Incomplete Categorical Data Design: Non-Randomized Response Techniques for Sensitive Questions in Surveys

Incomplete Categorical Data Design: Non-Randomized Response Techniques for Sensitive Questions in Surveys

Volumn , Issue , 2016, Pages 1-293

  Tian, Guo Liang ^a   Tang, Man Lai ^b  

^a UNIVERSITY OF HONG KONG   (Hong Kong)

^b HONG KONG BAPTIST UNIVERSITY   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85122909324     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Book

References (175)

1. Abernathy, J.R., Greenberg, B.G. and Horvitz, D.G. (1970). Estimates of induced abortion in urban North Carolina. Demography 7(1), 19-29.
2. Abul-Ela, A.L.A., Greenberg, B.G. and Horvitz, D.G. (1967). A multiproportions randomized response model. Journal of the American Statistical Association 62, 990-1008.
3. Agresti, A. and Coull, B.A. (1998). Approximate is better than "exact" for interval estimation of binomial proportions. The American Statistician 52(2), 119-126.
4. Ahart, A.M. and Sackett, P.R. (2004). A new method of examining relationships between individual difference measures and sensitive behavior criteria: Evaluating the unmatched count technique. Organizational Research Methods 7(1), 101-114.
5. Albert, J.H. and Gupta, A.K. (1983). Estimation in contingency tables using prior information. Journal of the Royal Statistical Society, B 45, 60-69.
6. Albert, J.H. and Gupta, A.K. (1985). Bayesian methods for binomial data with applications to a non-response problem. Journal of the American Statistical Association 80, 167-174.
7. Anderson, D.A., Simmons, A.M., Milnes, S.M. and Earleywine, M. (2007). Effect of response format on endorsement of eating disordered attitudes and behaviors. International Journal of Eating Disorders 40(1), 90-93.
8. Arnold, S.F. (1993). Gibbs sampling. In Handbook of Statistics: Computational Statistics (C.R. Rao, ed.), Vol. 9, 599-625. Elsevier Science Publishers B.V., New York.
9. Atkinson, A.C. and Donev, A.N. (1992). Optimum Experimental Designs. Clarendon Press, Oxford.
10. Avetisyan, M. and Fox, J.P. (2012). The Dirichlet-multinomial model for multivariate randomized response data and small samples. Psicologica 33, 362-390.
11. Bar-Lev, S.K., Bobovitch, E. and Boukai, B. (2003). A common conjugate prior structure for several randomized response models. Test 12(1), 101-113.
12. Bar-Lev, S.K., Bobovitch, E. and Boukai, B. (2004). A note on randomized response models for quantitative data. Metrika 60(3), 255-260.
13. Barton, A.H. (1958). Asking the embarrassing question. Public Opinion Quarterly 22(1), 67-68.
14. Berman, J., McCombs, H. and Boruch, R. (1977). Notes on the contamination method: Two small experiments in assuring confidentiality of Responses. Sociological Methods & Research 6(1), 45-62.
15. Bhargava, M. and Singh, R. (2002). On the efficiency comparison of certain randomized response strategies. Metrika 55, 191-197.
16. Bockenholt, U. and van der Heijden, P.G.M. (2007). Item randomizedresponse models for measuring noncompliance: Risk-return perceptions, social influences, and self-protective responses. Psychometrika 72(2), 245-262.
17. Boruch, R.F. (1971). Assuring confidentiality of responses in social research: A note on strategies. The American Sociologist 6(4), 308-311.
18. Bourke, P.D. (1974). Multi-proportions randomized response using the unrelated question technique. Report No. 74 of the Errors in Surveys Research Project. Institute of Statistics, University of Stockholm (Mimeo).
19. Bourke, P.D. (1982). Randomized response multivariate designs for categorical data. Communications in Statistics-Theory and Methods 11, 2889-2901.
20. Bourke, P.D. and Dalenius, T. (1973). Multi-proportions randomized response using a single sample. Report No. 68 of the Errors in Surveys Research Project. Institute of Statistics, University of Stockholm (Mimeo).
