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?
Which is the difference between these two models? Are they both correct, or
is one better than the other?
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
(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
> 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.
Best Regards, Donald McLaren
D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General Hospital
Harvard Medical School
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