Introduction to Quantitative Text Analysis

Most social science concepts are not directly observable, text analysis can provide a useful method in which we can measure quantities of interest that are otherwise difficult to estimate. For instance, by analysing the speeches of legislators, we can classify them as charismatic, populist, authoritarian, liberal, and so on. Similarly, by analysing the content of newspaper editorials, we can infer whether the media in question were biased in favour of a particular candidate during an election campaign.

Text analysis is a specific case of content analysis, typically defined as a method whose goal is to summarize a body of information in the form of text, in order to make inferences about the actor behind this body of information. This implies that text analysis can be seen as a data reduction method since its goal is to reduce the text material in to more manageable bits of information. Text analysis can be also seen as a method for descriptive inference. Weber (1990, p. 9) for instance, defines content analysis as ‘a method that uses a set of procedures to make valid inferences from text’. The idea is that, by analysing the textual output of an actor, we can infer something about this actor. This conceptualization of content and text analysis implies that we can use it as a tool for measurement in the social sciences. In this view of content analysis we are concerned with replicability and objectivity, (Neuendorf 2002, pp. 10-15), and therefore we should distinguish text analysis from other approaches/methods such as discourse analysis, rhetorical analysis, constructivism, ethnography and so on.

The course intends to familiarize participants with both manual and computer-assisted text analysis. Following Krippenforff (2004) and Neuendorf (2002), the course will introduce participants to the basic concepts and building blocks in content analysis designs. For instance, the following questions will be addressed and discussed during the course:

• Coding scheme (What are the theoretical underpinnings of the coding scheme? How are the categories selected and operationalized? What are the coding units? How is coding performed? Is our coding scheme valid?)
• Selection of documents (What guides the selection of texts? Are texts sufficiently comparable? Are our documents valid and reliable indicators of the quantities of interest? How can we acquire and process text for computer-assisted text analysis?)
• Aggregation (Are texts coded by different coders? If so, how are their results aggregated? If not, how can we ensure inter-coder reliability? What statistical measures can be used to estimate inter-coder reliability?)
• Scaling (Are we estimating the quantities of interest directly? If not, how do we scale data in order to estimate the quantities of interest? Is our scaling valid and reliable?)

For manual text analysis, the course will also look at the, often overlooked, distinction between the analysis of manifest content and judgemental coding. For computer-assisted text analysis, the course will offer an introduction to a variety of popular methods, such as the use of  content analysis dictionaries (including sentiment analysis), scaling methods (wordscores, wordfish), and supervised and unsupervised learning approaches (including topic models). The course will look discuss the relationship between reliability and validity, illustrate methods for estimating inter-coder reliability, and explore the links between manual and computer-assisted text analysis in terms of validation and training of supervised classificatio methods..

The course will be taught in a mix of lectures and seminars and participants will have the opportunity to practice on hands-on exercises. The examples used to illustrate the promises as well as the pitfalls of content analysis will be concerned with various applications across the social sciences (e.g. sentiment analysis of the press, frames analysis of social movements, estimating the positions of political actors, agenda-setting in the EU), while the majority of the exercises will be completed by following, step-by-step, code provided in the R statistical software, so that previous knowledge of R will not be necessary. In most of the seminars we will use R Studio. Follow the link for download instructions: https://www.rstudio.com/products/rstudio/download/ In addition, participants will be able to present their own project in class and receive feedback.

Participants are expected to be familiar with basic statistical concepts such as measures of central tendency (mean, median), dispersion (standard deviation), tests of association (Pearson’s r) and inference (χ², t-test). These material are covered in the first few chapters of introductory statistics or data analysis textbooks. A useful example is Pollock P.H. III, The Essentials of Political Analysis, fourth edition (Washington, DC: CQ Press, 2012), Chapters 2, 3, 6, and 7. Some familiarity with R statistical software is also desirable but not necessary. In most of the seminars we will use R Studio.

Each course includes pre-course assignments, including readings and pre-recorded videos, as well as daily live lectures totalling at least three hours. The instructor will conduct live Q&A sessions and offer designated office hours for one-to-one consultations.

Please check your course format before registering.

Online courses

Live classes will be held daily for three hours on a video meeting platform, allowing you to interact with both the instructor and other participants in real-time. To avoid online fatigue, the course employs a pedagogy that includes small-group work, short and focused tasks, as well as troubleshooting exercises that utilise a variety of online applications to facilitate collaboration and engagement with the course content.

In-person courses

In-person courses will consist of daily three-hour classroom sessions, featuring a range of interactive in-class activities including short lectures, peer feedback, group exercises, and presentations.

Disclaimer

This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc.). Registered participants will be informed at the time of change.

By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, please contact us before registering.