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

Autor/inn/en	Fong, Duncan K. H.; Ebbes, Peter; DeSarbo, Wayne S.
Titel	A Heterogeneous Bayesian Regression Model for Cross-Sectional Data Involving a Single Observation per Response Unit
Quelle	In: Psychometrika, 77 (2012) 2, S.293-314 (22 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0033-3123
DOI	10.1007/s11336-012-9252-x
Schlagwörter	Monte Carlo Methods; Social Sciences; Computation; Models; Bayesian Statistics; Data Analysis; Evaluation Methods; Psychology; Observation; Feedback (Response) + Suchen Sie Ihr Suchwort? Monte-Carlo-Methode; Social science; Sozialwissenschaften; Gesellschaftswissenschaften; Analogiemodell; Auswertung; Psychologie; Beobachtung
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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10