Reporting the Use of Multiple Imputation for Missing Data in 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. In particular, it has been shown to be preferable to listwise deletion, which has historically been a commonly employed method for quantitative research. However, our 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 in research where MI is used, 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 develop a set of reporting recommendations based on a synthesis of the MI methodological literature and offer a discussion of these recommendations oriented toward applied researchers. The recommended MI reporting practices involve describing the nature and structure of any missing data, describing the imputation model and procedures, and describing any notable imputation results.