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
--
Gu Li, MS
PhD Candidate
University of Cambridge
Department of Psychology
Free School Lane, Cambridge, CB2 3RQ
United Kingdom