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Nonresponse Bias Analysis of Body Mass Index Data in the Eating and Health Module

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  • Hamrick, Karen S.

Abstract

The ERS Eating and Health Module, a supplement to the American Time Use Survey (ATUS), included questions on height and weight so that respondents’ Body Mass Index (BMI—a measure of body fat based on height and weight) could be calculated and analyzed with ATUS time-use data in obesity research. Some respondents did not report height and/or weight, and BMIs could not be calculated for them. Analyses focusing on correlations between BMIs and time use could be biased if respondents who did not report height and/or weight differ significantly in other observable characteristics from the rest of the survey respondents. However, findings reveal that any nonresponse bias associated with the height and weight data appears to be small and would not affect future analyses of BMIs and time-use pattern correlations.

Suggested Citation

  • Hamrick, Karen S., 2012. "Nonresponse Bias Analysis of Body Mass Index Data in the Eating and Health Module," Technical Bulletins 131556, United States Department of Agriculture, Economic Research Service.
    • Handle: RePEc:ags:uerstb:131556
    DOI: 10.22004/ag.econ.131556
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    File URL: https://ageconsearch.umn.edu/record/131556/files/tb1934%20_3_.pdf
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    References listed on IDEAS

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    1. Burkhauser, Richard V. & Cawley, John, 2008. "Beyond BMI: The value of more accurate measures of fatness and obesity in social science research," Journal of Health Economics, Elsevier, vol. 27(2), pages 519-529, March.
    2. Harley Frazis & Jay Stewart, 2012. "How to Think about Time-Use Data: What Inferences Can We Make about Long- and Short-Run Time Use from Time Diaries?," Annals of Economics and Statistics, GENES, issue 105-106, pages 231-245.
    3. Hamrick, Karen S. & Hopkins, David & McClelland, Ket, 2008. "How Much Time Do Americans Spend Eating?," Amber Waves:The Economics of Food, Farming, Natural Resources, and Rural America, United States Department of Agriculture, Economic Research Service, pages 1-2, June.
    4. Hamrick, Karen S. & Andrews, Margaret & Guthrie, Joanne & Hopkins, David & McClelland, Ket, 2011. "How Much Time Do Americans Spend on Food?," Economic Information Bulletin 291940, United States Department of Agriculture, Economic Research Service.
    5. Danubio, Maria Enrica & Miranda, Gaetano & Vinciguerra, Maria Giulia & Vecchi, Elvira & Rufo, Fabrizio, 2008. "Comparison of self-reported and measured height and weight: Implications for obesity research among young adults," Economics & Human Biology, Elsevier, vol. 6(1), pages 181-190, March.
    6. Rebecca R. Andridge & Roderick J. A. Little, 2010. "A Review of Hot Deck Imputation for Survey Non‐response," International Statistical Review, International Statistical Institute, vol. 78(1), pages 40-64, April.
    7. Kyureghian, Gayaneh & Capps, Oral, Jr. & Nayga, Rodolfo M., Jr., 2011. "General Remedies to Local Problems: An Applied Researcher’s Manual to Multiple Imputation," 2011 Annual Meeting, July 24-26, 2011, Pittsburgh, Pennsylvania 108266, Agricultural and Applied Economics Association.
    8. Harley Frazis & Jay Stewart, 2012. "How to Think about Time-Use Data: What Inferences Can We Make about Long- and Short-Run Time Use from Time Diaries?," Annals of Economics and Statistics, GENES, issue 105-106, pages 231-245.
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    Cited by:

    1. Hamrick, Karen S. & McClelland, Ket, 2016. "Americans' Eating Patterns and Time Spent on Food: The 2014 Eating & Health Module Data," Economic Information Bulletin 262141, United States Department of Agriculture, Economic Research Service.

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