This research examined the significance of facet selection in a multi-facet Rasch model analysis. The residuals or remaining error in a multi-facet Rasch model were further studied in the context of a full and reduced data-to-model fit chi-square, given the specific design. In addition, main effect facet contributions to person measures and the interaction among elements of two facets were investigated. Seventy-four subjects participated, with the variables or facets studied being subjects, judges, sessions, topics, and tasks. Each subject was rated by a sample of 6 of the total of 31 judges on recall, interpretation, and application of history, geography, and earth science domains. Fixed chi-square values were significant for all facets included in the model, indicating that the elements for each facet differed significantly and had different effects on the subject's scores that needed to be accounted for through adjustment to scores or ability estimates. Examination of models in which one facet was excluded further indicated a facet's contribution to the overall data-model fit. The chi-square test can indicate how the facet elements differ, and calibrated measures indicate how much the subject ability estimates should be adjusted to account for the characteristics of the particular elements encountered by a subject. Appendix A shows entry of the original coded data, and Appendix B presents sample measurement report. (Contains one figure, five tables, and five references.) (SLD)