Using logistic regression in PLS-SEM: Dichotomous endogenous variables

The article below discusses how one can use logistic regression with the probit approach, to avoid the problems associated with having dichotomous endogenous variables, in the context of structural equation modeling via partial least squares (PLS-SEM).

Kock, N. (2023). Using logistic regression in PLS-SEM: Dichotomous endogenous variables. Data Analysis Perspectives Journal, 4(4), 1-6.

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Abstract:

A dichotomous endogenous variable would be impossible to occur at the population level, which an empirical sample is assumed to represent, because the structural error term associated with the endogenous variable is expected to be a random variable with many distinct values. Consequently, the endogenous variable is also expected to have many distinct values. This paper discusses how to address this problem, using logistic regression with the probit approach, in the context of structural equation modeling via partial least squares (PLS-SEM). Our discussion is based on an illustrative model analyzed with the software WarpPLS.

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