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Structural Equation Modeling: Applications Using Mplus by Xiaoqian Wang, Jichuan Wang

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4.3 Multi-Process LGM

In previous sections of this chapter, our modeling focuses only on a single outcome growth process. The LGM can be extended to simultaneously model multiple outcome growth processes, which could be either parallel or sequential (Muthén and Muthén, 1998–2010). In this section we will discuss and demonstrate parallel-process LGM. The following equations describe an unconditional parallel multi-process LGM:

(4.14) equation

(4.15) equation

(4.16) equation

where Equation (4.14) is referred to as the within subject model, in which img is the ith observed outcome measures at time point tt for the mth outcome measure. Model parameters img and img are the latent intercept and slope growth factors for the mth outcome growth process. Equations (4.15) and (4.16) are referred to as between subject models for the mth outcome measure, in which and serve as dependent variables, represents the estimated overall mean ...

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