Contents
How do you run multiple imputation in SPSS?
Analyze > Multiple Imputation > Impute Missing Data Values…
- Select at least two variables in the imputation model.
- Specify the number of imputations to compute.
- Specify a dataset or IBM® SPSS® Statistics-format data file to which imputed data should be written.
How much missing data is too much for Multiple Imputation?
Statistical guidance articles have stated that bias is likely in analyses with more than 10% missingness and that if more than 40% data are missing in important variables then results should only be considered as hypothesis generating [18], [19].
What is Multiple Imputation for missing data?
Multiple imputation is a general approach to the problem of missing data that is available in several commonly used statistical packages. It aims to allow for the uncertainty about the missing data by creating several different plausible imputed data sets and appropriately combining results obtained from each of them.
How can one create a pooled dataset in SPSS for further?
You can use the “splitdata.sav” file for further analysis directly and SPSS should take care of pooling. Click Data -> Split File. In the dialog box select “Imputation_” and move it to “Groups Based On:” box. Check “Compare Groups”.
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 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.