The Positive and Negative Affect Schedule (PANAS): Psychometric Properties of a Mongolian Version in University Students

1) Background: Understanding affective states is essential in psychology and psychiatry, yet few validated scales remain for evaluating affect in Mongolia. The Positive and Negative Affect Schedule (PANAS) is widely used globally, but cultural and linguistic differences necessitate its validation for specific populations. This study aimed to assess the psychometric properties of the Mongolian PANAS among university students, a group navigating academic and socio-emotional challenges where emotional well-being is crucial. 2) Methods: After a comprehensive translation and back-translation procedure with expert validation, we administered the 20-item Mongolian PANAS to 480 university students (mean age = 20.5, SD = 2.0; 54% female). We used exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) to assess structural validity, Cronbach's alpha to evaluate internal consistency, and test-retest reliability over 14 days to determine temporal stability. Item-scale correlations were examined to test construct validity. All statistical analyses were conducted using SPSS version 26, Amos version 29, and RStudio. 3) Results: The factor analysis supported a two-factor structure, consistent with the original PANAS, which explained 44.9% of the total variance and demonstrated a moderate model fit (CFI = 0.905, RMSEA = 0.066). The scale exhibited good internal consistency (Cronbach’s alpha = 0.788) and strong temporal stability (ICC = 0.806–0.831), confirming its reliability in measuring affect among Mongolian university students. 4) Conclusion: The Mongolian PANAS is a psychometrically sound tool for assessing affective states in university students, which supports its use in research and educational settings. However, additional refinement may be needed to enhance its cultural sensitivity and applicability. Future research should explore its use in clinical populations and longitudinal studies to assess its predictive validity over time.