A Property of the CHAID Partitioning Method for Dichotomous Randomized Response Data and Categorical Predictors
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In this paper, we present empirical and theoretical results on classification trees for randomized response data. We considered a dichotomous sensitive response variable with the true status intentionally misclassified by the respondents using rules prescribed by a randomized response method. We assumed that classification trees are grown using the Pearson chi-square test as a splitting criterion, and that the randomized response data are analyzed using classification trees as if they were not perturbed. We proved that classification trees analyzing observed randomized response data and estimated true data have a one-to-one correspondence in terms of ranking the splitting variables. This is illustrated using two real data sets.
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Journal of Classification
Volume 29, Issue 1 , pp 76-90
- Cover Date
- Print ISSN
- Online ISSN
- Additional Links
- Pearson chi-square
- Forced response method
- Prevalence estimation
- CHAID method
- Industry Sectors
- Author Affiliations
- 1. Department of Economics and Statistics, University of Calabria, Via P. Bucci, Cubo 0C, 87036, Arcavacata di Rende, Italy
- 2. Department of Methodology and Statistics, Utrecht University, PO Box 80.140, 3508, TC Utrecht, The Netherlands