Abstract
Study design and subsequent data analysis is ideally a collaborative endeavour between applied researchers and statistical experts (e.g. methodologists, data scientists). Applied researchers know what research questions need to be answered and statistical experts, in conjunction with the applied researchers, plan study details (e.g. sample size, measurement instruments) and the analytic approach to best answer the research questions. Research questions regarding change processes are especially challenging as there are more study design decisions and more analytic options to consider. Moreover, the optimal analytic approach may not be feasible given the available longitudinal data. Additionally, many studies of change utilize secondary data, where decisions regarding the measures, the number and timing of assessments and sample size were not planned for the current research questions. In these cases, longitudinal model development is often a compromise between what is ideal and what is feasible given the available data. In this paper, we discuss two empirical projects and how collaboration between applied researchers and developmental methodologists informed the analytic models.