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Get Over It! A Multilevel Threshold Autoregressive Model for State-Dependent Affect Regulation.

De Haan-Rietdijk S, Gottman JM, Bergeman CS, Hamaker EL - Psychometrika (2014)

Bottom Line: One of these models is the multilevel autoregressive model, which psychologists have applied successfully to study affect regulation as well as alcohol use.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.

View Article: PubMed Central - PubMed

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.

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.

No MeSH data available.


Scatterplot of the estimated level-1 inertias for less intense negative affect () and more intense negative affect (). Since most of the points fall above the diagonal line, we can conclude that the majority of the persons was characterized by stronger regulation during episodes of more intense negative affective behavior. The implication is that they experienced prolonged episodes with only little negative affect, and when they did experience more intense negative affect, they recovered quickly.
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Fig6: Scatterplot of the estimated level-1 inertias for less intense negative affect () and more intense negative affect (). Since most of the points fall above the diagonal line, we can conclude that the majority of the persons was characterized by stronger regulation during episodes of more intense negative affective behavior. The implication is that they experienced prolonged episodes with only little negative affect, and when they did experience more intense negative affect, they recovered quickly.

Mentions: Based on the estimates for the level-2 inertia difference (, with a 95 % CI of ) we conclude that the regulation of negative affect depended on affect intensity. The estimates for the mean inertias () indicate that the average person had a lower inertia during episodes of more intense negative affect (with effect size Cohen’s ). The inertia estimates for the individual persons are depicted in Figure 6. Considering the threshold (), these results together reflect the fact that, even in the subsample that we selected for analysis, many of the persons reported experiencing very little negative affect on most days. When they did experience more intense negative affect, they recovered quickly. We see that even though these data do not perfectly meet all the assumptions of the TAR model, the estimated model parameters do provide meaningful information. The relatively high mean inertia parameter for the lower state reflects the stability by which the average person could maintain an absence of negative affect for prolonged periods, while the low inertia parameter for the upper state indicates that the average person was very quick to recover from an episode of increased negative affect.Fig. 6


Get Over It! A Multilevel Threshold Autoregressive Model for State-Dependent Affect Regulation.

De Haan-Rietdijk S, Gottman JM, Bergeman CS, Hamaker EL - Psychometrika (2014)

Scatterplot of the estimated level-1 inertias for less intense negative affect () and more intense negative affect (). Since most of the points fall above the diagonal line, we can conclude that the majority of the persons was characterized by stronger regulation during episodes of more intense negative affective behavior. The implication is that they experienced prolonged episodes with only little negative affect, and when they did experience more intense negative affect, they recovered quickly.
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4764683&req=5

Fig6: Scatterplot of the estimated level-1 inertias for less intense negative affect () and more intense negative affect (). Since most of the points fall above the diagonal line, we can conclude that the majority of the persons was characterized by stronger regulation during episodes of more intense negative affective behavior. The implication is that they experienced prolonged episodes with only little negative affect, and when they did experience more intense negative affect, they recovered quickly.
Mentions: Based on the estimates for the level-2 inertia difference (, with a 95 % CI of ) we conclude that the regulation of negative affect depended on affect intensity. The estimates for the mean inertias () indicate that the average person had a lower inertia during episodes of more intense negative affect (with effect size Cohen’s ). The inertia estimates for the individual persons are depicted in Figure 6. Considering the threshold (), these results together reflect the fact that, even in the subsample that we selected for analysis, many of the persons reported experiencing very little negative affect on most days. When they did experience more intense negative affect, they recovered quickly. We see that even though these data do not perfectly meet all the assumptions of the TAR model, the estimated model parameters do provide meaningful information. The relatively high mean inertia parameter for the lower state reflects the stability by which the average person could maintain an absence of negative affect for prolonged periods, while the low inertia parameter for the upper state indicates that the average person was very quick to recover from an episode of increased negative affect.Fig. 6

Bottom Line: One of these models is the multilevel autoregressive model, which psychologists have applied successfully to study affect regulation as well as alcohol use.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.

View Article: PubMed Central - PubMed

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.

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.

No MeSH data available.