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A Monte Carlo Investigation of Conjoint Analysis Index-of-Fit: Goodness of Fit, Significance and Power

Published online by Cambridge University Press:  01 January 2025

U. N. Umesh  and
Sanjay Mishra
U. N. Umesh*
Affiliation:
Washington State University
Sanjay Mishra
Affiliation:
Washington State University
*
Requests for reprints should be sent to U. N. Umesh, College of Business and Economics, Washington State University, Pullman, WA 99164.
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Abstract

Researchers in the field of conjoint analysis know the index-of-fit values worsen as the judgmental error of evaluation increases. This simulation study provides guidelines on the goodness of fit based on distribution of index-of-fit for different conjoint analysis designs. The study design included the following factors: number of profiles, number of attributes, algorithm used and judgmental model used. Critical values are provided for deciding the statistical significance of conjoint analysis results. Using these cumulative distributions, the power of the test used to reject the null hypothesis of random ranking is calculated. The test is found to be quite powerful except for the case of very small residual degrees of freedom.

Keywords

conjoint analysisindex-of-fitmonotone regressionMonte Carlo simulationpower
Type
Original Paper
Information
Psychometrika , Volume 55 , Issue 1 , March 1990 , pp. 33 - 44
Copyright
Copyright © 1990 The Psychometric Society

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Footnotes

The authors thank the editor, the three reviewers and Ellen Foxman for helpful comments on the paper. Sanjay Mishra was a doctoral student at Washington State University at the time this research was completed. He is currently in the Department of Marketing at the University of Kansas.

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