Hey, does anybody know a good/statistically sound/widely used method to estimate missing values in a repeated measures design? References? Software to apply the method?
I have this problem: I tested subjects on reading times of words belonging to 4 conditions, resulting from 2 within-subject variables with two levels For example: participant long low frequency long high frequency short low frequency short high frequency 1 mean RTs of 10 words in that conditione 2 3 => 2 within-subject factors: word length and frequency However, due to a prior phase (not important) some of my cells are empty (10%), approximately MAR. I don't want to use multiple imputation, so I want to impute one value for each cell, and then use standard ANOVA statistics(I am aware that this results in an underestimation of within-condition variance) to test the effect of word length (short vs. long) , word frequency and its interaction. I read about the EM algorithm (Little & Rubin, 1987) in SPSS which is good for imputation when variables are independently, but what in such repeated measures designs? Can (or should) I use the xtra info on the interdependence of the variables in the imputation procedure? If so, which procedure does this? Tanx! elvis . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
