Introduction to Manual and Computer-Assisted Content Analysis

Most social science concepts are not directly observable, content analysis can provide a useful method in which we can measure quantities of interest that are otherwise difficult to estimate. For instance, by content analysing the speeches of legislators, we can classify them as charismatic, populist, authoritarian, liberal, and so on. Similarly, by content 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.

Content analysis is therefore typically defined as a method whose goal is to summarize a body of information, often in the form of text, and to make inferences about the actor behind this body of information. This implies that content 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. Content 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 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 content 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 content 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 content 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?)

The course will focus on both manual and computer-assisted content analysis. For manual content analysis, the course will also look at the, often overlooked, distinction between the analysis of manifest content and judgemental coding. For computer-assisted content analysis, the course will offer an introduction to a variety of popular methods, such as the use of  content analysis dictionaries, scaling methods (wordscores, wordfish), and supervised learning approaches. The course will look discuss the relationship between reliability and validity, illustrate methods for estimating inter-coder reliability, and methods for validating the results produced by computer-assisted content analysis.

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.