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A REVIEW OF STATISTICAL METHODS FOR ADDRESSING MISSING DATA
Keywords:
missing data, imputation, multiple imputationDOI:
https://doi.org/10.17654/0972361725054Abstract
Ensuring consistent and complete data is a fundamental requirement in any research study involving data analysis, as it forms the basis for producing reliable, meaningful, and actionable results. Missing data or censored data poses a big challenge for the analysis which may even lead to unreasonable and misleading results. Omitting such kind of data leads to information loss, which may affect the precision of the result. Hence, using such data requires analytical techniques which should be coined explicitly to combat this problem. Many researchers have been dealing with the topic of missing data since 1960s. This paper reviews key statistical methods that have been developed to address the challenges of missing data.
Received: March 27, 2025
Accepted: May 21, 2025
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