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How are multiple imputation and pooling parameter estimates used?
Analyses are done on all five datasets and then the estimates/parameters from the analysis are averaged to get a pooled estimate. In that circumstance, I’m not sure where mode would be used… 2) Original values are not used when pooling estimates, only the imputed datasets are used. I hope this is helpful! Best.
Is it easy to calculate pooled standard error?
The pooled estimate of the regression coefficients is easy. Calculating the pooled estimate of the standard error is a bit more tricky, but still relatively simple.
How to do pooled data analysis after multiple imputation?
Information is provided in the row called Pooled about the parameter estimates, i.e. regression coefficients, standard errors, t-values, p-values and confidence interval. Further, information is provided about the Fraction of Missing Information, Relative Increase Variance and Relative Efficiency.
When to use pooled standard deviation in statistics?
Note that the pooled standard deviation should only be used when the standard deviations between the two groups can be assumed to be roughly equal. Also note that because the pooled standard deviation is a weighted average, it will give more “weight” to the group with the larger sample size.
How to perform multiple imputation using SPSS v.19.0.1?
I am using (or trying to use) SPSS v. 19.0.1 to perform multiple imputation. Because SPSS seems to provide only some pooled results (e.g., for t, unstandardized b), how would I go about obtaining other pooled parameter estimates (e.g., F statistic, B, R square change, confidence intervals, df’s, partial eta squared, etc)?
What are the next steps after multiple imputation?
After Multiple Imputation has been performed, the next steps are to apply statistical tests in each imputed dataset and to pool the results to obtain summary estimates. In SPSS and R these steps are mostly part of the same analysis step.