Modeling Developmental Processes Using Accelerated Cohort-Sequential Data
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Studying the time-related course of psychological processes is a challenging endeavor, particularly over long developmental periods. Accelerated longitudinal designs (ALD) allow capturing such periods with a limited number of assessments in a much shorter time framework. In ALDs, participants from different age cohorts are measured repeatedly but the measures provided by each participant cover only a fraction of the study period. It is then assumed that the common trajectory can be studied by aggregating the information provided by the different converging cohorts. In this presentation, I report results from recent studies examining the performance of discrete- and continuous-time latent change score (LCS) models for recovering the trajectories of a developmental process from data obtained through different ALDs. These results support the effectiveness of LCS models to study developmental processes using data from ALDs under various conditions of sampling. When all cohorts are drawn from the same population, both types of models are able to recover the parameters defining the underlying developmental process. However, discrete-time models yield estimates with bias when time lags between observations are not constant. When cohorts are not from the same population and lack convergence, both discrete- and continuous-time models show bias in some dynamic parameters.