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How is multiple imputation used in missing data?
Multiple imputation (MI) is a statistical technique for dealing with missing data. In MI the distribution of observed data is used to estimate a set of plausible values for missing data. The missing values are replaced by the estimated plausible values to create a “complete” dataset.
Why is the proportion of missing data important?
The proportion of missing data provides limited information about the bias and efficiency gains that can be made from multiple imputation. Increasing the number of auxiliary variables included in an imputation model does not always result in efficiency gains.
How are missing data used in a regression equation?
The best predictors are selected and used as independent variables in a regression equation. The variable with missing data is used as the dependent variable. Cases with complete data for the predictor variables are used to generate the regression equation; the equation is then used to predict missing values for incomplete cases.
When to delete data from a complete case analysis?
Listwise deletion (complete-case analysis) removes all data for an observation that has one or more missing values. Particularly if the missing data is limited to a small number of observations, you may just opt to eliminate those cases from the analysis.
I’ve long been an advocate of multiple imputation for handling missing data. For example, in my two-day Missing Data seminar, I spend about two-thirds of the course on multiple imputation, using PROC MI in SAS and the mi command in Stata. The other third covers maximum likelihood (ML).
Which is better, maximum likelihood or multiple imputation?
Multiple imputation is rapidly becoming a popular method for handling missing data, especially with easy-to-use software like PROC MI. In this paper, however, I argue that maximum likelihood is usually better than multiple imputation for several important reasons.
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