2010
DOI: 10.1177/1536867x1001000211
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Stata Tip 87: Interpretation of Interactions in Nonlinear Models

Abstract: When fitting a nonlinear model such as logit (see [R] logit) or poisson (see [R] poisson), we often have two options when it comes to interpreting the regression coefficients: compute some form of marginal effect or exponentiate the coefficients, which will give us an odds ratio or incidence-rate ratio. The marginal effect is an approximation of how much the dependent variable is expected to increase or decrease for a unit change in an explanatory variable; that is, the effect is presented on an additive scal… Show more

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Cited by 407 publications
(226 citation statements)
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“…Control variables of gender, race, family income, and maternal education were modeled as categorical variables; the remaining continuous predictors were mean-centered first. All analyses were conducted in Stata statistical software using the zinb command, and the results were further probed and plotted using the margins and marginsplot command (Buis, 2010;Jann, 2013;StataCorp., 2013). The results were plotted following the standard procedures for probing significant interactions in regression-based models (Bauer & Curran, 2005;Buis, 2010;Curran, Bauer, & Willoughby, 2006;Jann, 2013;Preacher, Curran, & Bauer, 2006).…”
Section: Discussionmentioning
confidence: 99%
“…Control variables of gender, race, family income, and maternal education were modeled as categorical variables; the remaining continuous predictors were mean-centered first. All analyses were conducted in Stata statistical software using the zinb command, and the results were further probed and plotted using the margins and marginsplot command (Buis, 2010;Jann, 2013;StataCorp., 2013). The results were plotted following the standard procedures for probing significant interactions in regression-based models (Bauer & Curran, 2005;Buis, 2010;Curran, Bauer, & Willoughby, 2006;Jann, 2013;Preacher, Curran, & Bauer, 2006).…”
Section: Discussionmentioning
confidence: 99%
“…The proportion of zeroes is lower in Lambeth than the London-wide average because Lambeth is a high-incidence borough. 33 Following Buis (2010), given that both interacted variables are binary, the average interaction effect on each margin can be calculated by: first, using the Tobit estimates to produce the conditional expected value of admissions for the four Lambeth × policy period (PP qy or P qy ) cells (e.g, Lambeth = 0, PP qy = 0; Lambeth = 1, PP qy = 0; Lambeth = 0, PP qy = 1; Lambeth = 0, PP qy = 0); and, second, taking the double difference of those conditional expected admission rates. The average interaction effect for the intensive margin in the postpolicy period is therefore equal to the following:…”
Section: A2 Tobit Estimatesmentioning
confidence: 97%
“…In the probit model, however, the estimated coefficients show the multiplicative effect, or a ratio change of the dependent variable for each unit change in explanatory variable (Buis 2010). Therefore, for analysis the estimated coefficients needed to be transformed into marginal effects.…”
Section: Data Analysesmentioning
confidence: 99%