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Generalized Randomized Response Questioning Designs

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Pseudo-Populations

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Abstract

When questions on sensitive subjects, such as harassment at work, domestic violence, illegal employment, number of abortions, income, or voting behavior, are asked by direct questioning, nonresponse and untruthful answering will occur. As can be seen from Eq. (3.1), in the presence of both, the HT estimator t HT, for instance, is decomposed into three sums: one over the truthful answering set s t of sample s, another over the untruthful answering set s u , and a third over the missing set s m . Hence, such behavior by a respondent may cause serious problems in the analysis of sample and population data because the estimators of population parameters based only on a survey’s available cases may strongly be biased. It is therefore essential for data collectors to not ignore nonresponse or untruthful answering. Before applying such methods as weighting adjustment and data imputation (see Sects. 3.2 and 3.3) to compensate for nonresponse that has already occurred, data collectors should do everything to make the rates of both nonresponse and untruthful answering as small as possible.

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References

  • Alfons, A., Kraft, S., Templ, M., & Filzmoser, P. (2011a). Simulation of close-to-reality population data for household surveys with application to EU-SILC. Statistical Methods and Applications, 20(3), 383–407.

    Article  MathSciNet  MATH  Google Scholar 

  • Alfons, A., Templ, M., Burgard, J., Filzmoser, P., Hulliger, B., Kolb, J. -P., et al. (2011b). The AMELI simulation study. Research Project Report WP6 (D6.1), FP7-SSH-2007-217322 AMELI. Available on [September 10, 2015]: https://www.uni-trier.de/index.php?id=24676.

  • Antal, E., & TillĂ©, Y. (2011). A direct bootstrap method for complex sampling designs from a finite population. Journal of the American Statistical Association, 106(494), 534–543.

    Article  MathSciNet  MATH  Google Scholar 

  • Ardilly, P., & TillĂ©, Y. (2006). Sampling methods: Exercises and solutions. New York: Springer.

    MATH  Google Scholar 

  • Baker, R., Brick, J. M., Bates, N. A., Battaglia, M., Couper, M. P., Dever, J. A., et al. (Eds.). (2013). Report of the AAPOR task force on non-probability sampling. American Association of Publich Opinion Research, AAPOR Publishing. Available on [September 10, 2015]: http://www.aapor.org/AM/Template.cfm?Section=Reports1&Template=/CM/ContentDisplay.cfm&ContentID=5963.

  • Bar-Lev, S. K., Bobovitch, E., & Boukai, B. (2004). A note on randomized response models for quantitative data. Metrika, 60, 255–260.

    Article  MathSciNet  MATH  Google Scholar 

  • Barbiero, A., & Mecatti, F. (2010). Bootstrap algorithms for variance estimation in Ď€PS sampling. In P. Mantovan, P. Secchi (Eds.), Complex data modeling and computationally intensive statistical methods (pp. 57–69). Milan: Springer.

    Chapter  Google Scholar 

  • Bauer, A. (2011). Genauigkeitsanalysen zu den Ă–sterreich-Ergebnissen der PISA-Studie 2009. Master Thesis, Johannes Kepler University Linz.

    Google Scholar 

  • Beatty, P., & Herrmann, D. (1995). A framework for evaluating “Don’t Know” responses in surveys. Proceedings of the Survey Research Methods Section of the American Statistical Association, 1005–1010.

    Google Scholar 

  • Beatty, P., & Herrmann, D. (2002). To answer or not to answer: Decision processes related to survey item nonresponse. In R. M. Groves, D. A. Dillman, J. L. Eltinge, & R. J. A. Little (Eds.), Survey nonresponse (pp. 71–86). New York: Wiley.

    Google Scholar 

  • Bellhouse, D. R. (1988). A brief history of random sampling methods. In P. R. Krishnaiah, & C. R. Rao (Eds.), Handbook of statistics (Vol. 6, pp. 1–14). Amsterdam: Elsevier.

    Google Scholar 

  • Bethlehem, J. (2002). Weighting nonresponse adjustments based on auxiliary information. In R. M. Groves, D. A. Dillman, J. L. Eltinge, & R. J. A. Little (Eds.), Survey nonresponse (pp. 275–287). New York: Wiley.

