title: Constrained Stochastic Extended Redundancy Analysis.
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).
authors: Smeal College of Business, Pennsylvania State University, University Park, PA, 16802, USA, wsd6@psu.edu.
authors: Wayne S
authors: WS
authors: DeSarbo
authors: Heungsun
authors: H
authors: Hwang
authors: Ashley
authors: A
authors: Stadler Blank
authors: Eelco
authors: E
authors: Kappe
mesh_terms: D000465:Algorithms; D001484:Baseball / Q000706:statistics & numerical data*; D006801:Humans; D016013:Likelihood Functions; D040541:Marketing / Q000706:statistics & numerical data; D011594:Psychometrics; D013269:Stochastic Processes