21. Bourke, P.D. and Moran, M.A. (1988). Estimating proportions from randomized response data using the EM algorithm. Journal of the American Statistical Association 83, 964-968.
22. Bradburn, N.M., Sudman, S. and Associates (1979). Improving Interview Method and Questionnaire Design: Response Effects to Threatening Questions in Survey Interviews. Jossey-Bass, A Publishing Unit of John Wiley & Sons, San Francisco, California.
23. Brown, L.D., Cai, T.T. and DasGupta, A. (2001). Interval estimation for a binomial proportion. Statistical Science 16(2), 101-133.
24. Casella, G. and Berger, R.L. (2002). Statistical Inference. (2nd Ed.). Duxbury, Boston.
25. Chang, H.J. and Liang, D.H. (1996). A two-stage unrelated randomized response procedure. Australian Journal of Statistics 38, 43-51.
26. Chaudhuri, A. (2011). Randomized Response and Indirect Questioning Techniques in Surveys. Chapman & Hall/CRC, Boca Raton.
27. Chaudhuri, A. and Christofides, T.C. (2007). Item count technique in estimating the proportion of people with a sensitive feature. Journal of Statistical Planning and Inference 137, 589-593.
28. Chaudhuri, A. and Dihidar, K. (2009). Estimating means of stigmatizing qualitative and quantitative variables from discretionary responses randomized or direct. Sankhya, B 71(1), 123-136.
29. Chaudhuri, A. and Mukerjee, R. (1988). Randomized Response: Theory and Technique. Marcel Dekker, New York.
30. Chaudhuri, A. and Stenger, H. (1992). Survey Sampling: Theory and Methods. Marcel Dekker, New York.
31. Chow, S.C., Shao, J. and Wang, H.S. (2003). Sample Size Calculations in Clinical Research. Chapman & Hall/CRC, Boca Raton.
32. Christofides, T.C. (2005). Randomized response technique for two sensitive characteristics at the same time. Metrika 62(1), 53-63.
33. Clark, S.J. and Desharnais, R.A. (1998). Honest answers to embarrassing questions: Detecting cheating in the randomized response model. Psychological Methods 3(2), 160-168.
34. Clopper, C.J. and Pearson, E.S. (1934). The use of confidence or fiducial limits illustrated in the case of binomial. Biometrika 26, 404-413.
35. Cochran, W.G. (1977). Sampling Techniques. (3rd Ed.). Wiley, New York.
36. Coutts, E. and Jann, B. (2011). Sensitive questions in online surveys: Experimental results for the randomized response technique and the unmatched count technique (UCT). Sociological Methods & Research 40(1), 169-193.
37. Dalenius, T. (1980). Privacy and telephone surveys. Proceedings of the Business and Economics Statistics Section, American Statistical Association, 83-85.
38. Dalton, D.R., Wimbush, J.C. and Daily, C.M. (1994). Using the unmatched count technique (UCT) to estimate base rates for sensitive behavior. Personnel Psychology 47(4), 817-829.
39. Dalton, D.R., Daily, C.M. and Wimbush, J.C. (1997). Collecting "sensitive" data in business ethics research: A case for the unmatched count technique (UCT). Journal of Business Ethics 16, 1049-1057.
40. Daniel, W.W. (1993). Collecting Sensitive Data by Randomized Response: An Annotated Bibliography. (2nd Ed.). Research Monograph No. 107. Georgia State University Business Press, Atlanta.
41. Dempster, A.P., Laird, N.M. and Rubin, D.B. (1977). Maximum likelihood from Incomplete Data via the EM algorithm (with discussions). Journal of the Royal Statistical Society, B 39(1), 1-38.
42. Diana, G. and Perri, P.F. (2011). A class of estimators for quantitative sensitive data. Statistical Papers 52(3), 633-650.
43. DiPietro, M. (2004). Bayesian randomized response as a class project. The American Statistician 58(4), 303-309.
44. Dowling, T.A. and Shachtman, R.H. (1975). On the relative efficiency of randomized response models. Journal of the American Statistical Association 70, 84-87.
