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Multiple imputation involves three steps, namely, imputation, analysis, and pooling as determined by [56].
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Missing values were imputed via 5-fold multiple imputation involving repeated sampling the empirical multivariate distribution using the Amelia package [ 46] within R statistical programming environment [ 47].
The steps for multiple imputation involve: Imputation: This step involves the imputation of the missing values several times depending on the number of imputations stated.
In practical terms, the multiple imputation method involves imputing each missing score several times, say r times.
Incomplete data sets are frequently analyzed using multiple imputation, which involves creating multiple complete versions of the data with missing values imputed through random draws from distributions inferred from observed data (2).
Multiple imputation usually involves much more complicated statistical modelling than the single regression analyses commonly reported in medical research papers.
Imputation involves concluding what the parties would have intended, whereas inference involves concluding what they did intend.
One such imputation procedure is multiple imputation, 3 which involves a Bayesian estimation procedure to average outcomes across multiple imputed datasets.
Data analysis involves multiple imputation and multiple regression analysis to predict variations in HRQoL in relation to different factors.
Multiple imputation is computationally intensive and involves approximations.
In this paper, we document a study that involved applying a multiple imputation technique with chained equations to data drawn from the 2007 iteration of the TIMSS database.
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