# Multiple imputation

This post is part of 'Statistics | General' series

Multiple imputation is a statistical method used to handle missing data. It is a way to generate multiple plausible values for missing data points, based on the information that is available in the dataset. The idea behind multiple imputation is to use the information in the dataset to infer the missing values, rather than simply deleting observations with missing values.

The process of multiple imputation involves the following steps:

1. Model the missing data mechanism: The first step is to understand how the missing data were generated. This step helps in identifying the variables that are associated with the missing data and in understanding the pattern of missingness.

2. Create imputed values: Once the missing data mechanism is understood, the next step is to generate multiple sets of imputed values for the missing data. This is typically done using a technique called "Markov Chain Monte Carlo" (MCMC) which generates multiple imputed datasets by sampling from the posterior distribution of the missing values given the observed data.

3. Analyze the data: Once multiple imputed datasets are generated, the next step is to analyze the data. The results from each imputed dataset are combined to produce a single estimate and its associated standard error.

4. Assess the uncertainty: The final step is to assess the uncertainty in the imputed values by measuring the variability of the results across the multiple imputed datasets.

Multiple imputation is a powerful technique for handling missing data, as it allows to account for the uncertainty in the imputed values and provides more accurate estimates of the parameters of interest than methods that simply delete observations with missing values. It's important to mention that multiple imputation is not appropriate for all missing data scenarios, and the choice of imputation method depends on the specific research question and assumptions of the data.

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