Thanks again! so then I will go along with setting up the analysis
without covariates :)
cheers,
Alex
Michael Harms wrote:
> Yes, I understand that you were inquiring about controlling for gender
> across subjects (gender presumably isn't changing within a subject
> unless you're conducting a study involving sex change operations!). But
> in a repeated measures design, in which you have an EV with a column of
> 1's at your 4 timepoints for each subject (to model the mean of that
> specific subject), and you have such an EV for each subject, then your
> gender EV can always be expressed as linear combination of the subject
> specific mean EVs, and thus you would have a rank deficient design
> matrix. The same reasoning applies to age if you were to use the same
> age value at all 4 timepoints for a given subject.
>
> cheers,
> -MH
>
> On Thu, 2011-05-12 at 20:14 +0200, Alexander Olsen wrote:
>
>> Ok. I see. However, I don't thing this was what I wanted to do. What I
>> want to do is to control for age and gender differences between
>> subjects, not within subjects (e.g. control for the fact that I have
>> more younger than older participants, look at gender*time effects etc.).
>> Sorry if I'm not being very clear.
>>
>> Alexander
>>
>> Michael Harms wrote:
>>
>>> You can't covary for gender in your repeated measure model, for the
>>> reason that I explained. Try including a gender covariate and you'll
>>> find that your design matrix will be rank deficient. As for age, it
>>> would only be an option to consider if you want to use a different value
>>> for age for each repeated visit of a subject, and even then I think
>>> you'll get a poorly conditioned design matrix unless your age at the
>>> repeated visits for a subject differed in an appreciable manner (where
>>> "appreciable" would be relative to the variation in ages across
>>> subjects).
>>>
>>> cheers,
>>> -MH
>>>
>>> On Thu, 2011-05-12 at 19:44 +0200, Alexander Olsen wrote:
>>>
>>>
>>>> Hi Michael,
>>>> Thank you so much for helping me. So that means that I should set up the
>>>> design matrices without any covariates for age and gender?
>>>>
>>>> cheers,
>>>> Alexander
>>>>
>>>>
>>>> Michael Harms wrote:
>>>>
>>>>
>>>>> Hi Alexander,
>>>>> Given that you are modeling the mean of each subject using a repeated
>>>>> measures design, that mean already incorporates that subject's age and
>>>>> gender as part of its estimate. That is, if you added EVs for age
>>>>> and/or gender, those EVs can be represented as a linear combination of
>>>>> the columns modeling each subject's overall mean, and thus you would get
>>>>> a degenerate design matrix. (This wouldn't strictly be true for age IF
>>>>> the value you used for age differed across the repeated scans of a
>>>>> subject, but unless your scans were spaced far apart temporally, you
>>>>> still might get a poorly conditioned design matrix).
>>>>>
>>>>> cheers,
>>>>> -MH
>>>>>
>>>>> On Thu, 2011-05-12 at 01:12 +0100, Alexander Olsen wrote:
>>>>>
>>>>>
>>>>>
>>>>>> Dear FLS experts,
>>>>>>
>>>>>> I'm struggling a bit with setting up my analysis. What I would like to do is a repeated measures design with four timepoints, as in this example:
>>>>>>
>>>>>> http://www.fmrib.ox.ac.uk/fsl/feat5/detail.html#ANOVA1factor4levelsRepeatedMeasures
>>>>>>
>>>>>> However, I would also like to add age and gender as covariates in order to "remove" these effects. I hope anyone can help me, or direct me to an example which describes this.
>>>>>>
>>>>>> Thank you so much in advance.
>>>>>>
>>>>>> Alexander
>>>>>>
>>>>>>
>>>>>>
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