Building Empirical Models for Data from Factorial Designs with Time Series Responses: Toward Fraud Prevention and Detection

Quality Engineering, 1996/1997

Posted: 22 Jun 2004

See all articles by Ella Mae Matsumura

Ella Mae Matsumura

University of Wisconsin-Madison - Department of Accounting and Information Systems

Robert R. Tucker

Fordham University - Accounting Area

Abstract

This paper examines how four factors (audit fee, penalty, required testing, and internal control strength) affected the percentage of times fraud was committed and detected in an experimental economics study. A factorial experiment resulted in time-series responses in each cell, with the factors' effects and interactions possibly a function of time. We introduce novel techniques for empirical model building and analysis for this type of data. The experiment indicated that the lowest fraud committed occurred when all four factors were high. The highest fraud detection occurred when internal control was low and the remaining factors were high.

Keywords: Experimental design, data exploration, contrast series, cumulative sum series, multiresponse

JEL Classification: M40

Suggested Citation

Matsumura, Ella Mae and Tucker, Robert R., Building Empirical Models for Data from Factorial Designs with Time Series Responses: Toward Fraud Prevention and Detection. Quality Engineering, 1996/1997, Available at SSRN: https://ssrn.com/abstract=557066

Ella Mae Matsumura (Contact Author)

University of Wisconsin-Madison - Department of Accounting and Information Systems ( email )

WI
United States

Robert R. Tucker

Fordham University - Accounting Area ( email )

Graduate School of Business
113 W. 60th Street
New York, NY 10023
United States
212-636-6121 (Phone)

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