Former module 3 student, Bethany Huckle, recommended this book to me. As we are moving swiftly into week 4, I decided to read chapters 8, 9 & 10 to assist in the completion of the second task - having a go at some analysis and sending it to our tutor for feedback. I've begun executing research activities but have no idea how to go about analysing the data collected and structuring the write up of my analysis. I decided to write this blog pin pointing some main ideas from the book to help my BAPP community should they also be struggling with this.
Chapter 8: Preparing to analyse data (P. 211)
Things to think about prior to analysis...
1. The shape of your data - the condition your research is in and the facilities available for analysis
2. The nature of the data - meaning of numbers/words
3. Managing your data - coding, reducing and summarising raw data
4. Computer based data management and analysis - using software packages
5. The process of analysis - thinking about and planning your analysis
Is your data ORDERED (highlighted, labelled, eat, clear) or CHAOTIC (illegible, scraps, unorganised)?
I'm using qualitative data in my inquiry i.e. WORDS, consisting of:
1. Directly written or spoken words from participants
2. Written notes put together during/post research activities
3. Carefully considered words from literature & sources
It is important to remember that "some analysis has already occurred" and that "neither form of data is intrinsically better, more accurate or 'real'" (p. 219)
Particularly in literary sources, people think about what they want to say and have already formed some conclusions and opinions themselves. These sources could have been drafted over and over. Additionally, the data is all of equal value - it is down to personal interpretation and significance.
After assembling the data we have what is known as the MANAGERIAL PROCESS - sorting, reducing, summarising, significance. We can help this process by:
1. Annotating data - highlighting, underlining, making notes in the margins et
2. Labelling - applying key words that give direction to analysis
3. Selecting - choosing important pieces of information
4. Summary - providing an overview and retaining variables of original data
"Analysis is about the search for explanation and understanding, in the course of which concepts and theories will likely be advanced, considered and developed."
Back in school, you probably used the simple P.E.E method I.e. Point, evidence & explanation. A more advanced description of the analysis process would be:
CONCEPTS (point/idea) THEORIES (evidence) EXPLANATION (reasons as to why) and UNDERSTANDING (what is means in relation to inquiry, any issues or questions, it's application and relevance).
Chapter 9: Analysing your data (p. 228)
There are 2 main approaches to analysing the data:
1. POSITIVIST - exploring and testing an idea or hypotheses
2. INTERPRATIVE/CRITICAL - gathering data to see what light it sheds on a topic of interest
The former I would argue is an approach applicable to more statistical data collections and specific questions. The latter being the approach applicable to my 'professional inquiry'.
For my inquiry I will be using data in the form of documents/sources, interviews and practical demonstrations. With these can come varying analytical processes.
COMPARATIVE ANALYSIS: helps to develop our understanding of ideas, issues and policies. In relation to my working environment, comparison between venues in individual departments particularly helps to develop the understanding of skills required in relation to my inquiry topic. Comparing different roles within this company or comparing the same roles but across companies will help to develop understanding in relation to relationships and creativity.
THEMATIC ANALYSIS: looking for common themes across each activity, finding similarities and dissimilarities across interview questions, looking for any other themes.
EXTENDED ANALYSIS: practical demonstrations and shadowing/observation helps to give context to information acquired but I feel it is healthy to recognise how, in this overt approach, it can show only part and be selective/affected in its display.
When analysing data it is important to consider:
1. Significance
2. Generalisability
3. Validity
4. Reliability
During interpretation and analysis, make explicit the role/position of both the researcher and participant to highlight any limitations, influences and biases. Incorporate shared understanding I.e. How you or the data both agree and disagree with others and the reasons/relevance.
Chapter 10: Writing Up (p. 250)
It is so hard to just get going! Here are some tips for overcoming procrastination:
1. Make notes from activities, sources, etc
2. Draft a contents page
3. Type out any references
4. Draft the structure for a section
5. Type up any important points/quotes
6. Talk it through with others I.e. Tutor, SIG's etc
Hope this helps everyone!
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