    Google Scholar 

  • Bethlehem, J. (2009). The rise of survey sampling (Discussion paper (09015)). The Hague/Heerlen: Statistics Netherlands. Available on [September 10, 2015]: http://www.cbs.nl/NR/rdonlyres/BD480FBC-24CF-42FA-9A0D-BBECD4F53090/0/200915x10pub.pdf.

  • Bhargava, M., & Singh, R. (2000). A modified randomization device for Warner’s model. Statistica, LX, 315–321.

    Google Scholar 

  • Bickel, P. J., & Freedman, D. A. (1984). Asymptotic normality and the bootstrap in stratified sampling. The Annals of Statistics, 12(2), 470–482.

    Article  MathSciNet  MATH  Google Scholar 

  • Boeije, H., & Lensvelt-Mulders, G. (2002). Honest by chance: A qualitative study to clarify respondents’ (non-)compliance with computer-assisted randomized response. Bulletin de Methodologie Sociologique, 75, 24–39.

    Article  Google Scholar 

  • Booth, J. G., Butler, R. W., & Hall, P. (1994). Bootstrap methods for finite populations. Journal of the American Statistical Association, 89(428), 1282–1289.

    Article  MathSciNet  MATH  Google Scholar 

  • Boruch, R. F. (1971). Assuring confidentiality of responses in social research: A note on strategies. The American Sociologist, 6, 308–311.

    Google Scholar 

  • Botman, S. L., & Thornberry, O. T. (1992). Survey design features correlates of nonresponse. Proceedings of the Survey Research Methods Section of the American Statistical Association, 309–314.

    Google Scholar 

  • Bourke, P. D. (1984). Estimation of proportions using symmetric randomized response designs. Psychological Bulletin, 96(1), 166–172.

    Article  Google Scholar 

  • Bourke, P. D., & Dalenius, T. (1976). Some new ideas in the realm of randomized inquiries. International Statistical Review, 44(2), 219–221.

    Article  MathSciNet  MATH  Google Scholar 

  • Bourke, P. D., & Moran, M. M. (1988). Estimating proportions from randomized response data using the EM algorithm. Journal of the American Statistical Association, 83(404), 964–968.

    Article  Google Scholar 

  • Buckland, S. T. (1984). Monte Carlo confidence intervals. Biometrics, 40, 811–817.

    Article  MATH  Google Scholar 

  • Casella, G., & Berger, R. L. (2002). Statistical inference (2nd ed.). Cengage Learning, Brooks/Cole.

    MATH  Google Scholar 

  • Chang, H. -J., Wang, C. -L., & Huang, K. -C. (2004). On estimating the proportion of a qualitative sensitive character using randomized response sampling. Quality & Quantity, 38, 675–680.

    Article  Google Scholar 

  • Chao, M. -T., & Lo, S. -H. (1985). A bootstrap method for finite populations. Sankhya, Series A, 47, 399–405.

    MathSciNet  MATH  Google Scholar 

  • Chao, M. -T., & Lo, S. -H. (1994). Maximum likelihood summary and the bootstrap method in structured finite populations. Statistica Sinica, 4, 389–406.

    MathSciNet  MATH  Google Scholar 

  • Chapman, D. G. (1951). Some properties of the hypergeometric distribution with applications to zoological censuses. University of California Publications in Statistics, 1, 131–160. Available on [September 10, 2015]: http://babel.hathitrust.org/cgi/pt?id=wu.89045844248#view=1up;seq=22.

  • Chaudhuri, A. (2001). Using randomized response from a complex survey to estimate a sensitive proportion in a dichotomous finite population. Journal of Statistical Planning and Inference, 94, 37–42.

    Article  MathSciNet  MATH  Google Scholar 

  • Chaudhuri, A. (2011). Randomized response and indirect questioning techniques in surveys. Boca Raton: CRC Press.

    MATH  Google Scholar 

  • Chaudhuri, A., & Christofides, T. C. (2013). Indirect questioning in sample surveys. Heidelberg: Springer.

    Book  MATH  Google Scholar 

  • Christofides, T. C. (2003). A generalized randomized response technique. Metrika, 57, 195–200.

    Article  MathSciNet  MATH  Google Scholar 

  • Church, A. H. (1993). Estimating the effect of incentives on mail survey response rates: A meta-analysis. The Public Opinion Quarterly, 57(1), 62–79.

    Article  Google Scholar 

  • Cochran, W. G. (1977). Sampling techniques (3rd ed.). New York: Wiley.

