Abstract
Kenny and Judd (1984) formulated a model with interaction effects of two latent variables and suggested using product variables to estimate the model. Using one and four product variables and simulation techniques, Yang-Jonsson (1997) studied the estimation of this model with three methods: maximum likelihood (ML), weighted least squares (WLS), and weighted least squares based on the augmented moment matrix (WLSA). Because the model implies non-normality, one would expect WLS and WLSA to be better than ML at least in large samples, but Yang-Jonsson (1997) found that ML often works well over a range of sample sizes from 400 to 3,200, except that asymptotic standard errors and chi-squares of ML estimates are, in principle, incorrectly computed. In this chapter, we show that both asymptotic standard errors and chi-squares for ML can be corrected for non-normality using Satorra-Bentler type scaling corrections.
Keywords
Least-squares function approximationComputer scienceMathematicsStatistics
Related Publications
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...
Scaled test statistics and robust standard errors for non‐normal data in covariance structure analysis: A Monte Carlo study
Research studying robustness of maximum likelihood (ML) statistics in covariance structure analysis has concluded that test statistics and standard errors are biased under sever...
Evaluating the Fit of Structural Equation Models: Tests of Significance and Descriptive Goodness-of-Fit Measures
For structural equation models, a huge variety of fit indices has been developed. These indices, however, can point to conflicting conclusions about the extent to which a model ...
Interactions of latent variables in structural equation models
Interactions of variables occur in a variety of statistical analyses. The best known procedures for models with interactions of latent variables are technically demanding. Not o...
The many weak instruments problem and Mendelian randomization
Instrumental variable estimates of causal effects can be biased when using many instruments that are only weakly associated with the exposure. We describe several techniques to ...
Publication Info
- Year
- 2001
- Type
- book-chapter
- Pages
- 179-192
- Citations
- 25
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
25
OpenAlex
0
Influential
1
CrossRef
Cite This
Fan Yang‐Wallentin,
Karl G. Jöreskog
(2001).
Robust Standard Errors and Chi-Squares for Interaction Models.
Psychology Press eBooks
, 179-192.
https://doi.org/10.4324/9781410601858-11
Identifiers
- DOI
- 10.4324/9781410601858-11