Missing Data Imputation for Voter Turnout Using Auxiliary Margins

Missing data is one of the essential problems while conducting analysis related to data. Typically, researchers make strong assumptions or constraints to handle them such as missing at random. However, these assumptions are improvable sometimes and could even introduce severe bias while data is missing systematically. Under such circumstances, it is desirable to consider nonignorable missing mechanisms. Since the missing values are inaccessible based on the observed data itself, the missing data with auxiliary margins (MD-AM) framework proposed by Akande et al. (2019) provides a fexible method to characterize nonignorable missing models by combining auxiliary margins. Previous research applied the MD-AM framework on CPS voter turnout data with few variables. In this thesis, I extend the MD-AM framework on turnout data with nine primary variables. By changing the assumptions about how vote aects missing, I specify two chain models for joint distribution of primary variables, their associated missing indicator models and unit nonresponse model. Furthermore, I conduct sensitivity check for two models and compare results.