Robust structural equation modeling with missing data and auxiliary variables The paper develops a two-stage robust procedure for structural equation modeling (SEM) and an R package rsem to facilitate the use of the procedure by applied researchers. In the first stage, M-estimates of the saturated mean vector and covariance matrix of all variables are obtained. Those corresponding to the substantive variables are then fitted to the structural model in the second stage. A sandwich-type covariance matrix is used to obtain consistent standard errors (SE) of the structural parameter estimates. Rescaled, adjusted as well as corrected and F-statistics are proposed for overall model evaluation. Using R and EQS, the R package rsem combines the two stages and generates all the test statistics and consistent SEs. Following the robust analysis, multiple model fit indices and standardized solutions are provided in the corresponding output of EQS. An example with open/closed book examination data illustrates the proper use of the package. The method is further applied to the analysis of a data set from the National Longitudinal Survey of Youth 1997 cohort, and results show that the developed procedure not only gives a better endorsement of the substantive models but also yields estimates with uniformly smaller standard errors than the normal-distribution-based maximum likelihood.
Keywords for this software
References in zbMATH (referenced in 3 articles , 1 standard article )
Showing results 1 to 3 of 3.
- Yuan, Ke-Hai; Chan, Wai; Tian, Yubin: Expectation-robust algorithm and estimating equations for means and dispersion matrix with missing data (2016)
- Yuan, Ke-Hai; Savalei, Victoria: Consistency, bias and efficiency of the normal-distribution-based MLE: the role of auxiliary variables (2014)
- Yuan, Ke-Hai; Zhang, Zhiyong: Robust structural equation modeling with missing data and auxiliary variables (2012)