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Replication data for: Multiple Imputation for Continuous and Categorical Data: Comparing Joint Multivariate Normal and Conditional Approaches

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

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Title Replication data for: Multiple Imputation for Continuous and Categorical Data: Comparing Joint Multivariate Normal and Conditional Approaches
 
Identifier https://doi.org/10.7910/DVN/24672
 
Creator Kropko, Jonathan
Goodrich, Ben
Gelman, Andrew
Hill, Jennifer
 
Publisher Harvard Dataverse
 
Description We consider the relative performance of two common approaches to multiple imputation (MI): joint multivariate normal (MVN) MI, in which the data are modeled as a sample from a joint MVN distribution; and conditional MI, in which each variable is modeled conditionally on all the others. In order to use the multivariate normal distribution, implementations of joint MVN MI typically assume that categories of discrete variables are probabilistically constructed from continuous values. We use simulations to examine the implications of these assumptions. For each approach, we assess (1) the accuracy of the imputed values, and (2) the accuracy of coefficients and fitted values from a model fit to completed datasets. These simulations consider continuous, binary, ordinal, and unordered-categorical variables. One set of simulations uses multivariate normal data and one set uses data from the 2008 American National Election Study. We implement a less restricti
ve approach than is typical when evaluating methods using simulations in the missing data literature: in each case, missing values are generated by carefully following the conditions necessary for missingness to be ``missing at random'' (MAR). We find that in these situations conditional MI is more accurate than joint MVN MI whenever the data include categorical variables.
 
Subject multiple imputation