• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1397-1405
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• 2014

Usually unit-nonresponse or item-nonresponse occurs in the survey. In case the rate of nonresponse is high, the analysis ignoring nonresponse may cause the wrong effect. The characterization of nonresponse is required. In a cross-sectional data, it is possible to study the characteristics of item-nonresponse but it is hard to study the characteristics of the unit-nonresponse. In order to identify the characteristics of the unit-nonresponse, this study used the first-year student of middle schools in the Korea children and youth panel survey (KCYPS) data. We investigated the handling situation of nonresponse and analyzed the characteristics of the unit-nonresponse. Most of the papers applied the way of getting rid of nonresponse, so that there was little paper using weights. In this paper, we compared the results of the analyses depending on whether the weight is used or not. The method of using weights showed statistically significant results much more than that of removing. More discussion will be needed.

• Communications for Statistical Applications and Methods
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• v.18 no.3
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• pp.367-376
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• 2011

BLS weight adjustment is a widely used method for business surveys with non-responses and outliers. Recent surveys show that the non-response weight adjustment of the BLS method is the same as the ratio imputation method. In this paper, we suggested a modified BLS weight adjustment method by imputing missing values instead of using weight adjustment for non-response. Monthly labor survey data is used for a small Monte-Carlo simulation and we conclude that the suggested method is superior to the original BLS weight adjustment method.

• Proceedings of the Korean Statistical Society Conference
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• 2002.05a
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• pp.13-18
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• 2002

이중추출(two-phase)접근방법 이용의 주목적은 관심변수와 보조변수사이의 관계를 이용해서 더 좋은 추정을 하고자 하는 것이다. 특히 이 방법은 층화, 무응답 문제에 적용하는 경우 상당히 효과적이다. 본 논문에서는 무시할 수 있는 무응답이 발생했을 때 이중추출기법을 이용해서 g-가중치와 응답확률을 각 단계별로 조정해줌으로써 무응답 보정추정량과 분산추정량을 구했다.

• Communications for Statistical Applications and Methods
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• v.16 no.1
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• pp.103-113
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• 2009

Weighting is a common form of unit nonresponse adjustment in sample surveys where entire questionnaires are missing due to noncontact or refusal to participate. A common approach computes the response weight as the inverse of the response rate within adjustment cells based on covariate information. In this paper, we consider the efficiency and robustness of nonresponse weight adjustment bated on the response propensity and predictive mean. In the simulation study based on 2000 Fishry Census in Korea, the root mean squared errors for assessing the various ways of forming nonresponse adjustment cell s are investigated. The simulation result suggest that the most important feature of variables for inclusion in weighting adjustment is that they are predictive of survey outcomes. Though useful, prediction of the propensity to response is a secondary. Also the result suggest that adjustment cells based on joint classification by the response propensity and predictor of the outcomes is productive.

• The Korean Journal of Applied Statistics
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• v.27 no.7
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• pp.1229-1241
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• 2014

Most of sampling surveys have outliers and non-response missing values simultaneously. In that case, due to the effect of outliers, the result of imputation is not good enough to meet a given precision. To overcome this situation, outlier treatment should be conducted before imputation. In this paper in order for reducing the effect of outlier, we study outlier imputation methods and outlier weight adjustment methods. For the outlier detection, the method suggested by She and Owen (2011) is used. A small simulation study is conducted and for real data analysis, Monthly Labor Statistic and Briquette Consumption Survey Data are used.

• Proceedings of the Korean Association for Survey Research Conference
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• 2005.12a
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• pp.169-183
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• 2005

This paper considers formation of nonresponse weighting adjustment cell for handling unit nonresponse in sample surveys. We propose a multivariate regression tree mehtod for segmentation using the variable of interest and the estimated response probability simultaneously to construct effective nonresponse adjustment cell. One is using only response data and the other is using response and nonresponse data. These two cases are compared in terms of bias.

