As I said in the earlier post that I have just come back after my data collection. My research is an experimental one which includes an intervention in a primary school in Bangladesh. Therefore it means I have double data compared to any other research methods- pre and post intervention data. These pile of things have put me on a 'denial' mood and I was trying to spend my times on every other thing except going back to data. Then I have got this part of a book 'Planning and Preparing the Analysis", Chapter 2 of the book 'Applied Thematic Analysis' published by SAGE.
I will just try to write in bullet points the key things I have learnt from this chapter-
1. The first and foremost thing is to establish your Analysis Objectives- what you are trying to achieve with the data and how you are going to achieve it. This requires going back to your research design. Your analysis objectives will always relate to your research questions.
2. When jotting down the analysis objectives you are also thinking the data you will need to achieve those objectives. Therefore quality data is required. Its required to
- categorize type of data
- measure overall quality of data
- look for missing components and
- find out if any other information is needed.
3. Once you know the objectives of data analysis and the type of data you have for those particular objectives you are ready for prioritizing your analysis, which objective you need to achieve in very recent times and start working with that. Identify target dates and milestones to keep track of your analysis.
4. Which approach you are going to take for your analysis depends on the practical purpose of data analysis- find solutions? build theory or evaluate something?
What is the analytic purpose- to identify? compare? explore? explain? confirm? All these should synchronize with your research objective.
5. The analysis should have direct connection with research questions. So review your research questions and objectives now and then.
6. Get the resources i.e. software and others in hand.
7. In case of large data set think accordingly- separate data for separate analysis?
Data integration is one of the most important things. Its better to use the same code book throughout your thesis.
Use only that data needed for a particular analysis.
8. In case of using the results for a journal or report, always keep in mind the audience you are going to address. Do they like narratives with quotes or do they have a preference tables and matrices?
This chapter helped me to enrich my research design table with analysis objectives and type of data needed for that. Another column will be added- analytic approach. After meeting with my second supervisor what I have decided is-
* I will begin with the analysis of the post intervention qualitative data as that is still fresh in my mind. Using of a software might be time consuming for my case as I have two data sets. I will start with transcribing the data and see how it goes!
Also I am going to use the data for my upcoming conference presentation in London. Its 'Lessons from Near and Far' and I am going to talk about how outdoor environment can increase children's motivation to learn.
** Simultaneously I will start sorting the data for quantitative analysis. I will have a sitting with my third supervisor and start working on that. Lets see whether I can use some in the conference presentation.
*** Though observation and behaviour mapping can be done manually but it seems more effective to use ArcGIS. My colleague will help me with the basic things of ArcGIS. One thing- Never hesitate to seek help from your colleagues.
Here where I stand at this moment. Hope I can see some progress in the next week!
I will just try to write in bullet points the key things I have learnt from this chapter-
1. The first and foremost thing is to establish your Analysis Objectives- what you are trying to achieve with the data and how you are going to achieve it. This requires going back to your research design. Your analysis objectives will always relate to your research questions.
2. When jotting down the analysis objectives you are also thinking the data you will need to achieve those objectives. Therefore quality data is required. Its required to
- categorize type of data
- measure overall quality of data
- look for missing components and
- find out if any other information is needed.
3. Once you know the objectives of data analysis and the type of data you have for those particular objectives you are ready for prioritizing your analysis, which objective you need to achieve in very recent times and start working with that. Identify target dates and milestones to keep track of your analysis.
4. Which approach you are going to take for your analysis depends on the practical purpose of data analysis- find solutions? build theory or evaluate something?
What is the analytic purpose- to identify? compare? explore? explain? confirm? All these should synchronize with your research objective.
5. The analysis should have direct connection with research questions. So review your research questions and objectives now and then.
6. Get the resources i.e. software and others in hand.
7. In case of large data set think accordingly- separate data for separate analysis?
Data integration is one of the most important things. Its better to use the same code book throughout your thesis.
Use only that data needed for a particular analysis.
8. In case of using the results for a journal or report, always keep in mind the audience you are going to address. Do they like narratives with quotes or do they have a preference tables and matrices?
This chapter helped me to enrich my research design table with analysis objectives and type of data needed for that. Another column will be added- analytic approach. After meeting with my second supervisor what I have decided is-
* I will begin with the analysis of the post intervention qualitative data as that is still fresh in my mind. Using of a software might be time consuming for my case as I have two data sets. I will start with transcribing the data and see how it goes!
Also I am going to use the data for my upcoming conference presentation in London. Its 'Lessons from Near and Far' and I am going to talk about how outdoor environment can increase children's motivation to learn.
** Simultaneously I will start sorting the data for quantitative analysis. I will have a sitting with my third supervisor and start working on that. Lets see whether I can use some in the conference presentation.
*** Though observation and behaviour mapping can be done manually but it seems more effective to use ArcGIS. My colleague will help me with the basic things of ArcGIS. One thing- Never hesitate to seek help from your colleagues.
Here where I stand at this moment. Hope I can see some progress in the next week!
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