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Analyses were conducted using STATA 11.0 SE (StataCorp LP, College Station TX) to derive full maximum-likelihood and variance estimates with model assumptions confirmed through the analysis of residuals.
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An approximate maximum likelihood (ML) algorithm was developed in [8] to achieve near-optimal performance without the complexity of "full" maximum likelihood estimation.
This procedure only allows full maximum likelihood estimation.
Full maximum likelihood estimation was used to adjust for missing data on latent class indicator variables.
Missing data were dealt with using the full maximum likelihood procedure in Mplus [ 31].
Multiple imputations and full maximum likelihood modelling of continuous missing covariates can be done in a similar way as multiple imputations and full maximum likelihood modelling of categorical missing covariates.
Estimates were obtained with Proc Mixed (SAS version 9.2) using full maximum likelihood and robust SEs (20).
We used the full maximum likelihood estimation method for parameter estimation and type 3 F-test for testing significance.
We imputed missing data using the full information maximum-likelihood method (Arbuckle 1996).
To handle missing data, the full-information maximum-likelihood estimation (FIML) implemented in Mplus was applied.
All analyses were performed in the software package Mx [53], using the raw-data full-information maximum-likelihood approach.
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