Re: Binary Logistic Regression for Rare Events

Posted by Rich Ulrich on Mar 16, 2021; 6:14pm
URL: http://spssx-discussion.165.s1.nabble.com/Binary-Logistic-Regression-for-Rare-Events-tp5740296p5740310.html

As the article cited by Bruce points out, there is nothing wrong with the

logistic /model/ for rare events.  ML /methods/ do not like small Ns if you

hope for robust results.  N=20 (smaller group) is somewhat small.

However, even LS regression (here, it would be discriminant function)

wants the number of predictors to be multiplied by 5 or 10 to get the N

for the smaller group.

As to those other comments and alternate problem:  "Likert" items have

a neutral midpoint; good likert items have generally symmetrical responses.

The example is not likert.  

Dichotomizing a 7-point scale is a waste of good information, presumably.

If those category labels are not (subjectively) equal enough as intervals, consider

transformations. Someone cited a procedure that finds optimum intervals --

I think that it works well, but it /might/ be off-putting to your potential audience.

For the 1-7 frequencies given -- if I did not want to use that metric, my

first alternative to improve the "equal intervals" would be to subtract from 7

and take the square root. My second alternative would be to subtract from

8 and take the log. My third guess, if I disbelieved the OP's impulse to combine

the low scores, would be to take average ranks and score as logits.  That assigns

wider spacing to the 1,2,3 scores (in search of subjective "equal intervals").

--

Rich Ulrich

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From: SPSSX(r) Discussion <[hidden email]> on behalf of John Amora <[hidden email]>
Sent: Tuesday, March 16, 2021 4:48 AM
To: [hidden email] <[hidden email]>
Subject: Binary Logistic Regression for Rare Events

 

Hi, SPSS Users:

Is there a specialized algorithm in SPSS that handles Binary Logistic Regression for rare events?  Rare events happen if, for example, you have a sample size of 1000 but only 20 events.

How rare events are handled in SPSS?

Thank you.

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