Modeling Evasive Response Bias in Randomized Response: Cheater Detection Versus Self-protective No-Saying.

Randomized response is an interview technique for sensitive questions designed to eliminate evasive response bias. Since this elimination is only partially successful, two models have been proposed for modeling evasive response bias: the cheater detection model for a design with two sub-samples with different randomization probabilities and the self-protective no sayers model for a design with multiple sensitive questions. This paper shows the correspondence between these models, and introduces models for the new, hybrid "ever/last year" design that account for self-protective no saying and cheating.

The analysis of randomized response "ever" and "last year" questions: A non-saturated Multinomial model.

Randomized response (RR) is a well-known interview technique designed to eliminate evasive response bias that arises from asking sensitive questions. The most frequently asked questions in RR are either whether respondents were "ever" carriers of the sensitive characteristic, or whether they were carriers in a recent period, for instance, "last year". The present paper proposes a design in which both questions are asked, and derives a multinomial model for the joint analysis of these two questions.

Refinement of the extended crosswise model with a number sequence randomizer: Evidence from three different studies in the UK.

The Extended Crosswise Model (ECWM) is a randomized response model with neutral response categories, relatively simple instructions, and the availability of a goodness-of-fit test. This paper refines this model with a number sequence randomizer that virtually precludes the possibility to give evasive responses. The motivation for developing this model stems from a strategic priority of WADA (World Anti-Doping Agency) to monitor the prevalence of doping use by elite athletes.

Examining conservation compliance with randomized response technique analyses.

Understanding violations of laws or social norms designed to protect natural resources from overexploitation is a priority for conservation research and management. Because direct questioning about stigmatized behaviors can produce biased responses, researchers have adopted more complex, indirect questioning techniques. The randomized response technique (RRT) is one of the most powerful indirect survey methods, yet analyses of these data require sophisticated statistical models.

The multidimensional randomized response design: Estimating different aspects of the same sensitive behavior.

The conventional randomized response design is unidimensional in the sense that it measures a single dimension of a sensitive attribute, like its prevalence, frequency, magnitude, or duration. This paper introduces a multidimensional design characterized by categorical questions that each measure a different aspect of the same sensitive attribute. The benefits of the multidimensional design are (i) a substantial gain in power and efficiency, and the potential to (ii) evaluate the goodness-of-fit of the model, and (iii) test hypotheses about evasive response biases in case of a misfit.

Estimating the prevalence of crack dependence using capture-recapture with institutional and field data: a three-city study in The Netherlands.

The aim of this study was to estimate the prevalence of crack dependence in the three largest Dutch cities (Amsterdam, Rotterdam, The Hague), stratified by gender and age. Three-sample capture-recapture, using data (collected between 2009 and 2011) from low threshold substitution treatment (n = 1,764), user rooms (n = 546), and a respondent-driven sample (n = 549), and applying log-linear modeling (covariates: gender, age, and city), provided a prevalence rate of 0.51% (95% CI: 0.

Point and interval estimation of the population size using a zero-truncated negative binomial regression model.

This paper presents the zero-truncated negative binomial regression model to estimate the population size in the presence of a single registration file. The model is an alternative to the zero-truncated Poisson regression model and it may be useful if the data are overdispersed due to unobserved heterogeneity. Horvitz-Thompson point and interval estimates for the population size are derived, and the performance of these estimators is evaluated in a simulation study.