Limits...
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.


Related in: MedlinePlus

Hypothetical negative affect scores for persons A, B and C, and corresponding state-space plots depicting the underlying autoregression. All three persons have the same equilibrium (15). Persons A and B are described by AR models with inertias () of 0.1 and 0.7, respectively. Therefore, person A is quicker to recover toward his equilibrium, and person B is characterized by more carry-over affect from one moment to the next, indicating regulatory weakness. Person C is described by a TAR model with  during episodes of increased negative affect (15), and  during decreased negative affect (15). Thus, person C has weaker affect regulation during episodes of increased negative affect.
© Copyright Policy - OpenAccess
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4764683&req=5

Fig1: Hypothetical negative affect scores for persons A, B and C, and corresponding state-space plots depicting the underlying autoregression. All three persons have the same equilibrium (15). Persons A and B are described by AR models with inertias () of 0.1 and 0.7, respectively. Therefore, person A is quicker to recover toward his equilibrium, and person B is characterized by more carry-over affect from one moment to the next, indicating regulatory weakness. Person C is described by a TAR model with during episodes of increased negative affect (15), and during decreased negative affect (15). Thus, person C has weaker affect regulation during episodes of increased negative affect.

Mentions: In the AR(1) model for affect regulation, there is one inertia parameter reflecting a person’s regulatory weakness, and this is the regression coefficient that links each observation to the immediately preceding observation. To illustrate this model, which we will simply call the AR model hereafter, we consider an example of two hypothetical persons. The time series plots in the upper two panels of Figure 1 depict their negative affect scores on 150 consecutive measurements, with higher scores indicating more intense negative affect. The horizontal line represents the equilibrium of the person, the baseline level of negative affect that the person tends toward. The time series plots show that the negative affect of both persons fluctuates around their equilibrium over time and that there is no long-term trend. The difference between the two persons is in their autoregressive coefficient , which represents their emotional inertia for negative affect. Person A has a of 0.1, while that of person B is 0.7. This implies that person B is characterized by more regulatory weakness, causing a larger carry-over of negative affect from one occasion to the next. It can be seen in Figure 1 that person B is more likely to have several consecutive scores above or below his/her equilibrium, while person A’s affect is quicker to recover toward his/her equilibrium.Fig. 1


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)

Hypothetical negative affect scores for persons A, B and C, and corresponding state-space plots depicting the underlying autoregression. All three persons have the same equilibrium (15). Persons A and B are described by AR models with inertias () of 0.1 and 0.7, respectively. Therefore, person A is quicker to recover toward his equilibrium, and person B is characterized by more carry-over affect from one moment to the next, indicating regulatory weakness. Person C is described by a TAR model with  during episodes of increased negative affect (15), and  during decreased negative affect (15). Thus, person C has weaker affect regulation during episodes of increased negative affect.
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4764683&req=5

Fig1: Hypothetical negative affect scores for persons A, B and C, and corresponding state-space plots depicting the underlying autoregression. All three persons have the same equilibrium (15). Persons A and B are described by AR models with inertias () of 0.1 and 0.7, respectively. Therefore, person A is quicker to recover toward his equilibrium, and person B is characterized by more carry-over affect from one moment to the next, indicating regulatory weakness. Person C is described by a TAR model with during episodes of increased negative affect (15), and during decreased negative affect (15). Thus, person C has weaker affect regulation during episodes of increased negative affect.
Mentions: In the AR(1) model for affect regulation, there is one inertia parameter reflecting a person’s regulatory weakness, and this is the regression coefficient that links each observation to the immediately preceding observation. To illustrate this model, which we will simply call the AR model hereafter, we consider an example of two hypothetical persons. The time series plots in the upper two panels of Figure 1 depict their negative affect scores on 150 consecutive measurements, with higher scores indicating more intense negative affect. The horizontal line represents the equilibrium of the person, the baseline level of negative affect that the person tends toward. The time series plots show that the negative affect of both persons fluctuates around their equilibrium over time and that there is no long-term trend. The difference between the two persons is in their autoregressive coefficient , which represents their emotional inertia for negative affect. Person A has a of 0.1, while that of person B is 0.7. This implies that person B is characterized by more regulatory weakness, causing a larger carry-over of negative affect from one occasion to the next. It can be seen in Figure 1 that person B is more likely to have several consecutive scores above or below his/her equilibrium, while person A’s affect is quicker to recover toward his/her equilibrium.Fig. 1

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.


Related in: MedlinePlus