Missing data: An introductory conceptual overview for the novice researcher
Canadian Journal of Nursing Research
algorithm, Algorithms, article, Bias (Epidemiology), Case mean substitution, Data Collection, Data Interpretation, Statistical, Deletion, epidemiology, Group mean substitution, human, Humans, Imputation, information processing, methodology, Missing data, nursing research, Patterns of missingness, regression analysis, reproducibility, Reproducibility of Results, Research Design, standard, statistical analysis
Missing data is a common issue in research that, if improperly handled, can lead to inaccurate conclusions about populations. A variety of statistical techniques are available to treat missing data. Some of these are simple while others are conceptually and mathematically complex. The purpose of this paper is to provide the novice researcher with an introductory conceptual overview of the issue of missing data. The authors discuss patterns of missing data, common missing-data handling techniques, and issues associated with missing data. Techniques discussed include listwise deletion, pairwise deletion, case mean substitution, sample mean substitution, group mean substitution, regression imputation, and estimation maximization. © McGill University School of Nursing.
El-Masri, M. M. and Fox-Wasylyshyn, S M.. (2005). Missing data: An introductory conceptual overview for the novice researcher. Canadian Journal of Nursing Research, 37 (4), 156-171.