I have a question about power analysis related to a t-test of some weights in multiple regression equations. I'm afraid I'm a cognitive psychologist, so if I give you to much info below about my experiment please accept my apology.
I've taken memory measures from younger and older adults at several different encoding times and I also have created constructs of popular cognitive resources as predictors (perceptual speed, working memory, vocabulary,..) I am interested in how the regression equations change in each age group as memory performance increases (as I give people more time to study). I ran the regression equations for younger and older adults at a given memory measure and computed a t-test for difference between the predictor weights for what one might expect to be different equations. As an example, at a 1 second encoding time the older adults have a significant beta weight for speed as a predictor (b = .31) but the young adults do not (b = .15), in the same equation young adults have a significant beta weight for vocabulary (b = .24) but older adults do not (b = .19). This casually looks like a difference when eyeballing the equations, speed is the only significant predictor for older adults and vocabulary is the only significant predictor for young adults at a 1 second encoding time for the memory material. Interestingly, when I look at the t-test for difference between the B weights, I fail to reject the null of no significant difference between the weights. The t-test for difference between the speed predictors is t = -.51 and t = .14 for the vocabulary predictors. Of course I do not want to accept the null, I am really curious as to how much power I had to detect a significant difference with my sample size of 80 younger and 80 older adults in each equation. I need help computing the effect size given my SPSS output from the regressions. I assume that I need to compute f as an effect size since this isn't teh typical t-test. Can anyone tell me how to do that given the weights and the standard errors for the weights from the output? I must be doing something wrong as my f's are way to large. Thanks, Chuck . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
