels of self-compassion have been shown to be significantly linked to empathetic concern for others among community adults (Neff & Pommier, 2013). Therefore, it is anticipated that self-compassion would be an important factor for increasing self-efficacy.

Emotional regulation is another personal development practice that is expected to contribute to higher levels of counseling self-efficacy. Gross and John (2003) found that when individuals utilized the common emotional regulation strategy of reappraisal, it resulted in increased levels of environmental mastery, personal growth, self-acceptance, and all around well-being. Using the emotional regulation technique led to outcomes associated with a more optimistic attitude, fewer depressive symptoms, increased self- esteem, and an overall greater life satisfaction (Gross & John, 2003).

Intentional personal growth is also a construct of personal development practice that is anticipated to contribute to counseling self-efficacy. Intentional personal growth constitutes the ability to change and adapt to new situations and has been shown to be an important factor in career adaptability, managing stressors, and mastering new skills (Robitschek et al., 2012). Intentional personal growth is encompassed by the construct of personal growth initiative, a set of skills for developing self-improvement (Robitschek, 1998). Research has indicated that higher levels of personal growth initiative contributed to increased overall well-being and decreased emotional and psychological distress (Hardin, Weigold, Robitschek, & Nixon, 2007; Robitschek & Kashubeck, 1999; Robitschek & Keyes, 2009). Altogether self-compassion, emotional regulation, and personal growth initiative are personal development practices that could be contributing factors to increasing counseling self-efficacy amongst counselors-in-training.

The purpose of this study is to assess how the healthy personal adjustment variables of self-compassion (Neff, 2003), emotional regulation (Gross & John, 2003) and personal growth initiative (Robitschek et al., 2012) predict counseling self-efficacy. These healthy personal adjustment constructs could be a focus of graduate counselor training programs if they are found to be salient predictors of counseling self-efficacy among graduate students. Moreover, professional development variables of age, prior counseling experience, and semester hours completed in counselor training of graduate students will be assessed and compared to the healthy personal adjustment variables. The research questions are:

Research Question 1: Will the set of professional development variables and the set of healthy personal adjustment variables both significantly predict counseling self-efficacy among graduated students in CACREP counselor education programs?

Research Question 2: Which specific variables from both the professional development and the healthy personal adjustment sets predict counseling self-efficacy?

Method

Participants

Two hundred and sixty-nine students responded to the survey. The data were assessed for completeness and missing data resulting in 59 participants lacking adequate responses for inclusion and 12 cases missing data on demographic items that could not be imputed, resulting in a study sample of N = 198. The sample consisted of 167 females (84%), 30 males (15%), and one person (1%) who did not identify their gender. Six participants self-identified as being African American (3%), 156 as European American (79%), six as Hispanic American (3%), six as Native American (3%), nine as Mixed Heritage (4%), and 15 participants identified as Other (8%). The average age of respondents was M = 30. 67 (SD = 9.99), with an age range of 21 to 66. The sample was comprised of students in various types of counseling programs with 4% of participants in addiction counseling, 3% in career counseling, 52% in clinical mental health counseling, 13% in marriage, couple, and family counseling, 21% in school counseling, 3% in student affairs and college counseling, and 4% in counseling programs not otherwise listed. The participants represented 24 universities covering the five major regions of the United States.

Procedure

Program directors listed by the Council for Accreditation of Counseling and Related Educational Programs (CACREP) from throughout the United States were sent e- mails requesting that they notify their students about the opportunity to choose to participate in this study during November, 2012. A link to the survey on Survey Monkey was provided and required approximately 15 minutes to complete. Interested students followed this link and completed the survey at their convenience. The survey consisted of four psychometric questionnaires and demographic questions. The psychometric questionnaires were the Counselor Self-Efficacy Scale (CSES), the Self-Compassion Scale (SCS), the Personal Growth Initiative Scale (PGIS), and the Emotional Regulation Questionnaire (ERQ). The information requested from the demographic questions included age, gender, ethnocultural background, academic program major, university, counseling experience, and counselor training. Two incentives were provided to participants for their participation in the survey, upon their request. First, students were provided with an interpretive report of their responses to the Self-Compassion Scale. Second, executive summary reports of the study results were made available to students who requested them. One hundred and fifty-three participants were e-mailed the executive summary of the study and 159 participants received Self-Compassion Scale profiles.

