Multiple Imputation and Higher Education Research

Higher education researchers using survey data often face decisions about handling missing data. Multiple imputation (MI) is considered by many statisticians to be the most appropriate technique for addressing missing data in many circumstances. However, our content analysis of a decade of higher education research literature reveals that the field has yet to make substantial use of this technique despite common employment of quantitative analysis, and that many recommended MI reporting practices are not being followed. We conclude that additional information about the technique and recommended reporting practices may help improve the quality of the research involving missing data. In an attempt to address this issue, we offer an annotated practical example focusing on decision points researchers often face