TPC Journal-Vol 9- Issue 4-FULL ISSUE

290 The Professional Counselor | Volume 9, Issue 4 Brief Resilience Scale (BRS). The BRS was developed to measure a person’s ability to recover from stress and cope with challenging situations (B. W. Smith et al., 2008). The BRS is used to measure the construct of resilience. As a 6-item self-report assessment, the BRS includes a 5-point Likert-type scale with responses ranging from strongly disagree to strongly agree . A sample item is “I usually come through difficult times with little trouble.” B. W. Smith and colleagues (2008) reported that the Cronbach’s alpha values of the BRS range from .80 to .91, and we calculated a Cronbach alpha of .89 for the current study. Flourishing Scale (FS). The FS was designed to measure individuals’ self-perceived success in areas like optimism and relationships (Diener et al., 2010) and used to measure the construct of wellness in this study. The FS is an 8-item self-report assessment with a 7-point Likert-type scale with responses ranging from strongly disagree to strongly agree (Diener et al., 2010). A sample item is “I lead a purposeful and meaningful life.” Diener and colleagues (2010) reported moderately high reliability with a .87 Cronbach’s alpha coefficient, and in the current study, the FS had a Cronbach alpha of .86. Data Analysis Statistical power analysis. We used an a priori type of the G*Power to set the minimum number of participants needed to detect statistical power for this research design. Based on an alpha of .05, a power level of .90, and four predictors (Faul, Erdfelder, Buchner, & Lang, 2009), the computation results suggested that a minimum of 73 participants was required to detect statistical significance with at least a moderate size effect (.15). We had 86 participants, suggesting adequate power. Preliminary analyses. We analyzed all data using the Statistical Package for the Social Sciences, Version 20 (SPSS; IBM Corporation, 2011). Before addressing our stated research questions, we cleaned the dataset and addressed missing data. We did not observe any pattern between missing data points. Therefore, the type of missing data was completely random, which was addressed using the series of mean function within the SPSS. Next, we calculated descriptive statistics and alpha coefficients for each scale used in the study (see Table 1). Before performing hierarchical regression analyses, we tested all associated model assumptions. First, we examined study variables based on their types and concluded each utilized a continuous scale. We then assessed normality with the Shapiro-Wilk test of normality ( W > .05), indicating data was normally distributed for the dependent variable. To identify outliers, we examined boxplots. Although there were a few mild outliers, no extreme scores were detected. We assessed linearity and homoscedasticity through inspection of standardized residual plots. To assess for the assumption of multicollinearity, we examined the correlation matrix of study variables to determine if any correlated highly. According to Field (2013), correlations above .80 are considered high and may indicate the presence of multicollinearity. In the present study, none of the correlation coefficients were above .50 (see Table 2). Collectively, these findings indicated no evidence suggesting any of the model assumptions had been violated. As a result, the dataset was deemed appropriate for analysis using a hierarchical regression design. Primary analysis. Descriptive statistics were calculated to organize the data by producing means, mode, median, standard deviations, and minimum and maximum scores for the study variables (Field, 2013). Individually, we reviewed descriptive statistics for the compassion fatigue variable, and results were reported to address the first research question. Next, we performed a three-step hierarchical linear regression to address the second research question.