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Conversation Analysis (CA) is one of the predominant methods for the detailed study of human social interaction. Bringing together thirty-four chapters written by a team of world-renowned experts, this Handbook represents the first comprehensive overview of conversation-analytic methods. Topics include how to collect, manage, and transcribe data; how to explore data in search of possible phenomena; how to form and develop collections of phenomena; how to use different types of evidence to analyze data; how to code and quantify interaction; and how to apply, publish, and communicate findings to those who stand to benefit from them. Each method is introduced clearly and systematically, and examples of CA in different languages and cultures are included, to show how it can be applied in multiple settings. Comprehensive yet accessible, it is essential reading for researchers and advanced students in disciplines such as Linguistics, Sociology, Anthropology, Communication and Psychology.
The goal of this chapter is to guide the reader interested in grammar in interaction through the entire research process, beginning with how to find a researchable phenomenon and culminating in how to reveal the larger significance of research findings on grammar. We focus primarily on grammatical phenomena that are morphosyntactic in nature but include discussion of how prosodic-phonetic and embodied practices can impact the exploration of morphosyntactic phenomena. We begin by addressing some of the multiple sources of inspiration for a new research project on grammar, including starting with an observation in the data, or with an observation from the linguistic literature, or with an observation from the CA/IL literature on a different language. We then explore how to delimit the phenomenon chosen and how to build a collection of pertinent instances. Finally, we turn to issues of analyzing the collection and constructing an argument, with a final discussion of how to probe the theoretical significance of grammatical findings. In conclusion, we note that because of its orderliness, grammar in general as well as language-specific grammatical practices contribute to establishing and maintaining the social order.
This chapter discusses different types of evidence that conversation analysts use to ground their claims about social action. We begin by reviewing the epistemological perspective of CA, which demands that evidence reflect participants’ orientations; as a critical part of understanding the terms ‘participant orientation’ and ‘relevance,’ here we also discuss two ways in which CA’s position and emphasis on them are commonly misunderstood. The bulk of this chapter then reviews and illustrates a range of types of participant-orientation evidence. We organize our presentation of types of evidence roughly by sequential position vis-à-vis the focal action about which the analyst is making claims, including evidence to be found in: (i) next-turn, (ii) same-turn (e.g., same-TCU self-repair, accounts), (iii) prior turn or sequence, (iv) third turn/position (e.g., repair after next turn, courses of action/activity), (v) fourth turn/position, and (vi) more distal positions. We also discuss other forms of evidence that are not necessarily defined by sequential position, including: (i) third-party conduct, (ii) reported conduct, (iii) deviant cases, and (iv) distributional evidence. We conclude by offering some brief reflections on bringing different types and positions of evidence together toward the construction of an argument.
While the preceding chapters of the Handbook have focused on practical skills in CA research methods, this chapter looks towards the path ahead. A diverse group of conversation analysts were asked to outline possible projects, point readers toward un- or under-described interactional phenomena, and discuss persistent issues in the field. The contributions address future advances in data collection, specific interactional practices, the complex interplay between language and the body, and cross-cultural and crosslinguistic comparisons, among other issues. The chapter concludes with a concise reiteration of the bedrock principle that underpins all CA research methods.
We explore the necessarily comparative nature of CA’s methodology. We focus less on cross-linguistic comparisons, comparisons between talk-in-interaction in different settings, and comparisons between speakers from diverse speech communities. Instead, we consider the micro ways in which analysts work comparatively, ways that generally go unnoticed in accounts of CA’s methodology but which underpin our approach in data sessions, to building collections of phenomena, and even our research strategies when exploring certain linguistic or interactional forms. We demonstrate what can be learned from comparisons to be found in data, for example between the different responses by different participants to the same observation or question, or between different speakers’ versions of events, or from the different forms used by speakers when referring to the ‘same’ thing but in different action environments. We highlight the significance of speakers’ production of different versions of the ‘same’ something in their self-corrections. Finally, we illustrate the utility of a research strategy in which comparisons are made between speakers’ use of a certain reference form at one point in an interaction and the form they use at other points in the same interaction. In short, we explore the methodological significance of endogenous comparisons in data.
This chapter provides an overview of foundational principles that guide CA research, offered both on the basis of our own experiences as researchers, and from our discussions with other conversation analysts as they authored contributions for the present volume. We begin by briefly sketching of some of the fundamentals of human social interaction, in order to underscore CA’s central focus, the study of social action, and describe some of the basic features of how interaction is procedurally organized. These basic features of interaction, which CA research has rigorously evidenced and which guide our examination of new data, are then shown directly to inform CA as a research methodology. Put another way, it is precisely due to the procedural infrastructure of action in interaction that conversation analysts use and work with interactional data in particular ways. We conclude with advice for readers as they continue to explore the volume’s contents.