Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-24T19:13:13.176Z Has data issue: false hasContentIssue false
  • English
  • Français

Beyond Logit and Probit: Cox Duration Models of Single, Repeating, and Competing Events for State Policy Adoption

Published online by Cambridge University Press:  25 January 2021

Bradford S. Jones  and
Regina P. Branton
Bradford S. Jones
Affiliation:
University of Arizona
Regina P. Branton
Affiliation:
Rice University
Get access Rights & Permissions [Opens in a new window]

Abstract

Since 1990, the standard statistical approach for studying state policy adoption has been an event history analysis using binary link models, such as logit or probit. In this article, we evaluate this logit-probit approach and consider some alternative strategies for state policy adoption research. In particular, we discuss the Cox model, which avoids the need to parameterize the baseline hazard function and, therefore, is often preferable to the logit-probit approach. Furthermore, we demonstrate how the Cox model can be modified to deal effectively with repeatable and competing events, events that the logit-probit approach cannot be used to model.

Type
The Practical Researcher
Information
State Politics & Policy Quarterly , Volume 5 , Issue 4 , Winter 2005 , pp. 420 - 443
Copyright
Copyright © 2005 by the Board of Trustees of the University of Illinois

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Allison, Paul D. 1982. “Discrete-Time Methods for the Analysis of Event Histories.” In Sociological Methodology 1982, ed. Leinhardt, S.. Beverly Hills, CA: Sage.Google Scholar
Allison, Paul D. 1984. Event History Analysis: Regression for Longitudinal Data. Sage University Paper Series on Quantitative Application in the Social Sciences, series no. 07-041. Newbury Park, CA: Sage.CrossRefGoogle Scholar
Allison, Paul D. 1995. Survival Analysis Using the SAS System. Cary, NC: SAS Institute.Google Scholar
Andersen, Per Kragh, and Gill, R.D.. 1982. “Cox's Regression Model for Counting Processes: A Large Sample Study.” Annals of Statistics 10:1100–20.CrossRefGoogle Scholar
Balla, Steven J. 2001. “Interstate Professional Associations and the Diffusion of Policy Innovations.” American Politics Research 29:221–45.CrossRefGoogle Scholar
Beck, Nathaniel, and Jackman, Simon. 1998. “Beyond Linearity by Default: Generalized Additive Models.” American Journal of Political Science 42:596–627.CrossRefGoogle Scholar
Beck, Nathaniel, Katz, Jonathan N., and Tucker, Richard. 1998. “Taking Time Seriously: Time-Series-Cross-Section Analysis with a Binary Dependent Variable.” American Journal of Political Science 42:1260–88.CrossRefGoogle Scholar
Bennett, Scott. 1998. “Integrating and Testing Models of Rivalry Termination.” American Journal of Political Science 42:1200–32.CrossRefGoogle Scholar
Bergström, R., and Edin, P.A.. 1992. “Time Aggregation and the Distributional Shape of Unemployment Duration.” Journal of Applied Economics 7:5–30.CrossRefGoogle Scholar
Berry, Frances Stokes, and Berry, William D.. 1990. “State Lottery Adoptions as Policy Innovations: An Event History Analysis.” American Political Science Review 84:395–416.CrossRefGoogle Scholar
Berry, Frances Stokes, and Berry, William D.. 1992. “Tax Innovation in the States: Capitalizing on Political Opportunity.” American Journal of Political Science 36:715–42.CrossRefGoogle Scholar
Berry, Frances Stokes, and Berry, William D.. 1994. “The Politics of Tax Increases in the States.” American Journal of Political Science 38:855–9.CrossRefGoogle Scholar
Box-Steffensmeier, Janet M., and Jones, Bradford S.. 2004. Event History Modeling. New York: Cambridge University Press.CrossRefGoogle Scholar
Box-Steffensmeier, Janet M., and Zorn, Christopher J.W.. 2001. “Duration Models and Proportional Hazards in Political Science.” American Journal of Political Science 45:951–67.CrossRefGoogle Scholar
Box-Steffensmeier, Janet M., and Zorn, Christopher J.W.. 2002. “Duration Models for Repeated Events.” Journal of Politics 64:1069–94.CrossRefGoogle Scholar
Brace, Paul, Hall, Melinda Gann, and Langer, Laura. 1999. “Judicial Choice and the Politics of Abortion: Institutions, Context, and the Autonomy of Courts.” Albany Law Review 62:1265–302.Google Scholar
Brace, Paul, Hall, Melinda Gann, and Langer, Laura. 2001. “Placing Courts in State Politics.” State Politics and Policy Quarterly 1:81–108.CrossRefGoogle Scholar
Brace, Paul, and Langer, Laura. 2001. “State Supreme Courts and the Preemptive Power of the Judiciary.” Presented at the Annual Meeting of the Midwest Political Science Association, Chicago.Google Scholar
