Partial Least Square Structural Equation Modeling (PLS-SEM) with Biner Data (Case Study: Knowledge Creation on Dairy Cooperative in Indonesia)

Structural Equation Modeling (SEM) is an analysis method that consists of two models: the measurement and structural model. The assumtion of SEM modeling is multivariate normally distribution and large relatively sample size. In some cases there are data that doesn't meet these assumptions so that required some handeling. In this study, the handling is done by using the approach Partial Least Square (PLS). Furthermore, changing the likert scale into binary scale. Finally, compare the model of knowledge creation in Indonesian dairy cooperatives using PLS-SEM analysis of data likert scale questionnaire and the likert scale that has been converted into binary categories. The author uses the data creation knowledge for example the application of PLS-SEM. The results obtained that the binary data is no less good than the likert scale data. It is shown from the R-square value, F-square, Q-square, RMSEA, SRMR, NFI, and GFI these two models are not much different. Likewise indicated by the Composite Reliability and Cronbach Alpha was good. Based on the t-statistic value, a likert scale of only 14 of the 24 indicators were valid. Whereas the binary scale, there are 21 valid indicators. Thus, the contruction of the questionnaire can use the binary scale.