Missing values were present in the dataset because of the longitudinal nature of the research design, but adequate covariance coverage was present (ranging from 0.59 to 0.88). Missing data in all models were managed with the full information maximum likelihood (FIML) procedure used by Mplus version 6. This method has been shown to be very efficient when analyzing data from samples with moderate levels of missing values, and it is adequate even when data are not missing completely at random, as long as the predictors of missingness are included in the model (Widaman 2006). When using FIML, the estimation of each parameter is made on the basis of all available information from each participant. Consequently, we can retain in the analysis participants with missing data so they contribute to model estimation.
