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How to do data analysis after multiple imputation?
Figure 5.5: T-test for difference in mean Tampascale values between patients with and without Radiation in the leg applied in multiple imputed datasets. 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.
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.
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.
What happens when a dataset is multiple imputed?
Than SPSS recognizes the dataset as an “imputed” dataset and is able to generate pooled analyses results. If SPSS does not recognize the dataset as a multiple imputed dataset, the data will be treated as one large dataset.
How is multiple imputation used in stochastic imputation?
Multiple imputation is essentially an iterative form of stochastic imputation. However, instead of filling in a single value, the distribution of the observed data is used to estimate multiple values that reflect the uncertainty around the true value.
How can I interpret GAM results for regression?
This can happen where the GCV profile is relative flat and random variation can lead to the algorithm converging at too wiggly a fit. Fitting via REML (use method = “REML” in the gam () call) or ML has been shown by Simon Wood and colleagues to be much more robust to under smoothing, but at computational expense.