Additive and Multiplicative Method Effects in Applied Psychological Research: An Empirical Assessment of Three Models
研究了加性和乘性方法效应的性质与不同潜在因子模型拟合优度的关系，评估了三种模型在17个多特质多方法数据集上的表现，发现Marsh的相关唯一性技术总体上更有效。
Method effects can be additive (independent of trait correlations) or multiplicative (associated with trait correlations). This study is the first to empirically assess the relationship between the nature of method effects and the goodness-of--t of different latent factor models. Specifically, we examined method effects in I7 published multitrait-multimethod data sets and evaluated the usefulness of confirmatory factor analysis, the direct product approach, and Marsh S correlated uniqueness technique for modeling these effects. While each of the models fit some of the data sets well, Marsh’s technique appeared to be generally more effective. Also, Campbell and O'Connell’s slope index indicated that additive models (confirmatory factor analysis and the correlated uniqueness approach) were not more likely than a multiplicative model (the direct product model) to provide a good fit to data with additive method effects; nor did a multiplicative model provide a better fit than additive models when method effects were multiplicative.