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Moving up. Applying aggregate level time series analysis in the study of media coverage

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Abstract

In this article the advantages of aggregate level time series analysis for the study of media coverage are discussed. This type of analysis offers the opportunity to answer questions relating to causes and effects of media attention for issues and all kind of other content characteristics. Data that ask for a time series approach have become widely available during the past years, due to the rise of digital archives and social media such as Twitter and Facebook. This type of analysis allows for answering a set of interesting research questions and strong inferences about causal processes. Common challenges in time series analysis, relating to stationarity, accounting for a series’ past and autoregressive conditional heteroscedasticity are discussed. Two useful approaches, ARIMA and VAR, are introduced stepwise. An empirical example, dealing with intermedia agenda-setting between different newspapers in the Netherlands, demonstrates how both techniques can be applied and how they provide insightful answers to interesting research problems.

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Notes

  1. Officially, stationarity also requires that the variance of the series does not show an upward or downward trend (ever increasing or decreasing; variance-stationarity). Since for the type of data used in this article’s analyses (as with almost all data used by social scientists), it is highly unlikely that the variance is ever increasing or decreasing, I assume variance-stationarity and limit the discussion to the stationarity of the mean (mean-stationarity).

  2. Contrary to the ARIMA-model building process, if any of the series is non-stationary, some scholars advise against differencing because it removes long-term dynamics. They suggest the use of a different technique, Vector Error Correction Models (VECM) (Brandt and Williams 2007; Hamilton 1994). I will not discuss VECM here, for a good overview on error correction models, see De Boef and Keele (2008).

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Acknowledgments

Many thanks to David Hollanders for useful comments on an earlier draft and for our continuous discussion about many of the topics addressed in this paper. The research was supported through a VENI-grant from the Dutch science foundation (NWO).

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  1. Amsterdam School of Communication Research (ASCoR), University of Amsterdam, Kloveniersburgwal 48, 1012 CX , Amsterdam, The Netherlands

    Rens Vliegenthart

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Correspondence to Rens Vliegenthart.

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Vliegenthart, R. Moving up. Applying aggregate level time series analysis in the study of media coverage. Qual Quant 48, 2427–2445 (2014). https://doi.org/10.1007/s11135-013-9899-0

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