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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 ===================== 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 prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email.