Hello list members! I am writing to ask about methods of pooling Amelia outputs for standard deviation, Cohen's d, and model fit statistics such as F-statistic and R-squared. Specifically: (1) For SD, can I use mi.meld() to pool SDs estimated from individual imputed datasets, similarly to pooling standard errors for regression coefficients? (2) For Cohen's d, can I use zelig-ls to pool the t-statistic for the dummy predictor, and then transform the pooled t-statistic into Cohen's d? Alternatively, can I calculate Cohen's d by each imputed dataset and then calculate the mean of the ds? Or a third approach, to calculate Cohen's d based on pooled mean and SD? - These approaches do not always lead to identical results, which one is the best? Or is there yet another better approach? (3) For R-squared - I understand that Dr. King recommends not to focus on model fit statistics - but just out of curiosity: mice has a function that uses the procedure proposed by Harel (2009): http://www.tandfonline.com/doi/pdf/10.1080/02664760802553000 a) In each ‘complete’ data, • calculate R2 • take its squared root - R • use Fisher z-transformation 2) With the m sets of estimates and variances, • combine results using Is this approach superior to taking the mean of estimated R-squared's from the imputed datasets directly? (4) For the F-statistic - Is there any recommendation other than taking the mean of Fs from the imputed datasets? My apologies for the many questions! Thank you in advance for any of your help! :) Best wishes, Gu -- Gu Li, MS PhD Candidate University of Cambridge Department of Psychology Free School Lane, Cambridge, CB2 3RQ United Kingdom