ABSTRACT
In the present study, we investigated the relative impact of age- versus schooling-related growth in school readiness skills using four modeling approaches that leverage natural variation in longitudinal data collected within the preschool year. Our goal was to demonstrate the applicability of different analytic techniques that do not rely on assumptions inherent in commonly applied methods (e.g., the school entrance cutoff method, regression discontinuity design) that selection into subsequent grades is based on birthdate alone and that the quality of experiences between grades are not responsible for differences in outcomes. Notably, these alternative methods also do not require data collected across multiple grades. Participants included 316 children (Mage = 54.77 months; 47.15% male) who mostly identified as White (64%) or Latinx (20%). A little over half of the sample attended Head Start preschools (54.75%). Four modeling techniques that leverage data collected at two timepoints in preschool were used to examine schooling effects on children's preliteracy, emergent math, and executive function (EF) skills. Results replicate evidence from previous research using traditional methods. Specifically, findings across all models demonstrate a schooling effect on preliteracy skills during the preschool year, above and beyond maturation, but not on emergent math or EF. We discuss the advantages and disadvantages of each analytical tool for researchers who are interested in answering questions about the effects of schooling with diverse data collection strategies, as well as broader implications for the integrity of educational and developmental science. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
