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

[Patrick (Malone Quantitative)]

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 using aikq.de> wrote:

> Hi Pat,
>
> thanks very much for your help! Helps me see things a bit more clearly.
> Well, the present values aren't the only ones that could exist. There are
> questions like "How long is your shift", which could be 3, 4, or 5 hours;
> "How many shifts per week do you have", which could be between 1 and 7, or
> "how many callers do you have per semester" which could be - in theory -
> between 0 and thousands. Of course, there's only one response to every
> question that's actually true.
> (Maybe I'm misunderstanding your question, though, cause you probably
> didn't mean whether there could be only one possible response to every
> 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 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
>>
>> #Film: Der unberührte Garten - eine ungewöhnliche Geschichte übers
>> Erwachsenwerden (www.vimeo.com/148014360)
>>
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>>
>>
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>>
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>
>
> --
> Patrick S. Malone, Ph.D., Malone Quantitative
> NEW Service Models: http://malonequantitative.com
>
> He/Him/His
>
> --
> Dr. Diana Michl
> Kastanienallee 4
> 14471 Potsdam
> Tel: 0331 – 27 34 15 10
> 01577 – 3065650
> dianamichl using aikq.de
>
> #www.diana-michl.de
>
> #Film: Der unberührte Garten - eine ungewöhnliche Geschichte übers
> Erwachsenwerden (www.vimeo.com/148014360)
>
> #Musik: Singer-Songwriter (www.youtube.com/user/ghiaghiafy)
>


-- 
Patrick S. Malone, Ph.D., Malone Quantitative
NEW Service Models: http://malonequantitative.com

He/Him/His

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