Bayesian Estimation and Clustering of Latent Attitudinal Parameters Using Cross-Sectional Survey Data: Application to an Online Banking Survey

Abstract

Firms routinely collect cross-sectional survey data on attitudinal constructs such as satisfaction, brand awareness, behavioral intentions etc. from current and prospective customers. The single observation per individual in such survey data offers limited scope for estimation and inference of heterogeneous response parameters, and consequently, for downstream analyses that presuppose a heterogeneous response. We present a Bayesian treatment to this problem. We estimate these individual-specific latent attitudinal parameters, segment the respondents on the basis of these parameters, and finally, empirically validate our clustering solutions. Our approach considers both finite and infinite mixtures of component densities and enables exact finite sample inference on both individual level parameters and cluster level parameters. We examine the performance of the latent class, the mixture of normals and the semiparametric Bayesian estimators under varying conditions of latent data structure, cluster separation and individual-level idiosyncratic variance.