Marginal and Conditional Confounding Using Logits
Open Access
- 9 April 2021
- journal article
- research article
- Published by SAGE Publications in Sociological Methods & Research
- Vol. 52 (4) , 1765-1784
Abstract
This article presents two ways of quantifying confounding using logistic response models for binary outcomes. Drawing on the distinction between marginal and conditional odds ratios in statistics, we define two corresponding measures of confounding (marginal and conditional) that can be recovered from a simple standardization approach. We investigate when marginal and conditional confounding may differ, outline why the method by Karlson, Holm, and Breen recovers conditional confounding under a “no interaction”-assumption, and suggest that researchers may measure marginal confounding by using inverse probability weighting. We provide two empirical examples that illustrate our standardization approach.Keywords
Funding Information
- Medical Research Council (MC_UU_12017/13)
- Chief Scientist Office (SPHSU13)
This publication has 9 references indexed in Scilit:
- Interpreting and Understanding Logits, Probits, and Other Nonlinear Probability ModelsAnnual Review of Sociology, 2018
- On Group Comparisons With Logistic Regression ModelsSociological Methods & Research, 2018
- Education and Social Mobility: New Analytical ApproachesEuropean Sociological Review, 2013
- Total, Direct, and Indirect Effects in Logit and Probit ModelsSociological Methods & Research, 2013
- Comparing Regression Coefficients Between Same-sample Nested Models Using Logit and ProbitSociological Methodology, 2012
- Logistic Regression: Why We Cannot Do What We Think We Can Do, and What We Can Do About ItEuropean Sociological Review, 2009
- Confounding and Collapsibility in Causal InferenceStatistical Science, 1999
- Models for Longitudinal Data: A Generalized Estimating Equation ApproachBiometrics, 1988
- Regression Models with Ordinal VariablesAmerican Sociological Review, 1984