Contents
How do you check for multiple imputation?
A useful initial check is to explore the imputed values that have been generated by the imputation model. This can be done using graphical displays of the imputed data using plots such as histograms or boxplots. The imputed data can also be checked numerically by generating descriptive statistics.
What are the types of multiple imputation treatments?
Complete Case Analysis: This methods involves deleting cases in a particular dataset that are missing data on any variable of interest.
Why do we use multiple imputation?
Multiple imputation is a general approach to the problem of missing data that is available in several commonly used statistical packages. It aims to allow for the uncertainty about the missing data by creating several different plausible imputed data sets and appropriately combining results obtained from each of them.
When can you use multiple imputation?
When it is plausible that data are missing at random, but not completely at random, analyses based on complete cases may be biased. Such biases can be overcome using methods such as multiple imputation that allow individuals with incomplete data to be included in analyses.
What is the purpose of conditional multiple imputation?
Conditional Multiple Imputation: Conditional MI, as indicated in its name, follows an iterative procedure, modeling the conditional distribution of a certain variable given the other variables. This technique allows users to be more flexible as a distribution is assumed for each variable rather than the whole dataset.
What are the different approaches to multiple imputation?
There are two approaches to multiple imputation, implemented by different packages in R: 1 Joint Multivariate Normal Distribution Multiple Imputation: The main assumption in this technique is that the observed… 2 Conditional Multiple Imputation: Conditional MI, as indicated in its name, follows an iterative procedure, modeling the… More
How is multiple imputation used in latent variable models?
Mortaza Jamshidian, Matthew Mata, in Handbook of Latent Variable and Related Models, 2007 Multiple imputation consists of producing, say m, complete data sets from the incomplete data by imputing the missing data m times by some reasonable method.
How does multivariate normal distribution multiple imputation work?
Joint Multivariate Normal Distribution Multiple Imputation: The main assumption in this technique is that the observed data follows a multivariate normal distribution. Therefore, the algorithm that R packages use to impute the missing values draws values from this assumed distribution. Amelia and norm packages use this technique.