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Students of public opinion research have argued that voters show very little consistency and structure in their political attitudes. A model of the survey response is proposed which takes account of the vagueness in opinion survey questions and in response categories. When estimates are made of this vagueness or “measurement error” and the estimates applied to the principal previous study, nearly all the inconsistency is shown to be the result of the vagueness of the questions rather than of any failure by the respondents.

Many social scientists believe that dumping long lists of explanatory variables into linear regression, probit, logit, and other statistical equations will successfully “control” for the effects of auxiliary factors. Encouraged by convenient software and ever more powerful computing, researchers also believe that this conventional approach gives the true explanatory variables the best chance to emerge. The present paper argues that these beliefs are false, and that without intensive data analysis, linear regression models are likely to be inaccurate. Instead, a quite different and less mechanical research methodology is needed, one that integrates contemporary powerful statistical methods with deep substantive knowledge and classic data—analytic techniques of creative engagement with the data.

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s Abstract The past two decades have brought revolutionary change to the field of political methodology. Steady gains in theoretical sophistication have combined with explosive increases in computing power to produce a profusion of new estimators for applied political researchers. Attendance at the annual Summer Meeting of the Methodology Section has multiplied many times, and section membership is among the largest in APSA. All these are signs of success. Yet there are warning signs, too. This paper attempts to critically summarize current developments in the young field of political methodology. It focuses on recent generalizations of dichotomous-dependentvariable estimators such as logit and probit, arguing that even our best new work needs a firmer connection to credible models of human behavior and deeper foundations in reliable empirical generalizations.