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To authenticate to the full-text of this article, please visit this link: http://dx.doi.org/10.1111/j.1540-5907.2010.00440.x Byline: Michael MacKuen (1), Jennifer Wolak (2), Luke Keele (3), George E. Marcus (4) Abstract: Why do people practice citizenship in a partisan rather than in a deliberative fashion? We argue that they are not intractably disposed to one type of citizenship, but instead adopt one of two different modes depending on the strategic character of current circumstances. While some situations prompt partisan solidarity, other situations encourage people to engage in open-minded deliberation. We argue that the type of citizenship practiced depends on the engagement of the emotions of anxiety and aversion. Recurring conflict with familiar foes over familiar issues evokes aversion. These angry reactions prepare people for the defense of convictions, solidarity with allies, and opposition to accommodation. Unfamiliar circumstances generate anxiety. Rather than defend priors, this anxiety promotes the consideration of opposing viewpoints and a willingness to compromise. In this way, emotions help people negotiate politics and regulate the kinds of citizenship they practice. Author Affiliation: (1)University of North Carolina at Chapel Hill (2)University of Colorado (3)The Ohio State University (4)Williams College Article note: Michael MacKuen is Burton Craige Professor of Political Science, University of North Carolina at Chapel Hill, Department of Political Science, CB# 3265 Hamilton Hall, Chapel Hill, NC 27599-3265 (MacKuen@unc.edu). Jennifer Wolak is Assistant Professor, University of Colorado, Department of Political Science, 333 UCB, Boulder, CO 80309-0333 (wolakj@colorado.edu). Luke Keele is Associate Professor, The Ohio State University, 2140 Derby Hall, Columbus, OH 43201 (keele.4@polisci.osu.ed). George E. Marcus is Full Professor, Williams College, Department of Political Science, 2 Morey House, Williamstown, MA 01267 (George.E.Marcus@williams.edu).

To purchase or authenticate to the full-text of this article, please visit this link: http://dx.doi.org/10.1111/j.1540-5907.2007.00248.x Byline: Luke Keele (1) Abstract: It is well understood that trust in government responds to the performance of the president, Congress, and the economy. Despite improved government performance, however, trust has never returned to the levels witnessed in the 1950s and 1960s. Social capital may be the force that has kept trust low. If so, we need to assess the relative contributions of both government performance and social capital at the macro level. Using macrolevel data, the analysis, here, is designed to capture the variation over time in both social capital and government performance and let them compete to explain the macro variation in trust. The empirical results demonstrate that both government performance and social capital matter, but that social capital appears to be the force which accounts for the decline in trust over the last 40 years. Author Affiliation: (1)The Ohio State University Article note: Luke Keele is assistant professor of political science, The Ohio State University, 2140 Derby Hall, 154 N. Oval Mall, Columbus, OH 43210 (keele.4@osu.edu).

For some time, one line of research on trust in government has stressed that trust results from evaluations of the institutions of government. Here, trust reflects public sentiments toward the responsiveness of the political process. Another line of research has alternatively countered that trust tends to be a reflection of political leaders. But this research has been largely unable to demonstrate that the performance of authorities matters relative to evaluations of the political process. Changes in partisan control of Congress and the presidency, however, provides us with a natural experiment where authorities change while the political process is constant. Here, partisans should trust government more when their party controls Congress, the presidency, or both. I find that trust does respond to changes in partisan control of the Congress and presidency, which demonstrates that the effect of authorities matters relative to evaluations of the political process.

Social scientists use the concept of interactions to study effect dependency. In the causal inference literature, interaction terms may be used in two distinct type of analysis. The first type of analysis focuses on causal interactions, where the analyst is interested in whether two treatments have differing effects when both are administered. The second type of analysis focuses on effect modification, where the analyst investigates whether the effect of a single treatment varies across levels of a baseline covariate. While both forms of interaction analysis are typically conducted using the same type of statistical model, the identification assumptions for these two types of analysis are very different. In this paper, we clarify the difference between these two types of interaction analysis. We demonstrate that this distinction is mostly ignored in the political science literature. We conclude with a review of several applications where we show that the form of the interaction is critical to proper interpretation of empirical results. [ABSTRACT FROM AUTHOR]

It is well know that presidents with congressional majorities are better able to implement their legislative agendas. However, it is concluded that presidential attempts influence elections, and hence the findings indicative of presidential influence in previous congressional elections are discussed.

This article evaluates the usefulness of one currently available set of presidential recordings, those of President Lyndon Johnson. It demonstrates that these recordings constitute a sample of the president, phone conversations and a reasonable representation of his contacts with others. It demonstrates the use of these data in assessing presidential persuasion and activities. It also suggests how popular presentations of these data, through other published means, have distorted the picture of presidential activities., If presidents lead by persuasion, then how they communicate their ideas, with whom they speak, about what, and to what effect constitute the central evidence of leaders in action. Yet, [...]

Many areas of political science focus on causal questions. Evidence from statistical analyses is often used to make the case for causal relationships. While statistical analyses can help establish causal relationships, it can also provide strong evidence of causality where none exists. In this essay, I provide an overview of the statistics of causal inference. Instead of focusing on specific statistical methods, such as matching, I focus more on the assumptions needed to give statistical estimates a causal interpretation. Such assumptions are often referred to as identification assumptions, and these assumptions are critical to any statistical analysis about causal effects. I outline a wide range of identification assumptions and highlight the design-based approach to causal inference. I conclude with an overview of statistical methods that are frequently used for causal inference. [ABSTRACT FROM AUTHOR]

Identifying causal mechanisms is a fundamental goal of social science. Researchers seek to study not only whether one variable affects another but also how such a causal relationship arises. Yet commonly used statistical methods for identifying causal mechanisms rely upon untestable assumptions and are often inappropriate even under those assumptions. Randomizing treatment and intermediate variables is also insufficient. Despite these difficulties, the study of causal mechanisms is too important to abandon. We make three contributions to improve research on causal mechanisms. First, we present a minimum set of assumptions required under standard designs of experimental and observational studies and develop a general algorithm for estimating causal mediation effects. Second, we provide a method for assessing the sensitivity of conclusions to potential violations of a key assumption. Third, we offer alternative research designs for identifying causal mechanisms under weaker assumptions. The proposed approach is illustrated using media framing experiments and incumbency advantage studies. [ABSTRACT FROM PUBLISHER]

The Cox proportional hazards model is widely used to model durations in the social sciences. Although this model allows analysts to forgo choices about the form of the hazard, it demands careful attention to the proportional hazards assumption. To this end, a standard diagnostic method has been developed to test this assumption. I argue that the standard test for nonproportional hazards has been misunderstood in current practice. This test detects a variety of specification errors, and these specification errors must be corrected before one can correctly diagnose nonproportionality. In particular, unmodeled nonlinearity can appear as a violation of the proportional hazard assumption for the Cox model. Using both simulation and empirical examples, I demonstrate how an analyst might be led astray by incorrectly applying the nonproportionality test. [ABSTRACT FROM PUBLISHER]