• 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.

• Journal of the Korean Data and Information Science Society
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• v.23 no.6
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• pp.1183-1194
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• 2012

When it comes to a survey, no answer would occur frequently. Therefore various methods for handling nonresponse have been applied to analyse the survey. In this paper, the ratio of occurrence of two type of nonresponse cases - unit nonresponse and item nonresponse - is presented using previous real survey data, and we compared complete data and data with nonresponse. We suggest the reason of happening of nonresponse and the ratio of nonresponse using data collected through group interviews.

• 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.

• 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.

• The Korean Journal of Applied Statistics
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• v.26 no.3
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• pp.375-387
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• 2013

Unit non-response of surveys reduces the efficiency of the estimates and also causes non-response bias especially when there is large difference between respondents and non-respondents. Non-response weighting adjustments have usually been used to compensate for non-response. It is not easy to examine the non-response bias as well as to obtain information on the non-respondents in sample surveys. A household panel survey, called The Survey of Household Finances, was conducted in both 2010 and 2011. In this paper, we assume that non-response households in Wave 2 have strong non-response (non-cooperative) tendency. We classify those households into non-response households in Wave 1. Under this assumption, the characteristics of non-response households, the non-response bias and the effect of non-response adjustments are investigated.

• Proceedings of the Korean Statistical Society Conference
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• 2001.11a
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• pp.63-68
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• 2001

조사과정에서 필연적으로 발생하는 무응답을 보정하기 위해 보조정보를 사용한다. 이 때, 이용 가능한 보조정보의 차원이 크면, 계산과정에서 많은 시간이 소요되며 데이터를 다루기가 매우 어렵다. 또한 추정량의 분산이 보조정보의 차원에 의존하기 때문에 과소추정의 문제가 발생한다. 이러한 문제를 해결하기 위해 무응답 보정에서 적절한 보조정보의 선택 방법을 제안하였고, 이에 대한 효율성을 모의실험을 통해 살펴보았다.

• The Korean Journal of Applied Statistics
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• v.16 no.1
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• pp.33-44
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• 2003

Typically, it should be use auxiliary variable for calibrating the survey nonreponse in census or sampling survey. Where, if the dimension of auxiliary information is large, then it nay be spend a lot of computing time, and difficult to handle data set. Also because the variance estimator depends on the dimension of auxiliary variables, the variance estimator becomes underestimator. To deal with this problem, we propose the variable selection methods for calibration estimation procedure in unit nonreponse situation and we compare the efficiency by simulation study.

• Proceedings of the Korean Association for Survey Research Conference
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• 2001.06a
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• pp.57-70
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• 2001

In this paper we consider a various estimation methods including the post-stratification estimation, regression estimation and calibration estimation or a generalized raking estimation under a unit nonresponse. All of them have a common type of calibration estimation based on the post-stratification for a categorical auxiliary variables.

• The Korean Journal of Applied Statistics
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• v.34 no.5
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• pp.685-696
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• 2021

The Korean Economically Active Population Survey (KEAPS) is a national survey that produces employment-related statistics. The main purpose of the survey is to find out the economic activity status (employed/ unemployed/ non-employed) of the people. KEAPS has a unique characteristics caused by the survey method. In this study, through understanding of structural non-response and utilization of past data, we would like to present an improved imputation model. The performance of the proposed model is compared with the existing model through simulation. The performance of the imputation models is evaluated based on the degree of mathing/nonmatching rates. For this, we employ the KEAPS data in November 2019. For the randomly selected ones among the total 59,996 respondents, the six explanatory variables, which are critical in determining the economic activity states, are treated as non-response. The proposed model includes industry variable and job status variable in addition to the explanatory variables used in the precedent research. This is based on the linkage and utilization of past data. The simulation results confirm that the proposed model with additional variables outperforms the existing model in the precedent research. In addition, we consider various scenarios for the number of non-responders by the economic activity status.

• 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.