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The goal of the present study was to compare two imputation methods SMI and MMI for handling missing data in background variables in large-scale assessment programs.
Along with multiple imputation approaches, FIML is recommended as one of the best approaches to handling missing data [ 23, 24].
As a method handling missing data, K nearest neighbor (KNN) imputation gradually gains acceptance in empirical studies by its exemplary performance and simplicity.
One review of CRTs published in 2011 focused on imputation techniques with respect to handling missing data and did not discern between missing covariates or outcomes.
Imputation is one of the most commonly used approaches to handling missing data.
We compared popular methods to handle missing data with multiple imputation (a more sophisticated method that preserves data).
27 We handled missing data by the method of multiple imputation.
Mixed model uses maximum likelihood estimation to handle missing data, without the use of ad hoc imputations.
We used multiple imputation to handle missing data in the study.
We will use multiple imputation to handle missing data to enable an intention to treat analysis [ 63].
We used multiple imputation (MI) to handle missing data in the study.
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