A while back I posted a question something like this: a chi-square analysis enables you to conclude whether an observed frequency distribution differs significantly from the expected distribution. How can you determine which of the frequency categories is driving the overall departure from expectation?
Several of you wanted to see the responses I got. Here they are: 1. Hi, you need to compute the standardized residuals...standardized residual (R) tells you where differences lie: R = O - E/sqrt(E) where: O= observed freq E= expected freq when R > abs(2), you can conclude it's a major contributor to a significant X2 value. (source: Haberman, S. J. 1973. The analysis of residuals in cross-classified tables. Biometrics 29:205-220.) 2. It is possible to calculate the adjusted residuals from a Chi-sq test. An adjusted residual greater than about 2 or 3 in absolute value is an indication that the category is pretty different from predicted. 3. The adjusted residuals are calculated as (obs-exp)/sqrt(exp*(1-prob of being in the corresponding row)*(1-prob of being in the corresponding column)) 4. I have been using standardized residuals to determine which measurements differ from expected, using +/-2 as a cut-off for significance George P. Kraemer Leff Professor of Environmental Studies and Biology Chair, Environmental Studies Program Associate Dean, School of Natural and Social Sciences Purchase College 914-251-6640 (o) Website<http://www.purchase.edu/Departments/AcademicPrograms/faculty/GeorgeKraemer/GeorgeKraemer.aspx>
