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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.


Negative affect scores of four example subjects over the course of the 56 study days.
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Related In: Results  -  Collection


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Fig5: Negative affect scores of four example subjects over the course of the 56 study days.

Mentions: For an additional empirical illustration, we sought a data set including a person-level predictor variable, so that we could model the inertias and threshold. We analyzed a data set including a measure of trait neuroticism in addition to daily self-report measures of negative affect. These data were obtained from the older cohort (ages 50 and higher, ) of the Notre Dame Study of Health & Well-Being. Specifically, we considered the Negative Affect subscale of the PANAS (Watson, Clark, & Tellegen, 1988), which the participants filled out on 56 consecutive days. These daily scores ranged from 1 (very little or no negative affect) to 5 (very intense negative affect) in 0.1 increments, but as noted before by Wang, Hamaker, and Bergeman (2012), the negative affect scores of many individuals showed little variation over the course of the study. Some persons’ scores showed a floor effect because they repeatedly reported experiencing no negative affect whatsoever (score 1). Although skewed data can be generated by a TAR process with a large inertia difference, the lack of variation in scores violates the model’s assumption of normally distributed residuals (innovation). Thus, we chose to apply the multilevel TAR model only to those individuals whose negative affect scores had a standard deviation of 0.1 or higher. This criterion excluded the most stable score patterns, while avoiding overly subjective decisions on which participants to include. Figure 5 portrays the affect scores of four of the included individuals.Fig. 5


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)

Negative affect scores of four example subjects over the course of the 56 study days.
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig5: Negative affect scores of four example subjects over the course of the 56 study days.
Mentions: For an additional empirical illustration, we sought a data set including a person-level predictor variable, so that we could model the inertias and threshold. We analyzed a data set including a measure of trait neuroticism in addition to daily self-report measures of negative affect. These data were obtained from the older cohort (ages 50 and higher, ) of the Notre Dame Study of Health & Well-Being. Specifically, we considered the Negative Affect subscale of the PANAS (Watson, Clark, & Tellegen, 1988), which the participants filled out on 56 consecutive days. These daily scores ranged from 1 (very little or no negative affect) to 5 (very intense negative affect) in 0.1 increments, but as noted before by Wang, Hamaker, and Bergeman (2012), the negative affect scores of many individuals showed little variation over the course of the study. Some persons’ scores showed a floor effect because they repeatedly reported experiencing no negative affect whatsoever (score 1). Although skewed data can be generated by a TAR process with a large inertia difference, the lack of variation in scores violates the model’s assumption of normally distributed residuals (innovation). Thus, we chose to apply the multilevel TAR model only to those individuals whose negative affect scores had a standard deviation of 0.1 or higher. This criterion excluded the most stable score patterns, while avoiding overly subjective decisions on which participants to include. Figure 5 portrays the affect scores of four of the included individuals.Fig. 5

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.