Representativeness is a critical consideration in corpus linguistics as it ensures that the linguistic analyses conducted on a corpus can yield valid and generalizable insights about the target domain. Without adequate consideration for representativeness, findings may be skewed, thereby undermining the reliability of any conclusions drawn. Despite its importance, many corpus-based studies neglect planning for and evaluation of representativeness, which poses limitations to the accuracy and applicability of their results. Although corpus size has been recognized as a determinant of representativeness, domain analysis is an equally important element that has not received as much attention. This chapter utilizes the model of corpus representativeness proposed by Egbert, Biber, and Gray (2022), which advocates for a detailed approach to both domain analysis and sample planning. The model, which is rooted in statistical and logical rigor, underscores the importance of minimizing coverage and selection biases. The chapter applies this model in a case study on constructing a corpus of AI-simulated human conversations. By following the model, the case study illustrates the processes of domain specification, operationalization, and sampling to achieve a high level of representativeness for the corpus.

Any research question or application relating to language variation and/or use can be approached from a corpus linguistic perspective. The goals of The Cambridge Handbook of English Corpus Linguistics (CHECL) are to survey the breadth of these research questions and applications in relation to the linguistic study of English. The handbook addresses a range of topics, including chapters on lexical variation, grammatical variation, historical change, online language and social media, multimodal corpora, the linguistic description of dialects and registers, and applications to language teaching and translation. In each chapter, authors assess what we have learned from corpus-based investigations to date and provide detailed case studies that illustrate how corpus analyses can be employed for empirical descriptions, documenting surprising patterns of language use that are often unanticipated previously. By bringing together diverse perspectives and cutting-edge research, this volume serves as a comprehensive resource for researchers seeking to understand and apply a corpus-based approach to the study of English.

Chapter 4 argues for the benefits of an intra-individual research design and collecting different types of data from the same individuals. It shows that there is a developing interest towards an individual rather than commonalities across individuals in psychology, brain research and linguistics and discusses the differences between the individual and the communal level of language representation drawing on the usage-based perspective and complex systems. It then proposes a corpus of Master’s thesis drafts as a usage corpus, a personal corpus of references cited in the thesis as an exposure corpus and word associations as complementing psycholinguistic data. Given a long history of the word association method which was not always successful, the chapter also provides an extended discussion of its specifics. At the end, the chapter describes the corpus linguistic operationalization of meaning-shift units adopted in this study and data triangulation procedures.