Biases in SPSS 12.0 Missing Value Analysis

MVA can also impute values using the EM algorithm, but values are imputed without residual variation, so analyses that use the imputed values can be biased, and EM's implementation in MVA is limited to point estimates of means, variances, and covariances.

Abstract

In addition to SPSS Base software, SPSS Inc. sells a number of add-on packages, including a package called Missing Value Analysis (MVA). In version 12.0, MVAoffers four general methods for analyzing data with missing values. Unfortunately, none of these methods is wholly satisfactory when values are missing at random. The first two methods, listwise and pairwise deletion, are well known to be biased. The third method, regression imputation, uses a regression model to impute missing values, but the regression parameters are biased because they are derived using pairwise deletion. The final method, expectation maximization (EM), produces asymptotically unbiased estimates, but EM's implementation in MVA is limited to point estimates (without standard errors) of means, variances, and covariances. MVAcan also impute values using the EM algorithm, but values are imputed without residual variation, so analyses that use the imputed values can be biased.