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

Abstract

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.

Using structural equation modeling to study traits and states in intensive longitudinal data.

“…For state-trait SEMs, an important avenue for future research is therefore to allow for random loadings, autoregressive effects, or residual variances, which can be done by extending the models within the DSEM framework (Asparouhov et al, 2017(Asparouhov et al, , 2018. Alternatively, some authors have suggested a more bottom up approach (Adolf, Voelkle, Brose, & Schmiedek, 2017;Molenaar, 2004;Ram, Brinberg, Pincus, & Conroy, 2017), where each single-subject time series is first analyzed before pooling out results for the sample.…”

Time series analysis of intensive longitudinal data in psychosomatic research: A methodological overview

“…For instance, one can artificially refine the time scale by including missing values in the data, which are thus used to equalize the time intervals between observations up to a certain level of precision. On the modeling side, this then requires the introduction of so-called phantom variables and/or the use of estimation techniques that handle missing values (e.g., Adolf, Voelkle, Brose, & Schmiedek, 2017;Asparouhov et al, 2018;Rindskopf, 1984). The applicability of this solution depends on properties of the data.…”

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“…Time series have been used as a basis for forecasting future mood states [59]. There have also been some early attempts to take into account the role of environmental context [66].…”