A Monte Carlo investigation of conjoint analysis index-of-fit: Goodness of fit,significance and power

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.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.