Complex Applications of HLM in Studies of Science and Mathematics Achievement: Cross-Classified Random Effects Models

Autor/inn/en	Moreno, Mario; Harwell, Michael; Guzey, S. Selcen; Phillips, Alison; Moore, Tamara J.
Titel	Complex Applications of HLM in Studies of Science and Mathematics Achievement: Cross-Classified Random Effects Models
Quelle	In: School Science and Mathematics, 116 (2016) 6, S.338-351 (14 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0036-6803
DOI	10.1111/ssm.12191
Schlagwörter	Mathematics Achievement; Mathematics Instruction; Hierarchical Linear Modeling; Mathematics Curriculum; High School Students; Secondary School Mathematics; College Mathematics; College Students; Science Instruction; Science Achievement + Suchen Sie Ihr Suchwort? Mathmatics sikills; Mathmatics achievement; Mathematical ability; Mathematische Kompetenz; Mathematics lessons; Mathematikunterricht; High school; High schools; Student; Students; Oberschule; Schüler; Schülerin; Studentin; Collegestudent; Teaching of science; Science education; Natural sciences Lessons; Naturwissenschaftlicher Unterricht
Abstract	Hierarchical linear models have become a familiar method for accounting for a hierarchical data structure in studies of science and mathematics achievement. This paper illustrates the use of cross-classified random effects models (CCREMs), which are likely less familiar. The defining characteristic of CCREMs is a hierarchical data structure defined by multiple random factors at higher levels. We illustrate CCREMs using data for approximately 10,000 students from more than 250 high schools who attended one of 27 four-year postsecondary institutions. The appropriate use of CCREMs helps to ensure unbiased estimates of effects and credible inferences. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01