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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).

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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 attitudediscrepant 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.