    MATH  Google Scholar 

  • Dalenius, T., & Reiss, S. P. (1982). Data-swapping: A technique for disclosure control. Journal of Statistical Planning and Inference, 6, 73–85.

    Article  MathSciNet  MATH  Google Scholar 

  • Defays, D., & Anwar, M. N. (1998). Masking microdata using micro-aggregation. Journal of Official Statistics, 14(4), 449–461.

    Google Scholar 

  • Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B, 39(1), 1–38.

    MathSciNet  MATH  Google Scholar 

  • Deville, J.-C., & TillĂ©, Y. (2004). Efficient balanced sampling: The cube method. Biometrika, 91(4), 893–912.

    Article  MathSciNet  MATH  Google Scholar 

  • Diana, G., Giordan, M., & Perri, P. F. (2013). Randomized response surveys: A note on some privacy protection measures. Model Assisted Statistics and Applications, 8, 19–28.

    Google Scholar 

  • Diana, G., & Perri, P. F. (2009). Estimating a sensitive proportion through randomized response procedures based on auxiliary information. Statistical Papers, 50, 661–672.

    Article  MathSciNet  MATH  Google Scholar 

  • Diana, G., & Perri, P. F. (2011). A class of estimators for quantitative sensitive data. Statistical Papers, 52, 633–650.

    Article  MathSciNet  MATH  Google Scholar 

  • Diekmann, A. (2012). Making use of “Benford’s Law” for the randomized response technique. Sociological Methods & Research, 41(2), 325–334.

    Article  MathSciNet  Google Scholar 

  • Dillman, D. A. (1978). Mail and telephone surveys: The total design method. New York: Wiley InterScience.

    Google Scholar 

  • Dillman, D. A. (2000). Mail and Internet surveys. New York: Wiley.

    MATH  Google Scholar 

  • Dillman, D. A., Singer, E., Clark, J. R., & Treat, J. B. (1996). Effects of benefits appeals, mandatory appeals, and variations in statements of confidentiality on completion rates for census questionnaires. The Public Opinion Quarterly, 60(3), 376–389.

    Article  Google Scholar 

  • Drechsler, J., Bender, S. & Rässler, S. (2008). Comparing fully and partially synthetic datasets for statistical disclosure control in the German IAB establishment panel. Transactions on Data Privacy, 1, 1002–1050.

    MathSciNet  Google Scholar 

  • Efron, B. (1979). Bootstrap methods: Another look at the jackknife. Annals of Statistics, 7, 1–26.

    Article  MathSciNet  MATH  Google Scholar 

  • Efron, B. (1981). Censored data and the bootstrap. Journal of the American Statistical Association, 76(374), 312–319.

    Article  MathSciNet  MATH  Google Scholar 

  • Eichhorn, B. H., & Hayre, L. S. (1983). Scrambled randomized response methods for obtaining sensitive quantitative data. Journal of Statistical Planning and Inference, 7, 307–316.

    Article  Google Scholar 

  • Eurostat. (2012). Labour force survey in the EU, candidate and EFTA countries, 2012 Edition. Luxembourg: Publications Office of the European Union. Available on [September 10, 2015]: http://epp.eurostat.ec.europa.eu/portal/page/portal/employment_unemployment_lfs/publications/methods.

  • Fay, R. E. (1989). Theory and application of replicate weighting for variance calculations. Proceedings of the Survey Research Methods Section of the American Statistical Association, 212–217.

    Google Scholar 

  • Fienberg, S. E. (1992). Bibliography on capture-recapture modelling with application to census undercount adjustment. Survey Methodology, 18(1), 143–154.

    Google Scholar 

  • Fuller, W. A. (1993). Masking procedures for microdata disclosure limitation. Journal of Official Statistics, 9(2), 383–406.

    Google Scholar 

  • Gabler, S., & Häder, S. (2009). Die Kombination von Mobilfunk- und Festnetzstichproben in Deutschland. In M. Weichbold, J. Bacher, & C. Wolf (Eds.), Umfrageforschung (pp. 499–512). Wiesbaden: VS Verlag fär Sozialwissenschaften.

    Google Scholar 

  • Gabler, S., & Quatember, A. (2012). Das Problem mit der Repräsentativität von Stichprobenerhebungen. In vsms (Verband Schweizer Markt-und Sozialforschung) (Ed.), Jahrbuch 2012 (pp. 17–19). ZĂĽrich: vsms.

