Modeling the Co-Development of Correlated Processes with Longitudinal and Cross-Construct Effects

Conceptualizations of developmental trends are driven by the particular method used to analyze the period of change of interest. Various techniques exist to analyze developmental data, including: individual growth curve analysis in both observed and latent frameworks, cross-lagged regression to assess interrelations among variables, and multilevel frameworks that consider time as nested within individual. In this paper, we report on findings from a latent change score analysis of oral reading fluency and reading comprehension data from a longitudinal sample of approximately 16,000 students from first to fourth grade. Results highlight the utility of latent change score models compared to alternative specifications of linear and non-linear quadratic latent growth models, as well as implications for modeling change with correlated traits.