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Abstract Title: Modernizing use of regression models in physics education research: a review of hierarchical linear modeling
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 (a.k.a., multi-level models) account for this hierarchical nested structure in the data. In this presentation, 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 use hierarchical models when analyzing hierarchical datasets.
Abstract Type: Symposium Poster
Parallel Session: Learning by Analyzing More Than Just Correct Answers
Session Time: Parallel Sessions Cluster I
Room: Cascade C

Author/Organizer Information

Primary Contact: Ben Van Dusen
CSU Chico
Co-Author(s)
and Co-Presenter(s)
Jayson Nissen