Mplus Users Guide v6.. - Muthén & Muthén

CHAPTER 6slopes are also used to represent individual variation in the influence oftime-varying covariates on outcomes.Growth modeling in Mplus allows the analysis of multiple processes,both parallel and sequential; regressions among growth factors andrandom effects; growth modeling of factors measured by multipleindicators; and growth modeling as part of a larger latent variable model.Survival modeling in Mplus includes both discrete-time and continuoustimeanalyses. Both types of analyses consider the time to an event.Discrete-time survival analysis is used when the outcome is recordedinfrequently such as monthly or annually, typically leading to a limitednumber of measurements. Continuous-time survival analysis is usedwhen the outcome is recorded more frequently such as hourly or daily,typically leading to a large number of measurements. Survival modelingis integrated into the general latent variable modeling framework so thatit can be part of a larger model.All growth and survival models can be estimated using the followingspecial features:• Single or multiple group analysis• Missing data• Complex survey data• Latent variable interactions and non-linear factor analysis usingmaximum likelihood• Random slopes• Individually-varying times of observations• Linear and non-linear parameter constraints• Indirect effects including specific paths• Maximum likelihood estimation for all outcome types• Bootstrap standard errors and confidence intervals• Wald chi-square test of parameter equalitiesFor continuous, censored with weighted least squares estimation, binary,and ordered categorical (ordinal) outcomes, multiple group analysis isspecified by using the GROUPING option of the VARIABLE commandfor individual data or the NGROUPS option of the DATA command forsummary data. For censored with maximum likelihood estimation,unordered categorical (nominal), and count outcomes, multiple groupanalysis is specified using the KNOWNCLASS option of the98