Contextualizing the Dynamics of Affective Functioning: Conceptual and Statistical Considerations

Aktuelle Affektforschung betont die Bedeutung mikrolängsschnittlicher Daten für das Verstehen täglichen affektiven Funktionierens, da sie es erlauben affektive Dynamiken und potentiell zugrunde liegende Prozesse zu beschreiben. Dynamische Längsschnittmodelle werden entsprechend attraktiver. In dieser Dissertation komme ich Forderungen nach einer Integration kontextueller Informationen in die Untersuchung täglichen affektiven Funktionierens nach. Speziell modifiziere ich populäre dynamische Modelle so, dass sie kontextuelle Variationen einbeziehen. In einem ersten Beitrag werden Personen als in Kontexte eingebettet begriffen. Der vorgeschlagene Ansatz der festen moderierten Zeitreihenanalyse berücksichtigt systemische Reaktionen auf kontextuelle Veränderungen, indem Veränderungen in allen Parametern eines dynamischen Zeitreihenmodells auf kontextuelle Veränderungen bedingt schätzt werden. Kontextuelle Veränderungen werden als bekannt und assoziierte Parameterveränderungen als deterministisch behandelt. Folglich sind Modellspezifikation und -schätzung erleichtert und in kleineren Stichproben praktikabel. Es sind allerdings Informationen über den Einfluss kontextueller Faktoren erforderlich. Anwendbar auf einzelne Personen erlaubt der Ansatz die uneingeschränkte Exploration interindividueller Unterschiede in kontextualisierten affektiven Dynamiken. In einem zweiten Beitrag werden Personen als mit Kontexten interagierend begriffen. Ich implementiere eine Prozessperspektive auf kontextuelle Schwankungen, die die Dynamiken täglicher Ereignisse über autoregressive Modelle mit Poisson Messfehler abbildet. Die Kombination von Poisson und Gaußscher autoregressiver Modellierung erlaubt eine Formalisierung des dynamischen Zusammenspiels kontextueller und affektiver Prozesse. Die Modelle sind hierarchisch aufgesetzt und erfassen so interindividuelle Unterschiede in intraindividuellen Dynamiken. Die Schätzung erfolgt über simulationsbasierte Verfahren der Bayesschen Statistik.Recent affect research stresses the importance of micro-longitudinal data for understanding daily affective functioning, as they allow describing affective dynamics and potentially underlying processes. Accordingly, dynamic longitudinal models get increasingly promoted. In this dissertation, I address calls for an integration of contextual information into the study of daily affective functioning. Specifically, I modify popular dynamic models so that they incorporate contextual changes. In a first contribution, individuals are characterized as embedded in contexts. The proposed approach of fixed moderated time series analysis accounts for systemic reactions to contextual changes by estimating change in all parameters of a dynamic time series model conditional on contextual changes. It thus treats contextual changes as known and related parameter changes as deterministic. Consequently, model specification and estimation are facilitated and feasible in smaller samples, but information on which and how contextual factors matter is required. Applicable to single individuals, the approach permits an unconstrained exploration of inter-individual differences in contextualized affective dynamics. In a second contribution, individuals are characterized as interacting reciprocally with contexts. Implementing a process perspective on contextual changes, I model the dynamics of daily events using autoregressive models with Poisson measurement error. Combining Poisson and Gaussian autoregressive models can formalize the dynamic interplay between contextual and affective processes. It thereby distinguishes not only unique from joint dynamics, but also affective reactivity from situation selection, evocation, or anticipation. The models are set up as hierarchical to capture inter-individual differences in intra-individual dynamics. Estimation is carried out via simulation-based techniques in the Bayesian framework

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

Time series analysis of intensive longitudinal data provides the psychological literature with a powerful tool for assessing how psychological processes evolve through time. Recent applications in the field of psychosomatic research have provided insights into the dynamical nature of the relationship between somatic symptoms, physiological measures, and emotional states. These promising results highlight the intrinsic value of employing time series analysis, although application comes with some important challenges. This paper aims to present an approachable, non-technical overview of the state of the art on these challenges and the solutions that have been proposed, with emphasis on application towards psychosomatic hypotheses. Specifically, we elaborate on issues related to measurement intervals, the number and nature of the variables used in the analysis, modeling stable and changing processes, concurrent relationships, and extending time series analysis to incorporate the data of multiple individuals. We also briefly discuss some general modeling issues, such as lag-specification, sample size and time series length, and the role of measurement errors. We hope to arm applied researchers with an overview from which to select appropriate techniques from the ever growing variety of time series analysis approaches.status: publishe

PowerLAPIM : an application to conduct power analysis for linear and quadratic longitudinal actor–partner interdependence models in intensive longitudinal dyadic designs

The longitudinal actor–partner interdependence model (L-APIM) is used to study actor and partner effects, both linear and curvilinear, in dyadic intensive longitudinal data. A burning question is how to conduct power analyses for different L-APIM variants. In this paper, we introduce an accessible power analysis application, called PowerLAPIM, and provide a hands-on tutorial for conducting simulation-based power analyses for 32 L-APIM variants. With PowerLAPIM, we target the number of dyads needed, but not the number of repeated measurements for both partners (which is often fixed in longitudinal studies). PowerLAPIM allows to study moderation of linear and quadratic actor and partner effects by incorporating time-varying covariates or a categorical dyad-level predictor to test group differences. We also provide the functionality to account for serial dependency in the outcome variable by including autoregressive effects. Building on existing study that can yield estimates and thus plausible values of relevant model parameters, we illustrate how to perform a power analysis for a future study. In this illustration, we also demonstrate how to run a sensitivity analysis, to assess the impact of uncertainty about the model parameters, and of changes in the number of repeated measurements

Measurement invariance within and between individuals: A distinct problem in testing the equivalence of intra- and inter-individual model structures

We address the question of equivalence between modeling results obtained on intra-individual and inter-individual levels of psychometric analysis. Our focus is on the concept of measurement invariance and the role it may play in this context. We discuss this in general against the background of the latent variable paradigm, complemented by an operational demonstration in terms of a linear state-space model, i.e., a time series model with latent variables. Implemented in a multiple-occasion and multiple-subject setting, the model simultaneously accounts for intra-individual and inter-individual differences. We consider the conditions-in terms of invariance constraints-under which modeling results are generalizable (a) over time within subjects, (b) over subjects within occasions, and (c) over time and subjects simultaneously thus implying an equivalence-relationship between both dimensions. Since we distinguish the measurement model from the structural model governing relations between the latent variables of interest, we decompose the invariance constraints into those that involve structural parameters and those that involve measurement parameters and relate to measurement invariance. Within the resulting taxonomy of models, we show that, under the condition of measurement invariance over time and subjects, there exists a form of structural equivalence between levels of analysis that is distinct from full structural equivalence, i.e., ergodicity. We demonstrate how measurement invariance between and within subjects can be tested in the context of high-frequency repeated measures in personality research. Finally, we relate problems of measurement variance to problems of non-ergodicity as currently discussed and approached in the literature

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

<p>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 <i>observed</i> intra-individual heterogeneity (e.g., changes in emotional dynamics due to observed changes in daily stress). In comparison to existing approaches to <i>unobserved</i> 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.</p