Multiple Imputation for Dichotomous MNAR Items Using Recursive Structural Equation Modeling With Rasch Measures as Predictors

Abstract

Perfect data sets do not exist in the real world, and missing data are an authentic challenge facing social science analysts and researchers. Missing values can bias analyses, especially when high percentages are missing or there are patterns in the missingness (Allison, 2002;Osborne, 2013;Wang, Bartlett, & Ryan, 2017). The higher the percentage of missing values, the greater the potential problems (Bennett, 2001;Osborne, 2013). Consequently, the handling of missing data has been a topical issue in social sciences and methods dealing with missing values have grown exponentially (