Nonrandomly Missing Data in Multiple Regression: An Empirical Comparison of Common Missing-Data Treatments

Abstract

This research is an investigation of the effects of nonrandomly missing data in two-predictor regression analyses and the differences in the effectiveness of five common treatments of missing data on estimates of R2 and of each of the two standardized regression weights. Bootstrap samples of 50, 100, and 200 were drawn from three sets of actual field data. Nonrandomly missing data were created within each sample, and the parameter estimates were compared with those obtained from the same samples with no missing data. The results indicated that three imputation procedures (mean substitution, simple and multiple regression imputation) produced biased estimates of R2 and both regression weights. Two deletion procedures (listwise and pairwise) provided accurate parameter estimates with up to 30% of the data missing.