ERIC Number: ED329593
Record Type: RIE
Publication Date: 1991-Feb
Reference Count: 0
Randomly Missing Data in Multiple Regression: An Empirical Comparison of Common Missing Data Treatments.
Kromrey, Jeffrey D.; Hines, Constance V.
An investigation of the effects of randomly missing data in two-predictor regression analyses is described. The differences in the effectiveness of five common treatments of missing data on estimates of R-squared values and each of the two standardized regression weights is also investigated. Bootstrap sample sizes of 50, 100, and 200 were drawn from three sets of actual field data. Randomly 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 indicate that three imputation procedures (mean substitution, simple, and multiple regression imputation) produced biased estimates of R-squared values and both regression weights. Two deletion procedures (listwise and pairwise) provided accurate parameter estimates with up to 60% of the data missing. Twelve data tables, 9 figures, and a 20-item list of references are included. (Author/TJH)
Publication Type: Reports - Evaluative; Speeches/Meeting Papers
Education Level: N/A
Authoring Institution: N/A
Identifiers: Bootstrap Methods; Randomly Missing Data (Regression Analyses); R2 Values
Note: Paper presented at the Annual Meeting of the Eastern Educational Research Association (Boston, MA, February 13-16, 1991).