    Google Scholar 

  • Gabler, S., & Quatember, A. (2013). Repräsentativität von Subgruppen bei geschichteten Zufallsstichproben. AStA Wirtschafts- und Sozialstatistisches Archiv, 7, 105–119.

    Article  Google Scholar 

  • Gjestvang, C. R., & Singh, S. (2007). Forced quantitative randomized response model: A new device. Metrika, 66, 243–257.

    Article  MathSciNet  MATH  Google Scholar 

  • Gouweleeuw, J. M., Kooiman, P., Willenborg, L. C. R. J., & de Wolf, P. -P. (1998). Post randomisation for statistical disclosure control: Theory and implementation. Journal of Official Statistics, 14(4), 463–478.

    Google Scholar 

  • Greenberg, B. G., Abul-Ela, A. -L. A., Simmons, W. R., & Horvitz, D. G. (1969). The unrelated question randomized response model: Theoretical framework. Journal of the American Statistical Association, 64(326), 520–539.

    Article  MathSciNet  Google Scholar 

  • Greenberg, B. G., Kuebler, R. R. Jr., Abernathy, J. R., & Horvitz, D. G. (1971). Application of the randomized response technique in obtaining quantitative data. Journal of the American Statistical Association, 66(334), 243–250.

    Article  Google Scholar 

  • Groenitz, H. (2014). A new privacy-protecting survey design for multichotomous sensitive variables. Metrika, 77, 211–224.

    Article  MathSciNet  MATH  Google Scholar 

  • Gross, S. (1980). Median estimation in sample surveys. Proceedings of the Survey Research Methods Section of the American Statistical Association, 181–184.

    Google Scholar 

  • Groves, R. M., Fowler, F. J., Couper, M. P., Lepkowski, J. M., Singer, E., & Tourangeau, R. (2004). Survey methodology. Hoboken: Wiley.

    MATH  Google Scholar 

  • Groves, R. M., & Heeringa, S. G. (2006). Responsive design for household surveys: Tools for actively controlling survey errors and costs. Journal of the Royal Statistical Society, Series A, 169(3), 439–457.

    Article  MathSciNet  Google Scholar 

  • Guerriero, M., & Sandri, M. F. (2007). A note on the comparison of some randomized response procedures. Journal of Statistical Planning and Inference, 137, 2184–2190.

    Article  MathSciNet  MATH  Google Scholar 

  • Hansen, M. H., & Hurwitz, W. N. (1943). On the theory of sampling from finite populations. Annals of Mathematical Statistics, 14, 333–362.

    Article  MathSciNet  MATH  Google Scholar 

  • Heberlein, T. A., & Baumgartner, R. (1978). Factors affecting response rates to mailed questionnaires: A quantitative analysis of the published literature. American Sociological Review, 43(4), 447–462.

    Article  Google Scholar 

  • Holbrook, A. L., & Krosnick, J. A. (2010). Measuring voter turnout by using the randomized response technique. The Public Opinion Quarterly, 74(2), 328–343.

    Article  Google Scholar 

  • Holmberg, A. (1998). A bootstrap approach to probability proportional-to-size sampling. Proceedings of the Survey Research Methods Section of the American Statistical Association, 378–383.

    Google Scholar 

  • Holt, A., & Elliot, D. (1991). Methods of weighting for unit-nonresponse. The Statistician, 40(3), 333–342.

    Article  Google Scholar 

  • Horvitz, D. G., Shah, B. V., & Simmons, W. R. (1967). The unrelated question randomized response model. Proceedings of the Social Statistics Section of the American Statistical Association, 65–72.

    Google Scholar 

  • Horvitz, D. G., & Thompson, D. J. (1952). A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association, 47, 663–685.

    Article  MathSciNet  MATH  Google Scholar 

  • International Working Group for Disease Monitoring and Forecasting. (1995a). Capture-recapture and multiple-record systems estimation I: History and theoretical development. American Journal of Epidemiology, 142(10), 1047–1058.

    Google Scholar 

  • International Working Group for Disease Monitoring and Forecasting. (1995b). Capture-recapture and multiple-record systems estimation II: Applications in human diseases. American Journal of Epidemiology, 142(10), 1059–1068.

    Google Scholar 

  • James, J. M., & Bolstein, R. (1990). The effect of monetary incentives and follow-up mailings on the response rate and response quality in mail surveys. The Public Opinion Quarterly, 54(3), 346–361.

