Methods for handling missing data in social science data sets are reviewed. Limitations of common practical approaches, including complete-case analysis, available-case analysis and imputation, are illustrated on a simple missing-data problem with one complete and one incomplete variable. Two more principled approaches, namely maximum likelihood under a model for the data and missing-data mechanism and… Continue reading The Analysis of Social Science Data with Missing Values

A class of statistics Ĵ(ν) is proposed for constructing tests and confidence intervals for a linear combination of means ν using an incomplete multivariate normal sample. The class includes statistics based on maximum likelihood estimates for all the data, and simpler statistics such as those based on a subset of complete observations. A simple measure… Continue reading Inference About Means from Incomplete Multivariate Data

A model is proposed for continuous longitudinal data with non-ignorable or informative drop-out (ID). The model combines a multivariate linear model for the underlying response with a logistic regression model for the drop-out process. The latter incorporates dependence of the probability of drop-out on unobserved, or missing, observations. Parameters in the model are estimated by… Continue reading Informative Drop-Out in Longitudinal Data Analysis – Discussion