Using the Margins Command to Estimate and Interpret Adjusted Predictions and Marginal Effects

By Richard Williams. 2012

1,934 citations recorded by the source corpus.

Subfields: Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/f312bfba20033bb15586a937a396b1b62c793f9e

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In Sociology and Political Science

Citations in the corpus

Interpreting Results From the Multinomial Logit Model (2015, methodology): “This means that no matter how we decide to average the effects, we may be unintentionally obscuring important differences across the range of the predictor (Williams, 2012).”
Eight Simple Guidelines for Improved Understanding of Transformations and Nonlinear Effects (2021, methodology): “Adjusted prediction plots have been available in leading statistical software for years (Breheny & Burchett, 2017; Fox, 2003; Williams, 2012), and adding a scatterplot of the data is not difficult to do (e.g., Mitchell, 2012b, p. 448).”
Institutions and Entrepreneurship Quality (2018, methodology): “Building on Williams (2012) and Rising (2012), the beta coefficients in Table 4 provide averaged results of model estimation and are limited in capturing non-linear effects.”
Enhancing Rigor in Quantitative Entrepreneurship Research (2019, background): “To understand the effect of a change in the independent variable, marginal effects (especially average marginal effects) are often a helpful way to assess and communicate the effect sizes (e.g., Williams, 2012).”
What’s in a p? Reassessing best practices for conducting and reporting hypothesis-testing research (2017, background): “This is just one (simple) example of how to unpack the nature of the interaction effect (see also Williams, 2012, and Greene, 2010, for alternative approaches, and Hoetker 2007, Wiersema & Bowen, 2009, Zelner, 2009 for a discussion of logit and probit models, also including STATA do files).”
Interpreting Regression Results using Average Marginal Effects with R ’ s margins (2017, methodology): “The outline of this text is as follows: section 1 describes the statistical background of regression estimation and the distinctions between estimated coefficients and estimated marginal effects of righthand-side variables, Section 2 describes the computational implementation of margins used to obta”
Ordinal regression: A review and a taxonomy of models (2021, methodology): “For basic ordinal models Archer and Williams (2012) used the L1 penalty and Archer and Williams (2012) used elastic net versions.”
The Breakdown of the Spanish Two-Party System: The Upsurge of Podemos and Ciudadanos in the 2015 General Election (2016, background): “unemployed vs being employed) but they share the remaining characteristics of the sample (Williams 2012).”