Correlations and Non-Linear Probability Models

Research output: Contribution to journalJournal articlepeer-review


Although the parameters of logit and probit and other non-linear probability models are often explained and interpreted in relation to the regression coefficients of an underlying linear latent variable model, we argue that they may also be usefully interpreted in terms of the correlations between the dependent variable of the latent variable model and its predictor variables. We show how this correlation can be derived from the parameters of non-linear probability models, develop tests for the statistical significance of the derived correlation, and illustrate its usefulness in two applications. Under certain circumstances, which we explain, the derived correlation provides a way of overcoming the problems inherent in cross-sample comparisons of the parameters of non-linear probability models.
Original languageEnglish
JournalSociological Methods & Research
Issue number4
Pages (from-to)571-605
Number of pages35
Publication statusPublished - Nov 2014

Number of downloads are based on statistics from Google Scholar and

No data available

ID: 68078602