• The Korean Journal of Applied Statistics
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• v.23 no.5
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• pp.909-921
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• 2010

Cross-wave regression imputation and carry-over imputation method are generally used in the analysis of panel data with missing values. Recently it is known that the BLS non-response adjust method has good statistical properties. In this paper we show that the BLS method can be considered as an imputation method with a similar formula of a ratio-estimator. In addition, we show that the carry-over imputation and BLS imputation are approximately the same under the assumption that data follow a non-stationary process with drift. Small simulation studies and real data analysis are performed. For the real data analysis, a monthly labor statistic (2007) is used.

• The Korean Journal of Applied Statistics
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• v.22 no.6
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• pp.1301-1313
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• 2009

Efficiency and sensitivity of BLS adjustment method have been studied and the method is known to provide more accurate estimate of total by using properly adjusted weights of samples. However, BLS methods provide different efficiencies according to the magnitudes of correlation coefficients and the sizes of samples in strata. In this paper we study the efficiency of the BLS adjustment according to the sample sizes and correlations in strata. For this study, 2007 monthly labor survey data is used.

• Proceedings of the Korean Association for Survey Research Conference
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• 2006.12a
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• pp.11-40
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• 2006

시간의 흐름에 따라 사회 구성원들에 대한 행태 연구나 사회의 변화가 개인의 행동양식에 미치는 영향 등에 대한 조사에서는 어느 한 시점에서의 구성원들에 대한 횡단면 조사와는 다르게 다년간 지속적으로 조사개체를 추적조사 해야 하는 종단면 조사 또는 패널조사를 수행해야 한다. 패널조사는 횡단면 조사와는 달리 최초 표본이 시간이 지남에 따라 조사 대상 표본으로부터 탈락함으로서 발생하는 표본의 마모와 그에 따른 대표성 상실의 문제이다. 그러므로 이러한 표본의 대표성 상실 문제를 적절히 해결하기 위해 적용 가능한 방법이 가중치 조정 방법이다 횡단면 조사에서는 (1)추출가중치의 조정, (2)무응답 가중치 조정, (3)사후층화 가중치 조정과 같이 3단계의 가중치 조정과정을 수행하지만, 패널 조사의 경우 이와 더불어 원 표본의 대표성을 유지하기 위해 종단면 가중치(longitudinal weight)를 함께 고려해야 한다. 이러한 관점에서 본 연구에서는 다양한 패널형태에 따른 가중치 조정 방법에 대해 고찰하고, 향후 수행될 한국복지패널(Korea Welfare Panel Study: KWPS)의 가중치 산정에 관한 이론적 근거를 마련함과 동시에 현재 국내에서 수행되고 있는 패널조사의 가중치 조정방법과 비교하고자 한다.

• The Korean Journal of Applied Statistics
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• v.31 no.5
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• pp.587-597
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• 2018

Procedures, such as sampling technique, survey method, and questionnaire preparation, are required in order to obtain sample data in accordance with the purpose of a survey. An important procedure is the decision of the sample size formula. The sample size formula is determined by setting the target error and total cost according to the sampling method. In this paper, we propose a sample size formula using population changes over time, estimation error of the previous time and response rate of past data when the target error and the expected response rate are given in the simple random sampling. In actual research, we use estimators that apply complex weights in addition to design-based weights. Therefore, we induce a sample size formula for estimators using design-based weights and nonresponse adjustment coefficients, that can be a formula that reflects differences in response rates when survey methods are changed over time. In addition, we use simulations to compare the proposed formula with the existing sample size formula.

• Journal of the Korean Data and Information Science Society
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• v.27 no.2
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• pp.315-325
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• 2016

Thanks to the advantages of low cost and quick results, public opinion polls on election in Korea have been generally conducted by telephone survey, even though it has critical disadvantage of low response rate. In public opinion polls on election in Korea, the general method to handle nonresponses is adjusting the survey weight to estimate parameters. This study first drives mathematical expression of estimator and its bias with variance estimators with/without nonresponses in election polls in Korea. We also investigates the nonresponse rate of telephone survey on 2012 Korea presidential election. The average response rate was barely about 14.4%. In addition, we conducted a survey in April 2014 on the respondents's attitude toward telephone surveys. In the survey, the first reason for which respondents do not answer on public opinion polls on election was "feel bothered". And the aged 20s group, the most low response group, also gave the same answer. We here suggest that survey researchers motivate survey respondents, specially younger group, to participate surveys and find methods boosting response rate such as giving incentive.

• The Korean Journal of Applied Statistics
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• v.32 no.4
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• pp.631-642
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• 2019

Many methods have been developed to solve problems found in sample surveys involving a large number of item non-responses that cause inaccuracies in estimation. However, the non-response adjustment method used under the assumption of random non-response generates a bias in cases where the response rate is affected by the variable of interest. Chung and Shin (2017) and Min and Shin (2018) proposed a method to improve the accuracy of estimation by appropriately adjusting a bias generated when the response rate is a function of the variables of interest. In this study, we studied a case where the response rate function is linear and the error of the super population model follows normal distribution. We also examined the effect of the number of stratum population on bias adjustment. The performance of the proposed estimator was examined through simulation studies and confirmed through actual data analysis.

• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1185-1199
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• 2016

Two phase sampling (double sampling) is often used when there is inadequate population information for proper stratification. Many recent papers have been devoted to the estimation method to improve the precision of the estimator using first phase information. In this study we suggested outlier weight adjustment methods to improve estimation precision based on the weight of the generalized ratio-cum-product estimator. Small simulation studies are conducted to compare the suggested methods and the usual method. Real data analysis is also performed.

• The Korean Journal of Applied Statistics
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• v.21 no.3
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• pp.413-427
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• 2008

In sample design, it is usually planned by purpose and the range of the announcing statistics from the survey. After survey, getting a proper and decent statistics, applying the proper weights on the results of survey is very important and necessary. Therefore in this study, three estimation methods which are raking, BLS and general linear regression method are compared with MSE, Coverage, CV, LE and NC.

• Journal of Korean Society for Quality Management
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• v.26 no.3
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• pp.82-99
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• 1998

In sampling survey the nonresponse reduces the precision of the estimator becuase of the nonresponse bias of the estimator. Deville, et al.(1993) considered the generalized raking procedure with the auxiliary information under five distance measures for reducing the nonresponse bias of the estimator. This paper extends the classical weighting adjustment of Deville, et al.(1993) to the stratified sampling case with three among five measures.

• Communications for Statistical Applications and Methods
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• v.15 no.6
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• pp.843-858
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• 2008

BLS adjustment methods have been able to provide more accurate estimates of total and make samples represent population characteristics by post-adjustment of design weights of samples. However, BLS methods use additional data, for instance number of employee, without this information or using other information, give different weight adjustment factors. In this paper we studied the sensitivity of the variables used in BLS adjustment. The 2007 monthly labor survey data is used in analysis.