Consequences of Misspecifying Levels of Variance in Cross-Classified Longitudinal Data Structures.

The purpose of this study was to determine if modeling school and classroom effects was necessary in estimating passage reading growth across elementary grades. Longitudinal data from 8367 students in 2989 classrooms in 202 Reading First schools were used in this study and were obtained from the Progress Monitoring and Reporting Network maintained by the Florida Center for Reading Research. Oral reading fluency (ORF) was assessed four times per school year. Five growth models with varying levels of data (student, classroom, and school) were estimated in order to determine which structures were necessary to correctly partition variance and accurately estimate standard errors for growth parameters. Because the results illustrate that not modeling higher-level clustering inflated lower-level variance estimates and in some cases led to biased standard errors, the authors recommend the practice of including classroom cross-classification and school nesting when predicting longitudinal student outcomes.