The purpose of this study was to investigate how symptom distress, social role, interpersonal relationships, gender, age, number of supports, and education level predict client attrition in a community sample. Using binary logistic regression and cross‐sectional data, the authors examined the predictive impact of 8 variables on adult client attrition in a university‐based community counseling clinic. Results indicated that education level, interpersonal relationships, and number of supports significantly predicted attrition. In this sample, gender, age, symptom distress, social role, and race did not significantly predict attrition. Implications for clinical assessment and counseling practice are discussed.
The researchers employed a confirmatory tetrad analysis (CTA) using partial least squares–structural equation modeling (PLS-SEM) with Outcome Questionnaire–45.2 (OQ-45) data, examining the measurement model of the OQ-45 scores with a sample of male adult clients ( N = 1,558) receiving individual therapy at a university-based community counseling and research center (UBCCRC). Using CTA-PLS, this study examined the reflective and formative nature of each of the OQ-45 items and dimensions. These results identified the innovative second-order formative–formative three-factor model as a best alternative measurement model to represent and calculate the scores of OQ-45 scale.
The Child Behavior Checklist (CBCL) is one of the most frequently used assessments of social, emotional, and behavioral functioning; however, previous research has noted inconsistency in the factor structure and items included on the Child Behavior Checklist for Ages 6 to 18 Years (CBCL/6-18) when tested with diverse samples of client populations. Thus, the purpose of our investigation was to examine the factor structure of CBCL/6-18 scores ( N = 459) with diverse American children referred to receive school-based mental health counseling enrolled in five Title I elementary schools in the Southeastern United States. We performed confirmatory factor analysis (CFA) and principal component analysis (PCA) on CBCL/6-18 scores to examine the factor structure and internal consistency reliability of the data. Results demonstrated an inadequate fit for model and further data analyses resulted in a three-factor, 32-item model (41.40% of the variance explained). Implications of the findings support a new conceptual framework of the CBCL/6-18 to provide a more parsimonious model when working with diverse populations, specifically children from low-income families.
The researchers examined the factor structure and model specifications of the World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) with confirmatory tetrad analysis (CTA) using partial least squares–structural equation modeling (PLS-SEM) with a sample of adult clients ( N = 298) receiving individual therapy at a university-based counseling research center. The CTA and PLS-SEM results identified the formative nature of the WHODAS 2.0 subscale scores, supporting an alternative measurement model of the WHODAS 2.0 scores as a second-order formative–formative model.
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