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What Is Conversation Analysis?
Conversation Analysis (CA) focuses on how language is used in interaction, rather than simply what is being said. CA researchers recognize that conversation is orderly and that this orderliness can be observed and analyzed.
One of the goals of CA is to describe the procedures that people use to produce and understand conversation.
CA aims to understand how people use language to communicate and relies heavily on the analysis of naturally occurring conversations.
Through detailed examination of real-world conversations, conversation analysis illuminates how individuals use language to construct meaning, exercise power, and navigate the intricacies of social interactions in various settings.
Who introduced conversation analysis?
Sacks, Schegloff, and Jefferson laid the groundwork for conversation analysis through their pioneering work on the structure and organization of everyday talk.
They studied recordings of naturally occurring conversations, focusing on the sequential organization of talk, turn-taking, repair mechanisms, and the social actions performed through talk.
These studies, among others, established conversation analysis as a distinct field within the social sciences, providing a method for analyzing the intricate details of social interaction through talk.
When to use conversation analysis
Conversation Analysis (CA) helps researchers understand the meanings of real language and how people speak in natural settings, going beyond just the words themselves. CA is particularly useful in analyzing:
Researchers often choose CA because it relies on naturally occurring data, such as recordings of conversations. CA considers the context of the conversation crucial for understanding the meaning of what is said.
CA vs discourse analysis
CA is distinct from discourse analysis (DA). While both explore language in use, they have different focuses:
The choice between using CA or DA, or employing them together, depends on the research question.
For example, a researcher might use CA to study how doctors and patients negotiate treatment decisions or how lawyers use language to influence a jury.
Sequential organization of talk
Sequencing refers to the way in which turns and actions in a conversation are ordered and related to each other. Let’s look at how each of these elements relates to sequencing:
All these elements contribute to the sequential structure of conversation, which is a key focus of conversation analysis.
By studying how these elements are ordered and related to each other, conversation analysts aim to uncover the underlying structure and organization of talk-in-interaction.
1. Turn-Taking
People in conversations generally respond to each other in a structured process known as turn-taking.
Turn-taking is like sharing in a conversation so everyone gets a chance to speak. It makes sure conversations are orderly, with only one person talking at a time. Turn-taking lets people decide when to start and stop talking, making sure conversations are smooth and make sense.
This system ensures that conversations flow smoothly, with minimal overlapping or awkward silences
The foundational principle of turn-taking is that conversation unfolds one speaker at a time. While this may seem obvious, CA provides a detailed framework for understanding how this is accomplished in practice.
At a TRP, the current speaker can either continue speaking or offer the floor to another participant.
These techniques are central to the organization of turn-taking, ensuring the smooth exchange of speaking turns with minimal overlapping or interruptions.
There are two primary types of turn allocation techniques: current speaker selects next and self-selection
This technique involves the current speaker explicitly or implicitly choosing the next speaker.
For example, “Ben, do you want some?” explicitly selects Ben as the next speaker.
Affiliation with First Pair-Part:A broader category of utterances, termed “first pair-parts” in conversation analysis, also function as current speaker selects next techniques.
These include greetings, invitations, complaints, and other utterances that initiate specific types of adjacency pairs, creating an expectation for a particular type of response from a selected recipient.
Lexical Selection:The current speaker can also use specific words or phrases to indicate who should speak next, such as “never” or “ever” in a series of questions.
It’s important to note that addressing a party doesn’t automatically guarantee they will be the next speaker.
For instance, if B answers A’s question and addresses their response to A, this doesn’t necessarily mean A is selected to speak next.
The context and subsequent actions of the participants will determine the actual turn allocation.
In contrast to the current speaker selecting the next speaker, self-selection occurs when a participant who was not selected initiates their turn at atransition relevance place (TRP).
This ordering of techniques ensures that conversations maintain their sequential organization and avoid multiple speakers talking simultaneously.
A turn-taking system is considered “context-free” at its foundational level. This means the basic machinery for distributing turns operates consistently across diverse contexts, irrespective of who is speaking, what they are talking about, or their relationship.
This fundamental structure is what allows conversations to happen at all, ensuring that people mostly speak one at a time and transitions are generally smooth.
Understanding these techniques is crucial for researchers to analyze how conversations unfold, how participants manage their turns, and how meaning is co-constructed in interaction.
This context-free system becomes “context-sensitive” in its implementation. This means that while the underlying rules remain constant, how those rules are actually used to allocate turns can vary greatly depending on:
In essence, while the underlying system provides the framework, the actual dance of conversation, with its nuances and adjustments, emerges from the interplay of this context-free base with the ever-changing dynamics of context, relationships, and social expectations.
2. Adjacency Pairs
Adjacency pairs are pairs of turns in conversation that are closely related to each other.
