Evaluating Supplemental Samples in Longitudinal Research: Replacement and Refreshment Approaches

Despite the wide application of longitudinal studies, they are often plagued by missing data and attrition. The majority of methodological approaches focus on participant retention or modern missing data analysis procedures. This paper, however, takes a new approach by examining how researchers may <i>supplement</i> the sample with additional participants. First, <i>refreshment</i> samples use the same selection criteria as the initial study. Second, <i>replacement</i> samples identify auxiliary variables that may help explain patterns of missingness and select new participants based on those characteristics. A simulation study compares these two strategies for a linear growth model with five measurement occasions. Overall, the results suggest that <i>refreshment</i> samples lead to less relative bias, greater relative efficiency, and more acceptable coverage rates than <i>replacement</i> samples or not supplementing the missing participants in any way. Refreshment samples also have high statistical power. The comparative strengths of the refreshment approach are further illustrated through a real data example. These findings have implications for assessing change over time when researching at-risk samples with high levels of permanent attrition.