Statistical models for randomized response data Seminar

Abstract
It is well-known that, in surveys, questions about sensitive behaviour may not yield truthful responses. For this purpose randomized response has been developed. This presentation will discuss recent developments in the analysis of randomized response data. Many statistical tools are made available by us for the analysis of randomized response data and our presentations gives an overview that includes logistic regression (useful when the sensitive question is the response variable to be related to explanatory variables), loglinear analysis (useful to investigate relations between various sensitive questions), item response theory models (useful for investigating the extent of individual sensitive behaviour from a number of sensitive questions) and count data models (counting the number of sensitive behaviours). In addition, extensions to the above statistical models are proposed that accommodate respondents that do not follow the randomized response design by saying no to whatever sensitive question is asked (the untruthful-responses-problem). We argue that in the past randomized response has not been used as often as it could be, and that there are probably three reasons for this. Firstly, randomized response research is expensive as larger sample sizes are needed to obtain the same precision of estimates. Secondly, researchers mistakenly believe that randomized response only allows for the estimation of the prevalence of sensitive behaviour. Thirdly, randomized response is not believed to be solving the untruthful-answer problem. These three reasons can be refuted as follows: firstly, data collection over the internet has become easier over the years and can solve the sample size problem. Secondly, as is indicated above, a whole range of tools for the analysis of randomized response data exists. And thirdly, new statistical methodology is available to handle respondents that do not comply to the randomized response design.