Teach you how to use SPSS to perform multiple imputation for missing data

We often encounter the embarrassing situation of missing data in clinical research. Much missing data has affected our research results. Multiple imputation (MI) is a method used to fill missing values ​​in complex data. . It has appeared in many high-quality SCI papers in recent years.
Today we will demonstrate the SPSS multiple imputation function for missing data. First, open the data table, and you can see that there are many missing items.

We click Analyze-Multiple Imputation-Impute Missing Data Values ​​and

select all the variables to be imputed into the model. Generally, 5 data sets are imputed by default, SPSS will generate a new data set, we can give it a name, here is called data set 2

click OK, the new data set is generated, here you can see the interpolation method , Missing values ​​and imputation numbers. In the

upper right corner, you can see that a total of 5 sets of data have been generated. In the table, you can see that the yellow part is the SPSS inserted data.

Sometimes SPSS will report an error indicating that

we can change the measurement index name into a scale. After solving the problem

of how to analyze after generating 5 sets of data, you can put these 6 sets of data into the model and do it again, and then combine the effect value and the credible interval through R or stata. I won't go into details here. Those who are interested can Continue to follow my course.
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