Constrained Stochastic Extended Redundancy Analysis

Psychometrika, Volume 80, Issue 2, pp 516-534, 2015

Posted: 18 Jun 2016

See all articles by Wayne S. DeSarbo

Wayne S. DeSarbo

Pennsylvania State University

Heungsun Hwang

McGill University

Ashley Stadler Blank

University of St. Thomas

Eelco Kappe

Pennsylvania State University

Date Written: June 2015

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 analysis, maximum likelihood estimation, sports marketing, Major League Baseball, consumer psychology

Suggested Citation

DeSarbo, Wayne S. and Hwang, Heungsun and Stadler Blank, Ashley and Kappe, Eelco, Constrained Stochastic Extended Redundancy Analysis (June 2015). Psychometrika, Volume 80, Issue 2, pp 516-534, 2015, Available at SSRN: https://ssrn.com/abstract=2796796

Wayne S. DeSarbo (Contact Author)

Pennsylvania State University ( email )

University Park
State College, PA 16802
United States

Heungsun Hwang

McGill University ( email )

1001 Sherbrooke St. W
Montreal, Quebec H3A 1G5
Canada

Ashley Stadler Blank

University of St. Thomas ( email )

2115 Summit Avenue
AQU217
St. Paul, MN 55105
United States

Eelco Kappe

Pennsylvania State University ( email )

University Park
State College, PA 16802
United States

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