Cognitive discourse analysis
Cognitive discourse analysis is a research method which examines natural language data in order to gain insights into patterns in thought. The term was coined by Thora Tenbrink to describe a kind of discourse analysis that had been carried out by researchers in linguistics and other fields. As it is limited to examining verbalisable thought, CODA studies are often triangulated against other research methods. The method is theoretically neutral, and can therefore be used alongside a range of different models of cognition and grammar.
It is distinct from socio-cognitive discourse analysis, which is an analysis of the link between the text and structures in society.
Methodology
Because of its use in different fields, the precise methodology can vary between papers. A broad outline is as follows:- Selection of research question. The research question must be centred on some aspect of verbalisable thought in order for CODA to be a suitable research method. This includes mental representations and complex cognitive processes.
- Data collection. CODA is specifically for the analysis of natural language data. Because of that, it is important that questions be open-ended and not, for example, multiple-choice responses, though these are used alongside open-ended questions to get demographic information about participants.
- Transcription and data cleaning. Data collected through means other than typed responses will need to be transcribed before analysis. Responses that did not address the question will need to be removed.
- Analysis.
- # Dividing the data into units. This is also known as segmentation. Segments can be at various levels of granularity, including coherent statements and individual responses to questions.
- # Choosing the type of analysis. This can include a thematic analysis, or a content analysis. This analysis will lead to coding procedures for linguistic features in the text.
- Reliability checking. The coding procedures should be laid out in such a way that a layman could follow them, which allows for reliability checking using an independent coder, as well as replication of the findings by other researchers.
- Identification of relevant patterns. Patterns within the features identified during the analysis are identified here. For larger data sets, statistical procedures to identify patterns may be useful.
- Triangulation with other research methods. CODA can be triangulated with other research methods from related fields, including psycholinguistics and cognitive psychology. Surface-level linguistic representation is insufficient for the analysis of many cognitive processes, so triangulation allows both for a deeper analysis and a check on the validity of the conclusions.
Examples of use