    Article  Google Scholar 

  • Judkins, D. R. (1990). Fay’s method for variance estimation. Journal of Official Statistics, 6(3), 223–239.

    Google Scholar 

  • Kauermann, G., & KĂĽchenhoff, H. (2011). Stichproben. Heidelberg: Springer.

    Book  MATH  Google Scholar 

  • Kreuter, F. (2008). Interviewer effects. In P. J. Lavrakas (Ed.), Encyclopedia of survey research methods (pp. 369–371). Los Angeles: SAGE Publications.

    Google Scholar 

  • Kruskal, W., & Mosteller, F. (1980). Representative sampling, IV: The history of the concept in statistics, 1895–1939. International Statistical Review, 48, 169–195.

    Article  MathSciNet  MATH  Google Scholar 

  • Kuk, A. Y. C. (1989). Double bootstrap estimation of variance under systematic sampling with probability proportional to Size. Journal of Statistical Computation and Simulation, 31, 73–82.

    Article  MathSciNet  MATH  Google Scholar 

  • Lahiri, P. (2003). On the impact of bootstrap in survey sampling and small-area estimation. Statistical Science, 18(2), 199–210.

    Article  MathSciNet  Google Scholar 

  • Lensvelt-Mulders, G. J. L. M., Hox, J. J., van der Heijden, P. G. M., & Maas, C. J. M. (2005). Meta-analysis of randomized response research. Sociological Methods & Research, 33(3), 319–348.

    Google Scholar 

  • Little, R. J. A., & Rubin, D. B. (2002). Statistical analysis with missing data. Hoboken: Wiley.

    Book  MATH  Google Scholar 

  • Liu, P. T., & Chow, L. P. (1976). A new discrete quantitative randomized response model. Journal of the American Statistical Association, 71(353), 30–31.

    Article  MATH  Google Scholar 

  • Lohr, S. L. (2010). Sampling: Design and analysis (2nd ed.). Boston: Brooks/Cole.

    MATH  Google Scholar 

  • Lumley, T. (2010). Complex surveys - a guide to analysis using R. Hoboken: John Wiley.

    Google Scholar 

  • Mangat, N. S. (1992). Two stage randomized response sampling procedure using unrelated question. Journal of the Indian Society of Agricultural Statistics, 44, 82–87.

    MathSciNet  Google Scholar 

  • Mangat, N. S., & Singh, R. (1990). An alternative randomized response procedure. Biometrika, 77, 439–442.

    Article  MathSciNet  MATH  Google Scholar 

  • Mangat, N. S., & Singh, R. (1991). An alternative approach to randomized response survey. Statistica, 3, 327–332.

    MathSciNet  MATH  Google Scholar 

  • Mangat, N. S., Singh, S., & Singh, R. (1993). On the use of a modified randomization device in randomized response inquiries. Metron, 51, 211–216.

    MathSciNet  MATH  Google Scholar 

  • Mangat, N. S., Singh, S., & Singh, R. (1995). On use of a modified randomization device in Warner’s model. Journal of Indian Society of Statistics & Operations Research, 16, 65–69.

    MathSciNet  Google Scholar 

  • Matthews, G. J., & Harel, O. (2011). Data confidentiality: A review of methods for statistical disclosure limitation and methods for assessing privacy. Statistics Surveys, 5, 1–29.

    Article  MathSciNet  MATH  Google Scholar 

  • McCarthy, P. J., & Snowden, C. B. (1985). The bootstrap and finite population sampling. Vital and Health Statistics, Series 2, No. 95, Public Health Service, Washington, U.S. Government Printing Office.

    Google Scholar 

  • Mislevy, R. J. (1991). Randomization-based inference about latent variables from complex samples. Psychometrika, 56(2), 177–196.

    Article  MathSciNet  MATH  Google Scholar 

  • MĂĽnnich, R., Magg, K., Sostra, K., Schmidt, K., & Wiegert, R. (2004). Variance estimation for small area estimates. Research Project Report WP10 (D10.1 and 10.2), IST-2000-26057 DACSEIS. Available on [September 10, 2015]: http://www.uni-trier.de/index.php?id=29730.

  • MĂĽnnich, R., & SchĂĽrle, J. (2003). On the simulation of complex universes in the case of applying the German microcensus. DACSEIS Research Paper Series 4. Available on [September 10, 2015]: https://www.uni-trier.de/fileadmin/fb4/projekte/SurveyStatisticsNet/DRPS4.pdf.