Measures

There were two sets of three predictor variables used in this study. One set of variables is referred to as healthy personal adjustment and is comprised of self- compassion, emotional regulation, and personal growth initiative. A second set, referred to as professional development, is comprised of three variables, counseling experience, counselor training, and age. The dependent variable in this study is counselor self- efficacy.

Self-compassion was measured using the Self-Compassion Scale (SCS) developed by Neff (2003). It is a 26-item measure consisting of six subscales. The names of the six SCS sub-scales are Self-Kindness, Self-Judgment, Common Humanity, Isolation, Mindfulness, and Overidentification. The items making up the SCS sub-scales have a 5-point scale ranging from 1 = ‘almost never’ through 5 = ‘almost always.’ Higher scores in this study reflect higher self-compassion. Neff reported that an overall model Confirmatory Factor Analysis was conducted to assess the fit of the six intercorrelated factors to the 26 items selected for the final version of the SCS. The model was found to fit the data adequately well. Internal consistency reliability was .78 for the 5-item Common Humanity subscale, .79 for the 4-item Isolation subscale, .75 for the 4-item Mindfulness subscale, and .81 for the 4-item Overidentification subscale. Internal consistency for the 26-item SCS was .92 (Neff, 2003). Neff also found that self- compassion had a significant negative correlation with anxiety and depression and a significant positive correlation with life satisfaction. The SCS in this study produced a high coefficient alpha = .937

Intentional personal growth was measured using the Personal Growth Initiative Scale (PGIS) initially developed by Robitschek (1998). It is a 9-item measure employing a 6-point Likert scale ranging from ‘definitely disagree’ to ‘definitely agree.’ A high score reflects higher perceived personal growth initiative. Robitschek et al. (2012) reported that the majority of internal consistency measures in several samples were above .85 in the original PGIS. The authors reported construct validity evidence indicating a single factor for European American college students using confirmatory factor analysis. Convergent validity was demonstrated with high PGIS scores correlating with high levels of instrumentality, assertiveness, and locus of control. High PGIS scores were inversely related to chance locus of control (Robitschek et al., 2012). In this study, the coefficient alpha for the total scores of the sample participants was high at .875.

Emotional regulation was measured using the Emotional Regulation Questionnaire (ERQ) developed by Gross and John (2003). The ERQ is a 10-item scale measure consisting of two subscales: Cognitive Reappraisal and Expressive Suppression. The items making up the ERQ subscales have a 7-point Likert-type scale ranging from 1 (strongly disagree) to 7 (strongly agree). Gross and John (2003) reported an overall ERQ test-retest reliability of .69, and subscale measures of Reappraisal and Suppression of .79 and .73, respectively. The overall ERQ model was assessed using Exploratory and Confirmatory Factor Analyses. The fit of the two intercorrelated factors to the ten items selected for the final version of the ERQ was good. The average internal consistency reliability was .79 across four samples of the 6-item Reappraisal subscale and .73 for the 4-item Suppression subscale. Interestingly, Gross and John also found that individuals who repeatedly use reappraisal were no less (or more) likely to use suppression than individuals who use reappraisal less often. The overall ERQ coefficient alpha was acceptable in this study at .715.

Questions to measure the predictor variables of the professional development variables were presented in the demographic information section of the survey. The participants provided self-reports as to the years they provided counseling related services, the number of credit hours completed in their counseling program, and their age in years.