45. Droitcour, J., Caspar, R.A., Hubbard, M.L., Parsley, T.L., Visscher, W. and Ezzati, T.M. (1991). The item count technique as a method of indirect questioning: A review of its development and a case study application. In Measurement Errors in Surveys (P.P. Biemer, R.M. Groves, L.E. Lyberg, N.A. Mathiowetz and S. Sudman, eds.), 185-210. Wiley, New York.
46. Efron, B. and Tibshirani, R.J. (1993). An Introduction to the Bootstrap. Chapman & Hall/CRC, Boca Raton.
47. Eichhorn, B.H. and Hayre, L.S. (1983). Scrambled randomized response methods for obtaing sensitive quantitative data. Journal of Statistical Planning and Inference 7, 307-316.
48. Erdfelder, E., Auer, T.S., Hilbig, B.E., Aßfalg, A., Moshagen, M. and Nadarevic, L. (2009). Multinomial processing tree models: A review of the literature. Zeitschrift fur Psychologie/Journal of Psychology 217, 108-124.
49. Eriksson, S.A. (1973). A new model for randomizing response. International Statistical Review 41, 101-113.
50. Esponda, F. and Guerreroc, V.M. (2009). Surveys with negative questions for sensitive items. Statistics & Probability Letters 79(24), 2456-2461.
51. Esponda, F., Forrest, S. and Helman, P. (2009). Negative representations of information. International Journal of Information Security 8(5), 331-345.
52. Fidler, D.S. and Kleinknecht, R.E. (1977). Randomized response versus direct questioning: Two data-collection methods for sensitive information. Psychological Bulletin 84(5), 1045-1049.
53. Folsom, R.E., Greenberg, B.G., Horvitz, D.G. and Abernathy, J.R. (1973). The two alternate questions randomized response model for human surveys. Journal of the American Statistical Association 68, 525-530.
54. Fox, J.A. and Tracy, P.E. (1984). Measuring associations with randomized response. Social Science Research 13(2), 188-197.
55. Fox, J.A. and Tracy, P.E. (1986). Randomized Response: A Method for Sensitive Surveys (Series: Quantitative Applications in the Social Sciences). SAGE Publications, California.
56. Franklin, L.A. (1989). Randomized response sampling from dichotomous populations with continuous randomization. Survey Methodology 15, 225-235.
57. Franklin, L.A. (1998). Randomized response techniques. In Encyclopedia of Biostatistics (P. Armitage and T. Colton, eds.), 3696-3703. Wiley, New York.
58. Gelfand, A.E. and Smith, A.F.M. (1990). Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association 85, 398-409.
59. Gelman, A., Carlin, J.B., Stern, H.S. and Rubin, D.B. (1995). Bayesian Data Analysis. Chapman & Hall, London.
60. Geng, Q.X. (2011). Non-randomized response models for sensitive survey questions. Final Year Project for Bachelor of Science in Statistics and Operations Research under the Supervision of Dr. M.L. Tang. Department of Mathematics, Hong Kong Baptist University, Hong Kong.
61. Gilens, M., Sniderman, P.M. and Kuklinski, J.H. (1998). Affirmative action and the politics of realignment. British Journal of Political Science 28(1), 159-183.
62. Gjestvang, C.R. and Singh, S. (2006). A new randomized response model. Journal of the Royal Statistical Society, B 68, 523-530.
63. Goodstadt, M.S. and Gruson, V. (1975). The randomized response technique: A test on drug use. Journal of the American Statistical Association 70, 814-818.
64. Gould, A.L., Shah, B.V. and Abernathy, J.R. (1969). Unrelated question randomized response techniques with two trials per respondent. In 1969 Social Statistics Section Proceedings of the American Statistical Association, American Statistical Association, 351-359.
65. Greenberg, B.G., Abernathy, J.R. and Horvitz, D.G. (1986). Randomized response. In Encyclopedia of Statistical Sciences (S. Kotz and N.L. Johnson, eds.), Vol.7, 540-548. Wiley, New York.