  • MĂĽnnich, R., SchĂĽrle, J., Bihler, W., Boonstra, H. -J., Eckmair, D., Haslinger, A., et al. (2003). Monte-Carlo simulation study of European surveys. Research Project Report WP3 (D3.1 and 3.2), IST-2000-26057 DACSEIS. Available on [September 10, 2015]: http://www.uni-trier.de/index.php?id=29730.

  • Newcomb, S. (1881). Note on the frequency of use of the different digits in natural numbers. American Journal of Mathematics, 4, 39–40.

    Article  MathSciNet  MATH  Google Scholar 

  • OECD. (Ed.). (2012). PISA 2009 Technical Report. PISA, OECD Publishing. Available on [September 10, 2015]: http://dx.doi.org/10.1787/9789264167872-en.

  • Peeters, C. F. W., Lensvelt-Mulders, G. J. L. M., & Lasthuizen, K. (2010). A note on a simple and practical randomized response framework for eliciting sensitive dichotomous and quantitative information. Sociological Methods & Research, 39(2), 283–296.

    Google Scholar 

  • Perri, P. F. (2008). Modified randomized devices for Simmons’ model. Model Assisted Statistics and Applications, 3, 233–239.

    MathSciNet  MATH  Google Scholar 

  • Pollock, K. H., & Bek, Y. (1976). A comparison of three randomized response models for quantitative data. Journal of the American Statistical Association, 71(356), 884–886.

    Article  MATH  Google Scholar 

  • Poole, W. K. (1974). Estimation of the distribution function of a contiunous type random variable through randomized response. Journal of the American Statistical Association, 69(348), 1002–1005.

    Article  MathSciNet  MATH  Google Scholar 

  • Quatember, A. (1996a). Das Problem mit dem Begriff Reprsentativität. Allgemeines Statistisches Archiv, 80(2), 236–241.

    Google Scholar 

  • Quatember, A. (1996b). Das Quotenverfahren. Linz: Universitätsverlag Rudolf Trauner.

    Google Scholar 

  • Quatember, A. (2001a). Das Jahrhundert der Stichproben. Austrian Journal of Statistics, 30(1), 45–60.

    Google Scholar 

  • Quatember, A. (2001b). Die Quotenverfahren. Stichprobentheorie und -praxis. Aachen: Shaker Verlag.

    Google Scholar 

  • Quatember, A. (2009). A standardization of randomized response strategies. Survey Methodology, 35(2), 143–152.

    Google Scholar 

  • Quatember, A. (2012). An extension of the standardized randomized response technique to a multi-stage setup. Statistical Methods & Applications, 21(4), 475–484.

    Article  MathSciNet  Google Scholar 

  • Quatember, A. (2014a). A randomized response design for a polychotomous sensitive population and its application to opinion polls. Model Assisted Statistics and Applications, 9, 11–23.

    Google Scholar 

  • Quatember, A. (2014b). The finite population bootstrap - from the maximum likelihood to the Horvitz-Thompson approach. Austrian Journal of Statistics, 43(2), 93–102.

    Article  Google Scholar 

  • Quatember, A., & Bauer, A. (2012). Genauigkeitsanalysen zu den Ă–sterreich-Ergebnissen der PISA-Studie 2009. In F. Eder (Ed.), PISA 2009 - Nationale Zusatzanalysen (pp. 534–550). MĂĽnster: Waxmann Verlag.

    Google Scholar 

  • Quatember, A., & Hausner, M. C. (2013). A family of methods for statistical disclosure control. Journal of Applied Statistics, 40(2), 337–346.

    Article  MathSciNet  Google Scholar 

  • Ranalli, M. G., & Mecatti, F. (2012). Comparing recent approaches for bootstrapping sample survey data: A first step towards a unified approach. Proceedings of the Survey Research Methods Section of the American Statistical Association, 4088–4099.

    Google Scholar 

  • Rao, J. N. K. (2003). Small area estimation. Hoboken: Wiley.

    Book  MATH  Google Scholar 

  • Rao, J. N. K., & Thomas, D. R. (1988). The analysis of cross-classified categorical data from complex surveys. Sociological Methodology, 18, 213–269.

    Article  Google Scholar 

  • Rao, J. N. K., & Wu, C. F. J. (1988). Resampling inference with complex survey data. Journal of the American Statistical Association, 83(401), 231–241.

