Sampling weight adjustments in partial least squares structural equation modeling: guidelines and illustrations

Abstract

Applications of partial least squares structural equation modelling (PLS-SEM) often draw on survey data. While researchers go to great lengths to document reliability and validity statistics that support the generalisability of their findings, they often overlook or ignore a more fundamental issue related to data analysis—the representativeness of their sample. Addressing this concern, the present paper offers guidelines for using the weighted PLS-SEM (WPLS-SEM) algorithm to apply sampling weights in the model estimation. The results of the WPLS algorithm and the traditional PLS algorithm are then compared using a marketing research model. The findings show that researchers should routinely consider the procedure of the WPLS algorithm when using the PLS technique for assessment. The WPLS algorithm is a useful and practical approach for achieving better average population estimates in situations where researchers have a set of appropriate weights. This paper substantiates the use of the WPLS algorithm and provides business researchers and practitioners with the proper guidelines to assess, report, and interpret PLS-SEM results. It also illustrates that the use of the WPLS algorithm produces different inference test results in the structural model and different predictive relevance results. Thus, the study contributes to the advancement of PLS-SEM applications.