Assessing Goodness of Fit: Simple Rules of Thumb Simply Do Not Work
研究验证性因子分析模型的拟合指标，发现基于最大似然估计的简单经验法则不适用于对角加权最小二乘估计，并提供了根据条件选择临界值的回归方程，帮助组织研究者更准确评估模型拟合。
Confirmatory factor analytic (CFA) models are frequently used in many areas of organizational research. Due to their popularity, CFA models and issues about their fit have received a vast amount of attention during the past several decades. The purpose of this study was to examine several measures of fit and the appropriateness of previously developed ‘‘rules of thumb’’ for their interpretation. First, an empirical example is used to illustrate the effects of nonnormality on maximum likelihood (ML) estimation and to demonstrate the importance of diagonally weighted least squares (DWLS) estimation for organizational research. Then, the results of a simulation study are presented to show that appropriate cutoff values for DWLS estimation vary considerably across conditions. Finally, regression equations are described to aid researchers in selecting cutoff values for assessing the fit of DWLS solutions, given a desired level of Type I error. The results summarized here have important implications for the interpretation and use of CFA models.