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Longitudinal data analysis is gaining attention from researchers because it enables us to examine both between- and within-person effects simultaneously. Traditionally, centering has been used with multilevel models to estimate these two effects. However, recent studies found that centering could not disaggregate the between-person and within-person effects when a time-varying predictor shows time trends. This article develops methods for disaggregating the two effects in the presence of time trends using the latent curve model. The proposed methods reveal the link between centering and detrending, which are often seen as different preprocessing for different purposes. Two simulations are conducted to assess and compare the performance of the proposed and existing models. The results show that models with a slope factor behind a predictor can unbiasedly estimate the between- and within-person effects. Also, models with latent between-person predictors can unbiasedly estimate the between-person effect, while those with observed ones suffer from bias.

Latent Curve Detrending for Disaggregating Between-Person Effect and Within-Person Effect