Abstract: Structural Equation Modeling to Examine Psychometrics of the Geriatric Social Work Competency Scale II (GSWCS II): A Measure of Social Work Competencies in Aging and Geriatrics (Society for Social Work and Research 24th Annual Conference - Reducing Racial and Economic Inequality)

515P Structural Equation Modeling to Examine Psychometrics of the Geriatric Social Work Competency Scale II (GSWCS II): A Measure of Social Work Competencies in Aging and Geriatrics

Saturday, January 18, 2020
Marquis BR Salon 6 (ML 2) (Marriott Marquis Washington DC)
* noted as presenting author
Samantha Bates, PhD, Assistant Professor, Texas Christian University, TX
Scott Wilks, PhD, Associate Professor, Louisiana State University at Baton Rouge, Baton Rouge, LA
Background and Purpose: Exponential growth of the American older adult population is widely documented. The rapid aging of our nation reflects a need for effective curriculum and field training for social work students in geriatric/gerontology (gero) settings. Equally important is empirical measurement to assess practice competencies of social work students in gero-specific contexts. The Geriatric Social Work Competency Scale II (GSWCS II) was created by CSWE-Hartford Foundation for such assessment. Taken from CSWE Educational Policy and Accreditation Standards, the scale includes five 10-item measures of perceived knowledge and skills in (a) values, ethics and theories; (b) assessment; (c) intervention; (d) services, programs and policies; and (e) leadership. Scant research exists on the properties of these measures. To redress this literature gap, the purpose of this study was a psychometric examination of the GSWCS II.


Method: Survey data were collected from three recent cohorts of MSW advanced year students (N = 170) at a large southern university in the United States. Students completed the GSWCS II at the beginning of their MSW program. Majority of the sample self-identified as female, Caucasian, a 2-year program enrollee; the average age was approximately 27 years. Data were prepared and cleaned using Version 23 of the Statistical Pack for the Social Sciences. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were then employed using structural equation modeling techniques in Mplus Version 8. All EFA and CFA procedures for ordinal data utilized the weighted least squares means and variance adjusted estimator to extract factors (Bowen & Guo, 2012). During EFA procedures, eigenvalues over 1.0 provided preliminary evidence of the number of latent factors. Cutoffs and localized areas of strain identified in EFA informed CFA modeling. CFA re-examined relationships among observed items and latent factors and correlations among factors. In all model testing, an acceptable fit of a model was defined by the following: root mean square error of approximation (RMSEA) ≤ .05; comparative fit index (CFI) ≥ .95; Tucker-Lewis Index (TLI) ≥ .95 (see Bowen & Guo, 2012). Correlations among factors were considered significant at p < .05.

Results: Preliminary results of EFA showed adequate fit for a five-factor model (χ2 = 1445.24; RMSEA = .05; CFI = .98; TLI = .97). Several local areas of strain were identified and adjusted for prior to CFA procedures. Preliminary findings of CFA indicated adequate model fit for the five-factor model (χ2 = 1729.30; RMSEA = .05; CFI = .97; TLI = .97). All five factors were significantly (p < .05) and positively correlated with one another.

Conclusion/Implications: Results of psychometric testing indicate initial support for a five factor GSWCS II Scale designed to assess social work competencies in gero-specific contexts. The use of this measure can provide social work educators and the field at-large with valid and reliable information about students’ skills and competencies related to working with the growing older adult population. Additional implications for practice, policy, and future research will be discussed.