TPC Journal Volume 11 Issue 2

222 The Professional Counselor | Volume 11, Issue 2 Sensitivity to determine BPD-Tx participants’ scores of psychological distress using these two measures. We found strong correlations between the SCL-90-R factors and the BSI factors (Depression: r = .92, p < .001; Anxiety: r = .97, p < .001; Obsessive-Compulsion: r = .95, p < .001; Interpersonal Sensitivity: r = .90, p < .001; and Average Psychological Distress: r = .98, p < .001). Following Derogatis (1993), who found no significant difference in validity between the SCL-90-R and the BSI, we used only the BSI items to create symptom factors for both samples. The internal consistency of the BSI items for the BPD-Tx sample was very high (α = .95). Social Functioning The Network of Relationships Inventory-Behavioral Systems Version (NRI; Furman & Buhrmester, 2009) is a 33-item self-report measure of social support and negative interactions in various relationships (i.e., one’s mother, father, friends, and romantic partner). Participants rate the frequency of positive support or negative interactions on a Likert-scale from 1 ( little or none ) to 5 ( the most ). The Positive Support scale includes five subscales: Seeks Secure Base, Provides Secure Base, Seeks Safe Haven, Provides Safe Haven, and Companionship. The Negative Interactions scale includes three subscales: Conflict, Antagonism, and Criticism. Higher scores indicate more of each factor. The internal consistency was high for both samples (BPD-Tx, α = .93; student sample, α = .94). We calculated the mean score of the Positive Support subscales, including Seeks Secure Base, Seeks Safe Haven, and Companionship. We did not include Provides Secure Base or Provides Safe Haven because Furman and Buhrmester (2009) described these as “caretaking” factors rather than “attachment” or “affiliation” factors. We also calculated the mean score of all three Negative Interactions subscales. Data Analysis Plan To begin, we tested for the assumptions of analysis, following guidelines proposed by Tabachnick and Fidell (2019). We defined outliers as data points beyond three standard deviations from the mean. We evaluated outliers within each group and replaced them with the value that was three standard deviations above the group mean. We chose this more liberal approach to outliers to maximize variability in the data. It was especially important to maintain variability in the outcome variable of total SIB given that higher levels of SIB have great clinical significance. For skewness and kurtosis of the composite variables, we used ±2 as our acceptable range of values. We transformed variables that did not meet our criteria for normality. We also utilized the missing completely at random test and found no systematic patterns to missing data, and thus used the group means to replace missing values for analysis. To assess SIB in the two samples, we examined the intent of SIB acts separately for each sample and analyzed if SIB rates differed based on demographic information. To examine psychosocial predictors of SIB, we conducted a multiple linear regression. We used total SIB (including suicidal, nonsuicidal, and ambivalent SIB) as the outcome variable. We also examined differences in predictors of total SIB between the BPD-Tx and student samples by including interaction terms (e.g., psychological distress x sample). Statistically significant interactions were graphed to aid interpretation (Howell, 2013). For the multiple linear regression analysis, we used effect coding for sample type (Daly et al., 2016), which allows comparison of a sample mean to the overall mean instead of using one sample as a reference group. Additionally, we centered the predictor variables around the grand mean for the whole sample to reduce the risk of multicollinearity. We inspected the tolerance and variance inflation factors, and used multiple sources (e.g., correlations between variables, p -values, and the standard error of the regression coefficients) to determine if multicollinearity was an issue.