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Technology-based and multi-modal data analysis (Chapter 9)

Reflections on the eighth chapter of 'Creative Research Methods - A Practical Guide' by Helen Kara


This chapter was focussed on ‘the ethical aspects of using technology in, and mutli-modal approaches to, data analysis’ with ‘specific sections on analysis … video data’ (page 149) which was specifically relevant to my research and the methods I am adopting.

 

Kara writes, ‘technology can also help us to find solutions to ethical problems at the data analysis stage… Data analysis software improves researchers’ well-being by saving us time… (page 150). This point particularly resonated with the video aspects of my content creation; before I started using TikTok and discovered it had an incredibly accurate caption generator – I used to spend hours of time laboriously subtitling videos by hand. Nevertheless, I considered it worthwhile for two reasons: firstly, the process of listening, interpreting and writing the speech as text helps me to fully understand and remember what people have said during interviews or speeches; secondly, subtitling content is hugely beneficial for communication and dissemination purposes, as it improves comprehension and is very likely to achieve more engagements. In some ways, TikTok’s automated speech analysis detracts from that process, but it would be a lie if I said I didn’t appreciate the amount of time it has saved me – especially when captioning a lengthy Vlog which I have written and therefore would not benefit from the laborious process. Having said that, one thing about using this software which always makes me laugh is when it tries to analyse singing – the most hilarious results I ever generated were for my song ‘We Are Europe’ which includes 3 different languages (English, French and German). The software can only analyse one language at a time, so the “English language” transcription of the French and German lyrics was hilarious. I honestly thing I prefer them to the original.

 

Kara then address multi-modal data analysis, and although I will only be collecting qualitative data from my research participants, I think there is value in using different methods to analyse the findings. ‘A multi-modal approach to data analysis is essential if both qualitative and quantitative data have been gathered … It may also be useful with only one type of qualitative data, because analysing that data in different ways can provide a richer picture of the subject under investigation. This also has an ethical aspect: gathering less qualitative data and analysing it more thoroughly helps to reduce the burden on participants’ (page 150). I am also very conscious of not expecting too much time and energy from our participants; this is why I want to limit the video interviews to maximum two interviews of 1 hour duration per participant, and also why I am piloting the research process with 3 friends, who are (hopefully) more willing to be generous with their time and feedback.

 

The section on ‘Analysing video data’ was especially important for me to consider since I am planning to record video interviews with my project participants. ‘Video offers a myriad of possibilities, and enormous challenges, to the data analyst… It is also impossible to transcribe everything that could possibly be relevant: all the physical movements and gestures, directions of gaze and eye contact, handling of material objects, use of technology, details of the environment and so on (Hammersley 2010: 556)’ (page 154). I am not planning to write-up my research as a report, but rather edit and present the video interview data as a documentary – this will hopefully reduce the “challenges” Kara mentions. Presenting the video interviews as its original media alleviates the need to analyse the movements, gestures, etc.

 

Kara discusses multi-modal analysis, first describing ‘cultural consensus analysis, a quantitative method’ which is less relevant to my research and then ‘Cultural modelling is based on discourse analysis which enables researchers to understand participants’ perspectives on thoughts, knowledge and the meaning of language and is used to demonstrate and explain relationships between cultural elements in the data (Fairweather and Rinne 2012: 482)… both analytic techniques are used to investigate the extent to which culture is shared’ (page 156). This is worth me considering since my research is investigating participant’s experience of community, belonging and othering and therefore culture plays a big role in these questions. In a Zoom meeting I organised with my three ‘pilot’ research participants, they identified a shared cultural background as all three are originally from Eastern-European countries (Bulgaria, Czech Republic and Albania) – they suggested I could make the project to focus specifically on this region but that is not my intention.

 

Kara then discusses data integration, which again, I should consider since my research question is quite broad and there are many disparate aspects to my creative practice which are inter-connected but sometimes need a through analysis to understand and explain the connecting threads. ‘Data integration in multi-modal research can be conducted for a number of reasons, such as to address a research question from a variety of perspectives, or to bring together different parts of a phenomenon or process (Mason 2018: 39 -40). Within a research project, data integration has three main purposes: triangulation of data, development, development of richer analysis and illustration of findings (Fielding 2012: 124). The Aim is to synthesise equivalent or complementary findings, and to make further investigation of contradictory findings. (Fielding 2012: 125). The precise methods of integration will vary, depending on the datasets, but there are some basic questions that are likely in any case, such as the following:

1.        How far can each of your datsets contribute to answering your research question?

2.        To what extent can your findings be brought together to create an explanatory narrative?

3.        How much do the answers to 1 and 2 above benefit your research?’ (page 159)

 


Finally, Kara discusses the issue of diffraction (which in physics is ‘the process by which a beam of light or other system of waves is spread out as a result of passing through a narrow aperture or across an edge’ but in this context means ‘a way to map where the effects of difference appear’. Kara writes, ‘Some scholars have begun to question the reification of integrating findings in multi-modal research… multi-modal research is useful for addressing complex questions, and so integration can be difficult because the research may be ‘ontologically unstable’ with unclear boundaries (Uprichard and Dawney 2019: 20). They are not suggesting that diffraction should replace integration, but that it can provide a useful alternative when integration proves difficult or impossible’ (page 161). ‘Ontologically unstable’ and ‘with unclear boundaries’ seems to be a perfect description of me and my practice (I also like to think of myself of as blazing beam of white light which can be diffracted into a rainbow) – so mapping the effects of difference maybe a good approach to explore!

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