Linkage analysis is a sophisticated media effect research design that reconstructs the likely exposure to relevant media messages of individual survey respondents by complementing the survey data with a content analysis. It is an important improvement over survey-only designs: Instead of predicting some outcome of interest by media use and implicitly assuming what kind of media messages the respondents were exposed to, linkage analysis explicitly takes the media messages into account (de Vreese & Neijens, 2016; Scharkow & Bachl, 2017; Schuck, Vliegenthart, & de Vreese, 2016; Shoemaker & Reese, 1990; Slater, 2016; Valkenburg & Peter, 2013). The design in its modern form has been pioneered by Miller, Goldenberg, and Erbring (1979) and is today considered a “state-of-the art analysis of the impact of specific news consumption” (Fazekas & Larsen, 2015, p. 196). Its widespread use, especially in the field of political communication, and its still increasing popularity demonstrate the relevance of the design. The main advantage of a linkage analysis is the use of one or more message exposure variables which combine information about media use and media content. However, both constitutive sources are often measured with error: Survey respondents are not very good at reporting their media use reliably, and coders will often make some errors when classifying the relevant messages.In this article, we will first give a short overview on the prevalence and consequences of measurement error in both data sources. The arguments are based on a literature review and a simulation study which are published elsewhere in full detail (Scharkow & Bachl, 2017). We continue with a discussion of possible remedies in measurement and data analysis. Beyond the obvious need to improve the measures themselves, we highlight the importance of serious diagnostics of measurement quality. Such information can then be incorporated in the data analysis using estimation or imputation approaches, which are introduced in the main section of this chapter. We conclude by noting that 1) the improvement of measurements and the diagnosis of measurement error in both parts of a linkage analysis must be taken seriously; 2) many tools for correcting measurement error in single parts of a linkage analysis already exist and should be used; 3) methodological research is needed for the development of an integrated analysis workflow which accounts for measurement error and uncertainty in both data sources.
Calibration of Measurements
