Fit Indexes, Lagrange Multipliers, Constraint Changes and Incomplete Data in Structural Models

Peter M. Bentler

Multivariate Behavioral Research · 1990 · 514 citations · 18 references

Concepts

TL;DR

The paper discusses aspects of model modification and evaluation, noting differing viewpoints from Kaplan (1990) and examining how the Lagrange Multiplier statistic depends on estimated multipliers and constraint changes. The authors develop a sensitivity theorem that demonstrates how unit changes in constraints affect model fit, offering a tool for structural model modification. They extend recent incomplete‑data methods, originally developed by Kaplan and collaborators, to a broader range of situations. The study reaffirms the usefulness of Bentler–Bonett indexes and shows that the comparative fit index provides a general adequacy measure when misspecification is gauged by the noncentrality parameter of a chi‑square distribution, without requiring identification of misspecification sources.

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.

References

18