Using a naive Bayesian approach to identify academic risk based on multiple sources: A conceptual replication

The purpose of this study was to conduct a conceptual replication of Pendergast et al.'s (2018) study that examined the diagnostic accuracy of a nomogram procedure, also known as a naive Bayesian approach. The specific naive Bayesian approach combined academic and social-emotional and behavioral (SEB) screening data to predict student performance on a state end-of-year achievement test. Study data were collected in a large suburban school district in the Midwest across 2 school years and 19 elementary schools. Participants included 5753 students in Grades 3–5. Academic screening data included aimswebPlus reading and math composite scores. SEB screening data included Academic Behavior subscale scores from the Social, Academic, and Emotional Behavior Risk Screener. Criterion scores were derived from the Missouri Assessment Program (MAP) tests of English Language Arts and Mathematics. The performance of each individual screener was compared to the naive Bayesian approach that integrated pre-test probability information (i.e., district-wide base rates of risk derived from prior year MAP test scores), academic screening scores, and SEB screening scores. Post-test probability scores were then evaluated using a threshold model (VanDerHeyden, 2013) to determine the percentage of students within the sample that could be differentiated in terms of ruling in or ruling out intervention versus those who remained undifferentiated (as indicated by the need for additional assessment to determine risk status). Results indicated that the naive Bayesian approach tended to perform similarly to individual aimswebPlus measures, with all approaches yielding a large percentage (65%–87%) of undifferentiated students when predicting proficient performance. Overall, the results indicated that we likely failed to replicate the findings of the original study. Limitations and future directions for research are discussed.