The second paper in H809, from Wegerif and Mercer, uses computer-based language analysis as an opportunity to discuss qualitative and quantitative approaches in educational research. The paper dates from 1997 and, similar to the Hiltz and Meinke paper, its main objective seems to highlight the role novel computer-based technologies can play in research.
Quantitative data analysis enables testing of research hypotheses, creating evidence and making generalisations. This helps to build a body of knowledge and making predictions for new situations. Qualitative approaches allow much finer level of analysis and more attention to the particular context.
The time required for analysis and the space required for presentation mean that there is a de facto relationship between degree of abstraction useful in the data and the sample size of a study or the degree of generalisation. More concrete data such as video-recordings of events cannot be used to generalise across a range of events without abstracting and focusing on some key features from each event. (276)
Increasing computer power allows for analysis of much higher amounts of data in more detail and reduce the required level of abstraction in the categorisation. Large amounts of data can be analysed with more sensitivity to content and context.
We believe that the incorporation of computer-based methods into the study of talk offers a way of combining the strengths of quantitative and qualitative methods of discourse analysis while overcoming some of their main weaknesses. (271)
I agree that computer-based discourse analysis may overcome some weaknesses of both approaches, language may still be difficult to capture quantitatively because:
- nonverbal language plays an important part in communication
- meanings may be ambiguous
- meanings may change over time or vary among persons and among contexts.
I’m not sure it’s helpful to analyse language with the same tools and rigour as positive sciences, as context is much more prevalent in language than in positive sciences.
More computer power doesn’t mean that the subjective role of the researcher can be completely discarded. Researchers may have various motives for their research. As a researcher you always need to make interpretative decisions. Even with computer-based text analysis the researcher still decides which categories to use, which hypotheses to test and which excerpts to publish. I believe it’s best to document these decisions as well and transparently as possible. For example, the researchers could discuss limitations and weaknesses of their research or suggest alternative explanations (e.g. perhaps learners knew each other better the second time). Also making data publicly available would help, so other researchers can scrutinize the results (although few may have time and incentive to do this).