66. Greenberg, B.G., Abul-Ela, A.A., Simmons, W.R. and Horvitz, D.G. (1969). The unrelated question randomized response model: Theoretical framework. Journal of the American Statistical Association 64, 520-539.
67. Greenberg, B.G., Horvitz, D.G. and Abernathy, J.R. (1974). Comparison of randomized response designs. In Reliability and Biometry: Statistical Analysis of Life Length (F. Prochan and R.J. Serfling, eds.), 787-815. SIAM, Philadelphia.
68. Greenberg, B.G., Kuebler, R.R., Abernathy, J.R. and Horvitz, D.G. (1971). Application of the randomized response technique in obtaining quantitative data. Journal of the American Statistical Association 66, 243-250.
69. Grewal, I.S., Bansal, M.L. and Singh, S. (2003). Estimation of population mean of a stigmatized quantitative variable using double sampling. Statistica 63(1), 79-88.
70. Groenitz, H. (2012). A new privacy-protecting survey design for multichotomous sensitive variables. Metrika, in press.
71. Gupta, S., Gupta, B. and Singh, S. (2002). Estimation of sensitivity level of personal interview survey questions. Journal of Statistical Planning and Inference 100, 239-247.
72. Gupta, S., Shabbir, J. and Sehra, S. (2010). Mean and sensitivity estimation in optional randomized response models. Journal of Statistical Planning and Inference 140, 2870-2874.
73. Hedayat, A.S. and Sinha, B.K. (1991). Design and Inference in Finite Population Sampling. Wiley, New York.
74. Horvitz, D.G., Greenberg, B.G. and Abernathy, J.R. (1975). Recent developments in randomized designs. In A Survey of Statistical Design and Linear Models (J.N. Srivastava, ed.), 271-285. North Holland/ American Elsevier Publishing Co., New York.
75. Horvitz, D.G., Greenberg, B.G. and Abernathy, J.R. (1976). Randomized response: A data-gathering device for sensitive questions. International Statistical Review 44(2), 181-196.
76. Horvitz, D.G., Shah, B.V. and Simmons, W.R. (1967). The unrelated question randomized response model. In 1967 Social Statistics Section Proceedings of the American Statistical Association, American Statistical Association, 65-72.
77. Hu, X. and Batchelder, W.H. (1994). The statistical analysis of general processing tree models with the EM algorithm. Psychometrika 59, 21-47.
78. Hussain, Z. and Shabbir, J. (2007). Estimation of mean of a sensitive quantitative variable. Journal of Statistical Research 41(2), 83-92.
79. Imai, K. (2011). Multivariate regression analysis for the item count technique. Journal of the American Statistical Association 106(494), 407-416.
80. Jann, B. and Brandenberger, L. (2012). An experimental survey measuring plagiarism using the crosswise model: Codebook and documentation. The Institute of Sociology, University of Bern, Bern, Switzerland.
81. Jann, B., Jerke, J. and Krumpal, I. (2012). Asking sensitive questions using the crosswise model: An experimental survey measuring plagiarism. Public Opinion Quarterly 76(1), 32-49.
82. Janus, A.L. (2010). The influence of social desirability pressures on expressed immigration attitudes. Social Science Quarterly 91(4), 928-946.
83. Kim, J.M. and Elam, M.E. (2005). A two-stage stratified Warner's randomized response model using optimal allocation. Metrika 61, 1-7.
84. Kim, J.M. and Warde, W.D. (2004). A stratified Warner's randomized response model. Journal of Statistical Planning and Inference 120, 155-165.
85. Kim, J.M. and Warde, W.D. (2005). Some new results on the multinomial randomized response model. Communications in Statistics-Theory and Methods 34, 847-856.
86. Kim, J.M., Tebbs, J.M. and An, S.W. (2006). Extension of Mangat's randomized-response method. Journal of Statistical Planning and Inference 136, 1554-1567.
