• The Korean Journal of Applied Statistics
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• v.22 no.3
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• pp.667-676
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• 2009

This study investigates the effect of survey refusal and noncontact on the nonresponse error in the household survey. For this purpose we analyzed the data of the interviewer's field work report. The survey data quality is affected by nonresponse rate and nonresponse error, and also nonresponse rate measures the reliability of the survey data. The household survey mainly contains two types of nonresponses of refusals and noncontacts. These refusals and noncontacts have different effect on the nonresponse error. This could be a venue for future research interested in decreasing the error due to noncontacts and refusals.

• 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.9 no.3
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• pp.665-674
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• 2002

The advantage of internet survey compared to the traditional survey methods are speedy in data collection, cost-effective, high performed design and able to data process and analysis at the same time. The other side are difficult to select sample, come from serious nonresponse errors. We suggest the new internet survey method to the questionnaire design that have the high response rate, enough to advanced preparations and system stability.

• Communications for Statistical Applications and Methods
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• v.16 no.6
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• pp.1031-1036
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• 2009

Under recent dramatic declines in response rates, various procedures have been considered among survey practitioners to reduce nonresponse in order to avoid its potential impairment to the inference. In the random digit dialing telephone surveys, substantial efforts are often required to obtain the initial contact for the screener interview. To reduce a burden with higher data collection costs, refusal conversion can be administered only to a random portion of the sample, reducing nonresponse (bias) with an expense of sample variability increment due to the associated weight adjustment. In this paper, we provide ways to determine the optimal subsampling rate using a linear cost model. Our approach for refusal subsampling is to predetermine a random portion from the full sample and to apply refusal conversion efforts if needed only to the subsample.

• 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.

• Journal of the Korean Statistical Society
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• v.8 no.1
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• pp.3-14
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• 1979

When we estimate the population proportion of the individuals in the population for the attribute or the characteristic, we consider the sample survey. We can consider many methods of the sample survey, as mail questionnaire, visits, personal calls, etc. When we have the list of units in the population, we usually make use of the mail questionnaire. It is economical and free from the investigator's effect on the respondent, but it has some objections. The principal objection is that it involves a large nonresponse rate that might cause a singificant bias in the result. The bias arises from the different in the characteristics under investigation between those who respond and those who do not respond.

• 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.

• 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.28 no.3
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• pp.417-427
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• 2015

Measures in response rate used to measure the representativeness of the sample (the more high response rate) better explain the representativeness of the sample. However, we cannot often explain the representativeness of the sample because there is nonresponse even in the high response rate. Therefore, Schouten et al. (2009) presented a new R-indicator measure that can be described as a representative of the sample. We research the new estimator of the R-indicator in this paper because there are parameters that require estimations. We describe the meanings as representative of the R-indicator; consequently, the bias and efficiency of the proposed estimator for R-indicator are compared to the existing estimator under various simulations. The representativeness of the sample is also explained by applying the proposed estimators in the actual data.

• Journal of the Korean Society for Library and Information Science
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• v.40 no.2
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• pp.217-242
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• 2006

This Study reviewed 86 research articles using inferential statistics published in 2001-2004 in 4 korean core journals in the field of library and information science. Sampling methods, response rates and nonresponse bias, reliability test, and inferential statistic techniques used in the articles were critically reviewed and analyzed. Nonprobability sampling was mostly used. Average response rate was 74.47%. Parametric statistics were mostly used. Some misunderstandings in using each inferential statistics, especially Reliability Test, Multiple Regression, Factor Analysis, MDS, etc. were reported in this study.