A Reexamination of Public-Versus Private-School Achievement: The Case for Missing Data

The purpose of this research was to evaluate the effects of four data-replacement techniques on the High School and Beyond data set. A review of several previous studies indicated that estimating missing values by employing regression-based techniques is superior to other methods, particularly the variable-mean method. Because previous studies examined the effects of Missing Data Techniques (MDTs) on generated data sets where values were deleted at random, the current study was designed to evaluate the impact of the missing data techniques on a real data set for which the missing data pattern was unknown. Three studies that had analyzed the High School and Beyond data set were replicated. Missing data were first replaced using the variable mean, simple regression, and iterative regression methods, and then the analyses were repeated. Results from the analyses of the treated data sets were compared with the results of the original analyses. The findings indicated that the use of data-replacement techniques did affect the conclusions of the three studies examined; the findings indicated a convergence of the conclusions toward a moderate positive effect favoring private schooling.