87. Kuk, A.Y.C. (1990). Asking sensitive questions indirectly. Biometrika 77, 436-438.
88. Kuklinski, J.H., Cobb, M.D. and Gilens, M. (1997). Racial attitudes and the "new south." Journal of Politics 59(2), 323-349.
89. LaBrie, J.W. and Earleywine, M. (2000). Sexual risk behaviors and alcohol: Higher base rates revealed using the unmatched-count technique. Journal of Sex Research 37(4), 321-326.
90. Lakshmi, D.V. and Raghavarao, D. (1992). A test for detecting untruthful answering in randomized response procedures. Journal of Statistical Planning and Inference 31(3), 387-390.
91. Lange, K. (2002). Mathematical and Statistical Methods for Genetic Analysis. (2nd Ed.). Springer, New York.
92. Lara, D., Strickler, J., Olavarrieta, C.D. and Ellertson, C. (2004). Measuring induced abortion in Mexico: A comparison of four methodologies. Sociological Methods & Research 32(4), 529-558.
93. Lavender, J.M. and Anderson, D.A. (2008). A novel assessment of behaviors associated with body dissatisfaction and disordered eating. Body Image 5(4), 399-403.
94. Lee, R.M. (1993). Doing Research on Sensitive Topics. Sage Publications, London.
95. Lensvelt-Mulders, G.J.L.M., Hox, J.J. and van der Heijden, P.G.M. (2005). How to improve the efficiency of randomized response designs. Quality & Quantity 39, 253-265.
96. Lensvelt-Mulders, G.J.L.M., Hox, J.J., van der Heijden, P.G.M. and Maas, C.J.M. (2005). Meta-analysis of randomized response research: Thirty-five years of validation. Sociological Methods & Research 33(3), 319-348.
97. Li, H. and Zhang, K.L. (1998). The progress of social behavior sciences related to HIV and AIDS. Chinese Journal of Preventive Medicine 2, 120-124.
98. Liu, P.T. and Chow, L.P. (1976). The efficiency of the multiple trial randomized response technique. Biometrics 32, 607-618.
99. Liu, P.T., Chow, L.P. and Mosley, W.H. (1975). Use of the randomized response technique with a new randomizing device. Journal of the American Statistical Association 70, 329-332.
100. Liu, Y. and Tian, G.L. (2013a). Multi-category parallel models in the design of surveys with sensitive questions. Statistics and Its Interface 6(1), 137-149.
101. Liu, Y. and Tian, G.L. (2013b). A variant of the parallel model for sample surveys with sensitive characteristics. Computational Statistics & Data Analysis 67, 115-135.
102. Mangat, N.S. (1994). An improved randomized response strategy. Journal of the Royal Statistical Society, B 56, 93-95.
103. Mangat, N.S. and Singh, R. (1990). An alternative randomized response procedure. Biometrika 77(2), 439-442.
104. Migon, H.S. and Tachibana, V.M. (1997). Bayesian approximations in randomized response model. Computational Statistics & Data Analysis 24, 401-409.
105. Miller, J.D. (1983). The nominative technique: Method and heroin estimated from the 1982 national survey on drug abuse. Unpublished report of National Institute on Drug Abuse, Rockville, Maryland.
106. Miller, J.D. (1984). A new survey technique for studying deviant behavior. Unpublished doctoral dissertation, George Washington University.
107. Miller, J.D. (1985). The nominative technique: A new method of estimating heroin prevalence. In Self-Report Methods of Estimating Drug Use: Meeting Current Challenges to Validity (B.A. Rouse, N.J. Kozel and L.G. Richards, eds.), 104-124. National Institute on Drug Abuse Research Monograph 57, Rockville, Maryland.
108. Monto, M.A. (2001). Prostitution and fellatio. Journal of Sex Research 38(2), 140-145.
109. Moors, J.J.A. (1971). Optimization of the unrelated question randomized response model. Journal of the American Statistical Association 66, 627-629.
110. Moshagen, M. (2010). MultiTree: A computer program for the analysis of multinomial processing tree models. Behavior Research Methods 42(1), 42-54.
