Abstract
A brief outline of the general theory of estimating finite population totals and means based on a sample selected with a suitable sampling design is given. Initially it is assumed that direct responses are available and then the theory is developed in the case when the sensitivity of the data on the quantitative characteristic makes it necessary to implement suitable devices to collect randomized response data. Two different randomized response devices are considered. The theory of estimation is illustrated in case the sample is selected employing the Rao-Hartley-Cochran sampling scheme as well as in the case of a general sampling scheme and when the data are collected using either of the two devices. Techniques which allow for direct responses by participants are presented. Such approaches are based on the idea that some people may consider the item in question not sensitive enough and therefore both options for providing a direct response or a randomized one are available. The main advantage of these optional randomized response techniques is the variance reduction of the produced estimators.
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Arnab, R. (2004). Optional randomized response techniques for complex survey designs. Biometrical Journal, 46, 114–124.
Chaudhuri, A. (1992). Randomized response: Estimating mean square errors of linear estimators and finding optimal unbiased strategies. Metrika, 39, 341–357.
Chaudhuri, A. (2011). Randomized response and indirect questioning techniques in surveys. Boca Raton: Chapman & Hall, CRC Press, Taylor & Francis Group.
Chaudhuri, A., & Dihidar, K. (2009). Estimating means of stigmatizing qualitative and quantitative variables from discretionary responses randomized or direct. Sankhya Series B, 71, 123–136.
Chaudhuri, A., & Mukerjee, R. (1985). Optionally randomized response techniques. Calcutta Statistical Association Bulletin, 34, 225–229.
Chaudhuri, A., & Mukerjee, R. (1988). Randomized response: theory and techniques. New York: Marcel Dekker.
Chaudhuri, A., & Saha, A. (2005). Optional versus compulsory randomized response techniques in complex surveys. Journal of Statistical Planning and Inference, 135, 516–527.
Eichhorn, B.H., & Hayre, L.S. (1983). Scrambled randomized response methods for obtaining quantitative data. Journal of Statistical Planning and Inference, 7, 306–316.
Gupta, S., Gupta, B., Singh, S. (2002). Estimation of sensitivity level of personal interview survey question. Journal of Statistical Planning and Inference, 100, 239–247.
Gupta, S., Shabbir, J., Sehra, S. (2010). Mean and sensitivity estimation in optional randomized response models. Journal of Statistical Planning and Inference, 140, 2870–2874.
Huang, K.-C. (2010). Unbiased estimators of mean, variance and sensitivity level for quantitative characteristics in finite population sampling. Metrika, 71, 341–352.
Pal, S. (2008). Unbiasedly estimating the total of a stigmatizing variable from a complex survey on permitting options for direct or randomized responses. Statistical Papers, 49, 157–164.
Rao, J.N.K., Hartley, H.O., Cochran, W.G. (1962). On the simple procedure of unequal probability sampling without replacement. Journal of the Royal Statistical Society: Series B, 24, 482–491.
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© 2013 Springer-Verlag Berlin Heidelberg
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Chaudhuri, A., Christofides, T.C. (2013). Quantitative Issues Bearing Stigma: Parameter Estimation. In: Indirect Questioning in Sample Surveys. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36276-7_5
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DOI: https://doi.org/10.1007/978-3-642-36276-7_5
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