[ADJUST THE EXAMPLE INPUT AS DESIRED; THE COPY AND PASTE INTO A SPSS SYNTAX WINDOW AND RUN; OUTPUT BOTH TO SPREADSHEET AND OUTPUT WINDOW].

This program uses R-squared, the multiple correlation, as the effect size, which may also be expressed as Cohen's f= R-sq/(1-R-sq) (see http://en.wikipedia.org/wiki/Effect_size#Cohen.27s_d) as an effect size in regressions including one-way anovas as a special case.

From Cohen(1977, 1992) it follows R-squareds of 0.01, 0.0588 and 0.138 are suggested conventions for small, medium and large effect size.

Alpha is the type I error, df of the effect is the total number of degrees of freedom for the effect of interest, df of confounders is the total number of degrees of freedom of other predictors in the regression, ntot is the total sample size and rsq is the multiple correlation. The program then outputs the power. Power computation may also be done using a [attachment:regi.xls spreadsheet.]

df for each predictor equal k -1 for group predictor with k levels or 1 for each continuous variable.

DATA LIST free
/alpha dfreg dfc ntot rsq. 
BEGIN DATA. 
.05 2 0 40  0.3
END DATA. 
matrix.
get m /variables=alpha g ntot rsq  /missing=omit.
compute alpha=make(1,1,0).
compute dfreg=make(1,1,0).
compute dfc=make(1,1,0).
compute ntot=make(1,1,0).
compute rsq=make(1,1,0).
compute alpha=m(:,1).
compute dfreg=m(:,2).
compute dfc=m(:,3).
compute ntot=m(:,4).
compute rsq=m(:,5).  
end matrix.
COMPUTE power = 1 - NCDF.F(IDF.F(1-ALPHA,DFREG,NTOT-DFREG-DFC-1),DFREG,NTOT-DFREG-DFC-1,NTOT*RSQ/(1-RSQ)).
EXE.
formats ntot (f7.0) alpha (f5.2) dfreg (f5.2) dfc (f5.2) rsq (f5.2) power (f5.2).
variable labels ntot 'Total Sample Size' /alpha 'Alpha' /dfreg 'Df effect' /dfc 'Df confounders' /rsq 'R-squared' /power 'Power'.
report format=list automatic align(center)
  /variables=ntot alpha dfreg dfc rsq power 
  /title "Power in a multiple regression for given total sample size" .