Counselor self-efficacy was measured using the Counselor Self-Efficacy Scale (CSES) consisting of a 20-item measure developed by Melchert et al. (1996). The items making up the CSES have a 5-point Likert-type response scale indicating degree of agreement regarding respondent’s confidence in their counseling abilities. To protect against acquiescent response bias, half of the items are worded negatively, and the other half positively. The researchers used a Cronbach’s coefficient alpha to determine the internal consistency of the instrument and it was found to be .91 in the sample. Test-retest procedures were used to arrive at a reliability coefficient of r = .85 for the total scale scores. Additionally, convergent construct-related validity was measured by comparing the CSES to the Self-Efficacy Inventory (Friedlander & Snyder, 1983) and was found to be high (r = .83). Multiple regression was used to determine the correlation between CSES combined with level of training (r =.62), CSES with clinical experience (r = .55), and the correlation between level of training and amount of clinical experience (r = .48). We found a high coefficient alpha of .913 for the CSES in this study.

Data Analysis

Data were screened for accuracy and compliance to multivariate assumptions and important issues. Bivariate correlations and descriptive statistics of the seven variables were conducted using IBM SPSS 20. As Tabachnick and Fidell (2007) said about sequential multiple regression, “IVs can be entered one at a time or in blocks (p. 138).” Sets (blocks) of variables rather than single variables can be entered at a particular stage in the analysis (Meyers, Gamst, & Guarino, 2006). A block-entry linear multiple regression analysis was used in this study to enter two sets (blocks) of variables in a sequential manner. The first set (Professional Development) entered into the sequential multiple regression analysis was comprised of the three predictor variables age, counseling experience, and completed credit hours along with the dependent variable counseling self-efficacy. The second set (Healthy Personal Adjustment) of predictor variables was then entered into the analysis comprised of self-compassion, emotional regulation, and personal growth initiative.

A multiple correlation (R) was assessed for significance of the block of Professional Development predictor variables to counseling self-efficacy. Additionally, the effect size (R2 ) was interpreted for the percentage of variance in counseling self- efficacy explained by the Professional Development predictor variables (Vacha-Haase & Thompson, 2004). Next, the change (increase) in R and R2 after adding the second set of variables to the first set of variables was assessed for significance and variance in counseling self-efficacy explained by the Healthy Personal Adjustment variables. Then, R and R2 were assessed for the total model of the two sets of variables. Finally, each of the six variables was assessed as to their individual significance in the total model.

Observed power was calculated for the results of the multiple linear regression analysis.

Results

Data Screening

Initially, the data imported from a Survey Monkey file to an IBM SPSS file were assessed and found to be accurate. There were originally 269 surveys partially or fully completed by participants registered on the Survey Monkey file, 59 of which were missing more than one complete assessment and were deleted for incompleteness. A small number of missing values (< 5%) were discovered on the assessment scales and they were replaced using mean substitution based upon the variables with the missing data. Twelve cases had missing data on the demographic variables used in the multiple linear regression analysis resulting in a final sample size of N = 198. An a priori power analysis based upon a medium-size relationship, σ = .05, a power of .80, and six predictor variables was calculated using ratios of cases to predictor variables formulas specified by Tabachnick and Fidell (2007). Testing the multiple correlation requires a minimum of N ≥ 50 + 8 X (# of predictor variables) which is 98 cases needed and for testing the individual predictor variables, N ≥ 104 + (# of predictor variables) resulting in 110 cases needed. The sample size in this study of N = 198 exceeds both needed ratios of cases to predictor variables.

Multivariate screening for multicollinearity, multivariate normality, linearity, and homoscedasticity was conducted before the linear multiple regression analysis was performed. Multicollinearity was assessed using tolerance values and all values were greater than .50, an indication of confidence that there is not an issue of redundancy of variables. Analyses of residuals were utilized to assess for normality, linearity, and homoscedasticity. The regression standardized residual histogram distribution reflected normality of residuals. The regression standardized residual scatter-plot showed a concentration of points in the center of the plot and a normal distribution of residuals trailing off proportionately from the center (Tabachnick & Fidell, 2007), indicating normality. There was no indication of curvilinear relationships. There was also indication of homoscedasticity, the standard deviation of errors of prediction were approximately equal for the predicted variable. In conclusion, the analysis of residuals suggested that normality, linearity, and homoscedasticity existed among the variables used in the multiple linear regression analysis.

