Abstract
We discuss a mixed methodology for analyzing pile sorting data. We created a list of 14 barriers to colon cancer screening and recruited 18, 13, and 14 participants from three American Indian (AI) communities to perform pile sorting. Quantitative data were analyzed by cluster analysis and multidimensional scaling. Differences across sites were compared using permutation bootstrapping. Qualitative data collected during sorting were compiled by AI staff members who determined names for the clusters found in quantitative analysis. Results showed 5 clusters of barriers in each site although barriers in the clusters varied slightly across sites. Simulation demonstrated type I error rates around the nominal 0.05 level whereas power depended on the numbers of clusters, and between and within cluster variability.