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The overall odds ratio as an intuitive effect size index for multiple logistic regression: examination of further refinements

Title	The overall odds ratio as an intuitive effect size index for multiple logistic regression: examination of further refinements
Author	Le, H., Marcus, Justin
Publication Date:	2012-12
Publication Place	- Sage
Subject	Effect size, Logistic regression, Odds ratio, R square
Type	Periodical
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	1552-3888
Record ID	068e8ed4-7b3e-4194-980d-932094b9a438
Library Location	Psychology
Date	2012-12
Notes	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Sample Text	This study used Monte Carlo simulation to examine the properties of the overall odds ratio (OOR), which was recently introduced as an index for overall effect size in multiple logistic regression. It was found that the OOR was relatively independent of study base rate and performed better than most commonly used R-square analogs in indexing model strength. The authors also illustrate and test a jackknife procedure to correct for the bias in the OOR and estimate its standard error. An example applying the OOR to evaluate logistic regression models predicting organizational turnover is provided. The authors discuss implications and offer recommendations for using the OOR to quantify and compare the effectiveness of logistic regression models in applied research.
DOI	10.1177/0013164412445298
Cilt	72

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Özyeğin University

The overall odds ratio as an intuitive effect size index for multiple logistic regression: examination of further refinements

Author Le, H., Marcus, Justin

Publication Date 2012-12

Publication Place - Sage

Subject Effect size, Logistic regression, Odds ratio, R square

Type Periodical

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 1552-3888

Record ID 068e8ed4-7b3e-4194-980d-932094b9a438

Library Location Psychology

Date 2012-12

Notes Due to copyright restrictions, the access to the full text of this article is only available via subscription.

Sample Text This study used Monte Carlo simulation to examine the properties of the overall odds ratio (OOR), which was recently introduced as an index for overall effect size in multiple logistic regression. It was found that the OOR was relatively independent of study base rate and performed better than most commonly used R-square analogs in indexing model strength. The authors also illustrate and test a jackknife procedure to correct for the bias in the OOR and estimate its standard error. An example applying the OOR to evaluate logistic regression models predicting organizational turnover is provided. The authors discuss implications and offer recommendations for using the OOR to quantify and compare the effectiveness of logistic regression models in applied research.

DOI 10.1177/0013164412445298

Cilt 72