title: Get Over It! A Multilevel Threshold Autoregressive Model for State-Dependent Affect Regulation.
abstract: Intensive longitudinal data provide rich information, which is best captured when specialized models are used in the analysis. One of these models is the multilevel autoregressive model, which psychologists have applied successfully to study affect regulation as well as alcohol use. A limitation of this model is that the autoregressive parameter is treated as a fixed, trait-like property of a person. We argue that the autoregressive parameter may be state-dependent, for example, if the strength of affect regulation depends on the intensity of affect experienced. To allow such intra-individual variation, we propose a multilevel threshold autoregressive model. Using simulations, we show that this model can be used to detect state-dependent regulation with adequate power and Type I error. The potential of the new modeling approach is illustrated with two empirical applications that extend the basic model to address additional substantive research questions.
authors.affiliation: Methodology and Statistics, Faculty of Social and Behavioural Sciences, Utrecht University, P.O. Box 80140, 3508 TC, Utrecht, The Netherlands. s.rietdijk@uu.nl.
authors.forename: Silvia
authors.initials: S
authors.lastname: De Haan-Rietdijk
authors.affiliation: Relationship Research Institute, Seattle, WA, USA.
authors.forename: John M
authors.initials: JM
authors.lastname: Gottman
authors.affiliation: Department of Psychology, University of Notre Dame, Notre Dame, IN, USA.
authors.forename: Cindy S
authors.initials: CS
authors.lastname: Bergeman
authors.affiliation: Methodology and Statistics, Faculty of Social and Behavioural Sciences, Utrecht University, P.O. Box 80140, 3508 TC, Utrecht, The Netherlands.
authors.forename: Ellen L
authors.initials: EL
authors.lastname: Hamaker
mesh_terms: D001499:Bayes Theorem; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D055361:Multilevel Analysis; D011594:Psychometrics
keywords: affect regulation; bayesian estimation; dynamical modeling; intensive longitudinal data; threshold autoregression
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