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RFX in SPM5. Floris de Lange [email protected]. RFX Options. Conditions are MI LH (press left foot) and MI RH (press right foot); 8 subjects; threshold used is p20 (arbitrary) Methods used: One-sample T-test on difference images MI LH>MI RH - PowerPoint PPT Presentation
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RFX Options
Compare 2 conditions
Conditions are MI LH (press left foot) and MI RH (press right foot); 8 subjects; threshold used is p<0.001 uncorrected, k>20 (arbitrary)
Methods used:
• One-sample T-test on difference images MI LH>MI RH
• Paired-samples T-test on MI LH and MI RH
•Measurements assumed independent
•Measurements assumed dependent
• Two-samples T-test on MI LH and MI RH
• Multiple regression analysis on MI LH and MI RH
• Full factorial
• Flexible factorial
The gold standard: one-sample T-test
Paired T-test: dependence/indepence
Independent: error covariance matrix = identity matrix (check SPM.xVi.V!)
Dependent: error covariance matrix will be estimated (check SPM.xVi.V!)
Paired-samples T-test dep.: same
Paired-samples T-test indep.: same
Dependence/independence doesn’t make a difference here, because there’s only one sample to estimate covariance from
= identical
Multiple regression analysis: same
Two-samples T test indep: worse
Degrees of freedom ↑
Variance term ↑
Two-samples T test dep: better
• the correlation between the variance of the subjects in the first group and those in the second group is estimated
• this reduces the error term
Two-samples T test: dep vs indep
Dependent measures Independent measures
Two-samples T test: con images
Dependent measures Independent measures=
Two-samples T test: ResMS images
Dependent measures Independent measures<Error terms is reduced for dependent measures by modelling the dependencies
Full factorial dep. = 2-sample T dep
Full factorial indep. = 2-sample T indep
Flex factorial dep. = 2-sample T dep
Flex factorial indep = 2-sample T indep
Summary
•There are two types of models:
• Models that specify the subject factor (e.g., one-sample, paired-samples, MRA if you specify the factor yourself)
• Models that estimate the subject factor (e.g., two-samples T-test, full factorial, flexible factorial; measurements are dependent)
• If you don’t specify the subject factor, but also don’t estimate the error covariance, you are likely to shoot yourself in the foot because the errors will be assumed to be independent, and simply added, leading to much higher estimates of the error term
Is it valid to use 2-sample T test dep?
• It can be statistically beneficial to specify the model as a “between-subjects” model without modelling subject, but instead estimating the subject-induced regularities by specifying that measures may be dependent
• SPM5 manual suggests to do analyses this way
• But is it valid? Aren’t df’s inflated?
SPM5 manual