Yes. In more general terms:
If you are comparing a single beta to 0, you will want to use a one-sample t-test. This is irrespective if it represents condition1 versus implicit baseline or condition1-condition2. It only matters if its a single beta (or group of beta tested against 0 [e.g. (g1c1+g1c2+g1c3)/3]). In other words, if the comparison is not contrasting betas of two or more conditions against each other, then you need to collapse observations and use a one-sample t-test.
If you are comparing the beta for group1 condition 1 to group2 condition1, you will want a two-sample t-test. This is irrespective if it represents condition1 versus implicit baseline or condition1-condition2. It only matters if its a two betas (or group of betas are comparing group effects of the same betas [e.g. (g1c1+g1c2+g1c3)/3 versus(g2c1+g2c2+g2c3)/3 ]). In other words, if the comparison is not contrasting betas of two or more conditions against each other, then you need to collapse observations and use a two-sample t-test.
etc.

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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On Wed, Feb 29, 2012 at 5:05 PM, Bob Spunt <[log in to unmask]> wrote:

Thanks Donald. Just to clarify, I assume that when you say between-subject effects you mean both (a) the betas for each regressor (i.e., condition compared to the implicit baseline) (in this case use a one-sample t-test), and (b) main effects of group (in this case use two-sample t-test or ANOVA). Or use GLM_flex, of course. Is this correct? 

Thanks again,

Bob

On Wed, Feb 29, 2012 at 1:51 PM, MCLAREN, Donald <[log in to unmask]> wrote:

Bob,

You are absolutely correct that you don't need the main effects in the model; however, I have noticed small changes in the effects when comparing including them versus not including them. Best I can tell -- their inclusion/exclusion effects either the omnibus test used to decide which voxels to include OR the REML estimation of the variance-covariance pattern.

As an additional note, the main effects of between-subject factors are invalid because the error term and degrees of freedom are incorrect in the full and flexible factorial models. To properly estimate the between-subject effects you need to remove the repeated observations and use a one-sample t-test, two-sample t-test or ANOVA. Alternatively, GLM_flex properly estimates all effects (between and within-subjects) in a single model.

One other note in the tutorial, the main effect of condition is actually a linear trend contrast, rather than the main effect. The main effect F-contrast should have N-1 rows where N is the number of levels.


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
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On Wed, Feb 29, 2012 at 4:36 PM, Bob Spunt <[log in to unmask]> wrote:

Dear SPM experts,

I have a quick question regarding deciding which effects to include in a flexible factorial model. Consider the one-group case discussed in the Gläscher and Gitelman tutorial, which discusses a 2x3 within-subjects design (Pages 4-6). My question regards when to include the main effects in the model in addition to the interaction and subject effects, since it seems that all effects (namely, the two main effects and their interaction) can be tested in a model which includes only the interaction and subject effects. If this is indeed the case, then why would one ever include all effects in the model? 

Any light-shedding is much appreciated. 

Cheers,

Bob

-------------------------------------------------------------------------------

Bob Spunt
Postdoctoral Fellow

Social Cognitive and Affective Neuroscience Labs
Department of Psychology
University of California, Los Angeles