This chapter considers the main types of data used in Politics and International Relations, as well as the main criteria by which to judge whether the data collected is good or not. It first describes the steps involved in the process of thinking about what data or evidence is relevant to answering a research question before discussing the importance of addressing issues of validity and reliability in research. Some of these issues are illustrated by referring to recent attempts to measure corruption, a major topic of interest in Politics and International Relations. The chapter also examines the issue of case selection as well as the collection of qualitative and quantitative data using methods such as interviewing and observation. Finally, it analyses the so-called ‘big data’ revolution in data collection and analysis, and provides a data quality checklist.
Yannick Dufresne and Brittany I. Davidson
This chapter assesses big data. Within the social sciences, big data could refer to an emerging field of research that brings together academics from a variety of disciplines using and developing tools to widen perspective, to utilize latent data sets, as well as for the generation of new data. Another way to define big data in the social sciences refers to data corresponding to at least one of the three s of big data: volume, variety, or velocity.. These characteristics are widely used by researchers attempting to define and distinguish new types of data from conventional ones. However, there are a number of ethical and consent issues with big data analytics. For example, many studies across the social sciences utilize big data from the web, from social media, online communities, and the darknet, where there is a question as to whether users provided consent to the reuse of their posts, profiles, or other data shared when they signed up, knowing their profiles and information would be public. This has led to a number of issues regarding algorithms making decisions that cannot be explained. The chapter then considers the opportunities and pitfalls that come along with big data.
Todd Landman and Larissa C. S. K. Kersten
This chapter focuses on the measurement and monitoring of human rights. It explains the purpose, challenges, and types of human rights measures and discusses the main content of human rights that ought to be measured, including the different categories and dimensions of human rights. It also considers the different ways that human rights have been measured using various kinds of data and measurement strategies, such as events-based data, standards-based data, survey data, and socio-economic and administrative statistics. Furthermore, it looks at new trends in human rights measurement, with a focus on new ways to measure economic and social rights, ‘open source’, and ‘big’ data, and the mapping and visualization of human rights data. The chapter concludes by discussing the remaining challenges for human rights measurement and monitoring, including biased reporting, incomplete source material, and the importance of continued dialogue between different academic disciplines on the need for measurement.
Edited by Jean-Frédéric Morin, Christian Olsson, and Ece Özlem Atikcan
Research Methods in the Social Sciences features chapters that cover a wide range of concepts, methods, and theories. Each chapter begins with an introduction to a method, using real-world examples from a wide range of academic disciplines, before discussing the benefits and limitations of the approach, its current status in academic practice, and finally providing tips and advice on when and how to apply the method in research. The text covers both well-established concepts and emerging ideas, such as big data and network analysis, for qualitative and quantitative research methods.
The Application of Automatic Text Processing in the Social Sciences
This chapter examines automated text analysis (ATA), which describes the different methodologies that can be applied in order to perform text analysis with the use of computer software. ATA is a computer-assisted method for analysing text, whenever the analysis would be prohibitively labour-intensive due to the volume of texts to be analysed. ATA methods have become more popular due to current interest in big data, taking into account the volume of textual content that is made easily accessible by the digitization of human activity. Key to ATA is the notion of corpus, which is a collection of texts. A necessary step before starting any analysis is to collect together the necessary documents and construct the corpora that will be used. Which texts need to be included in this step is dictated by the research question. After text collection, some processing steps need to be taken before the analysis starts, for example tokenization and part-of-speech tagging. Tokenization is the process of splitting a text into its constituent words, also called tokens, whereas part-of-speech tagging assigns each word a label that indicates the respective part-of-speech.