Psychometric evaluation of the Social Support Questionnaire for Transactions in Turkish gender-based violence sample

Abstract

Aim: This study aims to investigate the reliability and predictive validity of the Social Support Questionnaire for Transactions (SSQT) scale. Design: A psychometric design using cross-sectional data. Method: This cross-sectional descriptive study was conducted between February–June 2021 using snowball sampling through an online survey panel. 204 Turkish survivors of gender-based violence (GBV) participated in the research. Results: The mean age of participants was 35.66 ± 12.50. Exploratory and confirmatory factor analysis examined the construct validity of the SSQT scale. The principle axis factoring (PAF) estimation method was performed, including oblique rotation (Promax) for EFA, and the diagonally weighted least squares (DWLS) estimation method was used for CFA. Test-retest reliability coefficients (r) were moderate to excellent, ranging from 0.48 to 0.88. The analyzes supported the 5-factor solution, and the reliability was evaluated with Cronbach’s Alpha coefficients for Social Friendship, Daily Emotional Support, Problem-Oriented Emotional Support, Daily Instrumental Support, and Problem-Focused Instrumental Support sub-dimensions, and the total score of SSQT showing 0.87, 0.84, 0.90, 0.73, 0.83, and 0.93, respectively. The relationships between sub-dimensions of SSQT showed moderate positive correlations ranging from 0.40 to 0.60. There were weak negative correlations between SSQT and PHQ-9, PHQ-15 and GAD-7, ranging from 􀀀 0.20 to 􀀀 0.34, which shows the external validity of the SSQT. Conclusion: The relationships between sub-dimensions of SSQT and PHQ-9, PHQ-15, GAD-7 are given in the results support using the SSQT scale as a research and clinical instrument for assessing women who had experienced GBV and have/have not received social support in Turkish culture.

Publication
Archives of Psychiatric Nursing, 45,184-191
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Ali Mertcan Köse
Ali Mertcan Köse
Ph.D. Candidate of Statistics

My research interests include latent variable modeling,supervised learning and bayesian statistics.