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dc.contributor.authorVan Dusen, Ben
dc.contributor.authorNissen, Jayson
dc.date.accessioned2019-11-03T12:43:28Z
dc.date.available2019-11-03T12:43:28Z
dc.date.created2018-10-14 23:11
dc.date.issued2018-07-17
dc.identifieroai:arXiv.org:1807.06510
dc.identifierhttp://arxiv.org/abs/1807.06510
dc.identifier.urihttp://hdl.handle.net/20.500.12424/2898806
dc.description.abstractPhysics education researchers (PER) often analyze student data with single-level regression models (e.g., linear and logistic regression). However, education datasets often 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 (a.k.a., multi-level models) account for this hierarchical nested structure in the data. In this publication, we outline the theoretical differences between how single-level and multi-level 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 multi-level datasets. To continue developing reliable and generalizable knowledge, PER should adopt the use of hierarchical models when analyzing hierarchical datasets. The supplemental materials include a sample dataset and R code to model the building and analysis presented in the paper.
dc.description.abstractComment: 13 pages, 4 figures, 6 tables, submitted as part of a collection on quantitative methods in physics education research
dc.subjectPhysics - Physics Education
dc.titleModernizing use of regression models in physics education research: a review of hierarchical linear modeling
dc.typetext
ge.collectioncodeOAIDATA
ge.dataimportlabelOAI metadata object
ge.identifier.legacyglobethics:15624317
ge.identifier.permalinkhttps://www.globethics.net/gel/15624317
ge.lastmodificationdate2018-10-14 23:11
ge.lastmodificationuseradmin@pointsoftware.ch (import)
ge.submissions0
ge.oai.exportid149801
ge.oai.repositoryid58
ge.oai.setnamePhysics (Other)
ge.oai.setspecphysics:physics
ge.oai.streamid2
ge.setnameGlobeEthicsLib
ge.setspecglobeethicslib
ge.linkhttp://arxiv.org/abs/1807.06510


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