Modernizing Use of Regression Models in Physics Education Research: A Review of Hierarchical Linear Modeling

Autor/inn/en	Van Dusen, Ben; Nissen, Jayson
Titel	Modernizing Use of Regression Models in Physics Education Research: A Review of Hierarchical Linear Modeling
Quelle	In: Physical Review Physics Education Research, 15 (2019) 2, Artikel 020108 (13 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2469-9896
Schlagwörter	Physics; Science Education; Educational Research; Hierarchical Linear Modeling; Introductory Courses; Multiple Regression Analysis; Science Tests; College Students; Force Concept Inventory + Suchen Sie Ihr Suchwort? Physik; Naturwissenschaftliche Bildung; Bildungsforschung; Pädagogische Forschung; Einführungskurs; Collegestudent
Abstract	Physics education researchers (PER) often analyze student data with single-level regression models (e.g., linear and logistic regression). However, education datasets can have hierarchical structures, such as students nested within courses, that single-level models fail to account for. The improper use of single-level models to analyze hierarchical datasets can lead to biased findings. Hierarchical models (also known as multilevel models) account for this hierarchical nested structure in the data. In this publication, we outline the theoretical differences between how single-level and multilevel models handle hierarchical datasets. We then present analysis of a dataset from 112 introductory physics courses using both multiple linear regression and hierarchical linear modeling to illustrate the potential impact of using an inappropriate analytical method on PER findings and implications. Research can leverage multi-institutional datasets to improve the field's understanding of how to support student success in physics. There is no "post hoc" fix, however, if researchers use inappropriate single-level models to analyze multilevel datasets. To continue developing reliable and generalizable knowledge, PER should use hierarchical models when analyzing hierarchical datasets. The Supplemental Material includes a sample dataset, R code to model the building and analysis presented in the paper, and an HTML output from the R code. (As Provided).
Anmerkungen	American Physical Society. One Physics Ellipse 4th Floor, College Park, MD 20740-3844. Tel: 301-209-3200; Fax: 301-209-0865; e-mail: assocpub@aps.org; Web site: http://prst-per.aps.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01