Multi-category parallel models in the design of surveys with sensitive questions

Statistics and its Interface

Volumn 6, Issue 1, 2013, Pages 137-149

  Liu, Yin ^a   Tian, Guo Liang ^b  

^a UNIVERSITY OF HONG KONG   (Hong Kong)

^b not available   (Hong Kong)

Author keywords

Chi squared test; Likelihood ratio test; Multi category parallel model; Multi category triangular model; Non randomized response technique

Indexed keywords

EID: 84876304409     PISSN: 19387989     EISSN: 19387997     Source Type: Journal    
DOI: 10.4310/SII.2013.v6.n1.a13     Document Type: Article

References (17)

1. Abul-Ela, A. A., Greenberg, B. G. and Horvitz, D.G. (1967). A multi-proportions randomized response model. Journal of the American Statistical Association 62 990-1008.
2. Agresti, A. and Coull, B. A. (1998). Approximate is better than "exact" for interval estimation of binomial proportions. The American Statistician 52 119-126
3. Bourke, P. D. (1974). Multi-proportions randomized response using the unrelated question technique. Technical Report No. 74 of the Errors on Surveys Research Project. Institute of Statistics, University of Stockholm (Mimeo).
4. Bourke, P. D. and Dalenius, T. (1973). Multi-proportions randomized response using a single sample. Report No. 68 of the Errors on Surveys Research Project. Institute of Statistics, University of Stockholm (Mimeo).
5. Brown, L. D., Cai, T. T. and DasGupta, A. (2001). Interval estimation for a binomial proportion. Statistical Science 16 101-133.
6. Clopper, C. J. and Pearson, E. S. (1934). The Use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26(4) 404-413.
7. Dempster, A. P., Laird, N. M. and Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm (with discussion). Journal of the Royal Statistical Society, Series B 39(1) 1-38.
8. Eriksson, S. A. (1973). A new model for randomized response. International Statistical Review 41 101-113.
9. Liu, Y. and Tian, G. L. (2012). A variant of the parallel model for sample surveys with sensitive characteristics. Technical Report of the Department of Statistics and and Actuarial Science, The University of Hong Kong.
10. Newcombe, R. G. (1998). Two-sided confidence intervals for the single proportion: comparison of seven methods. Statistics in Medicine 17(8) 857-872.
11. Tang, M. L., Tian, G. L., Tang, N. S. and Liu, Z. Q. (2009). A new non-randomized multi-category response model for surveys with a single sensitive question: Design and analysis. Journal of the Korean Statistical Society 38 339-349.
12. Tian, G. L. (2012). A new non-randomized response model: The parallel model. Technical Report of the Department of Statistics and and Actuarial Science, The University of Hong Kong.
13. Tian, G. L., Yu, J. W., Tang, M. L. and Geng, Z. (2007). A new non-randomized model for analyzing sensitive questions with binary outcomes. Statistics in Medicine 26(23) 4238-4252.
14. 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 Medicine Research 20(3) 159-173.
15. Warner, S. L. (1965). Randomized response: A survey technique for eliminating evasive answer bias. Journal of the American Statistical Association 60 63-69.
16. Williamson, G. D. and Haber, M. (1994). Models for threedimensional contingency tables with completely and partially cross-classified data. Biometrics 50 194-203.
17. 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.