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How to see pooled results after multiple imputation?
When imputation markings are turned on, a special icon is displayed in front of the statistical test procedures in the analyze menu. This icon shows you if a pooled result will be generated after multiple imputation is used ( (Figure 5.1 )).
What happens when you combine multiple imputation with propensity scores?
We investigate whether two different approaches to combining propensity scores and multiple imputation (Across and Within) lead to differences in the accuracy or precision of exposure effect estimates. Both approaches start by imputing missing values multiple times.
What to do when data is split by imputation?
Once your data is split by the “imputation” variable, SPSS should recognize that your dataset is multiply imputed and provide pooled estimates automatically (for the analyses that support multiple imputation in SPSS). I hope that helps! First thank you for your unconditional help.
When do you use multiple imputation and PSS?
Given that missing data and confounding are commonly faced challenges in analyzing observational data, where multiple imputation is often used to address the former and PSs are becoming increasingly popular for the latter, researchers may often require both techniques in their analysis.
How to analyze SPSS Data after multiple imputation?
It is therefore recommended to visit the help index in SPSS to expplore what kind of pooled information is provided by SPSS before MI is applied. Analyze > Descriptive Statistics. Figure 5.3 shows that in the “Pooled” row the mean values of the Tampascale variable are pooled.
How to calculate tampascale values after multiple imputation?
Figure 5.6: b.T-test for difference in mean Tampascale values between patients with and without Radiation in the leg applied in multiple imputed datasets. The result in the original dataset (including missing values) is presented in the row that is indicated by Imputation_ number 0.
How is the chi square test used in multiple imputation?
The Chi-square test is presented in the original dataset and in each imputed dataset, but a pooled Chi-square value and pooled p-value is missing. This is remarkable because when you choose for Descriptive Statistics -> Crosstabs to conduct the Chi-square test the special Multiple Imputation icon is shown.