Conventional and Piecewise Growth Modeling Techniques: Applications and Implications for Investigating Head Start Children's Early Literacy Learning

摘要

This article reviews the mechanics of conventional and piecewise growth models to demonstrate the unique affordances of each technique for examining the nature and predictors of children's early literacy learning during the transition from preschool through first grade. Using the nationally representative Family and Child Experiences Survey (FACES) data set, 1997 cohort, the authors show how piecewise models revealed discrete contributions of child, family, and classroom experiences to children's literacy skills within particular years, whereas conventional models, which considered the whole 3-year trajectory of change as a single outcome, revealed fewer of these nuanced contributions.