SIMULATION STUDY FOR UNDERSTANDING THE PERFORMANCE OF PARTIAL LEAST SQUARES–MODIFIED FUZZY CLUSTERING (PLSMFC) IN FINDING GROUPS UNDER STRUCTURAL EQUATION MODEL

Abstract

In structural equation modeling (SEM), it is usually assumed that all observations follow only one model. This becomes irrelevant if the observations contain natural groups, each of which has a different SEM model. Mukid et al (2002) have proposed the partial least squares-modified fuzzy clustering method (PLSMFC) as a way to find groups of observations and at the same time estimate the parameters of the SEM model. This research aims to understand the performance of the PLSMFC method in finding groups of observations characterized by different forms of structural equation models. The goal was achieved by conducting a simulation study involving factors such as SEM model specification and number of clusters. The procedure used is to force the generated data into a different number of segments. The segment validity measures used are the fuzziness performance index (FPI) and normalized classification entropy (NCE). The correct number of segments is indicated by the smallest FPI and NCE values. Based on simulation studies, it is known that the PLSMFC method can detect segments accurately, especially if the size of the segments used to reallocate observations is larger than the number of segments used to generate the data.

Keywords

• unobserved heterogenity
• partial least squares
• fuzzy clustering