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Abstract

This study compared the effectiveness of four imputation procedures (mean, conditional mean, hot deck, and regression) in a two-variable prediction system. A total of 18,869 participants were included in the sample. Two data matrices containing missing values were created: one with randomly missing data and the other with nonrandomly missing data. Values were missing for only one of the two independent variables. The results of the study provide some support for the following conclusions: (a) the grand mean procedure is not an appropriate procedure for handling missing data; (b) when estimating the regression coefficient of the variable with missing data, the conditional mean procedure should be used; and (c) when the prediction of the dependent variable is of interest, the regression procedure should be used.

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A Comparison of Four Imputation Procedures in a Two-Variable Prediction System

Abstract

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