Trait Outcomes
REGRESSION
The effect of the intervention on trait outcomes was examined by running 4 linear mixed models (LMMs) on an intention-to-treat basis, with baseline measures at T1 as a covariate, randomized group as a 2-level between-subjects factor, study phase (T2 and T3) as a 2-level repeated measures factor, an unstructured covariance matrix, and the restricted maximum likelihood estimation method. The statistical model was hierarchically balanced, with one 3-way interaction among covariate, phase, and randomized group, three 2-way interactions (covariate × phase, covariate × randomized group, phase × randomized group), and 3 main effects (covariate, phase, and randomized group). For the effect size estimation, we calculated partial eta-squared for each model factor.To estimate whether randomized group significantly contributed to the LMM, we ran four −2 log likelihood (LL) tests comparing the complete LMMs described earlier to LMMs without randomized group and its interactions. This model included baseline measures at T1 as a covariate, the study phase (T2 and T3) as a 2-level repeated measures factor, and one 2-way interaction (covariate × phase). For the −2 LL test, the 2 models were run using an unstructured covariance matrix and the maximum likelihood estimation method.For each trait outcome, 2 preplanned analyses of covariance (ANCOVAs) were run to separately verify the effect of randomized group at T2 and T3. The models included baseline measures as a covariate, randomized group as a fixed factor, and measures at either T2 or T3 as dependent variables. The main conclusions were drawn from these preplanned ANCOVAs.We also ran 2 post hoc repeated measures ANOVAs for each trait outcome, one for the control and one for the intervention group, testing for time trends across all 3 time points.Finally, preplanned dose-response effects were tested in the intervention condition by running multiple regression analyses with each trait outcome at T2 and T3 as a dependent variable and Helmert-coded engagement scores (ie, the number of videos watched) as independent variables. Helmert coding allows the comparison of each level of engagement against the remaining higher levels, thereby identifying potential jump points in the data [