Not accurately. See below.

The test statistics and effect sizes are computed with two components:

(1) The contrast (numerator) of the effect size will be correct both with the paired t-tests and ANOVA (if you use the flexible factorial).

and

(2) The variance term (denominator) of the effect size, which will only be correct using the paired t-test (all tests) or ANOVA/flexible factorial for the time and interaction effects. For the main effect of group, the ANOVA model will give the wrong variance term. The issue is that SPM only generates the within-subject error term in repeated-measure designs and you need the between-subject error/variance for the main effect effect size.

Thus, you should stick with the series of paired t-tests.

Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Research Fellow, Department of Neurology, Massachusetts General Hospital and
Harvard Medical School
Postdoctoral Research Fellow, GRECC, Bedford VA
Website: http://www.martinos.org/~mclaren
Office: (773) 406-2464
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On Mon, Jun 8, 2015 at 10:31 AM, María Díez Cirarda <[log in to unmask]> wrote:
Dear Helmut, 

Thank you very much for your prompt response. 
We have already calculated separately paired t tests for each group. However, the objetive of calculating ANOVA 2x2 main effect group and time, was to be able to calculate the effect size of ANOVA 2x2 interaction. We need those data, to calculate the effect size of the interaction anova. 

Is there any way to calculate it?

Thank you in advance, 

Maria. 

2015-06-05 15:12 GMT+02:00 H. Nebl <[log in to unmask]>:
Dear Maria,

As you have a mixed-design ANOVA I would suggest to construct as many contrasts as possible on single-subject level and forward these into two-sample t-tests (in your case) to ensure correct error terms and statistical validity. The procedure is maybe confusing (two different models for an ANOVA), but it reflects partioned error terms, which is also what we usually rely on for behavioral analyses with programs like SPSS. See Henson & Penny (2005, http://www.fil.ion.ucl.ac.uk/~wpenny/publications/rik_anova.pdf ) or Henson (2015, http://www.mrc-cbu.cam.ac.uk/wp-content/uploads/2015/03/Henson_EN_15_ANOVA.pdf ) on that issue.

Assuming your single-subject models include two sessions (pre and post), then two contrasts would be required, one for the average task effect (e.g. [1/2 ... 1/2 ...] with ... indicating zero-padding, averaging across pre condition 1 and post condition 1), and one for the differenctial task effect (e.g. [1/2 ... -1/2 ...]).

You would then set up two different two-sample t-tests,
#1 for the average task effect based on e.g. the con_0001 images: within that model set up F contrast [1 1] for average task activations, set up [1 -1] for main effect group.
#2 for the differential task effect based on e.g. the con_0002 images: within that model set up F contrast [1 1] for main effect time, set up [1 -1] for interaction group x time.
If you prefer T contrasts you would have to use the same vectors but also set up contrasts "into the other direction".

Alternatively, you could also work with GLM flex for group models, this package allows to set up ANOVAs with partioned error terms. Results should be identical to those obtained with the procedure described above.

Best

Helmut



--
Lda. María Díez Cirarda
Dpto. de Fundamentos y Métodos de la Psicología
Facultad de Psicología y Educación
Universidad de Deusto
Avda. de las Universidades 24, 48007, Bilbao
944139000 (ext 3212)