Adjacency pairs are inherently sequential, with the first part of the pair making the second part conditionally relevant
Here are some key features of adjacency pairs:
The concept ofconditional relevanceis central to understanding adjacency pairs. This principle states that the first part of an adjacency pair establishes an expectation for a particular type of second part.
If the expected second part does not occur, it is considered “noticeably absent.” This absence becomes significant for analyzing the conversation.
Adjacency pairs can be expanded in several ways:
It is important to note that while adjacency pairs are a fundamental aspect of conversation analysis, they are not the only way conversations are organized.
3. Repair Mechanisms
Rather than being a negative phenomenon, repair is a natural and essential self-regulating device crucial for maintaining coherence in conversation.
Types of repair
Repair mechanisms can be categorized based on who initiates the repair (the speaker or the recipient of the problematic utterance) and who carries out the repair. This results in four primary types of repair:
Preference for self-repair
While both speakers and recipients can initiate repair, there’s a general preference for self-initiated repair over other-initiated repair.
This suggests that the conversational system prioritizes speakers taking responsibility for their own utterances.
Other-initiated repair often takes the form of signaling a problem, prompting the original speaker to carry out the repair (other-initiated self-repair).
This highlights that even when others initiate repair, the system is often structured to ultimately facilitate self-repair.
Repair positions and turn-taking
Repair is also closely tied to turn-taking systems in conversation. The timing of repair initiation, relative to the problematic utterance, shapes how repair unfolds:
The sequential organization of repair, with opportunities for self-repair preceding those for other-repair, further emphasizes the system’s design favoring self-correction.
Repair in online communication
Repair in online communication, while sharing similarities with face-to-face interaction, also exhibits differences due to the nature of the medium.
One notable difference is the absence of same-turn repair in online interactions, as any corrections made during typing wouldn’t be visible to others.
Additionally, online communication might see a weakening of the preference for self-repair, as the recipient might resolve the issue more efficiently in some cases.
4. Non-Verbal Cues In Communication
Non-verbal cues are central to human communication, and though communication is possible without them (as in telephone calls), that does not make them peripheral to the process.
Human communication is inherentlymultimodal, meaning it uses all available modes to convey information between speakers and recipients. These can include less easily recorded modes like smell or taste.
How non-verbal cues shape meaning
Non-verbal elements like laughter, smiling, intonation, and stress act ascontextualization cues. They work alongside language to shape how words are understood in a given interaction.
This means that while they don’t inherently encode meaning on their own, they influence how spoken words are interpreted.
Gaze in conversation
Gaze, as an act of seeing and a communicative act, plays a significant role in social interaction. It signals what a participant is attending to and can be used to solicit a response, even without explicit verbal prompting.
For instance, if a speaker has not received a response to their talk, they might use gaze to elicit one, which could be verbal or take the form of a gesture.
Gesture as Communication
Gestures, which convey meaning through bodily action, are not incidental but a core part of interaction. They have a central communicative function that contributes to the overall meaning-making in conversation. Some of the roles gestures play within interactions include:
Integrating gesture, gaze, and talk
In conversation, gestures, gaze, and talk work in a coordinated way to construct meaning. Gestures can be used to introduce non-present entities into a conversation, functioning similarly to how pointing incorporates physically present objects.
For example, a speaker might use a gesture to indicate a specific location on a screen while simultaneously using the word “problem.” In this case, both the gesture and the spoken word aredeictic—they work together to direct attention to a particular spatial location and establish shared focus.
However, the gesture is not merely a repetition of the spoken word. The spoken word, in this instance, relies on the gesture to achieve its full meaning; without it, the spatial location might remain unclear.
This integration of gesture and talk creates alaminated action, where both modes are essential for conveying the intended meaning.
Non-verbal cues in conversation analysis
Conversation analysis (CA) emphasizes the significance of non-verbal cues in understanding social interaction.
Analyzing elements like speaking speed and intonation provides valuable context for comprehending the nuances of social interaction.
For instance, a speaker’s confidence level when answering a question can be inferred from their intonation and pauses.
Hesitations or pauses might suggest uncertainty as the speaker searches for the right words. Conversely, emphasizing certain words can convey authority and expertise.
By considering these non-verbal cues, CA provides a richer understanding of the meaning conveyed in interactions beyond the literal words spoken.
Steps for Conducting CA
Step 1:Data Collection
Data Collection in CA focuses on gathering recordings of these naturally occurring conversations. This could be conversations between friends, family members, or even strangers. The idea is to capture how people actually talk, not how we think they talk.
The goal is to gather data that accurately reflects real-world conversations for analysis.
Example
For example, a researcher studying how people apologize might collect recordings of conversations where apologies occur naturally, such as between friends who had a disagreement. They wouldn’t ask friends to stage an argument and apologize, as this wouldn’t reflect how people genuinely interact.
