PLS를 활용한 고차요인구조 추정방법의 비교

Abstract

Estimation approaches for casual relation model with high-order factors have strict restrictions or limits. In the case of ML(Maximum Likelihood), a strong assumption which data must show a normal distribution is required and factors of exponentiationis impossible due to the uncertainty of factors. To overcome this limitation many PLS (Partial Least Squares) approaches areintroduced to estimate the structural equation model including high-order factors. However, it is possible to yield biased estimatesif there are some differences in the number of measurement variables connected to each latent variable. In addition, any approachdoes not exist to deal with general cases not having any measurement variable of high-order factors. This study compare severalapproaches including the repeated measures approach which are used to estimate the casual relation model including high-orderfactors by using PLS (Partial Least Squares), and suggest the best estimation approach. In other words, the study proposes thebest approach through the research on the existing studies related to the casual relation model including high-order factors byusing PLS and approach comparison using a virtual model.