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

Voleti, S and Bharadwaj, S (2015) Bayesian Estimation and Clustering of Latent Attitudinal Parameters Using Cross-Sectional Survey Data: Application to an Online Banking Survey. Working Paper. SSRN. (Submitted)

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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.

Affiliation: Indian School of Business
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ISB Creators
ORCiD
Voleti, S
http://orcid.org/0000-0002-6858-014X
Item Type: Monograph (Working Paper)
Uncontrolled Keywords: Latent Variable Segmentation, Bayesian Mixture Model, Dirichlet Process prior, Crosssectional data, survey, e-banking
Subjects: Marketing
Depositing User: Mohan Dass
Date Deposited: 15 Apr 2019 06:19
Last Modified: 15 Apr 2019 06:19
URI: http://eprints.exchange.isb.edu/id/eprint/836
Publisher URL: https://papers.ssrn.com/sol3/papers.cfm?abstract_i...
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