They could ask their friends if they would be willing to be recorded having conversations in these types of situations.
The goal is to capture authentic apologies in their natural context, providing insights into how people use language to repair relationships and navigate social dynamics.
The Observer’s Paradox
Conversation Analysis (CA) researchers acknowledge that recording interactions for analysis might influence how naturally participants behave.
This is called the “observer’s paradox”: when people know they’re being watched, they may not act like they normally do. Ideally, CA seeks to understand how people interact when they arenotbeing observed.
There is some evidence suggesting that recording devices might not always significantly affect interaction. Speer and Hutchy (2003) argue that participants’ reactions to being recorded can be analyzed within the context of the interaction itself. The impact of recording is complex and varies depending on the specific participants and the situation.
Importantly,ethical considerationsin CA research require that participants are always aware they are being recorded and consent to it. While this awareness might impact the naturalness of their interaction, ethical research practices prioritize informed consent.
Step 2:Transcription
Transcribing talk in conversation analysis involves more than simply recording the words spoken. Conversation analysts need to know “how it was said” in addition to “what has been said”. The transcript should capture features like pauses, intonation, stress, and overlapping speech.
Here are key aspects of this transcription technique:
Conversation analysis emphasizes recording interactions in natural settings using audio and video technology. Despite limitations in capturing every detail, recordings offer significant advantages over relying on intuition or invented sentences.
Software applications that automatically generate transcripts from spoken language data offer limited use in conversation analysis.
While useful for basic transcription, these applications struggle with overlapping talk and lack the capacity to capture the detailed nuances crucial for in-depth analysis.
Specialized Symbols
The Jefferson Transcription System meticulously details the nuances of spoken interaction, going beyond mere words to include pauses, overlaps, intonation, and even non-verbal elements like laughter.
This system is not simply about accurate documentation; it provides a structured framework for analyzing the complexities of naturally occurring talk.
Turn-taking
Turn-taking is the systematic allocation of opportunities to talk, and the regulation of the size of those opportunities.
To account for turn-taking dynamics, transcripts aim to capture the details of how turns are taken in talk-in-interaction.
These details include the precise points at which turns begin and end, including overlaps, gaps, pauses, and audible breathing.
Speech Delivery
To account for the characteristics of speech delivery, transcripts mark noticeable features of stress, enunciation, intonation, and pitch.
For example, if a speaker noticeably extends a word, colons are inserted into the word at the point of extension. The longer the audible extension, the more colons are inserted.
While the Jefferson Transcription System excels in capturing the nuances of spoken language, it also acknowledges the importance of non-verbal elements in interaction.
Researchers have expanded the system to encompass visual information, especially in video-recorded data.
Descriptive annotations within double parentheses ( (()) ) provide context about actions, like a car turning a corner, enriching the understanding of the interaction’s setting and potential influence on the dialogue.
Step 3:Unmotivated Looking
Listen to the recordings multiple times without any pre-existing theories in mind.
Listening to the recordings
During the “Unmotivated Looking” stage of conversation analysis (CA), the researcher repeatedly listens to the same recordings.
This process aims to understand what transpires in the data without imposing preconceived theories or expectations.
The focus is on uncovering naturally occurring patterns and structures within the conversation.
Openness to discovery
Unmotivated looking encourages the analyst to be receptive to discovering unexpected phenomena in the data.
Rather than searching for specific pre-identified elements, the researcher maintains an open mind, allowing the data to guide their observations.
This approach helps in identifying subtle but significant aspects of social interaction that might otherwise be overlooked.
Noticing and identifying actions
The process involves carefully attending to the details of the talk, including seemingly insignificant features. The goal is to understand the actions being performed through language.
For example, a researcher might notice a particular phrase and try to identify its effect on the subsequent conversation.
This can involve identifying how participants use specific practices to achieve communicative goals, like making a request or offering an assessment.
Challenges and considerations
While seemingly straightforward, unmotivated looking presents challenges. Researchers acknowledge that achieving complete neutrality is difficult, as prior knowledge and research interests inevitably shape perception.
The process involves balancing openness to new discoveries with the existing body of knowledge in CA.
Despite these challenges, unmotivated looking remains a fundamental principle in CA, providing a foundation for identifying and understanding the intricate ways people communicate and interact.
Step 4: Identify Phenomena
The focus shifts to identifying recurring patterns in the data, such as turn-taking, repair strategies, and the use of specific words or phrases.
These patterns can manifest in various ways, including:
The goal is to move beyond individual instances and identify patterns that reveal how participants understand and navigate social interaction.
For example, analyzing instances of third-position repair, a pattern where a speaker clarifies their prior utterance after the recipient’s turn shows a problem in understanding.
Recognizing these recurring patterns helps researchers develop a deeper understanding of the practices and conventions governing conversation.
