Efficiency of Parameter Estimator of Various Resampling Methods on WarpPLS Analysis

Abstract

WarpPLS analysis has three algorithms, namely the outer model parameter estimation algorithm, the inner model, and the hypothesis testing algorithm which consists of several choices of resampling methods namely Stable1, Stable2, Stable3, Bootstrap, Jackknife, and Blindfolding. The purpose of this study is to apply the WarpPLS analysis by comparing the six resampling methods based on the relative efficiency of the parameter estimates in the six methods. This study uses secondary data from the questionnaire with 1 variable being formative and 2 variables being reflective. Secondary data for the Infrastructure Service Satisfaction Index (IKLI) were obtained from the Study Report on the Regional Development Planning for Economic Growth and the Malang City Gini Index in 2018, while secondary data for the Social Capital Index (IMS) and Community Development Index (IPMas) were obtained from the Research Report on Performance Indicators Regional Human Development Index and Poverty Rate of Malang City in 2018. The results of this study indicate that based on two criteria used, namely the calculation of relative efficiency and measure of fit as a model good, it can be concluded that the Jackknife resampling method is the most efficient, followed with the Stable1, Bootstrap, Stable3, Stable2, and Blindfolding methods.