Bayesian Inference for Structural Equation Models with Incomplete Data

Missing data can lead to bias and inefficiency in estimating the quantities of interest in scientific studies. This can be especially problematic in longitudinal studies which measure the same subjects at several different points in time. We consider the Bayesian approach to drawing inferences in structural equation models with missing data. The Bayesian approach and algorithms for performing the required calculations are described. An example from the Iowa Youth and Families Project (IYFP) being carried out at the Institute for Social and Behavioral Research at Iowa State University is used to illustrate the approach. Tools for assessing fit when Bayesian methods are used are also described.