Interpreting and Understanding Logits, Probits, and other Non-Linear Probability Models

Research output: Contribution to journalJournal articleResearchpeer-review

Methods textbooks in sociology and other social sciences routinely recommend the use of the logit or probit model when an outcome variable is binary, an ordered logit or ordered probit when it is ordinal, and a multinomial logit when it has more than two categories. But these methodological guidelines take little or no account of a body of work that, over the past 30 years, has pointed to problematic aspects of these nonlinear probability models and, particularly, to difficulties in interpreting their parameters. In this chapterreview, we draw on that literature to explain the problems, show how they manifest themselves in research, discuss the strengths and weaknesses of alternatives that have been suggested, and conclude by pointingpoint to lines of further analysis.
Original languageEnglish
JournalAnnual Review of Sociology
Pages (from-to)39-54
Publication statusPublished - Aug 2018

    Research areas

  • Faculty of Social Sciences - logit, probit, KHB method, Y-standardization, marginal effects, linear probability model, mediation

ID: 187579586