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To authenticate to the full-text of this article, please visit this link: http://dx.doi.org/10.1111/j.1460-2466.2009.01470.x Byline: R. Lance Holbert (1), R. Kelly Garrett (1), Laurel S. Gleason (1) Abstract: This article takes up Bennett and Iyengar's (2008) call for debate about the future of political communication effects research. We outline 4 key criticisms. First, Bennett and Iyengar are too quick to dismiss the importance of attitude reinforcement, long recognized as an important type of political media influence. Second, the authors take too narrow a view of the sources of political information, remaining fixated on news. Third, they offer an incomplete portrayal of selective exposure, exaggerating the extent to which individuals avoid attitude-discrepant information. Finally, they lean toward determinism when describing the role technologies play in shaping our political environment. In addition, we challenge Bennett and Iyengar's assertion that only brand new theory can serve to help researchers understand today's political communication landscape. We argue that existing tools, notably the Elaboration Likelihood Model (ELM), retain much utility for examining political media effects. Contrary to Bennett and Iyengar's claims, the ELM suggests that the contemporary political information environment does not necessarily lead to minimal effects. Author Affiliation: (1)School of Communication, The Ohio State University, Columbus, OH 43210, USA Article note: R. L. Holbert; e-mail: r.lance.holbert@gmail.com

To purchase or authenticate to the full-text of this article, please visit this link: http://dx.doi.org/10.1111/j.1460-2466.2008.00404.x Byline: William L. Benoit (1), R. Lance Holbert (2) Author Affiliation: (1)Department of Communication, University of Missouri-Columbia, Columbia, MO 65211 (2)School of Communication, The Ohio State University, Columbus OH 43210 Article note: William L. Benoit; e-mail: benoitw@missouri.edu

This study extends priming research in political communication by focusing on an alternative political information source (i.e., Michael Moore's Fahrenheit 9-11), affect rather than cognitions, and the existence of intra-affective ambivalence. In addition, two moderator variables are analyzed: political party identification and need for closure. There is a statically significant three-way interaction between the viewing of F 9-11, political party identification, and need for closure relative to the dependent variable of affective ambivalence toward George W. Bush. High need for closure Republicans who viewed F 9-11 exhibited pronounced levels of Bush-affective ambivalence. In addition, high need for closure Independents who viewed F 9-11 exhibited far lower ambivalence toward Bush relative to their control group peers. The findings are discussed relative to the roles performed by emerging alternative political media and the expansion of the theory of priming within the context of political communication.

An analysis of campaigning debate viewing as a mediator between news use and vote choice for the general voting populations is presented. The analysis shows that debate viewing acts as partisan reinforcement for democratic and republican voters.

This paper evaluates the perception of the role of the U.S. President by people who watch NBC's 'The West Wing.'

Structural equation modeling (SEM) is a viable multivariate tool used by communication researchers for the past quarter century. Building off Cappella (1975) as well as McPhee and Babrow (1987), this study summarizes the use of this technique from 1995-2000 in 37 communication-based academic journals. We identify and critically assess 3 unique methods for testing structural relationships via SEM in terms of the specification, estimation, and evaluation of their respective structural equation models. We provide general guidelines for the use of SEM and make recommendations concerning latent variable models, sample size, reporting parameter estimates, model fit statistics, cross-sectional data, univariate normality, cross-validation, nonrecursive modeling, and the decomposition of effects (direct, indirect, and total).