Total, Direct, and Indirect Effects in Logit and Probit Models

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This paper presents a method for estimating and interpreting total, direct, and indirect effects in logit or probit models. The method extends the decomposition properties of linear models to these models; it closes the much-discussed gap between results based on the "difference in coefficients" method and the "product of coefficients" method in mediation analysis involving nonlinear probability models models; it reports effects measured on both the logit or probit scale and the probability scale; and it identifies causal mediation effects under the sequential ignorability assumption. We also show that while our method is computationally simpler than other methods, it performs always as well as or better than these methods. Further derivations suggest a hitherto unrecognized issue in identifying heterogeneous mediation effects in nonlinear probability models. We conclude the paper with an application of our method to data from the National Educational Longitudinal Study of 1988.
Original languageEnglish
JournalSociological Methods & Research
Issue number2
Pages (from-to)164-191
Publication statusPublished - May 2013
Externally publishedYes

Bibliographical note

Defined as a highly cited paper in Web of Science as of 2016: "As of March/April 2016, this highly cited paper received enough citations to place it in the top 1% of the academic field of Social Sciences, general based on a highly cited threshold for the field and publication year."

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