Compare 2 conditions Conditions are MI LH (press left foot) and MI RH (press right foot); 8 subjects; threshold used is p 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
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
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