The MIANALYZE Procedure
- Overview
- Getting Started
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Syntax
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Details
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Examples
Reading Means and Standard Errors from Variables in a DATA= Data Set Reading Means and Covariance Matrices from a DATA= COV Data Set Reading Regression Results from a DATA= EST Data Set Reading Mixed Model Results from PARMS= and COVB= Data Sets Reading Generalized Linear Model Results Reading GLM Results from PARMS= and XPXI= Data Sets Reading Logistic Model Results from PARMS= and COVB= Data Sets Reading Mixed Model Results with Classification Variables Using a TEST statement Combining Correlation Coefficients
- References
| Multiple Imputation Efficiency |
The relative efficiency (RE) of using the finite 
imputation estimator, rather than using an infinite number for the fully efficient imputation, in units of variance, is approximately a function of and 
(Rubin 1987, p. 114):
 |
Table 57.2 shows relative efficiencies with different values of and .
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10% |
20% |
30% |
50% |
70% |
3 |
0.9677 |
0.9375 |
0.9091 |
0.8571 |
0.8108 |
5 |
0.9804 |
0.9615 |
0.9434 |
0.9091 |
0.8772 |
10 |
0.9901 |
0.9804 |
0.9709 |
0.9524 |
0.9346 |
20 |
0.9950 |
0.9901 |
0.9852 |
0.9756 |
0.9662 |
The table shows that for situations with little missing information, only a small number of imputations are necessary. In practice, the number of imputations needed can be informally verified by replicating sets of imputations and checking whether the estimates are stable between sets (Horton and Lipsitz 2001, p. 246).