Literaturnachweis - Detailanzeige
Autor/inn/en | Flunger, Barbara; Trautwein, Ulrich; Nagengast, Benjamin; Lüdtke, Oliver; Niggli, Alois; Schnyder, Inge |
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Titel | Using Multilevel Mixture Models in Educational Research: An Illustration with Homework Research |
Quelle | In: Journal of Experimental Education, 89 (2021) 1, S.209-236 (28 Seiten)Infoseite zur Zeitschrift
PDF als Volltext |
Zusatzinformation | ORCID (Flunger, Barbara) ORCID (Trautwein, Ulrich) ORCID (Nagengast, Benjamin) |
Sprache | englisch |
Dokumenttyp | gedruckt; online; Zeitschriftenaufsatz |
ISSN | 0022-0973 |
DOI | 10.1080/00220973.2019.1652137 |
Schlagwörter | Foreign Countries; Middle School Students; Grade 8; Multivariate Analysis; Models; Educational Research; Homework; Assignments; Profiles; Student Educational Objectives; Parent Participation; Teacher Influence; Classification; Predictor Variables; Switzerland Ausland; Middle school; Middle schools; Student; Students; Mittelschule; Mittelstufenschule; Schüler; Schülerin; School year 08; 8. Schuljahr; Schuljahr 08; Multivariate Analyse; Analogiemodell; Bildungsforschung; Pädagogische Forschung; Hausaufgabe; Assignment; Auftrag; Zuweisung; Charakterisierung; Profilanalyse; Elternmitwirkung; Classification system; Klassifikation; Klassifikationssystem; Prädiktor; Schweiz |
Abstract | The present study illustrates the utility of applying multilevel mixture models in educational research, using data on the homework behavior of 1,812 Swiss eighth-grade students in French as a second language. A previous person-centered study identified 5 homework learning types characterized by different patterns of high or low homework time and effort. Via multilevel latent profile analyses (MLPAs), the dependence of homework learning types on between-classroom differences was investigated. Based on the proportions of homework learning profiles across classrooms, 3 class-level profiles were identified: A "low time", "high time" and an "average" profile. Predictors of the latent profiles at the student and class levels were assessed. The study offers insights into the advantages of multilevel mixture models for educational research. (As Provided). |
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Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
Update | 2024/1/01 |