Modeling Dynamic Processes with Panel Data: An Application of Continuous Time Models to Prevention Research

Autor/inn/en	Deboeck, Pascal R.; Cole, David A.; Preacher, Kristopher J.; Forehand, Rex; Compas, Bruce E.
Titel	Modeling Dynamic Processes with Panel Data: An Application of Continuous Time Models to Prevention Research
Quelle	In: International Journal of Behavioral Development, 45 (2021) 1, S.28-39 (12 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Deboeck, Pascal R.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0165-0254
DOI	10.1177/0165025420905354
Schlagwörter	Intervention; Prevention; Individual Differences; Models; Comparative Analysis; Outcomes of Treatment; Behavior Change; Data Analysis; Goodness of Fit; Depression (Psychology); Parent Child Relationship; Cognitive Restructuring; Behavior Modification; Children; Adolescents; Anxiety; Correlation; At Risk Persons + Suchen Sie Ihr Suchwort? Prävention; Vorbeugung; Individueller Unterschied; Analogiemodell; Auswertung; Parents-child relationship; Parent-child-relation; Parent-child relationship; Eltern-Kind-Beziehung; Behaviour modification; Verhaltensänderung; Child; Kind; Kinder; Adolescent; Adolescence; Adoleszenz; Jugend; Jugendalter; Jugendlicher; Angst; Korrelation; Risikogruppe
Abstract	Many interventions are characterized by repeated observations on the same individuals (e.g., baseline, mid-intervention, two to three post-intervention observations), which offer the opportunity to consider differences in how individuals vary over time. Effective interventions may not be limited to changing means, but instead may also include changes to how variables affect each other over time. Continuous time models offer the opportunity to specify differing underlying processes for how individuals change from one time to the next, such as whether it is the level or change in a variable that is related to changes in an outcome of interest. After introducing continuous time models, we show how different processes can produce different expected covariance matrices. Thus, models representing differing underlying processes can be compared, even with a relatively small number of repeated observations. A substantive example comparing models that imply different underlying continuous time processes will be fit using panel data, with parameters reflecting differences in dynamics between control and intervention groups. (As Provided).
Anmerkungen	SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: http://sagepub.com
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01