Brace, Paul, and Langer, Laura. N.d. “The Preemptory Power of State Supreme Courts Adoption of Abortion and Death Penalty Laws.” University of Arizona. Typescript.Google Scholar
Buckley, Jack, and Westerland, Chad. 2004. “Duration Dependence, Functional Form, and Corrected Standard Errors: Improving EHA Models of State Policy Diffusion.” State Politics and Policy Quarterly 4:94–113.CrossRefGoogle Scholar
Cleves, Mario. 1999. “Analysis of Multiple Failure-Time Data with Stata.” Stata Technical Bulletin 49:30–9.Google Scholar
Collett, D. 1994. Modelling Survival Data in Medical Research. London: Chapman and Hall.CrossRefGoogle Scholar
Cox, D.R. 1972. “Regression Models and Life Tables.” Journal of the Royal Statistical Society B 34:187–220.Google Scholar
Crowder, Martin. 2001. Classical Competing Risks. Boca Raton, FL: Chapman and Hall.CrossRefGoogle Scholar
David, H.A., and Moeschberger, M.L.. 1978. The Theory of Competing Risks. London: Charles Griffin.Google Scholar
Davison, A.C., and Gigli, A.. 1989. “Deviance Residuals and Normal Scores Plots.” Biometrika 76:211–21.CrossRefGoogle Scholar
Dawson, Richard, and Robinson, James A.. 1963. “Inter-Party Competition, Economic Variables, and Welfare Policies in the American States.” Journal of Politics 25:265–89.CrossRefGoogle Scholar
Diermeier, Daniel, and Stevenson, Randy T.. 1999. “Cabinet Survival and Competing Risks.” American Journal of Political Science 43:1051–68.CrossRefGoogle Scholar
Dye, Thomas. 1966. Politics, Economics, and the Public: Policy Outcomes in the American States. Chicago: Rand McNally.Google Scholar
Fleming, Thomas R., and Harrington, David P.. 1991. Counting Processes and Survival Analysis. New York: Wiley.Google Scholar
Gordon, Sanford C. 2002. “Stochastic Dependence in Competing Risks.” American Journal of Political Science 46:200–17.CrossRefGoogle Scholar
Hays, Scott P., and Glick, Henry R.. 1997. “The Role of Agenda Setting in Policy Innovation—An Event History Analysis of Living-Will Laws.” American Politics Quarterly 25:497–516.CrossRefGoogle Scholar
Hougaard, Philip. 2000. Analysis of Multivariate Survival Data. New York: Springer-Verlag.CrossRefGoogle Scholar
Larsen, Ulla, and Vaupel, James W.. 1993. “Hutterite Fecundability by Age and Parity: Strategies for Frailty Modeling of Event Histories.” Demography 30:81–102.CrossRefGoogle ScholarPubMed
Lin, D. Y., and Wei, L.J.. 1989. “The Robust Inference for the Cox Proportional Hazards Model.” Journal of the American Statistical Association 84:1074–8.CrossRefGoogle Scholar
Mintrom, Michael. 1997a. “Policy Entrepreneurs and the Diffusion of Innovation.” American Journal of Political Science 41:738–70.CrossRefGoogle Scholar
Mintrom, Michael. 1997b. “The State-Local Nexus in Policy Innovation Diffusion: The Case of School Choice.” Publius: The Journal of Federalism 27:41–60.CrossRefGoogle Scholar
Mintrom, Michael, and Vergari, Sandra. 1998. “Policy Networks and Innovation Diffusion: The Case of State Education Reforms.” Journal of Politics 60:126–48.CrossRefGoogle Scholar
Mooney, Christopher Z. 2001. “The Public Clash of Private Values.” The Public Clash of Private Values: The Politics of Morality Policy, ed. Mooney, Christopher Z.. Chatham, NJ: Chatham House.Google Scholar
Mooney, Christopher Z., and Lee, Mei-Hsien. 1995. “Legislating Morality in the American States: The Case of Pre-Roe Abortion Regulation Reform.” American Journal of Political Science 39:599–627.CrossRefGoogle Scholar
Royston, Patrick. 2001. “Flexible Parametric Alternatives to the Cox Model, and More.” Stata Journal 1:1–28.CrossRefGoogle Scholar
Royston, Patrick, and Parmar, M.K.B.. 2002. “Flexible Parametric Models for Censored Survival Data, with Applications to Prognostic Modelling and Estimation of Treatment Effects.” Statistics in Medicine 21:1275–97.CrossRefGoogle Scholar
State Politics and Policy Quarterly Data Resource. http://www.ku.edu/pri/SPPQ/research.shtml (July 13, 2005).Google Scholar
Therneau, Terry M., and Grambsch, Patricia M.. 2000. Modeling Survival Data: Extending the Cox Model. New York: Springer-Verlag.CrossRefGoogle Scholar
True, Jacqui, and Mintrom, Michael. 2001. “Transnational Networks and Policy Diffusion: The Case of Gender Mainstreaming.” International Studies Quarterly 45:27–57.CrossRefGoogle Scholar
Wei, L. J., and Glidden, David V.. 1997. “An Overview of Statistical Methods for Multiple Failure Time Data in Clinical Trials.” Statistics in Medicine 16:833–9.3.0.CO;2-2>CrossRefGoogle ScholarPubMed