• Journal of Korean Society for Quality Management
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• v.16 no.1
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• pp.49-58
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• 1988

When a question in a sample survey is sensitive or highly personal, respondents may prefer not to give truthful answers. To eliminate the resulting evasive answer bias Warner suggested a randomized response model. In this paper, a modification of Warner's method is presented and compared with Warner's method. The relation between this modified method and an unrelated question method is also discussed.

• The Korean Journal of Applied Statistics
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• v.5 no.2
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• pp.157-167
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• 1992

This paper presents a new two-stage randomized response model to protect greater privacy of respondents for the sensitive characters. The conditions when the proposed model will be more efficient than Warner model, Liu-Chow's multiple trial model and Mangat-Singh model have been obtained for the case when the respondents are truthful in their answer, and the efficiency of the proposed model is also compared with Warner model, Liu-Chow's multiple trial model and Mangat-Singh model.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2895-2905
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• 2018

We present a mixed multiplicative quantitative randomized response model which added a unrelated quantitative attribute and forced answer to the multiplicative model suggested by Bar-Lev et al. (2004). We also try to set up theoretical grounds for estimating sensitive quantitative attribute according to circumstances whether or not the information for unrelated quantitative attribute is known. We also extend it into the stratified mixed multiplicative quantitative randomized response model for stratified population along with two allocation methods, proportional and optimum allocation. We can see that the various quantitative randomized response models such as Eichhorn-Hayre's model (1983), Bar-Lev et al.'s model (2004), Gjestvang-Singh's model (2007) and Lee's model (2016a), are one of the special occasions of the suggested model. Finally, We compare the efficiency of our suggested model with Bar-Lev et al.'s (2004) and see that the bigger the value of $C_z$, the more the efficiency of the suggested model is obtained.

• The Korean Journal of Applied Statistics
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• v.29 no.5
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• pp.897-906
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• 2016

We augment an unrelated quantitative attribute to Bar-Lev et al.'s model (2004) which is composed of sensitive quantitative variable and scrambled one to present a multiplicative unrelated quantitative randomized response model(MUQ RRM). We also establish theoretical grounds to estimate the sensitive quantitative attribute according to circumstances irrespective of known or unknown unrelated quantitative attribute. Finally, we explore the relationship among the suggested model, Eichhorn-Hayre model, Bar-Lev et al.'s model and Gjestvang-Singh's model, and compare the efficiency of our model with Bar-Lev et al.'s model.

• Communications for Statistical Applications and Methods
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• v.7 no.1
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• pp.211-224
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• 2000

The Bayes estimation of the proportion in successive occasions sampling with randomized response model is discussed by means of Acceptance Rejection sampling. Bayesian estimation of transition probabilities in two successive occasions is suggested via Markov Chain Monte Carlo algorithm and its applicability is represented in a numerical example.

• Communications for Statistical Applications and Methods
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• v.15 no.4
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• pp.597-603
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• 2008

In this paper, we propose the calibration procedure for the valiance reduction of the stratified Warner's randomized response estimators, which suggested by Hong et al. (1994) and Kim and Warde (2004), using auxiliary information at the population level. It is shown that the proposed calibration estimators are more efficient than the ordinary Warner's estimators.

• The Korean Journal of Applied Statistics
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• v.27 no.2
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• pp.239-247
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• 2014

For a sensitive survey in which the population is composed by several strata with quantitative attributes, we present an additive stratified quantitative attribute randomized response model which applied stratified random sampling instead of simple random sampling to the models of Himmelfarb-Edgell's additive quantitative attribute model and Gjestvang-Singh's. We also establish theoretical grounds to estimate the stratum mean of sensitive quantitative attributes as well as the over all mean. We deal with the proportional and optimal allocation problems in each suggested model and compare the relative efficiency of the suggested two models; subsequently, Himmelfarb-Edgell's model is more efficient than Gjestvang-Singh's model under the condition of stratified random sampling.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.339-345
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• 2004

This paper deals with a mixed logit model for ordered polytomous data. There are two types of factors affecting the response varable in this paper. One is a fixed factor with finite quantitative levels and the other is a random factor coming from an experimental structure such as a randomized complete block design. It is discussed how to set up the model for analyzing ordered polytomous data and illustrated how to estimate the paramers in the given model.

• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.247-256
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• 2003

In this study, we systemize the theoretical validity for applying RRM to three-stage cluster sampling method and derive the estimate and it's variance of sensitive parameter. We derive the minimum variance form under the optimal values of the subsample sizes when the costs are fixed. Under the some given precision, we obtain the optimal values of the subsample sizes and derive the minimum cost form by using them. We apply the three-stage cluster RRM to field survey and suggest some necessary points for practical use.

• The Korean Journal of Applied Statistics
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• v.17 no.2
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• pp.303-310
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• 2004

We suggest a method to procure information from the sensitive population which combine a direct survey method, BB and an indirect survey one, RRT, and a combined estimator that uses the stratified double sampling to estimate the sensitive parameter. We compare the efficiency of our estimator with that of Mangat and Singh model.