Data Integration Approaches to Longitudinal Growth Modeling

Autor/inn/en	Marcoulides, Katerina M.; Grimm, Kevin J.
Titel	Data Integration Approaches to Longitudinal Growth Modeling
Quelle	In: Educational and Psychological Measurement, 77 (2017) 6, S.971-989 (19 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164416664117
Schlagwörter	Growth Models; Longitudinal Studies; Mathematics Skills; Achievement Tests; Cognitive Tests; Mathematics Instruction; Data Collection; Bayesian Statistics; Children; Gender Differences; Socioeconomic Status; Woodcock Johnson Psycho Educational Battery + Suchen Sie Ihr Suchwort? Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung; Mathmatics achievement; Mathematics ability; Mathematische Kompetenz; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen; Kognitiver Fähigkeitstest; Mathematics lessons; Mathematikunterricht; Data capture; Datensammlung; Child; Kind; Kinder; Geschlechterkonflikt; Socio-economic status; Sozioökonomischer Status
Abstract	Synthesizing results from multiple studies is a daunting task during which researchers must tackle a variety of challenges. The task is even more demanding when studying developmental processes longitudinally and when different instruments are used to measure constructs. Data integration methodology is an emerging field that enables researchers to pool data drawn from multiple existing studies. To date, these methods are not commonly utilized in the social and behavioral sciences, even though they can be very useful for studying various complex developmental processes. This article illustrates the use of two data integration methods, the "data fusion" and the "parallel analysis" approaches. The illustration makes use of six longitudinal studies of mathematics ability in children with a goal of examining individual changes in mathematics ability and determining differences in the trajectories based on sex and socioeconomic status. The studies vary in their assessment of mathematics ability and in the timing and number of measurement occasions. The advantages of using a data fusion approach, which can allow for the fitting of more complex growth models that might not otherwise have been possible to fit in a single data set, are emphasized. The article concludes with a discussion of the limitations and benefits of these approaches for research synthesis. (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	2020/1/01