111. Moshagen, M., Musch, J. and Erdfelder, E. (2012). A stochastic lie detector. Behavior Research Methods 44, 222-231.
112. Nayak, T.K. (1994). On randomized response surveys for estimating a proportion. Communications in Statistics-Theory and Methods 23(11), 3303-3321.
113. Nazuk, A. and Shabbir, J. (2010). A new mixed randomized response model. International Journal of Business and Social Science 1(1), 186-190.
114. Newcombe, R.G. (1998). Improved confidence intervals for the difference between binomial proportions based on paired data. Statistics in Medicine 17, 2635-2650.
115. Ng, K.W. (1997). Inversion of Bayes formula: Explicit formulas for unconditional pdf. In Advances in the Theory and Practice in Statistics-A Volume in Honor of Samuel Kotz (N.L. Johnson and N. Balakrishnan, eds.), 571-584. Wiley, New York.
116. Ng, K.W., Tian, G.L. and Tang, M.L. (2011). Dirichlet and Related Distributions: Theory, Methods and Applications. Wiley, New York.
117. Ng, K.W., Tang, M.L., Tan, M. and Tian, G.L. (2008). Grouped Dirichlet distribution: A new tool for incomplete categorical data analysis. Journal of Multivariate Analysis 99(3), 490-509.
118. O'Hagan, A. (1987). Bayes linear estimators for randomized response models. Journal of the American Statistical Association 82, 580-585.
119. Orwin, R.G. and Boruch, R.F. (1982). RRT meets RDD: Statistical strategies for assuring response privacy in telephone surveys. Public Opinion Quarterly 46(4), 560-571.
120. Ostapczuk, M., Musch, J. and Moshagen, M. (2009). A randomized response investigation of the education effect in attitudes towards foreigners. European Journal of Social Psychology 39, 920-31.
121. Ostapczuk, M., Musch, J. and Moshagen, M. (2011). Improving self-report measures of medication non-adherence using a cheating detection extension of the randomized-response-technique. Statistical Methods in Medical Research 20, 489-503.
122. Ostapczuk, M., Moshagen, M., Zhao, Z. and Musch, J. (2009). Assessing sensitive attributes using the randomized response technique: Evidence for the importance of response symmetry. Journal of Educational and Behavioral Statistics 34(2), 267-287.
123. Petŕoczi, A., Nepusz, T., Cross, P., Taft, H., Shah, S., Deshmukh, N., Schaffer, J., Shane, M., Adesanwo, C., Barker, J., and Naughton, D.P. (2011). New non-randomised model to assess the prevalence of discriminating behaviour: A pilot study on mephedrone. Substance Abuse Treatment, Prevention, and Policy 6, 20.
124. Pitz, G.F. (1980). Bayesian analysis of random response models. Psychological Bulletin 87, 209-212.
125. Pollock, K.H. and Bek, Y. (1976). A comparison of three randomized response models for quantitative data. Journal of the American Statistical Association 71, 884-886.
126. Poole, W.K. (1974). Estimation of the distribution function of a continuous type random variable through randomized response. Journal of the American Statistical Association 69, 1002-1005.
127. Raghavarao, D. and Federer, W.T. (1979). Block total response as an alternative to the randomized response method in surveys. Journal of the Royal Statistical Society, B 41(1), 40-45.
128. Rao, C.R. (1973). Linear Statistical Inference and Its Applications. Wiley, New York.
129. Rao, J.N.K. and Sitter, R.R. (1995). Variance estimation under two-phase sampling with application to imputation for missing data. Biometrika 82, 453-460.
130. Rayburn, N.R., Earleywine, M. and Davison, G.C. (2003). Base rates of hate crime victimization among college students. Journal of Interpersonal Violence 18(10), 1209-1221.
131. Saha, A. (2007). Optional randomized response in stratified unequal probability sampling-A simulation based numerical study with Kuk's method. Test 16(2), 346-354.
