Access the full text.
Sign up today, get DeepDyve free for 14 days.
E. Norton, Hua Wang, C. Ai (2004)
Computing Interaction Effects and Standard Errors in Logit and Probit ModelsThe Stata Journal, 4
Jonathan Nagler (1991)
Where Is the Schema? Going Beyond the āSā Word in Political PsychologyAmerican Political Science Review, 85
Scott Basinger, Howard Lavine (2005)
Ambivalence, Information, and Electoral ChoiceAmerican Political Science Review, 99
Michael Miller (2012)
Economic Development, Violent Leader Removal, and DemocratizationAmerican Journal of Political Science, 56
William Berry, Matt Golder, D. Milton (2012)
Improving Tests of Theories Positing InteractionThe Journal of Politics, 74
Gary King, Langche Zeng (2001)
Improving Forecasts of State FailureWorld Politics, 53
G. Hoetker (2004)
Confounded Coefficients: Extending Recent Advances in the Accurate Comparison of Logit and Probit Coefficients Across GroupsEconometrics eJournal
M. Hanmer, Kerem Kalkan (2013)
Behind the Curve: Clarifying the Best Approach to Calculating Predicted Probabilities and Marginal Effects from Limited Dependent Variable ModelsAmerican Journal of Political Science, 57
Bennet Zelner (2009)
Using simulation to interpret results from logit, probit, and other nonlinear modelsSouthern Medical Journal, 30
H. Goemans, K. Gleditsch, G. Chiozza (2009)
Introducing Archigos: A Dataset of Political LeadersJournal of Peace Research, 46
William Berry, Jacqueline DeMeritt, J. Esarey (2010)
Testing for Interaction in Binary Logit and Probit Models: Is a Product Term Essential?American Journal of Political Science, 54
S. Morgan, Christopher Winship (2007)
Counterfactuals and Causal Inference: Methods and Principles for Social Research
A. Dreher, M. Gassebner (2008)
Do IMF and World Bank Programs Induce Government Crises? An Empirical AnalysisInternational Organization, 66
C. Ai, E. Norton (2003)
Interaction terms in logit and probit modelsEconomics Letters, 80
Matthew Holian (2009)
Outsourcing in US cities, ambulances and elderly votersPublic Choice, 141
Jonathan Nagler (2016)
THE EFFECT OF REGISTRATION LAWS AND EDUCATION ON U.S. VOTER TURNOUT
R. Reese, Christopher Achen, John Aldrich, F. Nelson (1986)
Interpreting and Using Regression Quantitative Applications in the Social Sciences, Paper 29; Linear Probability, Logit and Probit Models Quantitative Applications in the Social Sciences, Paper 45The Statistician, 35
Gary King, Langche Zeng (2006)
The Dangers of Extreme CounterfactualsPolitical Analysis, 14
Gary King, Michael Tomz, J. Wittenberg (2000)
Making the Most Of Statistical Analyses: Improving Interpretation and PresentationPSN: Computational Models (Games) (Topic)
W. Greene (2010)
Testing hypotheses about interaction terms in nonlinear modelsEconomics Letters, 107
H. Bowen (2012)
Testing Moderating Hypotheses in Limited Dependent Variable and Other Nonlinear ModelsJournal of Management, 38
M. Haspel, H. Knotts (2005)
Location, Location, Location: Precinct Placement and the Costs of VotingThe Journal of Politics, 67
Thomas Brambor, W. Clark, Matt Golder (2006)
Understanding Interaction Models: Improving Empirical AnalysesPolitical Analysis, 14
J. Gilbert, R. Oladi (2012)
Net campaign contributions, agricultural interests, andĀ votes on liberalizing trade with ChinaPublic Choice, 150
Christopher Achen (1982)
Interpreting and Using Regression
We assess the ability of logit, probit and numerous other parametric models to test a hypothesis that two variables interact in influencing the probability that some event will occur (Pr(Y)) in what we believe is a very common situation: when one's theory is insufficiently strong to dictate a specific functional form for the data generating process. Using Monte Carlo analysis, we find that many models yield overconfident inferences by generating 95% confidence intervals for estimates of the strength of interaction that are far too narrow, but that some logit and probit models produce approximately accurate intervals. Yet all models we study generate point estimates for the strength of interaction with large enough average error to often distort substantive conclusions. We propose an approach to make the most effective use of logit and probit in the situation of specification uncertainty, but argue that nonparametric models may ultimately prove to be superior.
American Journal of Political Science – Wiley
Published: Mar 1, 2016
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.