    Article  MathSciNet  MATH  Google Scholar 

  • Reiter, J. P. (2003). Inference for partially synthetic, public use microdata sets. Survey Methodology, 29(2), 181–188.

    Google Scholar 

  • RosĂ©n, B. (1997). On sampling with probability proportional to size. Journal of Statistical Planning and Inference, 62, 159–191.

    Article  MathSciNet  MATH  Google Scholar 

  • Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys. New York: Wiley.

    Book  MATH  Google Scholar 

  • Rubin, D. B. (1993). Discussion: Statistical disclosure limitation. Journal of Official Statistics, 9(2), 461–468.

    Google Scholar 

  • Ryu, J. -B., Kim, J. -M., Heo, T. -Y., & Park, C. G. (2005). On stratified randomized response sampling. Model Assisted Statistics and Applications, 1(1), 31–36.

    MathSciNet  MATH  Google Scholar 

  • Seber, G. A. F. (1970). The effect of trap response on tag recapture estimates. Biometrics, 26, 13–22.

    Article  Google Scholar 

  • Sekar, C., & Deming, E. W. (1949). On a method of estimating birth and death rates and extent of registration. Journal of the American Statistical Association, 44, 101–115.

    Article  MATH  Google Scholar 

  • Sen, A. R. (1953). On the estimate of the variance in sampling with varying probabilities. Journal of the Indian Society of Agricultural Statistics, 5, 119–127.

    MathSciNet  Google Scholar 

  • Shao, J., & Sitter, R. R. (1996). Bootstrap for imputed survey data. Journal of the American Statistical Association, 91(435), 1278–1288.

    Article  MathSciNet  MATH  Google Scholar 

  • Shao, J., & Tu, D. (1995). The jackknife and bootstrap. New York: Springer.

    Book  MATH  Google Scholar 

  • Singer, E. (2002). The use of incentives to reduce nonresponse in household surveys. In R. M. Groves, D. A. Dillman, J. L. Eltinge, & R. J. A. Little (Eds.), Survey nonresponse (pp. 163–178). New York: Wiley.

    Google Scholar 

  • Singer, E., Mathiowetz, N. A., & Couper, M. P. (1993). The impact of privacy and confidentiality concerns on survey participation: The case of the 1990 U.S. census. The Public Opinion Quarterly, 57(4), 465–482.

    Article  Google Scholar 

  • Singer, E., von Hoewyk, J., Gebler, N., Raghunathan, T., & McGonagle, K. (1999). The effect of incentives on response rates in interviewer-mediated surveys. Journal of Official Statistics, 15(2), 217–230.

    Google Scholar 

  • Singer, E., van Hoewyk, J., & Maher, M. P. (2000). Experiments with incentives in telephone surveys. The Public Opinion Quarterly, 64(2), 171–188.

    Article  Google Scholar 

  • Singer, E., van Hoewyk, J., & Neugebauer, R. J. (2003). Attitudes and behavior: The impact of privacy and confidentiality concerns on participation in the 2000 census. The Public Opinion Quarterly, 67(3), 368–384.

    Article  Google Scholar 

  • Singer, E., von Thurn, D. R., & Miller, E. R. (1995). Confidentiality assurances and response: A quantitative review of the experimental literature. The Public Opinion Quarterly, 59(1), 66–77.

    Article  Google Scholar 

  • Singh, S., Horn, S., Singh, R., & Mangat, N. S. (2003). On the use of modified randomization device for estimating the prevalence of a sensitive attribute. Statistics in Transition, 6, 515–522.

    Google Scholar 

  • Singh, S., & Sedory, S. A. (2011). Cramer-Rao lower bound of variance in randomized response sampling. Sociological Methods & Research, 40(3), 536–546.

    Article  MathSciNet  Google Scholar 

  • Singh, S., Singh, R., Mangat, N. S., & Tracy, D. S. (1994). An alternative device for randomized responses. Statistica, 54, 233–243.

    MathSciNet  MATH  Google Scholar 

  • Singh, R., Singh, S., Mangat, N. S., & Tracy, D. S. (1995). An improved two stage randomized response strategy. Statistical Papers, 36, 265–271.

    Article  MathSciNet  MATH  Google Scholar 

  • Sitter, R. R. (1992a). A resampling procedure for complex survey data. Journal of the American Statistical Association, 87(419), 755–765.

