Interaction Terms in Nonlinear Models

Health Services Research - Tập 47 Số 1pt1 - Trang 255-274 - 2012
Pinar Karaca‐Mandic1, Edward C. Norton2, Bryan Dowd1
1Division of Health Policy and Management, School of Public Health, University of Minnesota, Minneapolis, MN
2Department of Health Policy and Management Department of Economics School of Public Health University of Michigan Ann Arbor MI

Tóm tắt

ObjectivesTo explain the use of interaction terms in nonlinear models.Study DesignWe discuss the motivation for including interaction terms in multivariate analyses. We then explain how the straightforward interpretation of interaction terms in linear models changes in nonlinear models, using graphs and equations. We extend the basic results from logit and probit to difference‐in‐differences models, models with higher powers of explanatory variables, other nonlinear models (including log transformation and ordered models), and panel data models.Empirical ApplicationWe show how to calculate and interpret interaction effects using a publicly available Stata data set with a binary outcome. Stata 11 has added several features which make those calculations easier.LIMDEPcode also is provided.ConclusionsIt is important to understand why interaction terms are included in nonlinear models in order to be clear about their substantive interpretation.

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Health Services Research

Tập 47 Số 1pt1

255-274

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