useR! 2024

Multilevel regression and post-stratification (MRP; Gelman & Hill, 2006; Gelman et al., 2020) are developed to process data from demographically diverse groups in complex survey designs. To obtain a representative estimate for a specific group, a multilevel regression model combines an individual-level model using individual-level data and a population-level model using group-level data. MRP is divided into two stages. The first step is the multilevel regression step, which estimates a stratified model divided into an individual model and a population model into an individual-response model using priors for parameters. The multilevel regression model is intended to calculate estimates for each class used for later post-stratification. In the individual model, only variables that enable post-stratification can be used. In this study, the existing problem of MRP, which uses only categorical variables that can be used for post-stratification, was solved by proposing a method incorporating a projection pursuit tree and implementing it in R.