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

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Interpreting and Understanding Logits, Probits, and other Non-Linear Probability Models. / Breen, Richard; Karlson, Kristian Bernt; Holm, Anders.

In: Annual Review of Sociology, Vol. 44, 08.2018, p. 39-54.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Breen, R, Karlson, KB & Holm, A 2018, 'Interpreting and Understanding Logits, Probits, and other Non-Linear Probability Models', Annual Review of Sociology, vol. 44, pp. 39-54. https://doi.org/10.1146/annurev-soc-073117-041429

APA

Breen, R., Karlson, K. B., & Holm, A. (2018). Interpreting and Understanding Logits, Probits, and other Non-Linear Probability Models. Annual Review of Sociology, 44, 39-54. https://doi.org/10.1146/annurev-soc-073117-041429

Vancouver

Breen R, Karlson KB, Holm A. Interpreting and Understanding Logits, Probits, and other Non-Linear Probability Models. Annual Review of Sociology. 2018 Aug;44:39-54. https://doi.org/10.1146/annurev-soc-073117-041429

Author

Breen, Richard ; Karlson, Kristian Bernt ; Holm, Anders. / Interpreting and Understanding Logits, Probits, and other Non-Linear Probability Models. In: Annual Review of Sociology. 2018 ; Vol. 44. pp. 39-54.

Bibtex

@article{b2976fbb966f4cacaca6d6b8c109a6c0,
title = "Interpreting and Understanding Logits, Probits, and other Non-Linear Probability Models",
abstract = "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.",
keywords = "Faculty of Social Sciences, logit, probit, KHB method, Y-standardization, marginal effects, linear probability model, mediation",
author = "Richard Breen and Karlson, {Kristian Bernt} and Anders Holm",
year = "2018",
month = "8",
doi = "10.1146/annurev-soc-073117-041429",
language = "English",
volume = "44",
pages = "39--54",
journal = "Annual Review of Sociology",
issn = "0360-0572",
publisher = "Annual Reviews",

}

RIS

TY - JOUR

T1 - Interpreting and Understanding Logits, Probits, and other Non-Linear Probability Models

AU - Breen, Richard

AU - Karlson, Kristian Bernt

AU - Holm, Anders

PY - 2018/8

Y1 - 2018/8

N2 - 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.

AB - 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.

KW - Faculty of Social Sciences

KW - logit

KW - probit

KW - KHB method

KW - Y-standardization

KW - marginal effects

KW - linear probability model

KW - mediation

U2 - 10.1146/annurev-soc-073117-041429

DO - 10.1146/annurev-soc-073117-041429

M3 - Journal article

VL - 44

SP - 39

EP - 54

JO - Annual Review of Sociology

JF - Annual Review of Sociology

SN - 0360-0572

ER -

ID: 187579586