Interpreting and Understanding Logits, Probits, and Other Nonlinear Probability Models

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 guidelin...

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Bibliographic Details
Published inAnnual review of sociology Vol. 44; no. 1; pp. 39 - 54
Main Authors Breen, Richard, Karlson, Kristian Bernt, Holm, Anders
Format Journal Article
LanguageEnglish
Published Palo Alto Annual Reviews 30.07.2018
Annual Reviews, Inc
Subjects
Control theory
KHB method
Linear equations
linear probability model
logit
marginal effects
Mathematical models
mediation
Probability
probit
Social research
Social sciences
standardization
Textbooks
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Summary: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 review, 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 point to lines of further analysis.
ISSN:0360-0572
1545-2115
DOI:10.1146/annurev-soc-073117-041429