Our tutorials reference a dataset called "sample" in many examples. If you'd like to download the sample dataset to work through the examples, choose one of the files below:
When analyzing data, it is sometimes useful to temporarily "group" or "split" your data in order to compare results across different subsets. This can be useful when you want to compare frequency distributions or descriptive statistics with respect to the categories of some variable (e.g., Gender) - especially if you want separate tables of results for each group.
To split your dataset, click Data > Split File.
SPSS Version 25 Drop-Down Menu
SPSS Version 22 Drop-Down Menu
The Split File window will appear. By default, the dataset is not split according to any criteria; this is indicated by Analyze all cases, do not create groups.
You can choose one of two ways to split the data:
For both splitting methods, there are two considerations to be made:
When you no longer want to split your analyses by group, you can turn Split File off through the same window you used to turn it on.
You can now run all analyses normally again.
SPLIT FILE OFF.
What are the differences in the split file options?
The Compare and Organize options produce numerically identical results when the same grouping variable(s) are applied. This is true regardless of what statistical analysis is used. The difference between the two options is how the numeric results are presented.
The choice of which splitting method to use is entirely about what format the user wants their results in. Do you want a single table with all results, or separate tables for each group's results? A good rule of thumb is to choose Compare Groups if you want to be able to directly compare the results of your groups, and to choose Organize Output by Groups if the information is from separate trials or samples (such as cohorts from different years).
Suppose that we want to get a summary of the differences in height between males and females in the sample data. Let's couple the Split File procedure with the Descriptives procedure to get summary statistics for the two groups. We'll use both Split File methods so that we can compare what their outputs look like.
If you choose to split your data using the Compare groups option and then run a statistical analysis in SPSS, your output will be displayed in a single table that organizes the results according to the grouping variable(s) you specified.
To split the data in a way that will facilitate group comparisons:
After splitting the file, the only change you will see in the Data View is that data will be sorted in ascending order by the grouping variable(s) you selected.
Now let's view the aforementioned descriptive statistics for the variable Height with respect to Gender. Select Analyze > Descriptive Statistics > Descriptives. Double click on the Height variable, then click OK.
SORT CASES BY Gender.
SPLIT FILE LAYERED BY Gender.
DESCRIPTIVES VARIABLES=Height
/STATISTICS=MEAN STDDEV MIN MAX.
This table gives us a breakdown of how many observations were in each group (N), and the minimum, maximum, average, and standard deviation of each group. The '.' group contains cases with missing gender values and nonmissing height values. At a glance, we can quickly take note that in this sample:
Note: This combination of Split File: Compare Groups with Descriptives is very similar to what you would get with the Compare Means procedure. The major difference is that Split File includes the missing values in the grouping/splitting variable, whereas Compare Means excludes missing values in the grouping variable.
If you choose to split your data using the Organize output by groups option and then run a statistical analysis in SPSS, your output will be broken into separate tables for each category of the grouping variable(s) specified.
To split the data in a way that separates the output for each group:
After splitting the file, the only change you will see in the Data View is that data will be sorted in ascending order by the grouping variable(s) you selected.
Now we will re-run the same descriptive statistics procedure that we ran before. You can go through the menu system again (Analyze > Descriptive Statistics > Descriptives), or you can click on the Recall recently used dialogs icon, which will bring up a list of recently used procedures:
SORT CASES BY Gender.
SPLIT FILE SEPARATE BY Gender.
DESCRIPTIVES VARIABLES=Height
/STATISTICS=MEAN STDDEV MIN MAX.
After re-running the descriptive statistics, we see that the output is broken into three sections based on values of the Gender variable. The first section (“Gender = .”) reports the minimum, maximum, average, and standard deviation of Height for the students who had missing values for Gender. The second section reports those same statistics for the male students; the third section reports the statistics for the females.