Strengths and Difficulties Questionnaire (SDQ)
Category: Student Well-Being
Academic behaviors, as reflected in administrative indicators such as attendance and suspensions, are widely used in education research. However, limited guidance exists on how to measure them longitudinally and at scale. Using longitudinal data from Boston Public Schools students followed from kindergarten through high school (N = 12,232), we compare grade-specific point-in-time indicators with longitudinal composites constructed using factor analysis and principal components analysis (PCA). Factor analyses suggest that academic behaviors are multidimensional, reflecting distinct but correlated patterns of attendance and disciplinary behaviors across developmental periods. Despite this structure, PCA-based and factor-analytic composites demonstrated broadly similar predictive validity for eighth grade standardized test scores, on-time high school graduation, and immediate college enrollment. Eighth grade unexcused absence rate, a driver of both composites, performed comparably to longitudinal composites and also predicted later educational attainment above and beyond prior achievement. Findings suggest that, when prediction is the goal, longitudinal composites may yield limited incremental benefits over carefully selected point-in-time attendance measures. We offer practical guidance for researchers and policymakers seeking scalable, interpretable measures of student behavior to inform interventions and policies.
Keywords
academic behavior, PCA, factor analysis, psychometrics, noncognitive skills
Education level
Topics
Document Object Identifier (DOI)
10.26300/7h7f-0j56
