Estimating interest groups' policy positions through content analysis: a discussion of automated and human-coding text analysis techniques applied to studies of EU lobbying

Abstract

The promises and pitfalls of automated (computer-assisted) and human-coding content analysis techniques applied to political science research have been extensively discussed in the scholarship on party politics and legislative studies. This study presents a similar comparative analysis outlining the pay-offs and trade-offs of these two methods of content analysis applied to research on EU lobbying. The empirical focus is on estimating interest groups’ positions based on their formally submitted policy position documents in the context of EU policymaking. We identify the defining characteristics of these documents and argue that the choice for a method of content analysis should be informed by a concern for addressing the specificities of the research topic covered, of the research question asked and of the data sources employed. We discuss the key analytical assumptions and methodological requirements of automated and human-coding text analysis and the degree to which they match the identified text characteristics. We critically assess the most relevant methodological challenges research designs face when these requirements need to be complied with and how these challenges might affect measurement validity. We also compare the two approaches in terms of their reliability and resource intensity. The article concludes with recommendations and issues for future research.

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ORCID for Raimondas Ibenskas: orcid.org/0000-0003-4128-9464

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