LAUSR

The randomized pretest posttest design is common in psychology, as is the corresponding missing data concern. Although missing data handling has seen advances over the past several decades, effective and practical solutions for handling missing data in randomized pretest posttest designs are lacking, particularly when assumptions of commonly used statistical models are violated. Although analysis of covariance can capture the average treatment effect with complete data, even when assumptions are tenuous, this becomes more difficult with missing data. This investigation fills this gap in the literature by comparing a variety of analysis models for estimating the average treatment effect under violations of linearity and homogeneity of regression slopes, when data are missing by several plausible, but understudied, missing at random patterns for randomized pretest posttest studies. Two missing data handling techniques, listwise deletion and multiple imputation, were considered. Listwise deletion provided maximum likelihood estimates (unbiased and appropriately precise) of the average treatment effect as long as the analysis model was appropriately specified to handle the violated assumption and the pretest mean was estimated using all cases. Although multiple imputation was effective as long as the imputation model was correct, the results highlight to the importance of model specification in the context of missing data. Importantly, the specific pattern of missing at random data had implications for results, emphasizing the need to consider the particular pattern of missingness beyond the general appropriateness of the missing at random assumption. (PsycInfo Database Record (c) 2020 APA, all rights reserved).