Monte Carlo simulations for evaluating the performance of modern missing data techniques when estimating structural equation models with latent variables. A systematic analysis of different types of multiple imputation.

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In this research project, various modern missing data techniques (MDT) will be tested that can be used when estimating structural equation models with latent variables. These techniques include methods of multiple imputation, expectation maximization and Direct-ML estimation. Using Monte Carlo simulations the performance of MDTs will be checked in respect to certain endogenous test criteria (such as estimation bias, convergence and model fit) and under varying exogenous model parameters (such as different value distributions, varying measuring scales, different missing value mechanisms, different shares of missing values and various sample sizes etc.). Unlike former Monte Carlo-simulation studies evaluating the advantages of missing data techniques, we will concentrate on the performance of different techniques of multiple imputation. Especially, we will investigate the relevance of various data conditions for selecting a particular MI-technique when estimating structural equation models with latent variables. In addition, the results of our Monte Carlo-simulations will be demonstrated using real empirical data sets. This will show the consequences of not adequately applying a particular MDT (especially a wrong type of multiple imputation) when analyzing empirical data. The results of this project will generate guidelines for social research practice, so that researches can select the MDT (including the optimal type of multiple imputation) that best fits to their empirical data.