Each accelerated growth model was estimated in Mplus Version 6.1.1 (Muthén & Muthén, 1998–2011) using the diagonally weighted least squares estimator, which is appropriate for categorical data. Since this estimator yields robust results under the assumption of missing completely at random only, we performed multiple imputations with the Bayes estimation method (Asparouhov & Muthén, 2010). Variables included in the imputation models were risk behaviors at all three measurement points, age, gender, marital status, and education. We created 10 complete data sets, which were analyzed in parallel, yielding means and standard deviations for the outcomes and goodness-of-fit indices, across the 10 data sets.
