Quantifying a phenomenon without knowledge of individual data: the erased respondent method (ERM).
A new method (ERM-Erased Respondent Method) is presented that deletes every link between the respondent subject and the sensitive responses he provides. The method is alternative to Randomized Response Technique (RRT) by S.L. Warner (1965), moreover it is applicable also to dosages of biological sample pools. The ERM is based on the binomial distribution where, in place of the single trial (single individual) probability of success (positive result) is taken into account the probability of failure (negative result) of a group of n individuals. Applying the method to N groups, each one including n individuals, it is possible to calculate the proportion of "positive" individuals among the examined subjects (point estimation of the population proportion) by means of the proportion of "negative" groups observed. This method, which could also be used in studies concerning biological samples (forming N pools each one consisting of biological materials from n subjects), appears to be of interest for its applicability in phenomena of difficult observation, whereby the subjects are little inclined to provide the requested response or whereby linking the subject to his responses (or analytical results) could entail undesired consequences for him, even of ethical or legal liability. In conclusion, the method allows knowing certain characteristics of a population without emphasizing individual conditions. In this way, it appears probable that the ERM can be applicable to problems of confidentiality of the information, such as sexual abuse, exit-polls, some genetic diseases, doping agents, alcohol abuse or psychotropic substances addiction, especially linked to vehicles driving. In any case is to be expected that ERM method will be used especially for the analysis of biologic material pools, where the subject is not involved in any way in producing the results and is totally absent any linkage to him.
Dept. Environment and Prevention, Section Environment and Trauma, National Institute of Health, Rome, Italy. taggi@iss.it
F
F
Taggi
D016010:Binomial Distribution; D015233:Models, Statistical