Constrained Stochastic Extended Redundancy Analysis

Wayne S. DeSarbo, Heungsun Hwang, Ashley Stadler Blank, Eelco Kappe

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


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).

Original languageEnglish (US)
Pages (from-to)516-534
Number of pages19
Issue number2
StatePublished - Jun 9 2015

All Science Journal Classification (ASJC) codes

  • General Psychology
  • Applied Mathematics


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