Step 5:Analyze the Data
As you examine the conversations, take notes and modify the formal description of the phenomenon as needed.
Step 6:Develop an Analysis
The final step in analyzing conversational data is to develop a formal account of the phenomenon.
The criteria used to identify the phenomenon and its boundaries are key to this account, along with an analysis of variations across the entire collection of conversations.
The account should describe the phenomenon’s structure, including the linguistic forms and social actions involved. It should explain how it functions, the conditions in which variations arise, and the interactional problem it addresses.
Example of developing an analysis
The analysis of a specific conversational phenomenon, such as a particular type of question-response sequence, could involve examining how the use of specific linguistic forms, such as hedges, relates to the participants’ understanding of each other’s knowledge about the topic being discussed.
For instance, the use of the word “sounds” in the assessment “That sounds interesting” may indicate that the speaker assumes the recipient has limited knowledge of the assessable object.
A formal account of this phenomenon might propose that speakers use hedges like “sounds” to convey a lack of certainty about the assessment, which is appropriate when they believe the recipient has no direct experience with the object being assessed.
This account would need to be grounded in evidence from the collection of conversational data, showing that speakers consistently use hedges in these specific contexts.
This explanation would connect the observed linguistic behavior to a broader social function, demonstrating how the phenomenon contributes to the smooth flow of conversation.
Step 7:Contextualize the Analysis
Consider the context of the interaction.
When analyzing conversations, it is important to consider the social and cultural norms that might be influencing the interaction.
For example, the way people take turns speaking or the types of speech acts that are considered appropriate can vary depending on the culture.
Additionally, the social relationships between the speakers, such as whether they are friends, family members, or strangers, can also influence how they interact.
It is important to note that conversation analysis (CA) focuses on analyzing how participants in an interaction understand and shape the interaction, rather than imposing external assumptions about the influence of social categories or relationships.
Next-turn proof procedure
Next-turn proof procedure is a method in conversation analysis (CA) where a turn is analyzed as evidence of its speaker’s understanding of the prior turn. This method is a basic tool in CA because it illustrates how the structure of talk is oriented to the performance of speaking.
The next-turn proof procedure is based on the fact that the turn-taking system requires speakers to display their understanding of the prior turn in order to produce a relevant next turn.
For example, when a speaker produces a question, the next speaker is expected to produce an answer. By producing an answer, the next speaker displays their understanding that the prior turn was a question. This understanding is what is analyzed in next-turn proof procedure.
Next-turn proof procedure is a valuable tool for analysts because it allows them to understand how participants are making sense of each other’s turns.
This method ensures that the analysis is grounded in the participants’ own understanding of the interaction, rather than the analyst’s assumptions.
By considering these contextual factors, researchers can gain a more complete understanding of the interaction and how the participants are using language to achieve their goals.
Tips for Conducting Conversation Analysis
Further Information
Maynard, D. W., & Heritage, J. (2005).Conversation analysis, doctor–patient interaction and medical communication. Medical Education, 39(4), 428–435.
Peräkylä, A., Antaki, C., Vehviläinen, S., & Leudar, I. (Eds.). (2008). Conversation analysis and psychotherapy. Cambridge University Press.
Sacks, H., Schegloff, E. A., & Jefferson, G. (1978).A simplest systematics for the organization of turn taking for conversation. InStudies in the organization of conversational interaction(pp. 7-55). Academic Press.
Schegloff, E. A. (1987).Analyzing single episodes of interaction: An exercise in conversation analysis.Social psychology quarterly, 101-114.
Schegloff, E. A. (1992).Repair after next turn: The last structurally provided defense of intersubjectivity in conversation.American journal of sociology,97(5), 1295-1345.
Schegloff, E. A. (1993).Reflections on quantification in the study of conversation.Research on Language and Social Interaction, 26(1), 99–128.
Schegloff, E. A. (2002).18 Beginnings in the telephone.Perpetual contact: Mobile communication, private talk, public performance, 284.
Schegloff, E. A. (2007).Categories in action: Personreference and membership categorization.DiscourseStudies, 9(4), 433–461.
Schegloff, E. A. (2007).Sequence organization in interaction: A primer in conversation analysis I(Vol. 1). Cambridge university press.
Schegloff, E. A. (2007).A tutorial on membership categorization.Journal of pragmatics,39(3), 462-482.
Schegloff, E. A., & Sacks, H. (1973).Opening up closings.
Stivers, T. (2007).Prescribing under pressure: Parent-physician conversations and antibiotics. Oxford University Press.
Olivia Guy-Evans, MSc
BSc (Hons) Psychology, MSc Psychology of Education
Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.
Saul McLeod, PhD
BSc (Hons) Psychology, MRes, PhD, University of Manchester
Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.
