__Spreadsheet and SPSS macro inputs__
 
 * alpha is the likelihood of making a type I error (usually = 0.05)

 * etasq is partial $$\eta^text{2}$$/100 so, for example, 5.9% = 0.059

Partial $$\eta^text{2}$$ = 
$$ \frac{\mbox{SS(effect)}}{\mbox{SS(effect) + SS(its error)}}$$
where SS is the sum of squares associated with a particular term in the anova.

In other words, partial $$\eta^text(2)$$ represents the proportion of variance in outcome predicted by the effect after adjusting for other terms in the anova. 
Click [[attachment:etasqrp.pdf|here]] for further details on partial $$\eta^text{2}$$ and [[attachment:etasq.pdf|here.]] If SS are not available you can [[FAQ/power/rsqform| construct eta-squared]].

 * correlation = average correlation between repeated measures levels

 * num(erator) = df of term of interest = the product of the (number of levels of each factor -1) in term of interest

 * sum (B-1) = sum of dfs involving '''only''' between subject factors in anova or zero otherwise. df = Product of number of levels minus 1 of each between subject factor in term of interest. e.g. For a three way interaction involving three between subject factors, abc, we sum the dfs of the six lower order combinations: ab, ac and bc, a, b and c to that of abc.

 * Prod (W-1) = df of within subject effect if in term of interest or 1 otherwise. df = Product of number of levels minus 1 of each within subject factor in term of interest

 * Prod W = product of all within subject levels or one if there are none.

 * power is the power of the test

[[FAQ/power/powexample| Example input]]

Computation may be performed using an EXCEL [[attachment:anovan2.xls|spreadsheet]] or the below SPSS syntax. Power analysis software using Winer (1991, pp 136-138) for balanced anovas may be downloaded from [[http://www.soton.ac.uk/~cpd/anovas/datasets/|here]] with details of how to compute inputs [[FAQ/Doncaster| here.]]

[ COPY AND PASTE THE BOXED BELOW SYNTAX BELOW INTO A SPSS SYNTAX WINDOW AND RUN; ADJUST INPUT DATA AS REQUIRED]

{{{
DATA LIST free
/alpha etasq num bsum wdf corr power. 
BEGIN DATA. 
.05 0.059 2 1 2 0.3 0.85
.05 0.059 2 1 2 0.0 0.85
END DATA.

matrix.
get m /variables=alpha etasq num bsum wdf corr power  /missing=omit.
compute alpha=make(1,1,0).
compute etasq=make(1,1,0).
compute num=make(1,1,0).
compute bsum=make(1,1,0).
compute wdf=make(1,1,0).
compute corr=make(1,1,0).
compute power=make(1,1,0).
compute alpha=m(:,1).
compute etasq=m(:,2).
compute num=m(:,3).
compute bsum=m(:,4).
compute wdf=m(:,5).
compute corr=m(:,6).
compute power=m(:,7).
end matrix.


define apow   (!pos !tokens(1)
                   / !pos !tokens(1)
                   / !pos !tokens(1)
                   / !pos !tokens(1)
                   / !pos !tokens(1)
                   / !pos !tokens(1) 
                   / !pos !tokens(1)).

COMPUTE #POW = !7.

compute #conf = (1-!1).
compute #lc3 = 1.
compute #ind=0.
compute ntot = 700.000.
comment COMPUTE #LC1 = 2.000.
compute denom=(ntot-1-!4)*!5.
COMPUTE #CUMF2 = 1 - NCDF.F(IDF.F(#conf,!3,denom),!3,denom,(denom*(!2/(1-!2)))/(1-!6)).
COMPUTE #DIFF=1.
SET MXLOOPS=10000.
LOOP IF (#DIFF GT .00005) .
+	DO IF (#CUMF2 GT #pow) .
+		COMPUTE #LC3 = ntot.
+		COMPUTE ntot = (Ntot - rnd(1)).
+                      COMPUTE denom=(ntot-1-!4)*!5.                      
+		COMPUTE #CUMF2 = 1 - NCDF.F(IDF.F(#conf,!3,denom),!3,denom,(denom*(!2/(1-!2)))/(1-!6)).
+	ELSE .
+		COMPUTE #LC1 = ntot.
+		COMPUTE ntot = (ntot + #LC3)/2.
+                      COMPUTE denom=(ntot-1-!4)*!5.
+		COMPUTE #CUMF2 = 1 - NCDF.F(IDF.F(#conf,!3,denom),!3,denom,(denom*(!2/(1-!2)))/(1-!6)).
+          END IF. 
+	COMPUTE #DIFF = ABS(#CUMF2 - #pow) .
END LOOP .
compute pow2 = 1 - NCDF.F(IDF.F(#conf,!3,denom),!3,denom,(denom*(!2/(1-!2)))/(1-!6)).
if (ntot-trunc(ntot) gt 0.5) #ind=1.
if (#ind eq 0) ntot=trunc(ntot)+1.
if (#ind eq 1) ntot=rnd(ntot).
EXECUTE .
compute alpha=!1.
compute etasq=!2.
compute num=!3.
compute bsum=!4.
compute wdf=!5.
compute corr=!6.
compute power=!7.
compute denom=(ntot-1-bsum)*wdf.
formats ntot (f7.0) alpha (f5.2) num (f5.2) denom (f5.2) etasq (f5.2) corr (f5.2) power (f5.2).
variable labels ntot 'Total Sample Size Required' /alpha 'Alpha' /num 'Numerator' /denom 'Denominator' /etasq 'Partial Eta-Sq' /corr 'Correlation' /power 'Power'.
report format=list automatic align(center)
  /variables=ntot alpha num denom etasq corr power 
  /title "Anova term sample size for given power (any anova)" .
!enddefine.
matrix.
get m /variables=alpha etasq num bsum wdf corr power  /missing=omit.
compute alpha=make(1,1,0).
compute etasq=make(1,1,0).
compute num=make(1,1,0).
compute bsum=make(1,1,0).
compute wdf=make(1,1,0).
compute corr=make(1,1,0). 
compute power=make(1,1,0).
compute alpha=m(:,1).
compute etasq=m(:,2).
compute num=m(:,3).
compute bsum=m(:,4).
compute wdf=m(:,5).
compute corr=m(:,6).
compute power=m(:,7).
end matrix.
apow alpha etasq num bsum wdf corr power.
}}}
 
__Reference__

Doncaster CP and Davey AJH (2007) Analysis of covariance. How to choose and construct models for the life sciences. CUP:Cambridge.

Winer BJ, Brown DR and Michels KM (1991) Statistical principles in experimental design, 3rd edition. McGraw-Hill:New York.