Now that I have gathered data relating to my intervention I will look into methods of data analysis.
I read Chapter six: Analysing Data, from Kara, H. (2025) Creative Research Methods in the Social Sciences: A Practical Guide to gain an overview on the types of approaches to data analysis.
Up to this point my understanding of data analysis is that it falls into two categories (quantitative and qualitative) and I knew very little beyond these general terms.
The text helped open up my understanding to the kinds of approaches available, how they might suit different kinds of data and provided some examples that gave context to the methods.
My understanding is that I’ll be using the project outcomes as data: the typefaces that students have designed in response to the brief – to explore type design in relation to an aspect of their own identity. Presumably I will be using qualitative methods to do this. I also plan to collect data at the end of the intervention, in the form of interviews, questionnaires, and/or conversations between the students where they have the opportunity to reflect on what they have learned, explored, how their understanding of the canon, decolonisation, diversity in relation to typeface design has changed as a result of the intervention. With the latter, there is potential for quantitative analysis too, in terms of how many students mention particular themes, subjects, etc.
The article gives an example:
“Suppose that you hold
a focus group with eight first-generation immigrants from different countries of origin. You begin by having each person share some basic demographic data by way of introduction: where they have lived, how old they are, their occupation(s) before and after immigration, who and where their family members are. Then you facilitate a discussion of their experiences of emigration and immigration around themes drawn from the academic literature, including wealth and poverty, coercion and freedom, belonging, emotion, status, togetherness and separation. The resulting data would be amenable to quantitative and qualitative analysis”
This is very similar to what I might do with the discussions / conversations at the end of the intervention. With the themes drawn from the academic literature being: decolonisation, the western canon, diversity, etc. The article suggests that “The resulting data would be amenable to quantitative and qualitative analysis”, something I hadn’t fully considered.
The article talks about how conversations can also be analysed in other ways: “consider any silences, pauses or omissions in order to try to uncover what might have been left unsaid and why (Frost and Elichaoff 2010: 56, in Kara, 2015: 99). I’m not sure if this will apply to my data but really interesting to read about these alternative methods of analysis.
The article also refers to “more creative approaches to data analysis” but also the importance of “understanding where rules must be applied” (Kara, H 2015: 100). Firstly I’d like to learn more about possible creative approaches but this is perhaps also referring to how data analysis needs to be rigorous and guided by rules.
In terms of ethics the piece talks about the responsibility of the data analyst and that “It is essential that you do not invent or distort your data, or misuse statistical techniques” (Poon and Ainuddin 2011: 307, in Kara, 2015: 100).
I intend to record conversations and discussions with students and there is some useful information here about ‘data preparation and coding’, specifically about “large variety of decisions to be made about transcription” (Kara 2015: 100). Again, there is reference to “should you record non-speech sounds that people make”, something I had not previously considered.
So there is potential that I will use both qualitative and quantitative data analysis methods.
“Quantitative and qualitative data need to be analysed separately, using different techniques, and in research where both quantitative and qualitative data have been gathered, the datasets will be analysed separately before the analyses are integrated to produce the research findings.” (Kara 2015: 101)
The article later lists several kinds of qualitative data analysis, a few that I found most relevant to my research are
- content analysis – a semi-quantitative technique for counting the number of
instances of each category or code (Robson 2011: 349)
• thematic analysis – identifying themes from coded data (Robson 2011: 475) - conversation analysis – detailed analysis of the verbal and non-verbal content
of everyday interactions (Bryman 2012: 527) - phenomenological analysis – analysing participants’ stories from, and
descriptions of, their ‘life-worlds’, or individual experiences and perceptions,
with a focus on meaning (Papathomas and Lavallee 2010: 357; Mayoh, Bond
and Todres 2012: 28)
The last method in particular as potentially being connected to my project about identity and typeface design, how the projects are, in a way a kind of story about individual experiences, cultural backgrounds, etc.
A key part of the text was around Discourse Analysis (DA), “based on the concept that the way we talk about something affects the way we think about that phenomenon.” (Kara 2015: 105). Particularly as the author talks about how DA can be applied to other kinds of data, such as images.
Other potentially useful bits of information from this text were:
– idea of recording participants and how this might affect what they say, if they know they are being recorded. Some researchers looked at recording conversations in more natural settings, for longer periods (Gordon 2013: 314)
– Use of diagrams and maps to “help you visualise your data and the ideas and relationships that develop as you work through the analytic process (Kara 2015: 107). The example given here of researchers Charles Buckley and Michael Waring, “they found that creating
diagrams helped them to generate, explore, record and communicate insights
about their data” (Kara, 2015: 107) and “using diagrams in data analysis can help to uncover some otherwise hidden parts of the research process ((Buckley and Waring 2013: 150 in Kara 2015: 107).
– Also key to my data analysis is an example given on p.109 of researchers in Australia who used Mixed-method analysis and “developed an analytical procedure using three different methods to analyse children’s artworks” (Kara, 2015: 109). They used quantitative techniques (Content analysis, where they categorised visual elements in the drawings and counted number and frequency, and also used two qualitative methods: Interpretive analysis (looking at mood or atmosphere in the drawings), and developmental analysis (looking at correlating development with age). “The researchers conclude that this combination of analytic methods can ‘provide deep insights into young children’s understandings’
(Sorin, Brooks and Haring 2012: 29, in Kara, 2015: 109).
Another interesting example given is how researchers in the US looked at “how video could be used to represent young people’s identity” (Kara, 2015: 109), a very similar topic to my intervention. “They describe video data as ‘multimodal’ because it contains still and moving images, colour, a range of sounds and silences, sometimes text and so on” (Kara, 2015: 109).
“Halverson et al originally approached video analysis
by starting with dialogue, but then they encountered a film that had no dialogue, which engendered their decision to develop a multimodal approach. Their aim was to create a multimodal analytic framework, not to analyse data in different chunks, but to reflect how the interaction of different chunks of data can create new meanings. Following the work of Baldry and Thibault (2006), they divided the film into ‘phases and transitions’, which were units of analysis that had some kind of internal consistency, for example through a type of shot, a consistent voiceover or the same music. Then they devised a coding scheme, based on the work of Bordwell and Thompson (2004), for each unit of analysis. This involved four broad categories based on filmmakers’ key cinematic techniques:
1 mise-en-scène: anything visible within the camera’s frame, such as setting
and characters
2 sound: anything audible, such as dialogue and music
3 editing: the filmmaker’s interventions that create the film
4 cinematography: the filmmaker’s techniques for altering the image from that
seen through the camera’s lens.
Within each category, more detailed codes were developed, such as facial
expressions, clothing, sound effects, flashback, freeze frame, lighting and close-up.
Halverson et al say that using this system ‘to describe the phases and transitions of the films resulted in the creation of multilayered filmic transcripts that allow us to consider each mode individually, as well as how they connect to one another to help youth consider issues of identity in their films’ (Halverson et al 2012: 8).”
Lots to consider here, moving forward with my data analysis.
References
Kara, H (2015) Creative Research Methods in the Social Sciences : A Practical Guide, Policy Press, Bristol. Available from: ProQuest Ebook Central. [14 November 2023].