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Published 2011 | public
Journal Article

An empirical Bayes approach to estimating ordinal treatment effects


Ordinal variables—categorical variables with a defined order to the categories, but without equal spacing between them—are frequently used in social science applications. Although a good deal of research exists on the proper modeling of ordinal response variables, there is not a clear directive as to how to model ordinal treatment variables. The usual approaches found in the literature for using ordinal treatment variables are either to use fully unconstrained, though additive, ordinal group indicators or to use a numeric predictor constrained to be continuous. Generalized additive models are a useful exception to these assumptions. In contrast to the generalized additive modeling approach, we propose the use of a Bayesian shrinkage estimator to model ordinal treatment variables. The estimator we discuss in this paper allows the model to contain both individual group—level indicators and a continuous predictor. In contrast to traditionally used shrinkage models that pull the data toward a common mean, we use a linear model as the basis. Thus, each individual effect can be arbitrary, but the model "shrinks" the estimates toward a linear ordinal framework according to the data. We demonstrate the estimator on two political science examples: the impact of voter identification requirements on turnout and the impact of the frequency of religious service attendance on the liberality of abortion attitudes.

Additional Information

© The Author 2010. Published by Oxford University Press on behalf of the Society for Political Methodology. The views expressed here are those of the authors, not of any organization they are currently or formally associated with. This article was accepted by the previous editorial team in consultation with the officers of the Society for Political Methodology. Unforeseen delays resulted in it appearing in an issue edited by two of the authors. These two authors played no editorial role for this article. Formerly SSWP 1293.

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August 19, 2023
October 23, 2023