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Modeling heaped count data

Author

Listed:
  • Tammy H. Cummings

    (University of South Carolina)

  • James W. Hardin

    (University of South Carolina)

  • Alexander C. McLain

    (University of South Carolina)

  • James R. Hussey

    (University of South Carolina)

  • Kevin J. Bennett

    (University of South Carolina)

  • Gina M. Wingood

    (Emory University)

Abstract

We present motivation and new commands for modeling heaped count data. These data may appear when subjects report counts that are rounded or favor multiples (digit preference) of a certain outcome, such as the number of cigarettes reported. The new commands for fitting count regression models (Poisson, generalized Poisson, negative binomial) are also accompanied by real-world examples comparing the heaped regression model with the usual regression model as well as the heaped zero-inflated model with the usual zero-inflated model. Copyright 2015 by StataCorp LP.

Suggested Citation

  • Tammy H. Cummings & James W. Hardin & Alexander C. McLain & James R. Hussey & Kevin J. Bennett & Gina M. Wingood, 2015. "Modeling heaped count data," Stata Journal, StataCorp LP, vol. 15(2), pages 457-479, June.
    • Handle: RePEc:tsj:stataj:v:15:y:2015:i:2:p:457-479
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    Citations

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    Cited by:

    1. Mullahy, John, 2024. "Analyzing health outcomes measured as bounded counts," Journal of Health Economics, Elsevier, vol. 95(C).
    2. Qiang Fu & Tian‐Yi Zhou & Xin Guo, 2021. "Modified Poisson regression analysis of grouped and right‐censored counts," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1347-1367, October.
    3. Qihuang Zhang & Grace Y. Yi, 2023. "Zero‐inflated Poisson models with measurement error in the response," Biometrics, The International Biometric Society, vol. 79(2), pages 1089-1102, June.

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