197 The Professional Counselor | Volume 13, Issue 3 Instrument The Outcome Questionnaire-45.2 The OQ-45.2 is a self-report questionnaire that captures individuals’ subjective functionality in various aspects of life that can lead to common mental health concerns (e.g., anxiety, depression, substance use). The current three-factor structure of the OQ-45.2 has 45 items rated on a 5-point Likert scale, with rankings of 0 (never), 1 (rarely), 2 (sometimes), 3 (frequently), and 4 (almost always; Lambert et al., 2004). Nine OQ-45.2 items are reverse scored, with total OQ-45.2 scores calculated by summing all 45 items with a range from 0 to 180. Clinically significant changes are represented in a change score of at least 14, whether positive or negative (i.e., increased or reduced distress). The Symptom Distress subscale (25 items) evaluates anxiety, depression, and substance abuse symptoms, as these are the most diagnosed mental health concerns (Lambert et al., 1996). The Interpersonal Relations subscale (11 items) includes items that measure difficulties and satisfaction in relationships. The Social Role Performance subscale (nine items) assesses conflict, distress, and inadequacy related to employment, family roles, and leisure activities. The OQ-45.2 also includes four critical items (Items 8, 11, 32, and 44) targeting suicidal ideation, homicidal ideation, and substance use. The Cronbach’s alpha for the OQ-45.2 in the current study was calculated at .943. Data Analysis We calculated descriptive statistics on the total sample population, including the mean, standard deviations, and frequencies. Subsequently, we conducted preliminary descriptive analyses to test for statistical assumptions that included missing data, collinearity issues, and multivariate normality (Byrne, 2016). In the first analysis, we used confirmatory factor analysis (CFA) to test the factor structure of the OQ-45.2 with this population (N = 615) and subsequently used exploratory factor analysis (EFA) to evaluate revised OQ models. We conducted CFA utilizing the original three-factor oblique model (Lambert et al., 2004) as the a priori model to test the hypothesized structure of the latent variables. In addition, based on the results of the study, we tested a series of alternative structural models outlined by Bludworth and colleagues (2010). Given the non-normal distribution, we utilized MPlus (Version 8.4) with a robust maximum likelihood (MLR) parameter estimation (Satorra & Bentler, 1994). To address missing data, we employed a full information maximum likelihood (FIML) to approximate the population parameters and produce the estimates from the sample data (Enders, 2010). Results of the CFA were evaluated using several fit indices: (a) the chi-square test of model fit (χ2; nonsignificance at p > .05 indicate a good fit [Hu & Bentler, 1999]); (b) the CFI (values larger than .95 indicate a good fit [Bentler, 1990]); (c) TLI (values larger than .95 indicate a good fit [Tucker & Lewis, 1973]); (d) RMSEA with 90% CI (values between .05 and .08 indicate a good fit [Browne & Cudeck, 1993]); and (e) standardized root-mean-square residual (SRMR; values below .08 indicate good fit [Hu & Bentler, 1999]). Following the CFA, we conducted EFA because of poor model fit across all models and several items with outer loadings of less than 0.5 (Tabachnick & Fidell, 2019). Kline (2016) recommended researchers should not be constrained by the original factor structure when CFA indicates low outer loadings and should consider conducting an EFA because the data may not fit the original number of factors suggested. Accordingly, we conducted an EFA to test the number of factors derived from the 45-item OQ-45.2 within our population. We exceeded the recommended ratio (i.e., 10:1) of participants to the number of items (12.6:1; Costello & Osborne, 2005; Hair et al., 2010; Mvududu
RkJQdWJsaXNoZXIy NDU5MTM1