Analytical plan
Head Start-eligible
To examine the efficacy of each candidate component, intent-to-treat analyses can be utilized, using data from all eligible children and parents who participated in the study, regardless of absences or attrition. The main effects of each component can be examined using a full effects hierarchical linear model (random slopes with restricted maximum-likelihood estimation), with assessment periods nested within children, children nested in classrooms and classrooms nested within centers. Effect coding should be used to dichotomize each component, representing the presence (1) or absence (–1) of each intervention component (see Kugler et al.To further enhance the precision of statistical estimates, the hierarchical linear model can include children’s race, age, and gender as Level 1 covariates. To minimize the extent to which variation in treatment effects are attributable to classroom (cluster level) characteristics, Level 2 covariates can also include teachers’ implementation fidelity, classroom environment (food, mealtime, and physical activity), and classroom chaos. To account for center-level aggregate characteristics, models can include the center quality rating and an estimate of economic hardship faced by families (percentage of Head Start-eligible families) as a pretest covariate. To account for the repeated measures design, subject-level pretest, posttest, and follow-up scores on target outcomes for each component can be included as separate timepoints (repeated measures) in the model, or models examining mean differences in posttest and follow-up scores across conditions can be adjusted for pretest scores; both approaches are described in the work of Dziak et al.