TY - JOUR
T1 - A Multilevel AR(1) Model
T2 - Allowing for Inter-Individual Differences in Trait-Scores, Inertia, and Innovation Variance
AU - Jongerling, Joran
AU - Laurenceau, Jean Philippe
AU - Hamaker, Ellen L.
N1 - Funding Information:
for disclosure of potential conflicts of interest. Dr. Jongerling and Dr. Hamaker report a grant from the Netherlands Orga- nization for Scientific Research (NWO; VIDI Grant 452-10-007), during the conduct of the study.
Funding Information:
Funding: This work was supported by Grant 452-10-007 from the Netherlands Organization for Scientific Research (NWO).
Publisher Copyright:
© 2015, Copyright © Taylor & Francis Group, LLC.
PY - 2015/5/4
Y1 - 2015/5/4
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84931470688&partnerID=8YFLogxK
U2 - 10.1080/00273171.2014.1003772
DO - 10.1080/00273171.2014.1003772
M3 - Article
C2 - 26610033
AN - SCOPUS:84931470688
SN - 0027-3171
VL - 50
SP - 334
EP - 349
JO - Multivariate Behavioral Research
JF - Multivariate Behavioral Research
IS - 3
ER -