The Effects of Model Parsimony and Sampling Error on the Fit of Structural Equation Models
研究了结构方程模型中模型拟合指标受模型设定错误、简约性误差和抽样误差影响的程度，并通过模拟实验分析了简约性误差对完美测量模型的影响，为存在简约性误差时设定模型拟合标准提供依据。
The fit between a structural equation model and a data set is operationalized as the value of goodness-of-fit indices. The discrepancy between the estimated value and the value indicating perfect fit has three sources: misspecification, error arising from theoretical parsimony in the description of the model (parsimony error), and sampling error. Misspecification, which represents a disparity between “realworld” relationships and relationships in the model, is the most important source of error for researchers. It cannot be accurately assessed, however, unless parsimony error and sampling error are taken into account. Parsimony error occurs in measurement models when secondary relationships are excluded. Secondary relationships are defined here as secondary factor loadings and error term correlations that have small values, no theoretical bases, and no substantive meaning. A simulation was conducted to examine the effects of parsimony error on perfect measurement models and to establish appropriate criteria for model fit when parsimony error is present.