• 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.

• Asian Journal for Public Opinion Research
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• v.3 no.3
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• pp.111-130
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• 2016

Researchers have been perplexed by the issue of unit nonresponse since the beginning of practicing survey research and have been attentive to the causes that lead to the occurrence of unit nonresponse as well as the impact of unit nonresponse. However, because researchers have little to no information regarding unit nonrespondents, it is unlikely that they are able to examine the differences between participants and nonrespondents to estimate the loss of representativeness in the final sample compared with the target population. Therefore, the lack of information regarding the absentees complicates addressing the unit nonresponse bias in a satisfactory manner. Therefore, the purpose of this study was to determine the demographic characteristics and political attitudes of nonrespondents by relying on information provided by their spouses, which was obtained through a telephone survey conducted in Taiwan. It is found that demographic variables as well as political attitudes are related to the likelihood of an individual becoming a unit nonrespondent in telephone surveys.

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.665-678
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• 2003

In this paper we suggest the new calibration estimator, which is called to the recalibration estimator, and its variance estimator using two-phase sampling technique according to the auxiliary information having strong correlation with the variable of interest under the unit nonresponse. In this unit nonresponse situation, an available information may exists at the level of whole population or the first-phase sample. The proposed recalibration estimator derives from the first and second phase weights respectively.

• 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.

• Journal of Korean Society for Quality Management
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• v.28 no.2
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• pp.123-134
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• 2000

In the survey, it is very hard to get the complete response. Because the respondents tend to refuse to the questionnaire with something like incomes of the individual or may not be at home in the survey time. These nonresponses are classified into two groups as the item nonresponse and the unit nonresponse. When the nonresponse happen to the special item of the questionnaire, it is caned item nonresponse. On the other hand the unit nonresponse occurs to the totally missing in questionnaire. In this paper, we only consider to the unit nonresponse situation. We propose that the optimal estimator which is minimized the variance of the estimator under a fixed cost function for the survey and response.

• Journal of applied mathematics & informatics
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• v.9 no.2
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• pp.869-877
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• 2002

In the presence of unit nonresponse we perform the calibration estimation procedure for the population total corresponding to the levels of auxiliary information and derive the Taylor and the Jackknife variance estimators of it. We study the nonresponse bias reduction and the variance stabilization, and then show the efficiency of the Taylor and the Jackknife variance estimators by simulation study.

• 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.

• Communications for Statistical Applications and Methods
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• v.9 no.2
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• pp.479-489
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• 2002

The case which applies two-phase sampling to stratification and nonresponse problem, it is a poweful and effective technique. In this paper we study the calibration estimator and its variance estimator for the population total using two-phase sampling method according to the of auxiliary information for population and sample having strong correlation with an interested variable in unit nonresponse situation. The auxiliary information that available both at first-phase and second-phase sampling can be used to improve weights by the calibration procedure. A weight which corresponds to the product of sampling weights and response probability is calculated at each phase of sampling.

• 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.

• Journal of the Korean Data and Information Science Society
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• v.27 no.1
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• pp.245-254
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• 2016

Many surveys provide categorical data and there may be one or more missing categories. We describe a nonignorable nonresponse model for the analysis of two-way contingency tables from small areas. There are both item and unit nonresponse. One approach to analyze these data is to construct several tables corresponding to missing categories. We describe a hierarchical Bayesian model to analyze two-way categorical data from different areas. This allows a "borrowing of strength" of the data from larger areas to improve the reliability in the estimates of the model parameters corresponding to the small areas. Also we use a nonignorable nonresponse model with Bayesian uncertainty analysis by placing priors in nonidentifiable parameters instead of a sensitivity analysis for nonidentifiable parameters. We use the griddy Gibbs sampler to fit our models and compute DIC and BPP for model diagnostics. We illustrate our method using data from NHANES III data on thirteen states to obtain the finite population proportions.