The concept of the manifesto–media link implies that we can compare data based on electoral programs with data derived from media coverage. How-ever, such a comparison faces many challenges. In the following I will illus-trate how these challenges can be tackled. Most of the challenges discussed here apply to all different dataset combinations used in the empirical chap-ters.
Sampling of parties and media outlets
Using Manifesto Project Data to measure parties’ preferences and data based on media content during the electoral campaign implies an asymmetric cov-erage in terms of parties and media outlets. Whereas the data covcov-erage of parties’ electoral programs is close to a full coverage, the data coverage of media outlets and their reporting is necessarily limited and is the result
of a stricter sampling procedure. Although the Manifesto Project Dataset (and the Euromanifesto Dataset) do not cover all parties that compete at elections, they have a clear criteria covering all the relevant parties (namely the parties represented in parliament) and thereby providing a very good picture of the party system. In contrast, media systems are much more fragmented than party systems. On the one hand, there are different types of media outlets such as radio stations, tv channels, newspapers and online media and different types of formats within their coverage such as radio news, talkshows, opinion article, reports, and a vast amount of different on-line formats. On the other hand, even within these types, the number of competitors is often higher and the concentration is smaller than in party systems. Even large and well-funded projects of media content analysis do not have the resources to conduct a comprehensive analysis covering all content produced by all media outlets. A sampling of media outlets and content is therefore necessary. The guiding principle of the sampling is the same as the one applied to the sampling of electoral programs: the relevance.
Relevance is often measured in terms of circulation or outreach. As the rel-evance of different format is hard to compare, media content studies often sample the most relevant outlet among their competitors, for example the most read tabloid newspapers in a country, or the most watched tv news programs within a country. The exact data and sampling applied in the empirical chapters is described later. However, the sampling always follows the relevance criterion discussed here.
Different coding schemes
The empirical analysis requires data on media coverage and electoral pro-grams for a common set of issues. Most projects that generated large scale content analytical datasets on electoral programs or media coverage were started independent of each other and apply different issue coding schemes.
For example the issue coding of the Manifesto Project contains 56 main cat-egories which are distributed over seven domains. In contrast, the team from the European Election study used a more fine-grained coding scheme with a list of more than 150 different issues (Schuck et al., 2010). The general solution to come to comparable data is to map both issue coding schemes to a common scheme. If for example one scheme contains specific codes for different parts of the welfare state such as health system and pensions and the other scheme only contains one category for the welfare state, all the specific codes relating to the welfare state are merged and mapped on the welfare state code of the second coding scheme. The resulting scheme is -so to say - the largest common denominator of both schemes covering all issues with maximally the degree of detail of the coding scheme that covers the issue less extensive.
However, it is not always possible to find and equivalent category in two
3.6. Comparing Data from Manifestos and Media Coverage 59
different coding schemes, even after merging several categories in a scheme.
Media content analytical data based on news stories for example often con-tains a code covering stories dealing with the wheather (for example covering extraorindary heat or cold periods and their consequences - or wheather re-ports). Electoral programs hardly ever cover such a topic. Consequentially, the analysis excludes issues from the analysis where the category schemes cannot be mapped.
Different frequency of publication
Comparing electoral programs with media content comes along with the challenge to compare two very different types of documents. An electoral program is a document issued by a political party before the election usu-ally enacted by a party convention that illustrates a party’s policy goals and positions. Electoral programs shape the electoral campaign in provid-ing guidelines for the most important issues and positions used in leaflets, brochures, posters and press releases (Adams, Ezrow, and Somer-Topcu, 2011; Norris et al., 1999). The advantage to analyze electoral programs is that they are single documents that cover a broad range of issues and indicate priorities of parties becaues a party has to decide which issues to emphasize in their program. Where electoral programs are single documents issued once during the electoral campaign, media content is of a different nature. Media content is almost constantly published during the electoral campaign. Most newspapers are published on six days per week. TV news can be watched all day. Similar things can be said for radio and online news that are published constantly. The frequency of publication varies between different types of media, but all have a higher frequency of publication than electoral programs. So, the question is then how to compare the content of a single document with media coverage that is regularly or constantly published?
The solution here is to aggregate the media content over a longer period of time to a single value, for example the share of all articles on a specific issue published by a newspaper or the average issue position of a party during the electoral campaign. This necessarily also implies a loss of information and a reduction of complexity. First, when aggregating the information from different articles or news stories during the electoral campaign to one score for the whole campaign, we ignore differences that might occur during the electoral campaign. Second, by summing or averaging scores over different types of articles within the coverage of a media outlet, we ignore differences between for example opinion articles and reports. Both aspects could be studied with the data used here, however as the goal here is a cross-national research design and thereby a large scope of the study, a reduction of the complexity and the depth of the study is necessary to avoid an overcomplex design.