Table 1

Bivariate Correlations Between the Study Variables

Note: Scores on variables represent higher scores on the constructs assessed. CSES = Counselor Self-Efficacy Scale; Age = age in years; CE = counseling experience in years; CCH = completed credit hours; SCS = Self-Compassion Scale; ERQ = Emotional Regulation Questionnaire; PGIS = Personal Growth Initiative Scale.

*p < .05, ** p < .01, *** p <.001

Prediction of Professional Development Variables and Healthy Personal Adjustment Variables to Counseling Self-Efficacy

The bivariate correlations between the seven variables are reported in Table 1. All but one, emotional regulation, of the six predictor variables were significantly correlated (bivariate) to counseling self-efficacy. It is noteworthy that self-compassion showed a rather high correlation to personal growth initiative (r = .66), which will be discussed further in the regression analysis results.

A sequential multiple linear regression analysis with two blocks of variables was conducted using IBM SPSS 20 to answer the research questions. The set of three variables related to healthy personal adjustment and the set of three variables of professional development were analyzed in blocks for their contributions to explaining counseling self-efficacy. In the first block, the professional development set included years of professional helping experience, completed semester hours in a professional counseling program, and age. The professional development set of variables produced a model multiple correlation of R = .627 and the model was significant F (3, 194) = 41.985, p < .001. The R = .627 is at the moderate to high level (.40 - .70) and R2 = .39 indicating that approximately 39% of the variability in counseling self-efficacy can be explained by years of professional helping experience, completed semester hours in a professional counseling program, and age. However, age was not a significant contributor to counseling self-efficacy (p > .05).

Table 2

Sequential Multiple Linear Regression of the Professional Development and Healthy Personal Adjustment Predictors to Counseling Self-Efficacy

Note: Scores on variables represent higher scores on the constructs assessed. Age = age in years; CE = counseling experience in years; CCH = completed credit hours; SCS = Self-Compassion Scale; ERQ = Emotional Regulation Questionnaire; PGIS = Personal Growth Initiative Scale.

The set of three variables related to healthy personal adjustment were added in the second model to compare their contributions in explaining counseling self-efficacy. Healthy personal adjustment is comprised of self-compassion, emotional regulation, and personal growth initiative in this study. The model multiple correlation that included both sets of variables was R = .698 F(6, 191) = 30.252, p <.001. The multiple correlation was R = .698 nearly at the high level (.70) and R2 = .48 indicated that approximately 48% of variability in counseling self-efficacy was explained by both sets of variables related to professional development and healthy personal adjustment. The addition of the set of healthy personal adjustment variables showed a significant ΔR2 = .09, p < .001 (see Table 2). Explained variability increased approximately 9% by including the set of healthy personal adjustment variables to the professional development variables.

The two sets of variables produced a total of three variables that significantly predicted counseling self-efficacy (see Table 2). Two of the variables were from the professional development set, counseling experience (p <.001) and completed credit hours (p <.001). One variable, personal growth initiative, was significant (p < .01) from the healthy personal adjustment set. The observed power was 1.0 providing a high degree of confidence in the stability and generalizability of these findings for future similar studies.

Age, self-compassion, and emotional regulation variables did not contribute significantly to counseling self-efficacy in this model. However, as noted previously, self-compassion and personal growth initiative had a strong correlation with each other. Another sequential multiple linear regression was conducted with personal growth initiative removed and self-compassion was a significant predictor (p < .001) in the model along with counseling experience and completed credit hours producing a slightly lower model multiple correlation of R = .679. Either variable could be used in the model but not together since they share considerable variance.

Discussion

The first research question addressed the extent that a set of professional development variables and the set of healthy personal adjustment variables both significantly predict counseling self-efficacy among graduate students in CACREP programs. The professional development set of variables significantly predicted counseling self-efficacy explaining 39% of the variability. The addition of the set of healthy personal adjustment variables significantly increased the prediction of counseling self-efficacy by an increment of 9%. Nearly half of the variability in counseling self- efficacy (48%) was explained by both sets of variables.

