Journal of Data Science

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 the regression trees differ across the subject areas (i.e., reading and math) and between the performance levels and achievement gains. Through the use of real-world assessment datasets, this article demonstrates the strengths and limitations of CRT when analyzing student achievement data as well as the challenges. When achievement data such as achievement gains in our case study are not linearly strongly related to any continuous predictors, regression trees may make more accurate predictions than general linear models and produce results that are easier to interpret. Our study illustrates scenarios when CRT on achievement data is most appropriate and beneficial.

Defined as perseverance and passion for long term goals, grit represents an important psychological skill toward goal-attainment in academic and less-stylized settings. An outstanding issue of primary importance is whether age affects grit, ceteris paribus. The 12-item Grit-O Scale and the 8-item Grit-S Scale—from which grit scores are calculated—have not existed for a long period of time. Therefore, Duckworth (2016, p. 37) states in her book, Grit: The Power and Passion of Perseverance, that “we need a different kind of study” to distinguish between rival explanations that either generational cohort or age are more important in explaining variation in grit across individuals. Despite this clear data constraint, we obtain a glimpse into the future in the present study by using a within and between generational cohort age difference-in-difference approach. By specifying generation as a categorical variable and age-in-generation as a count variable in the same regression specifications, we are able to account for the effects of variation in age and generation simultaneously, while avoiding problems of multicollinearity that would hinder post-regression statistical inference. We conclude robust, significant evidence that the negative-parabolic shape of the grit-age profile is driven by generational variation and not by age variation. Our findings suggest that, absent a grit-mindset intervention, individual-level grit may be persistent over time.