Korean J Nutr.  2012 Dec;45(6):600-612. 10.4163/kjn.2012.45.6.600.

Complex sample design effects and inference for Korea National Health and Nutrition Examination Survey data

• Chung CE

• KMID: 2268777
• DOI: http://doi.org/10.4163/kjn.2012.45.6.600

Abstract

Nutritional researchers world-wide are using large-scale sample survey methods to study nutritional health epidemiology and services utilization in general, non-clinical populations. This article provides a review of important statistical methods and software that apply to descriptive and multivariate analysis of data collected in sample surveys, such as national health and nutrition examination survey. A comparative data analysis of the Korea National Health and Nutrition Examination Survey (KNHANES) was used to illustrate analytical procedures and design effects for survey estimates of population statistics, model parameters, and test statistics. This article focused on the following points, method of approach to analyze of the sample survey data, right software tools available to perform these analyses, and correct survey analysis methods important to interpretation of survey data. It addresses the question of approaches to analysis of complex sample survey data. The latest developments in software tools for analysis of complex sample survey data are covered, and empirical examples are presented that illustrate the impact of survey sample design effects on the parameter estimates, test statistics, and significance probabilities (p values) for univariate and multivariate analyses.

Keyword

Korea National Health and Nutrition Examination Survey (KNHANES); sample design; design effect; stratification; clustering; weighting; sampling variance

MeSH Terms

Cluster Analysis
Korea
Multivariate Analysis
Nutrition Surveys
Population Characteristics
Software
Statistics as Topic
Surveys and Questionnaires

Reference

1. Ministry of Health and Welfare. Korea National Health and Nutrition Examination Survey. Seoul: Ministry of Health and Welfare;Available from: http://knhanes.cdc.go.kr.

2. Heeringa SG, Liu J. Complex sample design effects and inference for mental health survey data. Int J Methods Psychiatr Res. 1998. 7(1):56–65.

3. Muthén BO, Satorra A. Complex sample data in structural equation modeling. Sociol Methodol. 1995. 25:267–316.

4. Koch GG, Lemeshow S. An application of multivariate analysis to complex sample survey data. Institute of Statistics Mimeo Series No. 802. 1972. Chapel Hill: University of North Carolina.

5. Chung CE. Evaluation of statistical methodology in national journals related with food science, cooking, and food culture. 2010. Seoul: Youlchon Foundation;591–703.

6. SAS version 9.3. 2011. Cary: SAS Institute Inc.;Available from: http://www.sas.com.

7. SPSS. 2012. Armonk: IBM;Available from: http://www.spss.com.

8. SUDAAN version 11. 2011. Research Triangle Park: RTI International.

9. Kish L, Groves RM, Krotki KP. World fertility survey. Sampling errors for fertility surveys. Occasional Paper, No. 17. 1975. Voorburg: International Statistical Institute.

10. Agresti A. Categorical data analysis. 2002. 2nd edition. New York: John Wiley & Sons.

11. Agresti A. An introduction to categorical data analysis. 2007. 2nd edition. New York: John Wiley & Sons.

12. Lee JH, Moon IO, Chung CE. Health statistics. 2008. Seoul: Power Book Co..

13. Roberts G, Rao JNK, Kumar S. Logistic regression analysis of sample survey data. Biometrika. 1987. 74(1):1–12.

14. Morel JG. Logistic regression under complex survey designs. Surv Methodol. 1989. 15:203–223.

15. Hosmer DW Jr, Lemeshow S. Applied logistic regression. 2000. 2nd edition. New York: John Wiley & Sons.

16. Cochran WG. Sampling techniques. 1977. New York: John Wiley & Sons.

17. Skinner CJ, Holt D, Smith TMF. Analysis of complex surveys. 1989. New York: John Wiley & Sons.

18. Särndal CE, Swensson B, Wretman J. Model assisted survey sampling. 1992. New York: Springer.

19. Binder DA, Roberts GR. Design-based and model-based methods for estimating model parameters. Analysis of Survey Data. 2003. New York: John Wiley & Sons.

20. Fuller WA. Sampling statistics. 2009. Hoboken: John Wiley & Sons.

21. Lohr SL. Sampling: design and analysis. 2010. 2nd edition. Boston: Brooks/Cole.

22. Kish L. Survey sampling. 1965. New York: John Wiley & Sons.

23. Goldstein H. Multi-level models in educational and social research. 1987. London: Oxford University Press.

24. Rust K. Variance estimation for complex estimators in sample surveys. J Off Stat. 1985. 1(4):381–397.

25. Wolter KM. Introduction to variance estimation. 1985. New York: Springer.

26. Woodruff RS. A simple method for approximating the variance of a complicated estimate. J Am Stat Assoc. 1971. 66(334):411–414.

27. Kish L, Frankel MR. Balanced repeated replications for standard errors. J Am Stat Assoc. 1970. 65(331):1071–1094.

28. Rao JNK, Shao J. Modified balanced repeated replication for complex survey data. Biometrika. 1999. 86(2):403–415.

29. Rao JNK, Wu CFJ. Resampling inference with complex survey data. J Am Stat Assoc. 1988. 83(401):231–241.

30. Rao JNK, Wu CFJ, Yue K. Some recent work on resampling methods for complex surveys. Surv Methodol. 1992. 18:209–217.

31. Williams RL. A note on robust variance estimation for cluster-correlated data. Biometrics. 2000. 56(2):645–646.

32. Wolter KM. Introduction to variance estimation. 2007. 2nd edition. New York: Springer.

33. Stata Corp. Stata statistical software: release 5. 1997. College Station: Stata Corp..

34. Brick JM, Broene P, James P, Severynse J. A user's guide to Wes-Var PC. 1996. Rockville: Westat Inc..