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A Heterogeneous Bayesian Regression Model for Cross-sectional Data Involving a Single Observation per Response Unit

Published online by Cambridge University Press:  01 January 2025

Duncan K. H. Fong,
Peter Ebbes and
Wayne S. DeSarbo
Duncan K. H. Fong*
Affiliation:
The Pennsylvania State University
Peter Ebbes
Affiliation:
The Ohio State University
Wayne S. DeSarbo
Affiliation:
The Pennsylvania State University
*
Requests for reprints should be sent to Duncan K.H. Fong, Marketing Department, Smeal College of Business, Pennsylvania State University, 456 Business Building, University Park, PA 16802, USA. E-mail: i2v@psu.edu
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Abstract

Multiple regression is frequently used across the various social sciences to analyze cross-sectional data. However, it can often times be challenging to justify the assumption of common regression coefficients across all respondents. This manuscript presents a heterogeneous Bayesian regression model that enables the estimation of individual-level-regression coefficients in cross-sectional data involving a single observation per response unit. A Gibbs sampling algorithm is developed to implement the proposed Bayesian methodology. A Monte Carlo simulation study is constructed to assess the performance of the proposed methodology across a number of experimental factors. We then apply the proposed method to analyze data collected from a consumer psychology study that examines the differential importance of price and quality in determining perceived value evaluations.

Keywords

Bayesian estimationcross-sectional analysisheterogeneityconsumer psychology

Information

Type
Original Paper
Information
Psychometrika , Volume 77 , Issue 2 , April 2012, pp. 293 - 314
Copyright
Copyright © 2012 The Psychometric Society

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Footnotes

Duncan K.H. Fong is Professor of Marketing and Statistics. Wayne S. DeSarbo is the Smeal Distinguished Research Professor of Marketing. Peter Ebbes is Visiting Assistant Professor.

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