132. Shimizu, I.M. and Bonham, G.S. (1978). Randomized response technique in a national survey. Journal of the American Statistical Association 73, 35-39.
133. Singh, S. and Kim, J.M. (2011). A pseudo-empirical log-likelihood estimator using scrambled responses. Statistics and Probability Letters 81, 345-351.
134. Singh, S., Kim, J.M. and Grewal, I.S. (2008). Imputing and jackknifing scrambled responses. Metron-International Journal of Statistics 66(2), 183-204.
135. Singh, S., Mangat, N.S. and Singh, R. (1997). Estimation of size and mean of a sensitive quantitative variable for a sub-group of a population. Communications in Statistics-Theory and Methods 26, 1793-1804.
136. Song, J.J. and Kim, J.M. (2012). Bayesian analysis of randomized response sum score variables. Communications in Statistics-Theory and Methods 41, 1875-1884.
137. Spurrier, J.D. and Padgett, W.J. (1980). The application of Bayesian techniques in randomized response. Sociological Methodology 11, 533-544.
138. Strauss, S.M., Rindskopf, D.M. and Falkin, G.P. (2001). Modeling relationships between two categorical variables when data are missing: Examining consequences of the missing data mechanism in an HIV data set. Multivariate Behavioral Research 36(4), 471-500.
139. Suzuki, T., Takahasi, K. and Sakasegawa, H. (1976). Some notes on randomized response techniques. Proceedings of the Institute of Statistical Mathematics 24(1), 1-13.
140. Swensson, B. (1974). Combined questions: A new survey technique for eliminating evasive answer bias (I)-Basic theory. Report No. 70 of the Errors in Surveys Research Project. Institute of Statistics, University of Stockholm.
141. Takahasi, K. and Sakasegawa, H. (1977). A randomized response technique without making use of any randomizing device. Annals of the Institute of Statistical Mathematics 29(1), 1-8.
142. Tan, M., Tian, G.L. and Ng, K.W. (2003). A non-iterative sampling method for computing posteriors in the structure of EM-type algorithms. Statistica Sinica 13(3), 625-639.
143. Tan, M., Tian, G.L. and Ng, K.W. (2010). Bayesian Missing Data Problems: EM, Data Augmentation and Non-iterative Computation. Chapman & Hall/CRC, Boca Raton.
144. Tan, M., Tian, G.L. and Tang, M.L. (2009). Sample surveys with sensitive questions: A non-randomized response approach. The American Statistician 63(1), 9-16.
145. Tang, M.L. and Wu, Q. (2013). Non-randomized response model for sensitive survey with non-compliance. Submitted.
146. Tang, M.L., Tian, G.L., Tang, N.S. and Liu, Z.Q. (2009). A new nonrandomized multi-category response model for surveys with a single sensitive question: Design and analysis. Journal of the Korean Statistical Society 38, 339-349.
147. Tang, M.L., Wu, Q., Tian, G.L. and Guo, J.H. (2012). Two-sample nonrandomized response techniques for sensitive questions. Communication in Statistics-Theory and Methods, in press.
148. Tanner, M.A. (1996). Tools for Statistical Inference: Methods for the Exploration of Posterior Distributions and Likelihood Functions. (3rd Ed.). Springer, New York.
149. Tanner, M.A. and Wong, W.H. (1987). The calculation of posterior distributions by data augmentation (with discussions). Journal of the American Statistical Association 82, 528-550.
150. Tian, G.L. (2012). A new non-randomized response model: The parallel model. Statistica Neerlandica, in revision.
151. Tian, G.L., Ng, K.W. and Geng, Z. (2003). Bayesian computation for contingency tables with incomplete cell-counts. Statistica Sinica 13, 189-206.
152. Tian, G.L., Tan, M. and Ng, K.W. (2007). An exact non-iterative sampling procedure for discrete missing data problems. Statistica Neerlandica 61, 232-242.
153. Tian, G.L., Yu, J.W., Tang, M.L. and Geng, Z. (2007). A new nonrandomized model for analyzing sensitive questions with binary outcomes. Statistics in Medicine 26(23), 4238-4252.
