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Constrained Stochastic Extended Redundancy Analysis

Published online by Cambridge University Press:  01 January 2025

Wayne S. DeSarbo ,
Heungsun Hwang ,
Ashley Stadler Blank  and
Eelco Kappe
Wayne S. DeSarbo*
Affiliation:
Pennsylvania State University
Heungsun Hwang
Affiliation:
McGill University
Ashley Stadler Blank
Affiliation:
Pennsylvania State University
Eelco Kappe
Affiliation:
Pennsylvania State University
*
Requests for reprints should be sent to Wayne S. DeSarbo, Marketing Department, Smeal College of Business, Pennsylvania State University, University Park, PA 16802, USA. E-mail: wsd6@psu.edu
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Abstract

We devise a new statistical methodology called constrained stochastic extended redundancy analysis (CSERA) to examine the comparative impact of various conceptual factors, or drivers, as well as the specific predictor variables that contribute to each driver on designated dependent variable(s). The technical details of the proposed methodology, the maximum likelihood estimation algorithm, and model selection heuristics are discussed. A sports marketing consumer psychology application is provided in a Major League Baseball (MLB) context where the effects of six conceptual drivers of game attendance and their defining predictor variables are estimated. Results compare favorably to those obtained using traditional extended redundancy analysis (ERA).

Keywords

redundancy analysismaximum likelihood estimationsports marketingMajor League Baseballconsumer psychology

Information

Type
Original Paper
Information
Psychometrika , Volume 80 , Issue 2 , June 2015 , pp. 516 - 534
Copyright
Copyright © 2013 The Psychometric Society

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