Inappropriate Survey Design Analysis of the Korean National Health and Nutrition Examination Survey May Produce Biased Results.
- Author:
Yangho KIM
1
;
Sunmin PARK
;
Nam Soo KIM
;
Byung Kook LEE
Author Information
1. Department of Occupational and Environmental Medicine, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, Korea.
- Publication Type:Original Article
- Keywords:
Korean National Health and Nutrition Examination Survey;
Complex survey design;
Design-based analysis;
Ordinary statistical data analysis;
Multi-stage stratified sampling
- MeSH:
Adult;
Aged;
Blood Pressure;
Bone Density;
Cadmium/blood;
Creatinine/blood;
Female;
Hemoglobins/analysis;
Humans;
Lead/blood;
Male;
Mercury/blood;
Middle Aged;
*Nutrition Surveys;
PubMed;
Republic of Korea;
*Research Design
- From:Journal of Preventive Medicine and Public Health
2013;46(2):96-104
- CountryRepublic of Korea
- Language:English
-
Abstract:
OBJECTIVES: The inherent nature of the Korean National Health and Nutrition Examination Survey (KNHANES) design requires special analysis by incorporating sample weights, stratification, and clustering not used in ordinary statistical procedures. METHODS: This study investigated the proportion of research papers that have used an appropriate statistical methodology out of the research papers analyzing the KNHANES cited in the PubMed online system from 2007 to 2012. We also compared differences in mean and regression estimates between the ordinary statistical data analyses without sampling weight and design-based data analyses using the KNHANES 2008 to 2010. RESULTS: Of the 247 research articles cited in PubMed, only 19.8% of all articles used survey design analysis, compared with 80.2% of articles that used ordinary statistical analysis, treating KNHANES data as if it were collected using a simple random sampling method. Means and standard errors differed between the ordinary statistical data analyses and design-based analyses, and the standard errors in the design-based analyses tended to be larger than those in the ordinary statistical data analyses. CONCLUSIONS: Ignoring complex survey design can result in biased estimates and overstated significance levels. Sample weights, stratification, and clustering of the design must be incorporated into analyses to ensure the development of appropriate estimates and standard errors of these estimates.
