14 August 2012

Solas 4.0

Missing data values are the bane of a statistician’s life. At best, when the distribution of missing values is truly random, they reduce sample size which has a knock-on effect on conclusions and confidence. At worst, when there is an extensive pattern of systematic omissions, they can render a whole data set completely useless.

At the same time, attempting to fill in (or, in the language of the trade, impute) the missing values is a fraught business full of pitfalls. There is an ever present danger of building assumptions into the data which are then expressed in analyses.

Very often, however, the benefits of imputation in retrieving at least part of a study for useful non-content-robust analysis outweigh purist qualms. The question is then how to go about the process in the best way and this is where Solas comes in... [more]

1 comment:

Jasmine said...

You know that my maths and tech skills leave a lot to be desired, so a lot of this is over my head. But I think I get the gist of it.

So basically you did all you could to try to trick this package, and it still worked? Awesome.

I wasn't aware that there were packages that could 'fill in' gaps in a data set. Bearing this in mind for uni next year!