ANALISIS FAKTOR 2-LEVEL DALAM MODEL PERSAMAAN STRUKTURAL; ( 2-LEVEL FACTOR ANALYSIS IN STRUCTURAL EQUATION MODELING )

Waluyo, Mohamad, Abdurakhman

2015 | Disertasi | FMIPA

Structural equation modeling (SEM) is very useful to know whether the models between a latent variable and indicators variable fit with the data obtained. If the obtained data are quite large and hierarchical then it is necessary to adjust the structural model analysis. The adjustment is to incorporate the effects of random variables each level of the hierarchy. Random variables for 2-level hierarchical model are 1st level effect and 2nd level effect random variable. The estimation method used in this research is the maximum likelihood (ML). The ML method is commonly used in the estimation of the parameters in the SEM , but it often gets problems when there are missing datas. So the expectation-maximization algorithm (EM) is required to estimate the missing data.

Kata Kunci : SEM; multilevel; ML; EM Algorithm.