Many authors report a positive relationship of education and political interest with political efficacy and trust, but it is well known that both of the former are associated with response styles, such as a tendency to “strongly agree.” Since they are related to both a substantive concept (political efficacy and trust), and to methodological effects (agreement bias and a tendency to give non-substantive responses) it is important to assess whether the substantive relationship is due to methodological artifacts. Applying multiple correspondence analysis to the 1984 Canadian National Election Study, we will discuss a method which allows to test a set of items for measurement effects such as ordinality and response sets. In the given example, ordinality of the political efficacy and trust items could be confirmed only for politically interested respondents. For respondents with low political interest, there is clear evidence of a response set resulting in a tendency to “strongly agree” regardless of the direction of the items. Taken together, these findings call into question the substantive relationships reported in the literature.

Binary, count, and duration data all code discrete events occurring at points in time. Although a single data generation process can produce all of these three data types, the statistical literature is not very helpful in providing methods to estimate parameters of the same process from each. In fact, only a single theoretical process exists for which known statistical methods can estimate the same parameters—and it is generally used only for count and duration data. The result is that seemingly trivial decisions about which level of data to use can have important consequences for substantive interpretations. We describe the theoretical event process for which results exist, based on time independence. We also derive a set of models for a time-dependent process and compare their predictions to those of a commonly used model. Any hope of understanding and avoiding the more serious problems of aggregation bias in events data is contingent on first deriving a much wider arsenal of statistical models and theoretical processes that are not constrained by the particular forms of data that happen to be available. We discuss these issues and suggest an agenda for political methodologists interested in this very large class of aggregation problems.

This article develops a model that simultaneously considers individual turnout and vote choice while also accounting for uncertainty about candidates. The theoretical development of this model implies that the effects of uncertainty on turnout vary with the strength of individual preferences. Application of the model to individual choice in the 1996 American presidential election confirms that decreasing uncertainty about the character traits of the candidates decreases the probability of abstention for individuals with strong preferences but increases the probability of abstention for individuals with weak preferences.

We argue that survey responses to economic evaluation questions exhibit instability and can be affected by fairly trivial changes in questionnaire wording. Our analyses make three empirical contributions to this area of survey research. First, we demonstrate that within the course of the interview there is considerable instability in economic evaluations. Second, one source of this instability is cues regarding economic performance, such as those provided by the media. We find that respondents can be persuaded to change their economic evaluations if they receive contradictory cues. Finally, we demonstrate that question placement can affect economic evaluations. More specifically, we demonstrate that proximity to political questions can contaminate economic evaluations. If economic evaluations closely follow political preference questions, respondents have a tendency to give economic responses that are “consistent” with their political responses. Our empirical analysis is based on economic evaluations of respondents to the Hungarian Markets and Democracy Survey administered during December 1997.

Political scientists often argue that political processes move together in the long run. Examples include partisanship and government approval, conflict and cooperation among countries, public policy sentiment and policy activity, economic evaluations and economic conditions, and taxing and spending. Error correction models and cointegrating relationships are often used to characterize these equilibrium relationships and to test hypotheses about political change. Typically the techniques used to estimate equilibrium relationships are based on the statistical assumption that the processes have permanent memory, implying that political experiences cumulate. Yet many analysts have argued that this is not a reasonable theoretical or statistical assumption for most political time series. In this paper I examine the consequences of assuming permanent memory when data have long but not permanent memory. I focus on two commonly used estimators: the Engle-Granger two-step estimator and generalized error correction. In my analysis I consider the important role of simultaneity and discuss implications for the conclusions political scientists have drawn about the nature, even the existence, of equilibrium relationships between political processes. I find that even small violations of the permanent memory assumption can present substantial problems for inference on long-run relationships in situations that are likely to be common in applied work in all fields and suggest ways that analysts should proceed.