Mariëlle Zondervan-Zwijnenburg

TL;DR: It is found in this review that the use of Bayes has increased and broadened in the sense that this methodology can be used in a flexible manner to tackle many different forms of questions.

TL;DR: It is argued that the use of informative priors should always be reported together with a sensitivity analysis to show that two issues often encountered during analysis of small samples, power and biased parameters, can be solved by including prior information into Bayesian analysis.

TL;DR: The first formal treatment of different forms of MI and their consequences for the validity of multi-group/multi-time comparisons is attributable to Meredith (1993), as well as a recent book by Millsap (2011) containing a general systematic treatment of the topic of MI.

TL;DR: This study provides general guidelines to collect prior knowledge and formalize it in prior distributions and demonstrates with an empirical application how prior knowledge can be acquired systematically.

TL;DR: Longitudinal research in high risk samples could benefit from using multiple imputation (MI) using predictive mean matching in future research to handle missing data.