Introduction

This program implements the statistical procedures for analyzing incomplete multivariate data developed in

Gary King, James Honaker, Anne Joseph, and Kenneth Scheve. ``Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation.'' American Political Science Review, Vol. 95, No. 1 (March, 2001): Pp. 49-69, copy available at http://GKing.Harvard.Edu.

Please read this paper before using ${\mathfrak{A}melia}$. The paper proposes, and this program implements, a remedy to the discrepancy between the way social scientists analyze data with missing values and the recommendations of the statistics community. With a few notable exceptions, statisticians and methodologists have agreed on a widely applicable approach to many missing data problems based on the concept of ``multiple imputation,'' but most social scientists still use listwise deletion (deleting all cases with at least one missing cell) to make inferences in the presence of missing data. This practice is always inefficient and often biased. The various other ad hoc methods available in commerically available statistical software (such as pairwise deletion, imputation from regressions, mean substitution, etc.) are no better. As it turns out, the failure to use superior methods has been largely due to the fact that the computational algorithms available to implement multiple imputation models have been slow, very difficult to use even for experts, and impossible to run with existing commercial software. In the paper, an existing algorithm is adapted for use as a general purpose, multiple imputation model for missing data. This algorithm, called EMis, is between dozens and hundreds of times faster than the leading method recommended in the statistics literature, gives the same answer, and requires no special expertise to use. ${\mathfrak{A}melia}$: A Program for Missing Data implements the EMis algorithm and thus offers a superior and easy-to-use alternative for statistical analyses of incomplete multivariate data.