Randomized Response Models (RRMs) aim at increasing the validity of measuring
sensitive attributes by eliciting more honest responses through anonymity protection of
respondents. This anonymity protection is achieved by implementing randomization in
the questioning procedure. On the other hand, this randomization increases the
sampling variance and, therefore, increases sample size requirements. The present work
aims at countering this drawback by combining RRMs with curtailed sampling, a
sequential sampling design in which sampling is terminated as soon as sufficient
information to decide on a hypothesis is collected. In contrast to open sequential
designs, the curtailed sampling plan includes the definition of a maximum sample size
and subsequent prevalence estimation is easy to conduct. Using this approach, resources
can be saved such that the application of RRMs becomes more feasible.
