I know this is a basic question but here goes:

I have categorical data, 0,1 which stands for incorrect (0) or correct (1) on a 
test item.

I have 25 items and I have a pretest and a posttest and I want to know on which 
items students improved significantly, and not just by chance. Just eyeballing 
the data I can tell that there are some on which the improved quite a bit, some 
not at all and some are someplace in the middle and I can't make a guess at 
all. That is why we have statistics. Yeah! .... hmmmm....bleh.....

As far as I know, the best thing to do is a chi-square test for each of 25 
items; but of course that will mean that with a .05 sig level I will have at 
least one false positive, maybe more, but most assuredly at least one. This 
seems to be a risk. At any rate I can use SPSS and the crosstabs command allow 
for calculation of the chi-square.

I know that when I do planned comparisons  with multiple t-tests, I can do a 
Simes' correction in which I can rank order my final, obtained alphas, and 
adjust for the number of comparisons and reject from the point from which the 
obtained alpha failed to exceed the corrected-for-number-of-comps alpha. But as 
far as I know, I cannot do that with 25 chi square tests. There is probably 
some reason why I can no more do that, that relates to the reason for why I 
cannot do 25 t-tests in this situation with categorical data.

Is there a better way to answer my research question? I need a major professor! 
Oh wait, that's me... drat! I need to hire a statistician. Oh wait, I'd need $$ 
for that and I don't have any. So I hope tipsters can stand in as a 
quasi-hired-statistician and help me out.

Oh, I get the digest. I don't mind waiting until tomorrow or the next day for a 
response, but a backchannel is fine. [email protected]

I will be at APS this year. Any other tipsters planning to be there? Let's have 
a party! I'd love to put personalities to names.

Thanks

Annette

Annette Kujawski Taylor, Ph. D.
Professor, Psychological Sciences
University of San Diego
5998 Alcala Park
San Diego, CA 92110
[email protected]
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