Search

Supplemental Material, sj-pdf-1-spr-10.1177_02654075221080128 for PowerLAPIM: An application to conduct power analysis for linear and quadratic longitudinal actor���partner interdependence models in intensive longitudinal dyadic designs by Ginette Lafit, Laura Sels, Janne K. Adolf, Tom Loeys and Eva Ceulemans in Journal of Social and Personal Relationships

Supplemental Material, sj-pdf-7-spr-10.1177_02654075221080128 for PowerLAPIM: An application to conduct power analysis for linear and quadratic longitudinal actor���partner interdependence models in intensive longitudinal dyadic designs by Ginette Lafit, Laura Sels, Janne K. Adolf, Tom Loeys and Eva Ceulemans in Journal of Social and Personal Relationships

Supplemental Material, sj-pdf-4-spr-10.1177_02654075221080128 for PowerLAPIM: An application to conduct power analysis for linear and quadratic longitudinal actor���partner interdependence models in intensive longitudinal dyadic designs by Ginette Lafit, Laura Sels, Janne K. Adolf, Tom Loeys and Eva Ceulemans in Journal of Social and Personal Relationships

Supplemental Material, sj-pdf-2-spr-10.1177_02654075221080128 for PowerLAPIM: An application to conduct power analysis for linear and quadratic longitudinal actor���partner interdependence models in intensive longitudinal dyadic designs by Ginette Lafit, Laura Sels, Janne K. Adolf, Tom Loeys and Eva Ceulemans in Journal of Social and Personal Relationships

Supplemental Material, sj-pdf-6-spr-10.1177_02654075221080128 for PowerLAPIM: An application to conduct power analysis for linear and quadratic longitudinal actor���partner interdependence models in intensive longitudinal dyadic designs by Ginette Lafit, Laura Sels, Janne K. Adolf, Tom Loeys and Eva Ceulemans in Journal of Social and Personal Relationships

Supplemental Material, sj-pdf-3-spr-10.1177_02654075221080128 for PowerLAPIM: An application to conduct power analysis for linear and quadratic longitudinal actor���partner interdependence models in intensive longitudinal dyadic designs by Ginette Lafit, Laura Sels, Janne K. Adolf, Tom Loeys and Eva Ceulemans in Journal of Social and Personal Relationships

Supplemental Material, sj-pdf-5-spr-10.1177_02654075221080128 for PowerLAPIM: An application to conduct power analysis for linear and quadratic longitudinal actor���partner interdependence models in intensive longitudinal dyadic designs by Ginette Lafit, Laura Sels, Janne K. Adolf, Tom Loeys and Eva Ceulemans in Journal of Social and Personal Relationships

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.

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.