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Sorting it out: pile sorting as a mixed methodology for exploring barriers to cancer screening

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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 five 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.

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References

  • Abdi, H., Valentin, D., Chollet, S., Chrea, C.: Analyzing assessors and products in sorting tasks: DISTATIS, theory and applications. Food Qual. Prefer. 18, 1–16 (2007)

    Article  Google Scholar 

  • Carroll, J.D., Chang, J.J.: Analysis of individual differences in multidimensional scaling via an \(n\)-way generalization of “Eckert–Young” decomposition. Psychometrika 35, 283–319 (1971)

    Article  Google Scholar 

  • Daley, C.M., James, A.S., Filippi, M., Weir, M., Braiuca, S., Kaur, B., Choi, W.S., Greiner, K.A.: American Indian community leader and provider views of need and barriers to colorectal cancer screening. J. Health Dispar. Res. Pract. 5(2), 10–23 (2012)

    Google Scholar 

  • Dietz, E.J.: Permutation tests for association between two distance matrices. Syst. Zool. 32, 21–26 (1983)

    Article  Google Scholar 

  • Dijksterhuis, G.B., Gower, J.C.: The interpretation of generalized procrustes analysis and allied methods. Food Qual. Prefer. 3, 67–87 (1991)

    Article  Google Scholar 

  • Efron, B., Tibshirani, R.J.: An Introduction to the Bootstrap. Chapman & Hall/CRC, New York (1994)

    Google Scholar 

  • Gower, J.C.: Generalized procrustes analysis. Psychometrika 40, 33–51 (1975)

    Article  Google Scholar 

  • Gower, J.C., Dijksterhuis, G.B.: Procrustes Problems. Oxford University Press, Oxford (2004)

    Book  Google Scholar 

  • Johnson, D.E.: Applied Multivariate Methods for Data Analysts, 1st edn. Duxbury Press, Pacific Grove (1998)

    Google Scholar 

  • Johnson, R.A., Wichern, D.W.: Applied Multivariate Statistical Analysis, 5th edn, pp. 690–692. Prentice Hall, Upper Saddle River (2001)

    Google Scholar 

  • Lavit, C.: Analyse conjointe de tableaux quantitatifs. Masson, Paris (1988)

    Google Scholar 

  • Mantel, N.: Detection of disease clustering and generalized regression approach. Cancer Res. 27, 209–220 (1967)

    Google Scholar 

  • Qannari, E.M., Wakeling, I., MacFie, H.J.H.: A hierarchy of models for analyzing sensory data. Food Qual. Prefer. 6, 309–314 (1995)

    Article  Google Scholar 

  • Schneider, J.W., Borlund, P.: Matrix comparison, part 2: measuring the resemblance between proximity measures or ordination results by use of the mantel and procrustes statistics. J. Am. Soc. Inf. Sci. Technol. 58, 1596–1609 (2007)

    Article  Google Scholar 

  • Sibson, R.: Studies in the robustness of multidimensional scaling: procrustes statistics. J. R. Stat. Soc. B 40, 234–238 (1978)

    Google Scholar 

  • Smith, J.J.: Using ANTHROPAC 3.5 and a spreadsheet to compute a free-list salience index. Cult. Anthropl. Methods 5, 1–3 (1993)

    Google Scholar 

  • Smith, J.J., Borgatti, S.P.: Salience counts—and so does accuracy: correcting and updating a measure for free-list-item salience. J. Linguist. Anthr. 7(2), 208–209 (1997)

    Google Scholar 

  • Takane, Y., Young, F.W., de Leeuw, J.: Nonmetric individual differences multidimensional scaling: an alternating least squares method with optimal scaling features. Psychometrika 42, 8–67 (1977)

    Article  Google Scholar 

  • Timm, N.H.: Applied Multivariate Analysis, pp. 522–533. Springer, New York (2002)

    Google Scholar 

  • Trotter, R.T., Potter, J.M.: Pile sorts, a cognitive anthropological model of drug and AIDS risks for Navajo teenagers: assessment of a new evaluation tool. Drug Soc. 7, 23–39 (1993)

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported in part by the National Institute on Minority Health and Health Disparities Center of Excellence Grant, Center for the American Indian Community Health (CAICH) P20MD004805 and by the National Cancer Institute (R03121828). HY and BJG were also supported by the NIH Grant 1UL1RR033179. DP was supported by NIH Grant U01 CA114642. The contents are solely the responsibility of authors and do not necessarily represent the official view of the NIH. Conflict of interest   The authors declare that they have no conflict of interests.

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Authors and Affiliations

  1. Department of Biostatistics, The University of Kansas Medical Center, Kansas City, KS, 66160, USA

    Hung-Wen Yeh & Byron J. Gajewski

  2. Center for American Indian Community Health, The University of Kansas Medical Center, Kansas City, KS, 66160, USA

    Hung-Wen Yeh, Byron J. Gajewski, Angel Cully, Lance Cully, K. Allen Greiner, Won S. Choi & Christine Makosy Daley

  3. School of Nursing, The University of Kansas Medical Center, Kansas City, KS, 66160, USA

    Byron J. Gajewski

  4. The American Indian Cancer Foundation, and Minnesota Gastroenterology P.A., Minneapolis, MN, 55441, USA

    David G. Perdue

  5. Department of Family Medicine, The University of Kansas Medical Center, Kansas City, KS, 66160, USA

    K. Allen Greiner & Christine Makosy Daley

  6. Department of Preventive Medicine & Public Health, The University of Kansas Medical Center, Kansas City, KS, 66160, USA

    K. Allen Greiner, Won S. Choi & Christine Makosy Daley

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  1. Hung-Wen Yeh
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  2. Byron J. Gajewski
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  3. David G. Perdue
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  4. Angel Cully
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  5. Lance Cully
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  6. K. Allen Greiner
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  7. Won S. Choi
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  8. Christine Makosy Daley
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Correspondence to Hung-Wen Yeh.

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Yeh, HW., Gajewski, B.J., Perdue, D.G. et al. Sorting it out: pile sorting as a mixed methodology for exploring barriers to cancer screening. Qual Quant 48, 2569–2587 (2014). https://doi.org/10.1007/s11135-013-9908-3

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  • DOI: https://doi.org/10.1007/s11135-013-9908-3

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