{"id":115,"date":"2020-06-03T15:09:44","date_gmt":"2020-06-03T15:09:44","guid":{"rendered":"http:\/\/postgradproofreader.co.uk\/blog\/?p=115"},"modified":"2021-06-17T16:36:42","modified_gmt":"2021-06-17T16:36:42","slug":"how-writing-can-help-your-qualitative-analysis","status":"publish","type":"post","link":"http:\/\/postgradproofreader.co.uk\/blog\/2020\/06\/03\/how-writing-can-help-your-qualitative-analysis\/","title":{"rendered":"How writing can help your qualitative analysis"},"content":{"rendered":"\n

By PGPR team member Elly Phillips<\/em><\/p>\n\n\n\n

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You\u2019re committed to completing a rigorous, thorough and well-developed analysis. You\u2019ve collected your data and spent hours immersing yourself in coding and organising your codes into themes. When are you ready to start writing up?<\/p>\n\n\n\n

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When is the best time to start writing?
Photo by Engin Akyurt <\/a>on Pixabay <\/figcaption><\/figure>\n\n\n\n

How final is the final\nreport?<\/strong><\/p>\n\n\n\n

It\u2019s useful to\naddress the perception of qualitative research as a linear process with writing\nup as the conclusion. If you follow this model, you may be reluctant to move on\nuntil you\u2019re sure your codes are \u2018finished\u2019. Perhaps that idea comes from quantitative\nresearch, where you analyse your data then write up your report to present to\nothers. Based on this, your concerns might be mostly about judging when you\u2019re \u2018ready\u2019\nto move onto writing. <\/p>\n\n\n\n

Writing as a\nprocess<\/strong><\/p>\n\n\n\n

Instead, consider\nwriting as an integral part of continuing your analysis. The use of writing as\nan analysis tool crops up across social sciences and qualitative methods. You\nmight have read about memo-writing in grounded theory, for instance.\nHermeneutic psychology writers have suggested writing should be an integral part\nof developing an analysis. <\/p>\n\n\n\n

In this vision, the\nwriting is less about reporting your fully formed ideas and more about refining\nyour thinking, ideas and arguments about your data. Codes only capture a small\npart of your thinking and don\u2019t allow you to explain or explore. As you write,\nyou can capture and develop initially nebulous ideas about your work.<\/p>\n\n\n\n

How does writing\nhelp?<\/strong><\/p>\n\n\n\n

Initially, writing\ncan be for yourself. You can test your arguments and reasoning. You might start\nsetting down your analytic claims about your data. How did you make sense of\nthe participants\u2019 words? Which parts of the text do you think were particularly\nrelevant? These don\u2019t have to be final ideas, but they can help you decide what\nmight be useful.<\/p>\n\n\n\n

Writing can also help you get feedback from others. A narrative with quotes, analytic commentary and overarching comments communicates your ideas with more depth than code names or standalone quotes. A written account helps an outsider understand the reasoning that took you from data to themes and how you\u2019re interpreting the data. <\/p>\n\n\n\n

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Sharing your ideas
Photo by Startupstockphotos via Pixabay <\/figcaption><\/figure>\n\n\n\n

A short, written analysis example can reassure your supervisor that you\u2019re engaging in a thorough analysis. They will then be able to see your work\u2019s structure, content and analysis style, which can be vital to ensure you\u2019re creating a well-developed piece of analysis.<\/p>\n\n\n\n

What to write?<\/strong><\/p>\n\n\n\n

There are many options for when to write and how much.\nPersonally, I like to write often, even if many of those narratives only\ncollect virtual cobwebs on my laptop (I always believe I\u2019ll use them one day<\/em>).\nYou can write a brief reflection on each interview and after transcription. You\nmight want to write up some key themes from one transcript, explain what you\nthink is important about a small group of quotes, or write a full analysis of\neach participant\u2019s account. <\/p>\n\n\n\n

Strange things\nhappen when you write. Despite rigorous attention to coding, our analytic ideas\ncan prove frustratingly slippery when we try to explain them. You might find that\nsome themes develop beyond their original scope as you find more to say about\nthem. Others may turn out to be uninteresting once you start writing. Either\nsituation should nudge you to return to your data and see what was happening\n(moving back around the hermeneutic circle). Are there multiple ideas within\none initial code that might benefit from more thought? Are there other parts of\nthe data that might elaborate your ideas and develop those dull themes? If you\nencounter these questions early, it\u2019s an exciting way to advance your analysis.\n<\/p>\n\n\n\n

How can PGPR help<\/strong><\/p>\n\n\n\n

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We can help you take the plunge
Image by Anja<\/a> via Pixabay<\/a> <\/figcaption><\/figure>\n\n\n\n

If you\u2019re unsure or\nreluctant to take the plunge, we can help. A Skype session might help make\ndecisions about writing. If your supervisor can\u2019t review all your developing\nideas, you can ask for feedback from our team. We can review early drafts and\nhelp you refine and focus further analysis.<\/p>\n\n\n\n

Starting to write and explain your ideas is an exciting time in your research. Take the plunge to see where it can take you.<\/p>\n\n\n

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