Hi all,
I'm new to Amelia and generally a statistics rookie and have been attempting to
impute a dataset using the Amelia II GUI with a small number of cases (25) and a large
number of variables (104). 25 of the variables have missing data of between 1 and 4
values and these are distributed fairly randomly across cases. Variables are mainly
ordinal but with a few continuous and nominal. The only way I can get the programme to
impute is by increasing the ridge/empirical prior to around 190. I've also used
range priors for the majority of variables with maximum certainty. Is this an acceptable
approach?
An alternative would be to drastically cut the number of variables but even then it still
requires a high empirical prior, much higher than the 5% of variables suggested as a
moderate ridge prior level in the manual. It may also mean generating a number of
separately imputed datasets containing a small number of variables for separate
multivariate analyses. Is there any obvious answer about which approach is most
acceptable, if any? I assume that either is better than an ad-hoc approach or forgetting
multivariate analysis with the data. I'm hoping to employ the non-parametric
multivariate approaches such as ANOSIM in the statistics package PRIMER with the imputed
data set.
I'd welcome any advice that can be offered.
Regards,
Dale Rodmell
Scarborough Centre for Coastal Studies
University of Hull
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