Marginal and Conditional Confounding using Logits

Kristian Bernt Karlson*, Frank Popham, Anders Holm

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

4 Citations (Scopus)
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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 (2012) 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.
Original languageEnglish
JournalSociological Methods & Research
Volume52
Issue number4
Pages (from-to)1765-1784
ISSN0049-1241
DOIs
Publication statusPublished - 2023

Keywords

  • Faculty of Social Sciences
  • logit
  • odds ratio
  • confounding
  • mediation
  • standardization

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