Staffan,   The sample size of the smaller of the 2 groups helps to determine the number of covariates that you can enter into your model.  You will want to have about 10 subject per covariate/variable.   Harrell, F. E., Jr. (2001). Regression modeling strategies: With applications to linear models, logistic regression, and survival analysis. New York: Springer-Verlag.   Scott ~~~~~~~~~~~ Scott R Millis, PhD, ABPP, CStat, CSci Board Certified in Clinical Neuropsychology, Clinical Psychology, & Rehabilitation Psychology Professor & Director of Research Dept of Physical Medicine & Rehabilitation Dept of Emergency Medicine Wayne State University School of Medicine 261 Mack Blvd Detroit, MI 48201 Email: aa3379@wayne.edu Email: srmillis@yahoo.com Tel: 313-993-8085 Fax: 313-966-7682

--- On Tue, 3/23/10, Staffan Lindberg <staffan.lindberg@anastat.se> wrote:

From: Staffan Lindberg <staffan.lindberg@anastat.se> Subject: Limit on the number of independent variables in binary logistic regression and redundancy among the independent variables To: SPSSX-L@LISTSERV.UGA.EDU Date: Tuesday, March 23, 2010, 6:21 AM

Dear list!   In factor analyses there is a rule of thumb (of many) that the number of cases should be appr. 4-5 times greater than the number of variables. Is there a corresponding rule as regards to binomial logistic regression?  Would it be feasible to have say c:a 150 independent variables (a mixture of scale, ordinal and nominal ones) with 600 cases? Or are there other considerations that this should not be done. And second are there any caveats if there are several redundant variables among the independent variables i.e age in 1-.year classes, 5-year classes and 10-year classes in the same set of independent variables.   thankful for any input on this   best   Staffan Lindberg Sweden