SPLIT FILE
SPLIT FILE [{LAYERED, SEPARATE}] BY VAR_LIST.
SPLIT FILE OFF.
SPLIT FILE allows multiple sets of data present in one data file to
be analyzed separately using single statistical procedure commands.
Specify a list of variable names to analyze multiple sets of data separately. Groups of adjacent cases having the same values for these variables are analyzed by statistical procedure commands as one group. An independent analysis is carried out for each group of cases, and the variable values for the group are printed along with the analysis.
When a list of variable names is specified, one of the keywords
LAYERED or SEPARATE may also be specified. With LAYERED, which
is the default, the separate analyses for each group are presented
together in a single table. With SEPARATE, each analysis is
presented in a separate table. Not all procedures honor the
distinction.
Groups are formed only by adjacent cases. To create a split using a variable where like values are not adjacent in the working file, first sort the data by that variable.
Specify OFF to disable SPLIT FILE and resume analysis of the
entire active dataset as a single group of data.
When SPLIT FILE is specified after TEMPORARY, it
affects only the next procedure.
Example
The file horticulture.sav contains data describing the yield of a
number of horticultural specimens which have been subjected to various
treatments. If we wanted to investigate linear statistics of the
yeild, one way to do this is using
DESCRIPTIVES. However, it is
reasonable to expect the mean to be different depending on the
treatment. So we might want to perform three separate procedures --
one for each treatment.1 The following syntax shows how this can be
done automatically using the SPLIT FILE command.
get file='horticulture.sav'.
* Ensure cases are sorted before splitting.
sort cases by treatment.
split file by treatment.
* Run descriptives on the yield variable
descriptives /variable = yield.
In the following output, you can see that the table of descriptive
statistics appears 3 times—once for each value of treatment. In this
example N, the number of observations are identical in all splits.
This is because that experiment was deliberately designed that way.
However in general one can expect a different N for each split.
Split Values
┌─────────┬───────┐
│Variable │ Value │
├─────────┼───────┤
│treatment│control│
└─────────┴───────┘
Descriptive Statistics
┌────────────────────┬──┬─────┬───────┬───────┬───────┐
│ │ N│ Mean│Std Dev│Minimum│Maximum│
├────────────────────┼──┼─────┼───────┼───────┼───────┤
│yield │30│51.23│ 8.28│ 37.86│ 68.59│
│Valid N (listwise) │30│ │ │ │ │
│Missing N (listwise)│ 0│ │ │ │ │
└────────────────────┴──┴─────┴───────┴───────┴───────┘
Split Values
┌─────────┬────────────┐
│Variable │ Value │
├─────────┼────────────┤
│treatment│conventional│
└─────────┴────────────┘
Descriptive Statistics
┌────────────────────┬──┬─────┬───────┬───────┬───────┐
│ │ N│ Mean│Std Dev│Minimum│Maximum│
├────────────────────┼──┼─────┼───────┼───────┼───────┤
│yield │30│53.57│ 8.92│ 36.30│ 70.66│
│Valid N (listwise) │30│ │ │ │ │
│Missing N (listwise)│ 0│ │ │ │ │
└────────────────────┴──┴─────┴───────┴───────┴───────┘
Split Values
┌─────────┬───────────┐
│Variable │ Value │
├─────────┼───────────┤
│treatment│traditional│
└─────────┴───────────┘
Descriptive Statistics
┌────────────────────┬──┬─────┬───────┬───────┬───────┐
│ │ N│ Mean│Std Dev│Minimum│Maximum│
├────────────────────┼──┼─────┼───────┼───────┼───────┤
│yield │30│56.87│ 8.88│ 39.08│ 75.93│
│Valid N (listwise) │30│ │ │ │ │
│Missing N (listwise)│ 0│ │ │ │ │
└────────────────────┴──┴─────┴───────┴───────┴───────┘
Example 13.3: The results of running DESCRIPTIVES with an active split
Unless TEMPORARY was used, after a split has been defined for a
dataset it remains active until explicitly disabled.
-
There are other, possibly better, ways to achieve a similar result using the
MEANSorEXAMINEcommands. ↩