Hi Diana
In addition to using descriptive statistics, I?would also recommend
using Partial Least Squares regression that was specifically designed
for the problem of small sample size and having many variables. (your
dependent?can be continuous, binary or multinomial in PLS). I have
successfully used PLS regression in medical / healthcare arena for
rare and orphan disease analyses where the affected population is very
small and getting data from 30 patients represents any where from 25%
to 60% of the overall?population.
I strongly recommend this?excellent resource (a detailed PDF document
- 235 pages)? by Gaston Sanchez on his website:
https://www.gastonsanchez.com/PLS_Path_Modeling_with_R.pdf
<https://www.gastonsanchez.com/PLS_Path_Modeling_with_R.pdf>
Hope this?helps. If you?have any questions or need additional
information please get?back to me and I can help you in identifying
whether PLS regression would be relevant and helpful for you.
Sree
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On Fri, Dec 25, 2020 at 12:08 PM Patrick (Malone Quantitative)
<malone at malonequantitative.com <mailto:malone at malonequantitative.com>>
wrote:
Diana,
cc'ing the list again in case anyone else has input
I was asking if the missing was structural--for example, hours per
shift if
someone is unemployed at the time of measurement. In that
scenario, you
could have missing "values" but still completely observed *data*.
Normally, I would assume that questions about missing data refer to
incomplete observation, but you clearly have a special situation,
which is
why I asked.
If your population data is completely observed, again, you don't need
inferential statistics.
If not, you do indeed have a sample of the data, not the
population, even
though you have most of it. I believe there are corrections that
need to be
made to inferential statistics for small populations. I don't have
experience with that, but that might get you started.
Pat
On Fri, Dec 25, 2020 at 9:55 AM Diana Michl <dianamichl at aikq.de
<mailto:dianamichl at aikq.de>> wrote:
> Hi Pat,
>
> thanks very much for your help! Helps me see things a bit more
> Well, the present values aren't the only ones that could exist.
> questions like "How long is your shift", which could be 3, 4, or
> "How many shifts per week do you have", which could be between 1
> "how many callers do you have per semester" which could be - in
> between 0 and thousands. Of course, there's only one response to
> question that's actually true.
> (Maybe I'm misunderstanding your question, though, cause you
> didn't mean whether there could be only one possible response to
> question, right?)
>
> Diana
>
>
> Am 24.12.2020 um 17:22 schrieb Patrick (Malone Quantitative):
>
> Diana,
>
> It depends on the nature of the missing. Are the present values
> ones that could exist? If so, you have the entire population's
> descriptive statistics are in fact preferable to inferential
> no need to run inferential statistics if you have the
> 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
<mailto:dianamichl at aikq.de>> wrote:
>> I have a repeated measures design with about 16 cases and 5-6
>> measuring. Sometimes, 1-4 full cases or some points of measure are
>> missing. (The measures are 20 numerical and categorical data
>> questionnaires.)
>>
>> The clue is: It's a small dataset with holes in it, but the 16
>> all that even exist. So they fully represent reality wherever
>> complete.
>>
>> I wanted to run logistic regressions with up to 6 predictors.
>> do that? I know about the many problems such small datasets
>> regression analysis - but do they matter as much if there
>> more cases in reality?
>> Are descriptive analyses the only ones I can use?
>>
>> Many thanks
>>
>> --
>> Dr. Diana Michl
>> #www.diana-michl.de <http://www.diana-michl.de>
>>
>> #Film: Der unber?hrte Garten - eine ungew?hnliche Geschichte ?bers
>> Erwachsenwerden (www.vimeo.com/148014360
>>
>> #Musik: Singer-Songwriter (www.youtube.com/user/ghiaghiafy
>>
>>
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>>
>> _______________________________________________
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>
> He/Him/His
>
> --
> Dr. Diana Michl
> Kastanienallee 4
> 14471 Potsdam
> Tel: 0331 ? 27 34 15 10
> 01577 ? 3065650
> dianamichl at aikq.de <mailto:dianamichl at aikq.de>
>
> #www.diana-michl.de <http://www.diana-michl.de>
>
> #Film: Der unber?hrte Garten - eine ungew?hnliche Geschichte ?bers
> Erwachsenwerden (www.vimeo.com/148014360
>
> #Musik: Singer-Songwriter (www.youtube.com/user/ghiaghiafy