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Measures and tests of heaping in discrete quantitative distributions

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  • John Roberts
  • Devon Brewer

Abstract

Heaping is often found in discrete quantitative data based on subject responses to open-ended interview questions or observer assessments. Heaping occurs when subjects or observers prefer some set of numbers as responses (e.g. multiples of 5) simply because of the features of this set. Although heaping represents a common type of measurement error, apparently no prior general measure of heaping exists. We present simple measures and tests of heaping in discrete quantitative data, illustrate them with data from an epidemiologic study, and evaluate the bias of these statistics. These techniques permit formal measurement of heaping and facilitate comparisons of the degree of heaping in data from different samples, substantive domains, and data collection methods.

Suggested Citation

  • John Roberts & Devon Brewer, 2001. "Measures and tests of heaping in discrete quantitative distributions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(7), pages 887-896.
    • Handle: RePEc:taf:japsta:v:28:y:2001:i:7:p:887-896
    DOI: 10.1080/02664760120074960
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    References listed on IDEAS

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    1. Robert Myers, 1976. "An instance of reverse heaping of ages," Demography, Springer;Population Association of America (PAA), vol. 13(4), pages 577-580, November.
    2. Klovdahl, A.S. & Potterat, J.J. & Woodhouse, D.E. & Muth, J.B. & Muth, S.Q. & Darrow, W.W., 1994. "Social networks and infectious disease: The Colorado Springs study," Social Science & Medicine, Elsevier, vol. 38(1), pages 79-88, January.
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    Cited by:

    1. Athanasakou, Vasiliki & Simpson, Ana, 2016. "Investor attention to rounding as a salient forecast feature," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1212-1233.
    2. Carletto, Calogero & Savastano, Sara & Zezza, Alberto, 2013. "Fact or artifact: The impact of measurement errors on the farm size–productivity relationship," Journal of Development Economics, Elsevier, vol. 103(C), pages 254-261.
    3. Ragui Assaad & Caroline Krafft & Shaimaa Yassin, 2018. "Comparing retrospective and panel data collection methods to assess labor market dynamics," IZA Journal of Migration and Development, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 8(1), pages 1-34, December.
    4. Ian B. Page & Erik Lichtenberg & Monica Saavoss, 2020. "Estimating Willingness to Pay from Count Data When Survey Responses are Rounded," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 75(3), pages 657-675, March.
    5. Zezza, Alberto & Federighi, Giovanni & Kalilou, Amadou Adamou & Hiernaux, Pierre, 2016. "Milking the data: Measuring milk off-take in extensive livestock systems. Experimental evidence from Niger," Food Policy, Elsevier, vol. 59(C), pages 174-186.
    6. Wollburg, Philip & Tiberti, Marco & Zezza, Alberto, 2021. "Recall length and measurement error in agricultural surveys," Food Policy, Elsevier, vol. 100(C).
    7. S. Zinn & A. Würbach, 2016. "A statistical approach to address the problem of heaping in self-reported income data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(4), pages 682-703, March.
    8. Boulaga,Amadou Adamou Kalilou & Federighi,Giovanni & Hiernaux, Pierre & Zezza,Alberto & Boulaga,Amadou Adamou Kalilou & Federighi,Giovanni & Hiernaux, Pierre & Zezza,Alberto, 2014. "Milking the data : measuring income from milk production in extensive livestock systems -- experimental evidence from Niger," Policy Research Working Paper Series 7114, The World Bank.
    9. Page, Ian B. & Lichtenberg, Erik & Saavoss, Monica, 2015. "Estimating Recreation Demand When Survey Responses are Rounded," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205653, Agricultural and Applied Economics Association.

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