title: On disaggregating between-person and within-person effects with longitudinal data using multilevel models.
abstract: This article extends current discussion of how to disaggregate between-person and within-person effects with longitudinal data using multilevel models. Our main focus is on the 2 issues of centering and detrending. Conceptual and analytical work demonstrates the similarities and differences among 3 centering approaches (no centering, grand-mean centering, and person-mean centering) and the relations and differences among various detrending approaches (no detrending, detrending X only, detrending Y only, and detrending both X and Y). Two real data analysis examples in psychology are provided to illustrate the differences in the results of using different centering and detrending methods for the disaggregation of between- and within-person effects. Simulation studies were conducted to further compare the various centering and detrending approaches under a wider span of conditions. Recommendations of how to perform centering, whether detrending is needed or not, and how to perform detrending if needed are made and discussed.
authors.affiliation: Department of Psychology, University of Notre Dame.
authors.forename: Lijuan Peggy
authors.initials: LP
authors.lastname: Wang
authors.affiliation: Department of Psychology, University of Notre Dame.
authors.forename: Scott E
authors.initials: SE
authors.lastname: Maxwell
mesh_terms: D006801:Humans; D008960:Models, Psychological; D015233:Models, Statistical; D055361:Multilevel Analysis