• The Korean Journal of Applied Statistics
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• v.36 no.6
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• pp.547-559
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• 2023

Proper handling of nonresponse in sample survey improves the accuracy of the parameter estimation. Various studies have been conducted to properly handle MAR (missing at random) nonresponse or MCAR (missing completely at random) nonresponse. When nonresponse occurs, the PSA (propensity score adjusted) estimator is commonly used as a mean estimator. The PSA estimator is known to be unbiased when known sample weights and properly estimated response probabilities are used. However, for MNAR (missing not at random) nonresponse, which is affected by the value of the study variable, since it is very difficult to obtain accurate response probabilities, bias may occur in the PSA estimator. Chung and Shin (2017, 2022) proposed a post-stratification method to improve the accuracy of mean estimation when MNAR nonresponse occurs under a non-informative sample design. In this study, we propose a double post-stratification method to improve the accuracy of the naive PSA estimator for MNAR nonresponse under an informative sample design. In addition, we perform simulation studies to confirm the superiority of the proposed method.

• Survey Research
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• v.12 no.3
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• pp.173-186
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• 2011

We introduced the methodologies used to construct the longitudinal weights and cross-sectional weight that are required for the analysis of Korea Youth Panel Survey. To analyze the longitudinal dynamic change of the population, we derived the longitudinal weight through nonresponse adjustment based on logistic regression and post-stratification. Cross-sectional weights that are necessary to produce an asymptotically unbiased estimator of the population parameter were constructed through simple nonresponse adjustment based on overall response rate and post-stratification.

• Survey Research
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• v.9 no.3
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• pp.71-91
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• 2008

Workplace Panel Survey(WPS) is the representative panel survey of workplace in Korea. WPS was newly sampled in 2005 and is to be used for the subsequent biennial survey. The main survey is divided into a questionnaire for human resources(HR) manager, a questionnaire for labor relations manager and a questionnaire for representatives of unions. The population of WPS 2005 included workplaces across the country with 30 or more employees. The WPS 2005 was composed of 1,905 workplaces including 290 workplaces in the public sector. The sample was selected by the stratified random sampling. Weighting process for the survey data was introduced to compensate for differential sampling and non-response rates. Personal interviews were conducted using the Computer Assisted Personal Interviewing(CAPI) system during visits by interviewers, along with survey via mail and e-mail concerning employment and financial issues. The CPAI system introduced for the WPS 2005 can by used for automatical detection for errors and inconsistencies which may occur during the survey process. The CAPI system played an important part in enhancing the reliability of the survey data.

• Environmental and Resource Economics Review
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• v.7 no.2
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• pp.31-51
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• 1998

우편설문조사를 이용한 조건부가치측정법을 통해 자료를 수집할 경우 무응답자의 발생은 보편적이며, 그러한 무응답자의 존재는 무응답자편의(無應答者偏倚) 가능성을 내재하고 있으므로, 표본의 통계치를 이용한 모집단에 대한 확대 적용을 위해서는 우선적으로 무응답자편의(無應答者偏倚)에 대한 검정(檢定)이 이루어져야 하며. 또한 무응답자편의(無應答者偏倚) 발생시 이에 대한 적절한 조치가 취해져야 한다. 본 연구에서는 다단계 우편설문발송법을 통해 수집된 자료를 이용하여 효과적인 무응답자편의(無應答者偏倚) 검정(檢定)의 수행과 무응답자군(無應答者群)의 평균지불의사액을 별도로 추정한 후 이를 이용하여 보다 합리적이고 정확한 총지불의사액(Aggregate WTP)의 도출방법을 제시하였다. 이는 우선적으로 무응답자편의(無應答者偏倚)를 분산분석을 통해 검정(檢定)한 후 선형외삽법으로 무응답자군(無應答者群)의 평균지불의사액을 추정하여 이를 모집단의 총지불의사액을 산출하는데 이용하는 방법으로 기존의 보수적인 방법들보다 상대적으로 저렴한 비용으로 무응답자편의(無應答者偏倚) 검정(檢定)을 가능하게 하며, 사회적 또는 인구특성상의 차이를 이용한 통계적 가중치 이용방법과는 달리 연구자의 직접적인 관심의 대상이 되는 지불의사액을 이용하여 무응답자편의(無應答者偏倚) 테스트를 하고 또한 무응답자군(無應答者群)의 평균지불의사액을 추정하여 보다 효율적이며 타당성이 있는 총지불의사액을 산출해 낼 수 있다는 장점이 있다.