[R-sig-ME] Regression analysis with small but complete dataset (fully representing reality)?

[Patrick (Malone Quantitative)]

Diana,

It depends on the nature of the missing. Are the present values the only
ones that could exist? If so, you have the entire population's data, and
descriptive statistics are in fact preferable to inferential ones. There's
no need to run inferential statistics if you have the population--they are
by definition for inferring population values from a sample.

Pat

On Thu, Dec 24, 2020 at 6:21 AM Diana Michl <dianamichl using aikq.de> wrote:

> I have a repeated measures design with about 16 cases and 5-6 points of
> measuring. Sometimes, 1-4 full cases or some points of measure are
> missing. (The measures are 20 numerical and categorical data taken from
> questionnaires.)
>
> The clue is: It's a small dataset with holes in it, but the 16 cases are
> all that even exist. So they fully represent reality wherever they're
> complete.
>
> I wanted to run logistic regressions with up to 6 predictors. But can I
> do that? I know about the many problems such small datasets have for
> regression analysis - but do they matter as much if there aren't any
> more cases in reality?
> Are descriptive analyses the only ones I can use?
>
> Many thanks
>
> --
> Dr. Diana Michl
> #www.diana-michl.de
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-- 
Patrick S. Malone, Ph.D., Malone Quantitative
NEW Service Models: http://malonequantitative.com

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