A Primer on Continuous-Time Modeling in Educational Research: An Exemplary Application of a Continuous-Time Latent Curve Model with Structured Residuals (CT-LCM-SR) to PISA Data

Autor/inn/en	Lohmann, Julian F.; Zitzmann, Steffen; Voelkle, Manuel C.; Hecht, Martin
Titel	A Primer on Continuous-Time Modeling in Educational Research: An Exemplary Application of a Continuous-Time Latent Curve Model with Structured Residuals (CT-LCM-SR) to PISA Data
Quelle	In: Large-scale Assessments in Education, 10 (2022), Artikel 5 (32 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Lohmann, Julian F.) ORCID (Hecht, Martin)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2196-0739
DOI	10.1186/s40536-022-00126-8
Schlagwörter	Longitudinal Studies; Structural Equation Models; Time; Achievement Tests; Foreign Countries; International Assessment; Secondary School Students; Educational Research; Reading Tests; Data Analysis; Trend Analysis; Coding; Syntax; Scores; Program for International Student Assessment + Suchen Sie Ihr Suchwort? Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung; Zeit; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen; Ausland; Sekundarschüler; Bildungsforschung; Pädagogische Forschung; Lesetest; Auswertung; Trendanalyse; Codierung; Programmierung
Abstract	One major challenge of longitudinal data analysis is to find an appropriate statistical model that corresponds to the theory of change and the research questions at hand. In the present article, we argue that "continuous-time models" are well suited to study the continuously developing constructs of primary interest in the education sciences and outline key advantages of using this type of model. Furthermore, we propose the "continuous-time latent curve model with structured residuals" (CT-LCM-SR) as a suitable model for many research questions in the education sciences. The CT-LCM-SR combines growth and dynamic modeling and thus provides descriptions of both trends and process dynamics. We illustrate the application of the CT-LCM-SR with data from PISA reading literacy assessment of 2000 to 2018 and provide a tutorial and annotated code for setting up the CT-LCM-SR model. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01