Confirmatory Factor Analysis of the PTSD Checklist for DSM-5 in Psychiatric Outpatients With PTSD

Objective Accumulating evidence suggests that the latent structure of post-traumatic stress disorder (PTSD) symptoms may be more differentiated than the four-factor model proposed in Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). While expanded factor models have been supported in trauma-exposed and community samples, their applicability in clinically diagnosed psychiatric outpatient populations remains unexplored. This study aimed to evaluate and compare competing DSM-5–based PTSD factor models in a general adult psychiatric outpatient sample using the Korean version of the PTSD Checklist for DSM-5 (K-PCL-5).Methods Psychological data were obtained from 229 adults who were first-visit psychiatric outpatients diagnosed with PTSD or probable PTSD at a university hospital in South Korea. PTSD symptoms were assessed using the K-PCL-5. Confirmatory factor analyses (CFA) were conducted to compare eight competing PTSD models, including one- and two-factor models, the DSM-5 four-factor model, and alternative multidimensional models (dysphoria, dysphoric arousal, externalizing behavior, anhedonia, and hybrid). Models were estimated using the weighted least squares mean- and variance-adjusted estimator for ordinal data. Model fit was evaluated using multiple fit indices.Results The DSM-5 four-factor model demonstrated a borderline fit. All alternative multidimensional models showed improved fit relative to the DSM-5 model. Among them, the seven-factor hybrid model provided the best overall fit, followed by the six-factor anhedonia and externalizing behavior models. One- and two-factor models demonstrated poor fit.Conclusion PTSD symptoms in Korean adult psychiatric outpatients are better represented by expanded multidimensional factor models than by the DSM-5 four-factor model. These findings support the clinical applicability of post-DSM-5 expanded PTSD models and highlight the need for further validation across diverse clinical populations.