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Modeling time-series count data: the unique challenges facing political communication studies.
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- Author(s): Fogarty BJ;Fogarty BJ; Monogan JE 3rd; Monogan JE 3rd
- Source:
Social science research [Soc Sci Res] 2014 May; Vol. 45, pp. 73-88. Date of Electronic Publication: 2014 Jan 09.- Publication Type:
Journal Article- Language:
English - Source:
- Additional Information
- Source: Publisher: Academic Press Country of Publication: United States NLM ID: 0330501 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1096-0317 (Electronic) Linking ISSN: 0049089X NLM ISO Abbreviation: Soc Sci Res
- Publication Information: Publication: New York, NY : Academic Press
Original Publication: New York, Seminar Press. - Subject Terms:
- Abstract: This paper demonstrates the importance of proper model specification when analyzing time-series count data in political communication studies. It is common for scholars of media and politics to investigate counts of coverage of an issue as it evolves over time. Many scholars rightly consider the issues of time dependence and dynamic causality to be the most important when crafting a model. However, to ignore the count features of the outcome variable overlooks an important feature of the data. This is particularly the case when modeling data with a low number of counts. In this paper, we argue that the Poisson autoregressive model (Brandt and Williams, 2001) accurately meets the needs of many media studies. We replicate the analyses of Flemming et al. (1997), Peake and Eshbaugh-Soha (2008), and Ura (2009) and demonstrate that models missing some of the assumptions of the Poisson autoregressive model often yield invalid inferences. We also demonstrate that the effect of any of these models can be illustrated dynamically with estimates of uncertainty through a simulation procedure. The paper concludes with implications of these findings for the practical researcher.
(Copyright © 2013 Elsevier Inc. All rights reserved.) - Contributed Indexing: Keywords: Count models; Political communication; Time series
- Publication Date: Date Created: 20140301 Date Completed: 20160105 Latest Revision: 20140228
- Publication Date: 20231215
- Accession Number: 10.1016/j.ssresearch.2013.12.008
- Accession Number: 24576628
- Source:
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