Regression analysis with small but complete dataset (fully representing reality)?
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 at 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
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Dr. Diana Michl
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