Dear Matthias,

One should not compare parameter estimates between models that are not identical in structure. Therefore, if your two groups differ wrt. the best model, then this is what you should report: that different mechanisms best explain the data in both groups. Depending on your question, it may also make sense to statistically test parameter estimates within each group separately - but not between groups.

Best wishes,

Klaas

One should not compare parameter estimates between models that are not identical in structure. Therefore, if your two groups differ wrt. the best model, then this is what you should report: that different mechanisms best explain the data in both groups. Depending on your question, it may also make sense to statistically test parameter estimates within each group separately - but not between groups.

Best wishes,

Klaas

----- Ursprüngliche Mail ----

Von: Matthias Schurz <[log in to unmask]>

An: [log in to unmask]

Gesendet: Mittwoch, den 30. Juli 2008, 09:22:52 Uhr

Betreff: [SPM] DCM model comparisons with 2 groups

Dear DCM experts,

I’ve got a question about DCM in the context of a group study:

Within 2 groups, I’ve have compared the same models, and I’ve found that

group 1 favors model A and group 2 favors model B. Normally, one compares

the parameter estimates between the 2 groups - for example, I could look at

the parameters of model A for group 1 (where this model is suitable) and for

group 2 (where it’s not) and then do t-tests on the parameters. Does this

make sense? Are the parameters for group 2 meaningful, although there is a

better model? Or would you rather only report the model-parameters for the

group where the model is suitable?

Many thanks in advance,

Matthias

Von: Matthias Schurz <[log in to unmask]>

An: [log in to unmask]

Gesendet: Mittwoch, den 30. Juli 2008, 09:22:52 Uhr

Betreff: [SPM] DCM model comparisons with 2 groups

Dear DCM experts,

I’ve got a question about DCM in the context of a group study:

Within 2 groups, I’ve have compared the same models, and I’ve found that

group 1 favors model A and group 2 favors model B. Normally, one compares

the parameter estimates between the 2 groups - for example, I could look at

the parameters of model A for group 1 (where this model is suitable) and for

group 2 (where it’s not) and then do t-tests on the parameters. Does this

make sense? Are the parameters for group 2 meaningful, although there is a

better model? Or would you rather only report the model-parameters for the

group where the model is suitable?

Many thanks in advance,

Matthias

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