Nonresponse Bias Analysis of Body Mass Index Data in the Eating and Health Module
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.
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- 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.
- John Cawley & Richard V. Burkhauser, 2006. "Beyond BMI: The Value of More Accurate Measures of Fatness and Obesity in Social Science Research," NBER Working Papers 12291, National Bureau of Economic Research, Inc.
- 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, 04.
- 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 ?,"
Annales d'Economie et de Statistique,
ENSAE, issue 105-106, pages 11.
- Harley Frazis & Jay Stewart, 2010. "How to Think About Time-Use Data: What Inferences Can We Make About Long- and Short-Run Time Use from Time Diaries?," Working Papers 442, U.S. Bureau of Labor Statistics.
- Frazis, Harley & Stewart, Jay, 2010. "How to Think About Time-Use Data: What Inferences Can We Make About Long- and Short-Run Time Use from Time Diaries?," IZA Discussion Papers 5306, Institute for the Study of Labor (IZA).
- 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.
- Hamrick, Karen S. & Hopkins, David & McClelland, Ket, 2008. "How Much Time Do Americans Spend Eating?," Amber Waves, United States Department of Agriculture, Economic Research Service, June.
- 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.
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