What is multiple imputation analysis?
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.
Can SPSS do multiple imputation?
From the menus choose: Analyze > Multiple Imputation > Impute Missing Data Values… Specify a dataset or IBM® SPSS® Statistics-format data file to which imputed data should be written. The output dataset consists of the original case data with missing data plus a set of cases with imputed values for each imputation.
How is multiple imputation done?
When using multiple imputation, all missing values in each generated dataset (imputation step) are replaced by a random sample of plausible values [22]. Hence, unless ‘a random seed’ is specified, each time a multiple imputation analysis is performed different results will be shown [22].
What is single and multiple imputation?
Single imputation usually identifies a particular record for a subject, e.g. baseline or just the previous non-missing value and repeats it for the missing data points. Multiple imputation uses a predicted value for a given subject and time point using statistical modelling of available data.
How are multiple imputations used for missing data?
Multiple Imputation for Missing Data: A Cautionary Tale, Sociological Methods and Research, 28, 301-309. Learn the different methods for dealing with missing data and how they work in different missing data situations. Take Me to The Video!
Which is the best method for multiple imputation in SAS?
Multiple Imputation is available in SAS, S-Plus, R, and now SPSS 17.0 (but you need the Missing Values Analysis add-on module). The second method is to analyze the full, incomplete data set using maximum likelihood estimation. This method does not impute any data, but rather uses each cases available data to compute maximum likelihood estimates.
Do you need to do multiple imputation in Amos?
AMOS doesn’t do multiple imputation, but you don’t need it to. It does maximum likelihood. You might find this helpful, though it’s not exactly what you’re doing: How to Use Full Information Maximum Likelihood in AMOS to Analyze Regression Models with Missing Data