Assignment to group was randomized by school rather than by individual; thus, data was analyzed using a three - level mixed random
regression model procedure, with time points nested within individual participants nested within schools.
Not exact matches
To ensure that the
model was not over-fitted, we repeated the stepwise
regression procedure with 70 % of the original data, selected randomly.
It looked like it worked until I tested it on a truncated data set against the lm (linear
models)
regression procedure and found a slight glitch.
procedures that... first infer a linear relation (
regression line) between ECS and variables... from
models and then use that linear relation to constrain ECS given observations... can be strongly influenced by «bad»
models that are not consistent with the data but exert large leverage on the inferred slope of the
regression line.
Multivariate logistic
regression models were used to estimate odds ratios (ORs) while adjusting for factors associated with obesity risk using the SAS PROC LOGISTIC
procedure (SAS Institute, Inc, Cary, North Carolina).
Models with dysfunctional emotion regulation as a mediation variable were tested via hierarchical multiple
regression analyses and bootstrapping
procedure.
A
model with cognitive efficiency as a mediator variable was tested using hierarchical multiple
regression analysis, with a bootstrapping
procedure to examine indirect effects.
This
procedure was repeated for the depressive symptoms
model; logistic
regression model predicting having at least two measures of depressive symptoms (n = 5,539).
We applied an intention - to - treat
procedure by conducting multiple imputation (Rubin 1987) to handle the missing data through a chain of conditional
regression models (fully conditional specification; Van Buuren et al. 1999).