Kim Minsun, Wang Ze
Department of Educational, School and Counseling Psychology, 14716University of Missouri, Columbia, MO, USA.
Eval Health Prof. 2022 Jun;45(2):157-167. doi: 10.1177/0163278721996794. Epub 2021 Mar 4.
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.
积极和消极情绪量表（PANAS）是评估情绪时使用最广泛的自我报告工具。然而，关于PANAS的因素结构存在不一致的研究结果。在本研究中，我们应用贝叶斯结构方程模型（BSEM），使用来自893名中国中学生样本的数据来研究PANAS的结构。测试了四个模型，即正交二因素模型、斜交二因素模型、三因素模型和双因素模型，并给出了包括近似零交叉负荷和残差协方差在内的先验规范。结果表明，对交叉负荷和残差相关性都指定了信息性先验的正交二因素模型具有最佳的模型拟合度。基于BSEM分析的修正，使用最大似然估计器（ML-CFA）进行的验证性因素分析显示，与基于频率主义分析的ML-CFA相比，模型拟合度有所提高，这证明了BSEM在解决模型设定错误方面的优点。
