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 to evaluate the normalized estimate and its variance (Q(i), V (i))

2) With the m sets of estimates and variances, • combine results using Rubin’s rules • the confidence interval (CI) for Q is QT ± z(α/2)√(QT) • inverse transform for the proportion scale • square your results.

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 Li, MS

PhD Candidate

University of Cambridge

Department of Psychology

Free School Lane, Cambridge, CB2 3RQ

United Kingdom