Hi On Tue, 18 Dec 2001, Benjamin Kenward wrote: > Let's say you have a repeatable experiment and each time the result can be > classed into a number of discrete categories (in this real case, seven). > If a treatment has no effect, it is known what the expected by chance > distribution of results between these categories would be. I know that a > good test to see if a distribution of results from a particular treatment > is different to the expected by chance distribution is to use a > chi-squared test. What I want to know is, is it valid to compare just one > category? In other words, for both the obtained and expected > distributions, summarise them to two categories, one of which is the > category you are interested in, and the other containing all the other > categories. If the chi-square result of the comparison of these categories > is significant, can you say that your treatment produces significantly > more results in particularly that category, or can you only think of the > whole distribution?
Yes, this is equivalent to planned contrasts (assuming it was planned) in ANOVA. In ANOVA with the critical condition as the last one, the contrast would be -1 -1 -1 -1 -1 -1 +6 (or some variation on that, e.g., normalized coefficients). I remember long ago in an epidemiology class learning how to partition chi^2, but I do not remember off hand whether the contrast ends up being equivalent to collapsing groups 1-6 and doing the 2-group chi^2, or whether there was a way to partition a SS for the numerator and use a common denominator from the 7-group chi^2 for the test of contrasts. The following link suggests that the two are not the same. http://www.sas.com/service/techsup/faq/stat_proc/freqproc919.html Best wishes Jim ============================================================================ James M. Clark (204) 786-9757 Department of Psychology (204) 774-4134 Fax University of Winnipeg 4L05D Winnipeg, Manitoba R3B 2E9 [EMAIL PROTECTED] CANADA http://www.uwinnipeg.ca/~clark ============================================================================ ================================================================= Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =================================================================
