Abstract
Certain aspects of model modification and evaluation are discussed, with an emphasis on some points of view that expand upon or may differ from Kaplan (1990). The usefulness of BentlerBonett indexes is reiterated. When degree of misspecification can be measured by the size of the noncentrality parameter of a x[SUP2] distribution, the comparative fit index provides a useful general index of model adequacy that does not require knowledge of sourees of misspecification. The dependence of the Lagrange Multiplier X[SUP2] statistic on both the estimated multiplier parameter and estimated constraint or parameter change is discussed. A sensitivity theorem that shows the effects of unit change in constraints on model fit is developed for model modification in structural models. Recent incomplete data methods, such as those developed by Kaplan and his collaborators, are extended to be applicable in a wider range of situations.
Keywords
Related Publications
Comparative fit indexes in structural models.
Normed and nonnormed fit indexes are frequently used as adjuncts to chi-square statistics for evaluating the fit of a structural model. A drawback of existing indexes is that th...
Maximum Likelihood Estimation of Misspecified Models
This paper examines the consequences and detection of model misspecification when using maximum likelihood techniques for estimation and inference. The quasi-maximum likelihood ...
Model Modification
An analysis of empirical data often leads to a rejection of a hypothesized model, even if the researcher has spent considerable efforts in including all available information in...
Evaluating and Modifying Covariance Structure Models: A Review and Recommendation
The purpose of this article is to present a strategy for the evaluation and modification of covariance structure models. The approach makes use of recent developments in estimat...
The Performance of ML, GLS, and WLS Estimation in Structural Equation Modeling Under Conditions of Misspecification and Nonnormality
Abstract This simulation study demonstrates how the choice of estimation method affects indexes of fit and parameter bias for different sample sizes when nested models vary in t...
Publication Info
- Year
- 1990
- Type
- article
- Volume
- 25
- Issue
- 2
- Pages
- 163-172
- Citations
- 505
- Access
- Closed
External Links
Social Impact
Social media, news, blog, policy document mentions
Citation Metrics
Cite This
Identifiers
- DOI
- 10.1207/s15327906mbr2502_3