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

Duncan K.H. Fong, Peter Ebbes, Wayne S. DeSarbo

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)293-314
Number of pages22
JournalPsychometrika
Volume77
Issue number2
DOIs
StatePublished - Apr 1 2012

All Science Journal Classification (ASJC) codes

  • Psychology(all)
  • Applied Mathematics

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