Simulation study demonstrates substantial bias in lagged causal effect estimates under time aggregation and undersampling, highlighting the necessity of temporal alignment in longitudinal research.
A common goal of longitudinal research in psychology and related disciplines is the study of causal effects over time. An often overlooked assumption in this context is temporal alignment, referring to a match between the timescale at which a longitudinal process operates, the timescale at which data were collected, and the time frame in measurements. We present multiple scenarios, representing both the analysis of intensive longitudinal data and panel data, in which this assumption is violated due to time aggregation (i.e., aggregating scores over multiple occasions) and/or systematic undersampling (i.e., measurement intervals that are too long in relation to the longitudinal process). Our simulations demonstrate the large biases that can occur in estimates of lag-0 and lag-1 effects using various modeling approaches, even when there is no effect in the data. We discuss implications of these results and briefly consider future lines of methodological research to address this problem.
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Mulder et al. (2026) studied this question.
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