    Article  MathSciNet  MATH  Google Scholar 

  • Sitter, R. R. (1992b). Comparing three bootstrap methods for survey data. The Canadian Journal of Statistics, 20(2), 135–154.

    Article  MathSciNet  MATH  Google Scholar 

  • Särndal, C. -E., Swensson, B., & Wretman, J. (1992). Model assisted survey sampling. New York: Springer.

    Book  MATH  Google Scholar 

  • Tan, M. T., Tian, G. L., & Tang, M. L. (2009). Sample surveys with sensitive questions: A nonrandomized response approach. The American Statistician, 63, 9–16.

    Article  MathSciNet  Google Scholar 

  • Templ, M. (2008). Statistical disclosure control for microdata using the R-package sdcMicro. Transactions on Data Privacy, 1, 67–85

    MathSciNet  Google Scholar 

  • Templ, M., MĂĽnnich, R., Alfons, A., Filzmoser, P., Hulliger, B., Kolb, J. -P., et al. (2011). Synthetic data generation of SILC data. Research Project Report WP6 (D6.2), FP7-SSH-2007-217322 AMELI. Available on [September 10, 2015]: https://www.uni-trier.de/index.php?id=24676.

  • Thomas, D. R., Singh, A. C., & Roberts, G. R. (1995). Independence tests for two-way tables under cluster sampling. Proceedings of the Survey Research Methods Section of the American Statistical Association, 885–890.

    Google Scholar 

  • Tourangeau, R., & Smith, T. W. (1996). Asking sensitive questions: The impact of data collection mode, question format, and question context. The Public Opinion Quarterly, 60(2), 275–304.

    Article  Google Scholar 

  • Traugott, M. W., Groves, R. M., & Lepkowski, J. M. (1987). Using dual frame designs to reduce nonresponse in telephone surveys. The Public Opinion Quarterly, 51(4), 522–539.

    Article  Google Scholar 

  • van den Hout, A., & van der Heijden, P. G. M. (2002). Randomized response, statistical disclosure control and misclassification: A review. International Statistical Review, 70(2), 269–288.

    Article  MATH  Google Scholar 

  • Warner, S. L. (1965). Randomized response: A survey technique for eliminating evasive answer bias. Journal of the American Statistical Association, 60, 63–69.

    Article  MATH  Google Scholar 

  • Warner, S. L. (1971). The linear randomized response model. Journal of the American Statistical Association, 66, 884–888.

    Article  Google Scholar 

  • Warner, S. L. (1976). Optimal randomized response models. International Statistical Review, 44(2), 205–212.

    Article  MathSciNet  MATH  Google Scholar 

  • Warner, S. L. (1986). The omitted digit randomized response model for telephone applications. Proceedings of the Social Survey Research Methods Section of the American Statistical Association, 441–443.

    Google Scholar 

  • Willenborg, L., & de Waal, T. (1996). Statistical disclosure control in practice. New York: Springer.

    Book  MATH  Google Scholar 

  • Willenborg, L., & de Waal, T. (2001). Elements of statistical disclosure control. New York: Springer.

    Book  MATH  Google Scholar 

  • Winkler, W. E. (2004). Masking and re-identification methods for public-use microdata: Overview and research problems. Research Report Series of the Statistical Research Division of the U.S. Bureau of the Census. 2004–2006.

    Google Scholar 

  • Wolter, K. M. (2007). Introduction to variance estimation. New York: Springer.

    MATH  Google Scholar 

  • Yates, F., & Grundy, P. M. (1953). Selection without replacement from within strata with probability proportional to size. Journal of the Royal Statistical Society, Series B, 15, 235–261.

    MATH  Google Scholar 

  • Young, T., & Ludloff, M. E. (2011). Privacy and big data. Sebastopol: O’Reilly Media.

    Google Scholar 

  • Zaizai, Y., Jingyu, W., & Junfeng, L. (2009). An efficiency and protection degree-based comparison among the quantitative randomized response strategies. Communications in Statistics: Theory and Methods, 38(3), 400–408.

    MathSciNet  MATH  Google Scholar 

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  1. Department of Applied Statistics, Johannes Kepler University Linz, Linz, Austria

    Andreas Quatember

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Quatember, A. (2015). Generalized Randomized Response Questioning Designs. In: Pseudo-Populations. Springer, Cham. https://doi.org/10.1007/978-3-319-11785-0_6

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