Handling Quasi-Nonconvergence in Logistic Regression: Technical Details and an Applied Example

by Jeffrey M. Miller and M. David Miller.

Abstract: Nonconvergence is a concern for any iterative data analysis process. However, there are instances in which convergence will be obtained for the overall solution but not for a specific estimate. For most software packages, it is not easy to notice this problem unless the researcher has a priori knowledge of reasonable solutions. Hence, faulty inferences can be disguised by a presumably correct estimation procedure known as “quasi-nonconvergence”. This type of nonconvergence occurs in logistic regression models when the data are quasi-completely separated. This is to say that prediction is completely or nearly completely perfect. Firth (1993) presented a penalized likelihood correction that was then extended by Heinze and Ploner (2003) to solve the quasi-nonconvergence problem. This procedure was applied to educational research data to demonstrate its success in eliminating the problem.

Key Words: Quasi-Nonconvergence, Nonconvergence, Logistic Regression

Jeffrey M. Miller, jeffmiller.research@gmail.com
M. David Miller, dmiller@coe.ufl.edu

Editor: Graf, R. G., rgraf@sunstroke.sdsu.edu

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