Latent Class Analysis With Distal Outcomes of Five-Factor Personality Traits and Their Association With Psychological Problems: A Cross-Sectional Study in Iran

Abstract

BackgroundPersonality traits substantially shape behavior, emotions, and cognition, yet the specific trait patterns most predictive of psychological problems remain unclear. This study applied latent class analysis with distal outcomes to elucidate these relationships.Material and MethodsIn this cross-sectional study, 746 adults from Isfahan, Iran, were assessed. The five-factor model (NEO-FFI) measured personality traits, and the Depression Anxiety Stress Scale (DASS-21) captured symptoms of depression, anxiety, and stress. We first conducted latent class analysis with distal outcomes to derive distinct personality profiles, then used ordinal logistic regression to evaluate associations between these profiles and psychological problem severity.ResultsTwo personality classes emerged: positive traits-predominant (56.3) and neuroticism (43.7). Adjusted ordinal logistic regression indicated that membership in the neuroticism-predominant class was associated with higher odds of depression (OR = 1.521, 95 CI: 1.102-2.098, p = 0.011) and anxiety (OR = 1.469, 95 CI: 1.068-2.021, p = 0.018).ConclusionsOur findings demonstrate that a neuroticism-dominant personality profile confers elevated risk for common psychological problems. Integrating latent class analysis with distal outcomes offers a robust approach for identifying high-risk trait configurations in population studies.