How to interpret coefficients of ordered probit?

In the context of a probit regression, the coefficient associated with a predictor variable such as age can be interpreted in a straightforward manner. Specifically, the coefficient can be seen as representing the change in the z-score for the probability of the event occurring for a one-unit increase in the predictor variable.

For instance, if the predictor variable is age and the event being modeled is the probability of being in a union, then the coefficient associated with age (denoted as βage) can be interpreted as the increase in the z-score for the probability of being in a union for a one-year increase in age.

This interpretation is useful because it allows researchers to quantify the impact of changes in the predictor variable on the probability of the event occurring. In the example given, it would allow researchers to estimate the effect of aging on the likelihood of an individual being in a union.

Probit regression is a statistical method used to model the probability of an event occurring as a function of one or more predictor variables. One important aspect of this model is the interpretation of the coefficients associated with the predictor variables.

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