Choosing a Pretest-Posttest Analysis

Publisher Website
Google Scholar
Abstract
Pretest-posttest designs serve as building blocks for other more complicated repeated-measures designs. In settings where subjects are independent and errors follow a bivariate normal distribution, data analysis may consist of a univariate repeated-measures analysis or an analysis of covariance. Another possible analysis approach is to use seemingly unrelated regression (SUR). The purpose of this article is to help guide the statistician toward an appropriate analysis choice. Assumptions, estimates, and test statistics for each analysis are approached in a systematic manner. On the basis of these results, the crucial choice of analysis is whether differences in pretest group means are conceived to be real or the result of pure measurement error. Direct consultation of the statistician with a subject-matter person is important in making the right choice. If pretest group differences are real, then a univariate repeated-measures analysis is recommended. If pretest group differences are the result of pure measurement error, then a conditional analysis or SUR analysis should be used. The conditional analysis and the SUR analysis will produce similar results. Smaller variance estimates can be expected based on the SUR analysis, but this gain is partially mediated by a lack of an exact distribution for test statistics.