On Feb 24, 2012, at 12:57 PM, MCLAREN, Donald wrote:
> On 2/24/12, Paola Valsasina <
[log in to unmask]> wrote:
>> Dear Donald,
>> This thread that wasn't generated by me, but I am interested in this
>> question as well, in particular to question 1) from Stefania.
>> I always have the doubt on how to introduce categorical covariates when I
>> perform second level analyses in SPM.
>>
>> Suppose e.g. that I want to compare GM between controls and patients,
>> accounting for sex (categorical variable).
>> Is it better that I do a full factorial design with a single factor
>> (controls/patients) and I add as covariate a gender vector all made by
>> zeroes and ones, or that I do a full factorial design with two factors
>> (controls/patients and males/females), insert in four separate cells male
>> controls, female controls, male patients, female patients, and then perform
>> the comparison between controls and patients by setting up a contrast like 1
>> 1 -1 -1?
>
> The two models that you described are quite different. If you test the
> group differences in the first model, then you are testing the
> difference in controls males and patient males (e.g. the y-intercept
> of each group). You have assumed that the effect of being female is
> the same in both groups.
>
> In model 2, you allow the the effect of gender to be different in both groups.
>
> I'll also suggest another model, add a single covariate of 1s and -1s
> to the model with patients and controls. This model will then test
> whether the groups - if they were balanced for males/females -- are
> different from each other. This assumes the effect of being
> male/female is the same for both groups. This can be thought of as
> covariate-adjusted group means.
>
>
>>
>> Which is the difference between these two models? Are they both correct, or
>> is one better than the other?
>
> Depending on the assumptions that you want to make and what you would
> like to test, different models would be most appropriate. Model 2
> would allow you to also test for the interaction of group and gender,
> which might be preferable. If there is a reason why there is an
> imbalance in gender between males/females, then model 3 could be
> problematic because you are testing whether or not the groups are the
> same if the genders were balanced. My personal preference would be
> model 2.
>
> The most important thing in the process is the transparency. You need
> to clearly state the model that you test and the interpretation of the
> model -- whether it is testing the group means, the mean of the males,
> or the mean of the group as if the genders were balanced
> (covariate-adjusted group means).
>
> Hope this helps.
>
>> Thanks
>> Kind regards
>> Paola
>>
>>
>>
>> -----Messaggio originale-----
>> Da: SPM (Statistical Parametric Mapping) [mailto:
[log in to unmask]] Per
>> conto di MCLAREN, Donald
>> Inviato: venerdì 24 febbraio 2012 15.10
>> A:
[log in to unmask]
>> Oggetto: Re: [SPM] Use of covariates
>>
>> The answer depends on the model. If the models are the same, then
>> programs will produce the same results. If the models are different,
>> then the results will be different.
>>
>> The General Linear Model will always produce the same results!!! The
>> differences in results is due to different models.
>>
>> Since you are using a full factorial, I assume that this is a between
>> subject design. To get the same results as SPSS, you will want to set
>> the variance to be equal between levels of your factors. This will
>> suppress the variance correction in SPM.
>>
>> (1) All columns are treated the same way. If you have 4 columns for
>> ethnicity in SPSS, then you need 4 columns in SPM. If you have one
>> column in SPSS, then you need 1 column in SPM. When you say you have a
>> ordinal variable in SPSS, then it treats it as N variables (each
>> variable represents one group). You can also verify this by creating
>> the dummy variables yourself. There are a few variations on the model
>> that are equivalent (see examples in FSL).
>>
>> (2) Yes. You believe that the there variables account for variance in your
>> data.
>>
>> (3) Generally speaking, yes. However, in brain data it is more
>> complicated because not every covariate will be significant in every
>> voxel. My solution to this is to keep the variable in the model if
>> there are any significant effects of the variable.
>>
>>
>> On 2/24/12, Stefania Tognin <
[log in to unmask]> wrote:
>>> Dear SPM’ers,
>>> I have few questions regarding the use of covariates in the statistical
>>> analysis with SPM. It is not clear to me if covariates in SPM are treated
>>> the same as if using a stats software (e.g. SPSS).
>>> I want to perform an Full factorial ANOVA with SPM on structural data (1
>>> factor 3 levels) and I want to control for (“remove”) the effect of age
>>> (continuous variable), gender (categorical, 2 levels), handedness
>>> (categorical, 3 levels) and ethnicity (categorical, 4 levels).
>>> 1) Under what assumptions is it correct to use categorical nuisance
>>> variables (e.g. gender, handedness) given that using stats software as for
>>> example SPSS, covariates should be continuous and not categorical?
>>> 2) If variables as for example age, gender or handedness do not differ
>>> across groups it is still correct to remove their effects, assuming that
>>> being right-handed, left-handed or ambidextrous (or male and female, older
>>> or younger) could result in brain differences?
>>> 3) To justify the use of a covariate should I use statistical criteria
>> or
>>> theoretical criteria that take into account the fact that we are working
>>> with the human brain?
>>> Thank you very much and I do apologize if they sound very basic questions.
>>> Regards,
>>> Stefania
>>>
>>
>>
>> --
>> Best Regards, Donald McLaren
>> =================
>> D.G. McLaren, Ph.D.
>> Postdoctoral Research Fellow, GRECC, Bedford VA
>> Research Fellow, Department of Neurology, Massachusetts General Hospital
>> and
>> Harvard Medical School
>> Website:
http://www.martinos.org/~mclaren
>> Office:
(773) 406-2464
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>>
>
>
> --
> Best Regards, Donald McLaren
> =================
> D.G. McLaren, Ph.D.
> Postdoctoral Research Fellow, GRECC, Bedford VA
> Research Fellow, Department of Neurology, Massachusetts General Hospital
> and
> Harvard Medical School
> Website:
http://www.martinos.org/~mclaren
> Office:
(773) 406-2464
> =====================
> This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED
> HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is
> intended only for the use of the individual or entity named above. If the
> reader of the e-mail is not the intended recipient or the employee or agent
> responsible for delivering it to the intended recipient, you are hereby
> notified that you are in possession of confidential and privileged
> information. Any unauthorized use, disclosure, copying or the taking of any
> action in reliance on the contents of this information is strictly
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