The purpose of the second research question was to identify which individual variables predict counseling self-efficacy among the students. The significant individual predictors of the professional development variables were counseling experience and completed credit hours and one variable from the set of healthy personal adjustment variables, personal growth initiative, was significant. However, it was found that self- compassion would have been a significant predictor if personal growth initiative was excluded. Self-compassion and personal growth initiative were highly correlated with each other.

These study findings support previous research showing that prior counseling experience and more training in counseling predicted counseling self-efficacy (Larson et al., 1992; Melchert et al., 1996). Additionally, this research adds increased understanding of counseling self-efficacy by showing that healthy personal development variables of higher self-compassion and higher personal growth initiative among counselors in training significantly predicted higher counseling self-efficacy.

There are limitations of this study to acknowledge. First, individuals volunteered to participate through e-mail (linked to Survey Monkey) and may not be representative of all CACREP counseling students. Second, all measures were self-reported, and because of this, scores of participants may reflect response style biases that could have interfered with the accuracy of the results.

The results of this study provide information regarding possible essential components of counselor self-efficacy. Prior professional helping experience and training semester hours completed in professional counseling programs play a large role in the development of counselor self-efficacy, and these elements are addressed in most graduate level educational settings. Professional experience will vary based upon individual differences in students who enter counseling programs and can be enhanced by curriculum opportunities (e.g., field-work experience, practicum and internship). The development of healthy personality beliefs and behaviors including self-compassion and personal growth initiative are amenable to change and could become added components to the curriculum of counselor education programs. For instance, Loving Kindness Meditation (LKM) has been used successfully to cultivate self-compassion in individuals (Fredrickson, Cohn, Coffey, Pek, & Finkel, 2008). Loving Kindness Meditation is a variation of meditation in which a particular phrase of words are thought or spoken, and are meant to generate feelings of goodwill and kindness toward the self (or others) and sets an intention of loving kindness without denying actual experience. Furthermore, Robitschek and her colleagues (2012) called for the creation of individualized counseling methods and workshop curriculum for people with low levels of intentional growth.

The findings of this study prompt further questioning and continued research. It would be beneficial to conduct replication studies to assess the stability of these findings across new samples and contexts. Further research to enhance an understanding of the connection between self-compassion and personal growth would be valuable for both educational and clinical purposes. It would be informative to compare self-efficacy between counseling students from CACREP programs that require engagement in healthy personal adjustment activities (i.e., personal counseling, attending workshops or conferences) and those that do not. Future research in this area is suggested as a means to discover what levels of these attributes counseling students bring into their programs (e.g., levels of personal growth initiative and self-compassion), how counseling courses contribute to levels of personal growth and self-compassion (e.g., through course reading and lecture topics), and how working with clients affects these measures.

Perhaps the most intriguing aspect of decoding the elements of counselor self- efficacy is the implication for future counselor educators in CACREP programs. This area of research has the potential to improve our understanding of essential educational elements for counselors-in-training, advance curriculum development, and as a result enhance student counseling self-efficacy. For instance, incoming counseling students could self-assess themselves on healthy personal adjustment constructs, and experiential and learning activities could be available to students to enhance their healthy personal adjustment. Identified supports could be implemented to address these specific areas of need and to provide opportunities for expansion in these areas with programs such as mentoring, individual or group counseling, and psycho-educational activities with a focus on self-compassion and personal growth. Some CACREP programs already incorporate personal counseling expectations of their students, as well as requirements for accumulating a certain number of hours from attending workshops, conferences, and other value added endeavors. Based on the results of this study, it is recommended to create supplemental psycho-educational activities toward self-compassion (e.g., Loving Kindness Meditation, addressing critical self-talk) and personal growth (e.g., self- discovery activities, goal setting, exploring personal interests, building positive life values). Identifying the components of counselor self-efficacy is an important step in understanding what it takes to create excellence in counseling students as well as the profession as a whole. Research in this area can be used to examine the ability of CACREP counselor education programs to identify potential students that embody these characteristics, to evaluate student’s strengths and challenges related to counselor self- efficacy, and to incorporate these identified needs in program development and training curriculum.