Education Commons^™

Tree-Based Methods: A Tool For Modeling Nonlinear Complex Relationships And Generating New Insights From Data, Ya Mo, Brian Habing, Nell Sedransk

Curriculum, Instruction, and Foundational Studies Faculty Publications and Presentations

Our paper introduces tree-based methods, specifically classification and regression trees (CRT), to study student achievement. CRT allows data analysis to be driven by the data’s internal structure. Thus, CRT can model complex nonlinear relationships and supplement traditional hypothesis-testing approaches to provide a fuller picture of the topic being studied. Using Early Childhood Longitudinal Study-Kindergarten 2011 data as a case study, our research investigated predictors from students’ demographic backgrounds to ascertain their relationships to students’ academic performance and achievement gains in reading and math. In our study, CRT displays complex patterns between predictors and outcomes; more specifically, the patterns illuminated by …