The impact of ignoring lags on developmental science: A re-analysis of meta-analyses using lag as moderator

Abstract

Longitudinal studies provide developmental science with invaluable information about how variables and the associations between variables change across time, but typically give limited attention to the length of time over which that change occurs. The present study re-analyzed data from previously published meta-analyses of longitudinal data across a broad range of developmental science to ascertain how lag may have impacted coefficients of stability ( kmeta-analyses = 6, kstudies = 157) and prediction ( kmeta-analyses = 15, kstudies = 270). We additionally analyzed how average participant age interacts with lag to test how the impact of lag might change across the lifespan. Findings indicate that conventional lags (e.g., 6 months, 12 months) were used at extremely high rates: More than 75% of lags were selected based on convention. Linear and nonlinear models indicated that lag moderated stability and predictive associations, although the significance, magnitude, and direction of this impact changed depending on the phenomenon under investigation. Average participant age interacted with lag in certain cases, providing a possibility for more time-specific developmental theory. However, these results should not be considered conclusive due to the high number of conventional lags in our sample, which likely restricted both variability in lags and the length of those lags. Future longitudinal studies should measure phenomena at varying lags, and future meta-analysts should consider both lag and average participant age when synthesizing longitudinal research. Both practices would enable developmental science to determine the interval over which a phenomenon occurs and facilitate advancements in developmental theory.