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Graham, J.W. and Hofer, S.M. (2000) Multiple Imputation in Multivariate Research. In: Little, T.D., Schnabel, K.U. and Baumert, J., Eds., Modeling Longitudinal and Multilevel Data, Psychology Press, New York, 189-204.
https://doi.org/10.4324/9781410601940-15

has been cited by the following article:

• TITLE: Study on the Missing Data Mechanisms and Imputation Methods

  AUTHORS: Abdullah Z. Alruhaymi, Charles J. Kim

  KEYWORDS: Missing Data, Mechanisms, Imputation Techniques, Models

  JOURNAL NAME: Open Journal of Statistics, Vol.11 No.4, August 11, 2021

  ABSTRACT: The absence of some data values in any observed dataset has been a real hindrance to achieving valid results in statistical research. This paper aimed at the missing data widespread problem faced by analysts and statisticians in academia and professional environments. Some data-driven methods were studied to obtain accurate data. Projects that highly rely on data face this missing data problem. And since machine learning models are only as good as the data used to train them, the missing data problem has a real impact on the solutions developed for real-world problems. Therefore, in this dissertation, there is an attempt to solve this problem using different mechanisms. This is done by testing the effectiveness of both traditional and modern data imputation techniques by determining the loss of statistical power when these different approaches are used to tackle the missing data problem. At the end of this research dissertation, it should be easy to establish which methods are the best when handling the research problem. It is recommended that using Multivariate Imputation by Chained Equations (MICE) for MAR missingness is the best approach to dealing with missing data.