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

TY - JOUR

T1 - Total, Direct, and Indirect Effects in Logit and Probit Models

AU - Breen, Richard

AU - Karlson, Kristian Bernt

AU - Holm, Anders

N1 - 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."

PY - 2013/5

Y1 - 2013/5

N2 - This article 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 always performs 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 article with an application of our method to data from the National Educational Longitudinal Study of 1988.

AB - This article 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 always performs 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 article with an application of our method to data from the National Educational Longitudinal Study of 1988.

U2 - 10.1177/0049124113494572

DO - 10.1177/0049124113494572

M3 - Journal article

SN - 0049-1241

VL - 42

SP - 164

EP - 191

JO - Sociological Methods & Research

JF - Sociological Methods & Research

IS - 2

ER -