title: A Multilevel AR(1) Model: Allowing for Inter-Individual Differences in Trait-Scores, Inertia, and Innovation Variance.
abstract: In this article we consider a multilevel first-order autoregressive [AR(1)] model with random intercepts, random autoregression, and random innovation variance (i.e., the level 1 residual variance). Including random innovation variance is an important extension of the multilevel AR(1) model for two reasons. First, between-person differences in innovation variance are important from a substantive point of view, in that they capture differences in sensitivity and/or exposure to unmeasured internal and external factors that influence the process. Second, using simulation methods we show that modeling the innovation variance as fixed across individuals, when it should be modeled as a random effect, leads to biased parameter estimates. Additionally, we use simulation methods to compare maximum likelihood estimation to Bayesian estimation of the multilevel AR(1) model and investigate the trade-off between the number of individuals and the number of time points. We provide an empirical illustration by applying the extended multilevel AR(1) model to daily positive affect ratings from 89 married women over the course of 42 consecutive days.
authors.affiliation: a Department of Methodology and Statistics , Utrecht University.
authors.forename: Joran
authors.initials: J
authors.lastname: Jongerling
authors.affiliation: b Department of Psychological and Brain Sciences , University of Delaware.
authors.forename: Jean-Philippe
authors.initials: JP
authors.lastname: Laurenceau
authors.affiliation: a Department of Methodology and Statistics , Utrecht University.
authors.forename: Ellen L
authors.initials: EL
authors.lastname: Hamaker
mesh_terms: D000339:Affect; D001499:Bayes Theorem; D035841:Behavioral Research; D003198:Computer Simulation; D005260:Female; D006801:Humans; D007206:Individuality; D016013:Likelihood Functions; D015233:Models, Statistical; D055361:Multilevel Analysis; D012044:Regression Analysis