Difference between revisions of "Krug2025a"
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| − | |DOI= | + | |DOI=10.1075/nb.00023.kru |
|Abstract=This paper explores integrating quantitative methods into Conversation Analysis (CA) to examine psychological reactance in face-to-face interactions. Although extensively studied in mediated contexts, psychological reactance – a motivational state triggered by perceived threats to autonomy – remains underexplored in natural conversations. Addressing this gap, the study investigates how reactance manifests and affects conversational dynamics, such as stance-taking and disalignments. Video-recorded interactions were collected where participants faced reactance-inducing conditions (e.g., limiting smartphone use). Using GAT2 transcription and multimodal annotations, a systematic triple manual coding was used to identify markers of reactance. For statistical tests, the averages of the coding decisions were used to utilize the interpretative flexibility inherent in coding. Quantitative analyses showed significant relationships: reactance correlated positively with perceived freedom restriction, negatively with stance, and predicted conversational disalignment. Findings demonstrate that integrating quantitative methods into CA enhances its capacity to analyze complex phenomena like reactance, linking interactional practices with psychological concepts and advancing methodological discussions within CA. | |Abstract=This paper explores integrating quantitative methods into Conversation Analysis (CA) to examine psychological reactance in face-to-face interactions. Although extensively studied in mediated contexts, psychological reactance – a motivational state triggered by perceived threats to autonomy – remains underexplored in natural conversations. Addressing this gap, the study investigates how reactance manifests and affects conversational dynamics, such as stance-taking and disalignments. Video-recorded interactions were collected where participants faced reactance-inducing conditions (e.g., limiting smartphone use). Using GAT2 transcription and multimodal annotations, a systematic triple manual coding was used to identify markers of reactance. For statistical tests, the averages of the coding decisions were used to utilize the interpretative flexibility inherent in coding. Quantitative analyses showed significant relationships: reactance correlated positively with perceived freedom restriction, negatively with stance, and predicted conversational disalignment. Findings demonstrate that integrating quantitative methods into CA enhances its capacity to analyze complex phenomena like reactance, linking interactional practices with psychological concepts and advancing methodological discussions within CA. | ||
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Latest revision as of 09:36, 26 September 2025
| Krug2025a | |
|---|---|
| BibType | ARTICLE |
| Key | Krug2025a |
| Author(s) | Maximilian Krug |
| Title | Integrating quantitative methods into conversation analysis: Exploring psychological reactance through interactional coding |
| Editor(s) | |
| Tag(s) | EMCA, Quantification, Psychological reactance, Disalignment, Conversation analysis, Statistics |
| Publisher | |
| Year | 2025 |
| Language | English |
| City | |
| Month | |
| Journal | Nota Bene: Journal for Linguistics in Belgium and The Netherlands |
| Volume | 2 |
| Number | 1 |
| Pages | 41–68 |
| URL | Link |
| DOI | 10.1075/nb.00023.kru |
| ISBN | |
| Organization | |
| Institution | |
| School | |
| Type | |
| Edition | |
| Series | |
| Howpublished | |
| Book title | |
| Chapter | |
Abstract
This paper explores integrating quantitative methods into Conversation Analysis (CA) to examine psychological reactance in face-to-face interactions. Although extensively studied in mediated contexts, psychological reactance – a motivational state triggered by perceived threats to autonomy – remains underexplored in natural conversations. Addressing this gap, the study investigates how reactance manifests and affects conversational dynamics, such as stance-taking and disalignments. Video-recorded interactions were collected where participants faced reactance-inducing conditions (e.g., limiting smartphone use). Using GAT2 transcription and multimodal annotations, a systematic triple manual coding was used to identify markers of reactance. For statistical tests, the averages of the coding decisions were used to utilize the interpretative flexibility inherent in coding. Quantitative analyses showed significant relationships: reactance correlated positively with perceived freedom restriction, negatively with stance, and predicted conversational disalignment. Findings demonstrate that integrating quantitative methods into CA enhances its capacity to analyze complex phenomena like reactance, linking interactional practices with psychological concepts and advancing methodological discussions within CA.
Notes
