A Bias-Corrected Estimator for the Crosswise Model with Inattentive Respondents

Abstract The crosswise model is an increasingly popular survey technique to elicit candid answers from respondents on sensitive questions. Recent studies, however, point out that in the presence of inattentive respondents, conventional estimator prevalence a attribute biased toward 0.5. To remedy this problem, we propose simple design-based bias correction using anchor question has item with known prevalence. We demonstrate can easily estimate and correct for arising without measuring individual-level attentiveness. also offer several useful extensions our estimator, including sensitivity analysis strategy weighting, framework multivariate regressions which latent trait used as outcome or predictor, tools power parameter selection. Our method be implemented through open-source software cWise .