562013-03-08 10:18:01localhostconverted to 1.6 markup552009-01-28 10:32:51PeterWatson542009-01-28 10:31:49PeterWatson532009-01-28 10:30:44PeterWatson522009-01-28 10:30:06PeterWatson512009-01-28 10:25:06PeterWatson502009-01-28 10:23:11PeterWatson492009-01-28 09:54:19PeterWatson482009-01-27 16:31:57PeterWatson472009-01-27 16:30:48PeterWatson462008-09-25 13:35:49PeterWatson452008-09-25 10:46:07PeterWatson442008-09-25 10:45:47PeterWatson432008-09-25 10:42:49PeterWatson422008-09-25 10:40:37PeterWatson412008-09-25 10:39:51PeterWatson402008-09-25 10:36:59PeterWatson392008-09-25 10:34:20PeterWatson382008-09-25 09:09:29PeterWatson372008-09-25 09:07:55PeterWatson362008-09-24 16:21:48PeterWatson352008-09-24 16:21:31PeterWatson342008-09-24 16:14:33PeterWatson332008-09-24 16:09:10PeterWatson322008-09-24 16:08:48PeterWatson312007-09-05 12:54:26PeterWatson302007-09-05 12:54:06PeterWatson292007-09-05 12:48:17PeterWatson282007-09-05 09:17:36PeterWatson272007-09-04 15:31:55PeterWatson262007-09-04 15:31:18PeterWatson252007-09-04 15:22:21PeterWatson242007-03-20 15:12:50PeterWatson232007-03-06 15:06:33PeterWatson222007-03-06 15:00:59PeterWatson212007-03-06 15:00:09PeterWatson202007-02-19 17:21:30PeterWatson192007-02-19 17:21:04PeterWatson182007-02-19 17:20:51PeterWatson172007-01-15 17:11:35PeterWatson162007-01-15 10:05:05PeterWatson152007-01-15 10:04:39PeterWatson142006-12-08 10:31:40PeterWatson132006-12-08 10:31:24PeterWatson122006-12-06 13:11:58PeterWatson112006-12-06 13:11:33PeterWatson102006-11-30 16:00:42PeterWatson92006-10-17 14:53:55PeterWatson82006-10-17 14:43:04PeterWatson72006-10-17 14:32:15PeterWatson62006-10-05 15:19:04PeterWatson52006-10-05 15:18:40PeterWatson42006-10-05 15:18:30PeterWatson32006-10-05 15:11:52PeterWatson22006-10-05 15:09:58PeterWatson12006-10-03 11:14:59PeterWatsonSpreadsheet 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 represents 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 here for further details on partial $$\eta^text{2}$$ and here. If the Sums of Squares are not available you canconstruct eta-squared. 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 the 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 = The product of all the ithin subject levels in term of interest corr is the average correlation between levels of the repeated measures (=0 if no within subjects factors) Total sample size Example input Power can be computed using an EXCEL spreadsheet or the SPSS syntax below. Power analysis software using Winer (1991, pp 136-138) for balanced anovas may be downloaded from here with details of how to compute inputs here. [ COPY AND PASTE THE SYNTAX IN THE BOX BELOW INTO A SPSS SYNTAX WINDOW AND RUN; ADJUST INPUT DATA AS REQUIRED] 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.