Attrition in longitudinal studies is a major threat to the representativeness of the data and the generalizability of the findings. Typical approaches to address systematic nonresponse are either expensive and unsatisfactory (e.g., oversampling) or rely on the unrealistic assumption of data missing at random (e.g., multiple imputation). Thus,…

Selective non-participation and attrition pose a ubiquitous threat to the validity of inferences drawn from observational longitudinal studies. We investigate various potential predictors for non-response and attrition of parents as well as young persons at different stages of a multi-informant study. Various phases of renewed consent from parents…

Inherent in applied developmental sciences is the threat to validity and generalizability due to missing data as a result of participant drop-out. The current paper provides an overview of how attrition should be reported, which tests can examine the potential of bias due to attrition (e.g., t-tests, logistic regression, Little's MCAR test,…

Most longitudinal studies are plagued by drop-out related to variables at earlier assessments (systematic attrition). Although systematic attrition is often analysed in longitudinal studies, surprisingly few researchers attempt to reduce biases due to systematic attrition, even though this is possible and nowadays technically easy. This is…

Latent growth curve modeling was used to describe longitudinal trends in community and religious involvement and Marcia's (1966) four identity statuses (diffusion, foreclosure, moratorium, and achievement), as well as to assess relations between involvement and identity change. Cross-lagged regression models explored temporal ordering of relations…