In the Horvitz model, the randomized response device presents to the sampled person labelled $i$ a box containing a large number of identical cards, with a proportion $p,(0 <p < 1)$ bearing the mark $A$ and the rest marked $B$ (an innocuous attribute whose population proportion $\alpha$ is known).

The response solicited denoted by $z_i$ takes the value $y_i$ if $i$ bears $A$ and the card drawn is marked $A$ or if $i$ bears $B$ and the card drawn is marked $B$. Otherwise $z_i$ takes the value 0.

The file data "HorvitzDataRealSurvey.rda" stores a sample, extracted by simple random sampling without replacement, of size 710. With these data we estimate the prevalence of students who have sensitive characteristics set out below.

The randomized response technique used is Horvitz model (Horvitz et al, 1967 and Greenberg et al., 1969) with parameter $p=0.5$

In this model an additional questionnaire with the following characteristics is required:

- The questions should not be sensitive.
- The questions should not be related to the theme of "sensitive" questions.
- For each of the questions that measure qualitative variables, the answers should have a known probability.

The survey is

To estimate results, you must have the response probabilities of non-sensitive questions:

In order to give more confidence to the respondent, he was given a booklet of instructions:

- Step 1: Flip a coin and did not show anyone the side obtained.
- Step 2: The question you must answer in each game depends on the side obtained on the coin:
- If you get head, answer the question preceded by the word HEAD
- If you get tail, answer the question preceded by the word TAIL

This procedure must be repeated for each question.

The respondent only had to put on the question sheet a cross in the chosen answers, and then he deposited the survey sheet in an urn.

We store the answers to senstive questions in data.frame (HorvitzDataRealSurvey.rda).

We use the Horvitz function to obtain the estimates:

library("RRTCS") N=10777 n=710 data(HorvitzDataRealSurvey) p=0.5 alpha=c(1/12,1/10,20/30,1/10,10/30,1/12) pi=rep(n/N,n) cl=0.95 out1=Horvitz(HorvitzDataRealSurvey$copied,p,alpha[1],pi,"mean",cl,N) out1 out2=Horvitz(HorvitzDataRealSurvey$fought,p,alpha[2],pi,"mean",cl,N) out2 out3=Horvitz(HorvitzDataRealSurvey$bullied,p,alpha[3],pi,"mean",cl,N) out3 out4=Horvitz(HorvitzDataRealSurvey$bullying,p,alpha[4],pi,"mean",cl,N) out4 out5=Horvitz(HorvitzDataRealSurvey$drug,p,alpha[5],pi,"mean",cl,N) out5 out6=Horvitz(HorvitzDataRealSurvey$sex,p,alpha[6],pi,"mean",cl,N) out6

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

Horvitz, D.G., Shah, B.V., Simmons, W.R. (1967).
*The unrelated question RR model.*
Proceedings of the Social Statistics Section of the American Statistical Association. 65-72. Alexandria, VA: ASA.

**Any scripts or data that you put into this service are public.**

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.