Analyzing Randomized Response Data with a Binomial Selection Procedure
The randomized response survey model introduced by Warner (1965) is reviewed and applied to the analysis of contaminated data, i. e., response or reported data which is truthful with probability less than one. Two generic mechanisms are distinguished: an active mechanism whereby the contamination is inserted into the process and hence becomes a statistical design parameter, and a passive mechanism whereby contamination of the response is suspected and hence becomes an analysis parameter. The impact of contamination on the operating characteristics of a subset ranking and selection procedure for binomial models is assessed.
Unable to display preview. Download preview PDF.
- Gupta, S. S. and McDonald, G. C. (1986). “A Statistical Selection Approach to Binomial Models.” Journal of Quality Technology 18, 103–115.Google Scholar
- Gupta, S. S. and Sobel, M. (1960). “Selecting A Subset Containing the Best of Several Binomial Populations.” Contributions to Probability and Statistics, Stanford University Press, 224–248.Google Scholar
- McDonald, G. C. (1993). “Adjusting for Data Contamination in Statistical Inference.” General Motors Research Publication GMR 7304 (Revised).Google Scholar
- Moors, J. J. A. (1985). “Estimation in Truncated Parameter Spaces,” Ph.D. Thesis, Kathalieke Hogeschool, Tilburg.Google Scholar
- Nathan, G. (1988). “A Bibliography on Randomized Response: 1965–1987.” Survey Methodology 14, 331–346.Google Scholar