STRUCTURAL EQUATION MODELING (SEM) MENGGUNAKAN ALTERNATIF PARTIAL LEAST SQUARE (PLS)

PUTRI AYUNING TYAS, NIM. 17106010004 (2021) STRUCTURAL EQUATION MODELING (SEM) MENGGUNAKAN ALTERNATIF PARTIAL LEAST SQUARE (PLS). Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

Structural Equation Modeling (SEM) is a combination of factor analysis with path analysis into a comprehensive statistical method. SEM method with variant-based alternatives or often called Partial Least Square (PLS) is one of the methods used when the assumptions that must be fulfilled are violated when using real data in the field. Partial Least Square (PLS) itself is an estimation method that maximizes the variance between latent variables. In this study, the authors examined SEM using PLS on the factors influencing the success of online learning for active mathematics students at UIN Sunan Kalijaga Yogyakarta during the Covid-19 pandemic, using reflective indicators. The covid-19 outbreak has hit all over the world including Indonesia, the impact of the pandemic requires the government to change the learning system that was initially face-to-face into distance learning. Reflective indicator itself is an indicator that assumes that the covariance between model measurements is explained by the variables which are the manifestation of the construct domain, or often called indicators that are influenced by latent variables. As for the results of the SmartPLS 3.0 output that the outer model evaluation shows all indicator variables weighted on each latent variable are valid and reliable. So does, the evaluation of the inner model produces a good Q-Square value. And the resulting structural model that variables of student characteristics negatively affect online learning variables.

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