154. Tian, G.L., Yuen, K.C., Tang, M.L. and Tan, M. (2009). Bayesian nonrandomized response models for surveys with sensitive questions. Statistics and Its Interface 2, 13-25.
155. Tian, G.L., Tang, M.L., Liu, Z.Q., Tan, M. and Tang, N.S. (2011). Sample size determination for the non-randomized triangular model for sensitive questions in a survey. Statistical Methods in Medical Research 20(3), 159-173.
156. Tracy, D.S. and Mangat, N.S. (1996). Some developments in randomized response sampling during the last decade-A follow up of review by Chaudhuri and Mukerjee. Journal of Applied Statistical Science 4, 533-544.
157. Tsuchiya, T. (2005). Domain estimators for the item count technique. Survey Methodology-A Journal of Published by Statistics Canada 31(1), 41-51.
158. Tsuchiya, T., Hirai, Y. and Ono, S. (2007). A study of the properties of the item count technique. Public Opinion Quarterly 71(2), 253-272.
159. Unnikrishnan, N.K. and Kunte, S. (1999). Bayesian analysis for randomized responses models. Sankhya, B 61(3), 422-432.
160. van den Hout, A. and Klugkist, I. (2009). Accounting for non-compliance in the analysis of randomized response data. Australian & New Zealand Journal of Statistics 51(3), 353-372.
161. van den Hout, A., Bockenholt, U. and van der Heijden, P.G.M. (2010). Estimating the prevalence of sensitive behavior and cheating with a dual design for direct questioning and randomized response. Journal of the Royal Statistical Society, C 59(4), 723-736.
162. Warner, S.L. (1965). Randomized response: A survey technique for eliminating evasive answer bias. Journal of the American Statistical Association 60, 63-69.
163. Warner, S.L. (1971). The linear randomized response model. Journal of the American Statistical Association 66, 884-888.
164. Warner, S.L. (1986). The omitted digit randomized response model for telephone applications. In Proceedings of the Social Survey Research Methods, American Statistical Association, 441-443.
165. Wiederman, M.W. (1997). The truth must be in here somewhere: Examining the gender discrepancy in self-reported lifetime number of sex partners. Journal of Sex Research 34, 375-386.
166. Williamson, G.D. and Haber, M. (1994). Models for three-dimensional contingency tables with completely and partially cross-classified data. Biometrics 50, 194-203.
167. Wimbush, J.C. and Dalton, D.R. (1997). Base rate for employee theft: Convergence of multiple methods. Journal of Applied Psychology 82(5), 756-763.
168. Winkler, R.L. and Franklin, L.A. (1979). Warner's randomized response model: A Bayesian approach. Journal of the American Statistical Association 74, 207-214.
169. Wiseman, F., Moriarty, M. and Schafer, M. (1975). Estimating public opinion with the randomized response model. Public Opinion Quarterly 39(4), 507-513.
170. Wu, J.Q., Zhao, P.F., Lu, H., et al. (1996). The Progress of Social Sciences in Reproductive Health. Chinese Population Publishing House, Beijing.
171. Youn, G. (2001). Perceptions of peer sexual activities in Korean adolescents. Journal of Sex Research 38(4), 352-360.
172. Yu, J.W., Tian, G.L. and Tang, M.L. (2008). Two new models for survey sampling with sensitive characteristic: Design and analysis. Metrika 67, 251-263.
173. Yu, J.W., Lu, Y. and Tian, G.L. (2013). A survey design for a sensitive binary variable correlated with another non-sensitive binary variable. Journal of Probability and Statistics, Volume 2013, Article ID 827048, 11 pages, http://dx.doi.org/10.1155/2013/827048.
174. Zdep, S.M. and Rhodes, I.N. (1976). Making a randomized response technique work. Public Opinion Quarterly 40(4), 531-537.
175. Zou, G.H. (1997). Two-stage randomized response procedures as single stage procedures. Australian Journal of Statistics 39, 235-236.