Re: Big odds ratio in binary regression output

Posted by Bruce Weaver on Aug 23, 2017; 1:31pm
URL: http://spssx-discussion.165.s1.nabble.com/Big-odds-ratio-in-binary-regression-output-tp5734731p5734734.html

With 52 events and 7 variables, you have 7.4 events per variable (EPV).  In order to avoid over-fitting a binary logistic regression model, you ought to have approximately 15 EPV.  See Mike Babyak's nice article on over-fitting, for example.  See also the 2016 article by Greenland et al. on "sparse data bias", as it could also be relevant.

https://www.ncbi.nlm.nih.gov/pubmed/15184705
http://www.bmj.com/content/352/bmj.i1981

HTH.

Sidra wrote

Dear fellows, I have run binary logitic regression in spss with 7 IVs (dichotmized). My DV is "dissatisfied with cildbirth care" with response options of 1=yes and 0=no. One of the Independent variables is "interpersonal care and communication" (0=High, 1= Low). When I get the output for binary regression, all the statistics seem normal except the odds ratio of "interpersonal communication and care". All ORs are in the range of 0.173 and 3.251, however OR for interpersonal communication and care is 23 which to me seems exceptionally large (If I'm right in believing so). The dichotomous response for this variable has been obtained from its factor score. All the VIFs and Tolerance values seem fine indicating that the problem of multicollinearity doesn't exist. My sample size is 317 with 52 responses in the outcome category that I want to predict (dissatisfaction). Could anybody point out to any other problems I might have in the data owing to which the huge OR is observed (if it is huge at all).

Furthermore, I would like to know that what statistics of binary regression output should I report while writing the results.
Thanks