2 - Advances in Analysis of Mean and Covariance Structure when Data are Incomplete

Abstract

This chapter discusses the advances in analysis of mean and covariance structure of incomplete data. Missing data arise in many areas of empirical research. One such area is in the context of structural equation models (SEM). A review of the methodological advances in fitting data to SEM and, more generally, to mean and covariance structure models when there is missing data, is presented in the chapter. This encompasses common missing data mechanisms and some widely used methods for handling missing data. The methods fall under the classifications of ad-hoc, likelihood-based, and simulation-based. A method is proposed for performing sensitivity analysis. A simulation study demonstrates the method using a three-factor factor analysis model, focusing on missing completely at random (MCAR) and missing not at random (MNAR) data. Parameter estimates from samples of all available data, in the form of box plots, are compared with parameter estimates from only the complete data. The results indicate a possible distinction for determining missing data mechanisms.