Hi James M. Clark Professor of Psychology 204-786-9757 204-774-4134 Fax [EMAIL PROTECTED]
>>> "Marc Carter" <[EMAIL PROTECTED]> 04-Apr-07 9:26:35 AM >>> I am also getting a sense that the field is moving away from factorial ANOVA and more into somewhat more straightforward regression models with interaction terms in them. Pedagogically, it seems to me that regression's an easier conceptual model than ANOVA (though quantitatively they're the same model). Why aren't we teaching that? Why are we teaching them things that are so badly out-of-date? In my honours stats class I do first teach regression and then anova (I have the luxury of a full year course). And when we start anova I demonstrate the equivalence between the two approaches. But it is incorrect to think that everything is as easy to do via the regression approach as via the anova approach (which Marc correctly points out are equivalent). One major area that anova wins hands down is with repeated measures designs, a common situation in psychological research. In addition to the initial (relatively minor) problem of generating subject vectors for large numbers of subjects (or using subject means and adjusting df, as per Kerlinger), factorial and follow-up analyses for within-s factors generally involve unique error terms as well as partitioning of the numerators. This is less readily accommodated by regression than by anova. There are undoubtedly other topics (e.g., the post hoc procedures that started this discussion) that are generally less available in regression packages than anova programs. Take care Jim --- To make changes to your subscription go to: http://acsun.frostburg.edu/cgi-bin/lyris.pl?enter=tips&text_mode=0&lang=english
