Modeling Affect Dynamics: State of the Art and Future Challenges

Hamaker, Ellen L.; Ceulemans, Eva; Grasman, Raoul P. P. P.; Tuerlinckx, Francis

doi:10.1177/1754073915590619

Cited by 183 publications

(137 citation statements)

References 57 publications

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“…These limitations on the estimation methods come with more limitations in the statistical models themselves. VAR modeling, especially, is not without problems and faces severe challenges (Hamaker, Ceulemans, Grasman, & Tuerlinckx, 2015;Hamaker & Wichers, 2017). We made several assumptions that can be problematic.…”

Section: Limitations and Challengesmentioning

confidence: 99%

The Gaussian Graphical Model in Cross-Sectional and Time-Series Data

Epskamp

Waldorp

Mõttus

et al. 2018

Multivariate Behavioral Research

778

929

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We discuss the Gaussian graphical model (GGM; an undirected network of partial correlation coefficients) and detail its utility as an exploratory data analysis tool. The GGM shows which variables predict one-another, allows for sparse modeling of covariance structures, and may highlight potential causal relationships between observed variables. We describe the utility in three kinds of psychological data sets: data sets in which consecutive cases are assumed independent (e.g., cross-sectional data), temporally ordered data sets (e.g., n = 1 time series), and a mixture of the 2 (e.g., n > 1 time series). In time-series analysis, the GGM can be used to model the residual structure of a vector-autoregression analysis (VAR), also termed graphical VAR. Two network models can then be obtained: a temporal network and a contemporaneous network. When analyzing data from multiple subjects, a GGM can also be formed on the covariance structure of stationary means-the between-subjects network. We discuss the interpretation of these models and propose estimation methods to obtain these networks, which we implement in the R packages graphicalVAR and mlVAR. The methods are showcased in two empirical examples, and simulation studies on these methods are included in the supplementary materials.

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Section: Limitations and Challengesmentioning

confidence: 99%

The Gaussian Graphical Model in Cross-Sectional and Time-Series Data

Epskamp

Waldorp

Mõttus

et al. 2018

Multivariate Behavioral Research

778

929

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show abstract

“…Most studies examining bidirectional processes in child and adolescent psychopathology relate to sleep, but many additional applications are possible. A key challenge in this area is temporally aligning EMA items with the timing of the bidirectional process – if EMA items are spaced too closely or too far apart in time, the process will be missed (Hamaker, Ceulemans, Grasman, & Tuerlinckx, ). However, if aligned to the specific research question, EMA provides a powerful means to study the nuances of bidirectional relationships.…”

Section: Review Of Ema Studies Of Child and Adolescent Mental And Behmentioning

confidence: 99%

“…Similarly, Bayesian multilevel models with time series elements may allow simultaneous estimation of temporal complexity and heterogeneity (Depaoli & Clifton, ; Hamaker, Asparouhov, Brose, Schmiedek, & Muthén, ). Advances in dynamic modeling also offer strategies for handling complexities present in EMA designs, including models that can capture non‐linear trends, decaying lag functions across continuous time, and state‐space grids defining the dynamic behavior of a two‐variable system (Hamaker et al, ). The increasing richness of modern ambulatory assessment paradigms makes a number of analytic applications possible, and the increasing intensity with which individuals can be sampled will likely push these modeling innovations forward.…”

Section: Future Directions Limitations and Conclusionmentioning

confidence: 99%

Annual Research Review: Ecological momentary assessment studies in child psychology and psychiatry

Russell

Gajos

2020

Child Psychology Psychiatry

150

106

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Background Enhancements in mobile phone technology allow the study of children and adolescents' everyday lives like never before. Ecological momentary assessment (EMA) uses these advancements to allow in‐depth measurements of links between context, behavior, and physiology in youths' everyday lives. Findings A large and diverse literature now exists on using EMA to study mental and behavioral health among youth. Modern EMA methods are built on a rich tradition of idiographic inquiry focused on the intensive study of individuals. Studies of child and adolescent mental and behavioral health have used EMA to characterize lived experience, document naturalistic within‐person processes and individual differences in these processes, measure familiar constructs in novel ways, and examine temporal order and dynamics in youths' everyday lives. Conclusions Ecological momentary assessment is feasible and reliable for studying the daily lives of youth. EMA can inform the development and augmentation of traditional and momentary intervention. Continued research and technological development in mobile intervention design and implementation, EMA‐sensor integration, and complex real‐time data analysis are needed to realize the potential of just‐in‐time adaptive intervention, which may allow researchers to reach high‐risk youth with intervention content when and where it is needed most.

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“…Dynamic models are increasingly employed in the analysis of intensive longitudinal measures of emotional phenomena (Hamaker, Ceulemans, Grasman, & Tuerlinckx, 2015;Montpetit, Bergeman, Deboeck, Tiberio, & Boker, 2010;Pe et al, 2015;Röcke & Brose, 2013). The aim of these applications is to get at the processes that structure change in daily affective experiences, among them emotion regulation (Boker, 2002;Kuppens, Oravecz, & Tuerlinckx, 2010;Kuppens & Verduyn, 2015;Ram & Gerstorf, 2009), and specific model parameters are interpreted as parameters of regulatory processes (e.g., auto-regressive strength as "emotional inertia"; Brose, Schmiedek, Koval, & Kuppens, 2015;Koval, Kuppens, Allen, & Sheeber, 2012;Suls, Green, & Hillis, 1998).…”

Section: General Rationalementioning

confidence: 99%

Capturing Context-Related Change in Emotional Dynamics via Fixed Moderated Time Series Analysis

Adolf

Voelkle

Brose

et al. 2017

Multivariate Behavioral Research

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Much of recent affect research relies on intensive longitudinal studies to assess daily emotional experiences. The resulting data are analyzed with dynamic models to capture regulatory processes involved in emotional functioning. Daily contexts, however, are commonly ignored. This may not only result in biased parameter estimates and wrong conclusions, but also ignores the opportunity to investigate contextual effects on emotional dynamics. With fixed moderated time series analysis, we present an approach that resolves this problem by estimating context-dependent change in dynamic parameters in single-subject time series models. The approach examines parameter changes of known shape and thus addresses the problem of observed intra-individual heterogeneity (e.g., changes in emotional dynamics due to observed changes in daily stress). In comparison to existing approaches to unobserved heterogeneity, model estimation is facilitated and different forms of change can readily be accommodated. We demonstrate the approach's viability given relatively short time series by means of a simulation study. In addition, we present an empirical application, targeting the joint dynamics of affect and stress and how these co-vary with daily events. We discuss potentials and limitations of the approach and close with an outlook on the broader implications for understanding emotional adaption and development.

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Modeling Affect Dynamics: State of the Art and Future Challenges

Cited by 183 publications

References 57 publications

The Gaussian Graphical Model in Cross-Sectional and Time-Series Data

The Gaussian Graphical Model in Cross-Sectional and Time-Series Data

Annual Research Review: Ecological momentary assessment studies in child psychology and psychiatry

Capturing Context-Related Change in Emotional Dynamics via Fixed Moderated Time Series Analysis

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