Factor Structure of the PANAS With Bayesian Structural Equation Modeling in a Chinese Sample

The Positive and Negative Affect Schedule (PANAS) is the most widely used self-report instrument for assessing affect. However, there are inconsistent findings regarding the factor structure of the PANAS. In this study, we applied Bayesian structural equation modeling (BSEM) to investigate the structure of the PANAS using data from a sample of 893 Chinese middle and high school students. Four models, the orthogonal two-, the oblique two-, the three-, and the bi-factor models were tested with prior specifications including approximately zero cross-loadings and residual covariances. The results indicated that the orthogonal two-factor model specified with informative priors for both cross-loadings and residual correlations has the best model fit. Confirmatory factor analysis with the maximum likelihood estimator (ML-CFA) based on modifications from BSEM analysis showed improved model fit compared to ML-CFA based on frequentist analysis, which is the evidence for the merit of BSEM for addressing misspecifications.