Our new command midiagplots makes diagnostic plots for multiple imputations created by mi impute. The plots compare the distribution of the imputed values with that of the observed values so that problems with the imputation model can be corrected before the imputed data are analyzed. We include...

Missing data is a very frequent obstacle in many social science studies. The absence of values on one or more variables can signi?cantly affect statistical analyses by reducing their precision and by introducing selection biases. Being unable to account for these aspects may result in severe...

Research using the Agricultural Resource Management Survey (ARMS) and other data shows that direct government payments to farmers increase rents and the price of land. However, some ARMS data is imputed and does not account for relationships between payments and other variables. We investigate...

We estimate how motorists value their time savings and characterize the degree of heterogeneity in these values by observable traits. We obtain these estimates by analyzing the choices that commuters make in a real market situation, where they are offered a free-flow alternative to congested...

We estimate how motorists value their time savings and characterize the degree of heterogeneity in these values by observable traits. We obtain these estimates by analyzing the choices that commuters make in a real market situation, where they are offered a free-flow alternative to congested...

Clustered data arise in many settings, particularly within the social and biomedical sciences. For example, multiple-source reports are commonly collected in child and adolescent psychiatric epidemiologic studies where researchers use various informants (for instance, parents and adolescents) to...

This article describes a substantial update to mvis, which brings it more closely in line with the feature set of S. van Buuren and C. G. M. Oudshoorn’s implementation of the MICE system in R and S-PLUS (for details, see http://www.multiple-imputation.com). To make a clear distinction from...

This paper introduces two new commands, smpred and smmatch, that implement the statistical matching procedure proposed by Rubin (1986). The purpose of statistical matching in Rubin's procedure is to generate a single dataset from various datasets, where each dataset contains a specific variable...

Researchers often impute continuous variables under an assumption of normality–yet many incomplete variables are skewed. We find that imputing skewed continuous variables under a normal model can lead to bias. The bias is usually mild for popular estimands such as means, standard...