2.5. Statistical Analyses
asthma, diabetes
ASTHMA, DIABETES
We examined the demographic characteristics of Head Start centers and study participants in three conditions using descriptive statistics (i.e., Chi-square test or Fisher’s exact test for categorical variables and Kruskal–Wallis H test for continuous variables). For each outcome of interest (i.e., GMQ, LS pctl, and BS pctl), we used a 3-level (time nested within child and child nested within center) linear mixed-effects model (LMM) to examine group differences with time (baseline vs. post test), treatment group (CBI vs. C + HBI vs. control), the interaction between time and treatment group, and center size (large vs. small) as fixed predictors that were kept in the model regardless of whether they were statistically significant. The LMM had one random term to account for the correlation among children nested within the same center and a second term to account for the correlation among two measures nested within the same child. Data were assumed to be missing at random. In the full LMMs, we included child’s age at baseline, age squared, gender, race/ethnicity, asthma, mother’s education, language spoken most often at home, parent marital status, and family history of diabetes as confounders. We employed backward model selection to select the final reduced model by removing one non-significant (