• The Korean Journal of Applied Statistics
• /
• v.32 no.4
• /
• pp.643-652
• /
• 2019

We propose a method for sample size determination using design effect formulas when a sample is resigned for a repeated survey. The proposed method enables the determination of the sample size by incorporating the impact of various design components to the sampling error through design effect formulas that are applicable under multistage sampling design and stratified multistage sampling designs.

• Journal of the Korean Data and Information Science Society
• /
• v.28 no.2
• /
• pp.443-449
• /
• 2017

Estimation of population proportion like the distribution rate of LED TV and the prevalence of a disease are often estimated based on survey sample data. Population proportion is generally considered as a special form of population mean. In complex sampling like stratified multistage sampling with unequal probability sampling, the denominator of mean may be random variable and it is estimated like ratio estimator. In this research, we examined the estimation of distribution rate based on stratified multistage sampling, and determined some numerical outcomes using stratified random sample data with about 25% of missing observations. In the data used for this research, the survey weight was determined by deterministic way. So, the weights are not random variable, and the population distribution rate and its variance estimator can be estimated like population mean estimation. When the weights are not random variable, if one estimates the variance of proportion estimator using ratio method, then the variances may be inflated. Therefore, in estimating variance for population proportion, we need to examine the structure of data and survey design before making any decision for estimation methods.

• The Korean Journal of Applied Statistics
• /
• v.36 no.4
• /
• pp.337-347
• /
• 2023

Sampling designs often use stratified sampling, where elements or clusters of the study population are divided into strata and an independent sample is chosen from each stratum. The stratification strategy consists of stratification and sample allocation, which are important issues that are repeatedly considered in survey sampling. Although a stratified multistage sample design is often used in practice, the literature tends to discuss simple sampling in terms of stratum effects or stratum efficiency. This study examines an existing stratum efficiency measure for two-stage sampling and further proposes additional stratum efficiency measures using the design effect model. The proposed measures are used to evaluate the stratification strategy of the sample design for high school students of the 4^th Korean National Environmental Health Survey (KoNEHS).

• Communications for Statistical Applications and Methods
• /
• v.9 no.3
• /
• pp.853-864
• /
• 2002

In the analysis of categorical data subject to misclassification errors, the observed cell proportions are adjusted by a misclassification probabilities and estimates of variances are adjusted accordingly. In this case, it is important to determine the extent to which misclassification probabilities are homogeneous within a population. This paper considers methods to evaluate the power of chi-squared tests for homogeneity with complex survey data subject to misclassification errors. Two cases are considered: adjustment with homogeneous misclassification probabilities; adjustment with heterogeneous misclassification probabilities. To estimate misclassification probabilities, logistic regression method is considered.

• Communications for Statistical Applications and Methods
• /
• v.10 no.2
• /
• pp.553-566
• /
• 2003

In many cases, the measurement error variances may be functions of the unknown true values or related covariates. This paper considers design-based estimators of the parameters of these variance functions based on the within-unit sample variances. This paper devotes to: (1) define an error scale factor $\delta$; (2) develop estimators of the parameters of the linear measurement error variance function of the true values under large-sample and small-error conditions; (3) use propensity methods to adjust survey weights to account for possible selection effects at the replicate level. The proposed methods are applied to medical examination data from the U.S. Third National Health and Nutrition Examination Survey (NHANES III).

• The Korean Journal of Applied Statistics
• /
• v.28 no.6
• /
• pp.1217-1225
• /
• 2015

Survey research, data is commonly collected through a sample design with complex design features that allow the relative efficiency on the precision of an estimator to be measured using the concept of the design effect compared to simple random sampling as a reference design. This concept is most useful when the design effect can be expressed as a function of various design features. We propose a design effect formula suitable under a stratified multistage sampling by generalizing Gabler et al. (1999, 2006)'s approaches for multistage sampling. Its use can either guide improvement in the design efficiency when in design stage or enable the evaluation of the adopted design features afterwards.

• 한국데이터정보과학회:학술대회논문집
• /
• 2003.05a
• /
• pp.1-14
• /
• 2003

In man cases, the measurement error variances may be functions of the unknown true values or related covariate. In some cases, the measurement error variances increase in proportion to the value of predictor. This paper develops estimators of the parameters of a linear measurement error variance function under stratified multistage random sampling design and additional conditions. Also, this paper evaluates and compares the power of an asymptotically unbiased test with that of an asymptotically biased test. The proposed method are applied to blood sample measurements from the U.S. Third National Health and Nutrition Examination Survey(NHANES III)

• Journal of the Korean Society of School Health
• /
• v.21 no.2
• /
• pp.61-73
• /
• 2008

Purpose: Several health behavior factors affect the incidence of type 2 diabetes. Especially, obesity, which causes insulin resistance, is the most important determinant of diabetes. Therefore, we expect the risk factors associated with insulin resistance and type 2 diabetes are affected by obesity and, additionally, the related factors with diabetes caused by obesity can be controlled. Methods: This study used data collected from the 2001 Korea National Health and Nutrition Examination Survey (KNHANES). A stratified multistage probability sampling method was applied and the final sample included 5,500 subjects over 30 years old who had completed necessary health examinations and health behaviors survey. Results: The risk factors associated with type 2 diabetes are affected by obesity. According to logistic regression model stratified by body mass index (BMI) and sex, abdominal obesity and age were the significant risk factors of diabetes regardless of sex and BMI. However, drinking, smoking, total energy consumption, and protein consumption were risk factors for women with normal BMI, while carbohydrate consumption was a risk factor for man with normal BMI. Sleeping hours affected diabetes for women with obesity and fiber consumption was a risk factor for both women and men with obesity. In addition, statistically the family history of diabetes was a significant risk factor only in the group with normal weight, not in the group with obesity. Conclusion: The study results will provide information for implementing a regional initiative of type 2 diabetes prevention by BMI.

• Proceedings of the Korean Association for Survey Research Conference
• /
• 2003.06a
• /
• pp.34-41
• /
• 2003

In many cases, the measurement error variances may be functions of the unknown true values or related covariates. This paper develops estimators of the parameters of a linear measurement error variance function based on wi thin-unit sample variaoces. This paper devotes to: (1) define measurement error scale factor $\delta$: (2) develop estimators of the parameters of the 1inear measurement error variance function under stratified multistage sampling design and small error conditions; (3) use propensity methods to adjust survey weights to account for possible selection effects at the replicate level. The proposed methods are applied to medical examination data from the U S Third National Health and Nutrition Examination Survey(NHANES III)

• Journal of Environmental Science International
• /
• v.22 no.12
• /
• pp.1589-1598
• /
• 2013

The objective of this study was to evaluate associations between airborne manganese and blood manganese in a general population of South Korean adults. The concentrations of airborne manganese in total suspended particulate (TSP) were calculated from data obtained from ambient air-monitoring stations (AAMSs) located in South Korea. Blood manganese data obtained Korean National Health and Nutrition Examination Survey (KNHANES) using a rolling sampling design involving a complex, stratified, multistage, probability cluster survey of a representative sample of the non-institutionalized civilian population of South Korea. Airborne manganese geometric means was 46.10 $ng/m^3$, blood manganese geometric means were 1.19 ${\mu}g/d{\ell}$ for male and 1.40 ${\mu}g/d{\ell}$ for female. In multiple linear regression analysis of log transformed blood manganeseas a continuous variable on airborne manganese, after adjusting for covariates including gender, age, job, smoking and drinking status, education level, BMI (body mass index). Airborne manganese was positively associated with blood manganese with statistical significance. The present study confirms that airborne manganese is a possible contributor to the increase of blood manganese in the adult general population.