Federico,
yes, this is equal.
Volkmar
Quoting Federico Tubaldi <[log in to unmask]>:
>
> Many thanks to Rik and Volkmar for their helps.
> Maybe this reply is too technical for me so I have a dout yet. Put in simple
> words: Is SPM2 anova within subjects statistical comparable (even if not
> exactly the same) to SPM5 factorial design when I specify one factor with
> four conditions (a1b1, a1b2, a2b1, a2b2)?
>
> Best,
>
> Federico.
>
> -----Messaggio originale-----
> Da: Volkmar Glauche [mailto:[log in to unmask]]
> Inviato: marted́ 6 marzo 2007 10.45
> A: Federico Tubaldi
> Cc: [log in to unmask]
> Oggetto: Re: R: [SPM] POOLED and PARTITIONED error in SPM5
>
> Dear Federico,
>
> actually, Rik Henson pointed out that my reply was not quite correct in
> statistical terms. SPM uses the factor-wise error covariance specification
> to model a single, non-spherical error process at each level of statistics.
> If you want to take into account only some of your sources of error, you
> will have to use different levels of analysis by taking con- (i.e. effect
> size) images from a number of analyses into a new GLM.
> Hope, I got this right now...
>
> Volkmar
>
> On Mon, 5 Mar 2007, Federico Tubaldi wrote:
>
>> Thank you for your support.
>>
>> Federico.
>>
>> -----Messaggio originale-----
>> Da: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]]
>> Per conto di Volkmar Glauche
>> Inviato: luned́ 5 marzo 2007 10.13
>> A: [log in to unmask]
>> Oggetto: Re: [SPM] POOLED and PARTITIONED error in SPM5
>>
>> Dear Federico,
>>
>> you are absolutely right in your conclusions: The design matrix for
>> both models will be identical, but the 2-factor model is more general
>> in terms of error partitioning. You can still pool your error, if you
>> specify equal variance and indepence for all factors and levels, but
>> you can also partition your error for either factor a or b or even both
> factors.
>>
>> Volkmar
>>
>> On Sat, 3 Mar 2007, Federico Tubaldi wrote:
>>
>>> Dear Experts,
>>>
>>> In full factorial design option is it different to specify one factor
>>> with four levels (a1b1; a1b2; a2b1; a2b2) or two factors (A,B) with
>>> two levels
>>> (a1,a2 and b1,b2)? I think that in the former situation I have a
>>> pooled error as when I used one-way anova within subjects in spm2
>>> (spm does not know the factorial structure) while in the latter
>>> situation I have a partitioned error (spm knows the factorial
>>> structure). Is this
>> correct?
>>> I have ran the two models: the design matrixes are the same and the
>>> results are strongly similar. Is this plausible or should I get some
>>> differences for example in matrix structure?
>>>
>>> Thank you.
>>>
>>> Federico.
>>>
>>>
>>>
>>
>> --
>> Volkmar Glauche
>> -
>> Department of Neurology
> [log in to unmask]
>> Universitaetsklinikum Freiburg Phone 49(0)761-270-5331
>> Breisacher Str. 64 Fax 49(0)761-270-5416
>> 79106 Freiburg http://fbi.uniklinik-freiburg.de/
>>
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>>
>>
>
> --
> Volkmar Glauche
> -
> Department of Neurology [log in to unmask]
> Universitaetsklinikum Freiburg Phone 49(0)761-270-5331
> Breisacher Str. 64 Fax 49(0)761-270-5416
> 79106 Freiburg http://fbi.uniklinik-freiburg.de/
>
> --
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