SPSS TUTORIALS BASICS ANOVA REGRESSION FACTOR CORRELATION

Regression over Many Dependent Variables

"I have a data file on which I'd like to carry out several regression analyses. I have four dependent variables, v1 through v4. The independent variables (v5 through v14) are the same for all analyses. How can I carry out these four analyses in an efficient way that would also work for 100 dependent variables?"

SPSS Python Syntax Example

*Run REGRESSION repeatedly over different dependent variables.

begin program.
import spss,spssaux
dependent = 'v1 to v4' # dependent variables.
spssSyntax = '' # empty Python string that we add SPSS REGRESSION commands to
depList = spssaux.VariableDict(caseless = True).expand(dependent) # create Python list of variable names
for dep in depList: # "+=" (below) concatenates SPSS REGRESSION commands to spssSyntax
spssSyntax += '''
REGRESSION
/MISSING PAIRWISE
/STATISTICS COEFF OUTS R ANOVA
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT %s
/METHOD=STEPWISE v5 to v14.
'''%dep # replace "%s" in syntax by by dependent var
print spssSyntax # prints REGRESSION commands to SPSS output window
end program.

*If REGRESSION commands look good, have SPSS run them.

begin program.
spss.Submit(spssSyntax)
end program.

Description

• That this syntax uses Python so you need to have the SPSS Python Essentials installed in order to run it;
• The syntax will simply run a standard SPSS regression analysis analysis over different dependent variables one-by-one;
• Except for the occurrence of `%s`, Python will submit to SPSS a textbook example of regression syntax generated by the GUI. It can be modified as desired.
• The TO and ALL keywords may be used for specifying the dependent and independent variables. The entire specification is enclosed in quotes.
• As a test file for this solution, you could use supermarket.sav.

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