RSS01.E3 Discourse Network Analysis
Discourse Network Analysis is a methodological toolbox for measuring and analyzing policy debates and their development over time. At its core, the software Discourse Network Analyzer (DNA) allows researchers to manually code actors’ opinions about policies in text data. In a way similar to other qualitative content analysis tools, the user annotates statements of political actors about their preferred or rejected concepts, policy instruments, frames, or beliefs. Useful text sources can be newspaper articles, parliamentary testimony, press agencies, or social media. DNA then allows the researcher to export various kinds of network data based on what the user coded.
The network data capture the relationships between political actors based on their congruence or conflict around concepts. As these relationships are aggregated into a network, the user can identify discourse coalitions or advocacy coalitions in these networks, identify brokers, opinion leaders, and central actors and concepts, examine the dimensionality of the discourse, find frames composed of different concepts through co-agreement by multiple actors, track the evolution of the policy debate over time (for example before policy change occurs), apply ideological scaling techniques to measure actors’ ideological ideal points relative to each other, or model the contributions by actors to the debate using statistical techniques.
The course explores the connections between discourse network analysis and a number of policy process theories. It introduces the mathematical foundations of creating discourse networks using annotated text datasets. We will cover best practices for coding statements in text data. The course will introduce a variety of statistical and exploratory methods to analyze the resulting network data. In addition to the methodological and theoretical foundations, we will dedicate about half of the time to software implementations, including the Discourse Network Analyzer, its associated R package rDNA, and visualization and analysis of discourse networks in the stand-alone network analysis software visone.
After attending the course, participants will be able to confidently code their own policy debates using the DNA software, possibly in teams, and will be able to analyze the resulting network data competently in visone and, if a participant has prior R skills, also using the rDNA package. Most importantly, participants will learn how to operationalize important aspects of policy process theories using discourse network analysis and apply the methodology to their own research questions.
|19 June 2023 - 23 June 2023|
Early Bird Regular: €895 (application deadline* April 1st)
|Scholarships and discounts||Find more information here|
*Your application is only completed when the course fee has been paid
|Course leader||Philip Leifeld|
|Level of participant||
|Admission requirements||Participants should be interested in thinking about social phenomena and policy making as complex systems. Basic experience with the statistical programming environment R would be a plus, but only a small part of the course will actually use R. If you are willing to listen without using R during these parts, R skills are not strictly required. Participants should ideally bring their own laptops.|
|Mode of Study||On Campus|
|ECTS||2 or 4 Find more information here|