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ERIC Number: EJ1117320
Record Type: Journal
Publication Date: 2016-Oct
Pages: 14
Abstractor: As Provided
ISSN: ISSN-0036-6803
Complex Applications of HLM in Studies of Science and Mathematics Achievement: Cross-Classified Random Effects Models
Moreno, Mario; Harwell, Michael; Guzey, S. Selcen; Phillips, Alison; Moore, Tamara J.
School Science and Mathematics, v116 n6 p338-351 Oct 2016
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.
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Publication Type: Journal Articles; Reports - Research
Education Level: High Schools; Secondary Education; Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A