Matching Pre-Processing of Split-Ballot Survey Data for the Analysis of Double Standards

Split-ballot data are often used to study double standards. The key problem of this design is that individual double standards cannot be identified. I propose a simple two-step approach based on a matching pre-processing of the data to estimate individual double standards. Once this preliminary first step is completed, any statistical technique (e.g., regression models) can be applied on the new data. I apply the method to gender double standards on attitudes toward the age one leaves home by using data from the third round of the European Social Survey. The proposed method simplifies regression analyses of the effects of covariates on double standards and offers new opportunities for research on double standards.