Qualitative software used to need you to format text files in very specific ways before they could be imported. These days the software is much more capable and means you can import nearly any kind of text data in any kind of formatting, which allows for a lot more flexibility.
However, that easy-going nature can let you get away with some pretty lazy habits. You’ll probably find your analysis (and even data collection and transcription) can go a lot smoother if you’ve set a uniform style or template for your data before hand. This article will cover some of the formatting and meta-data you might want to consider getting in a consistent form before you start it.
Part of this should also be a consistent way to record research procedures and your own reflections on the data collection. Sometimes this can be a little adhoc, especially when relying on a research diary, but designing a standard post-interview debriefing form for the interviewer at the same time as creating a semi-structured interview guide can make it much easier to compare interviewer reflections across sources.
So for example you could have a field to record how comfortable the interview setting was, whether the participant was nervous about sharing, if questions were missed or need follow-up. Having these as separate source property fields allows you to compare sources with similar contexts and see if that had an noticeable effect on the participants data.
For transcribed interviews, have a standard format for questions and answers, and make sure that it’s clear who is who. Formatting for focus groups demands particular attention to formatting, as some software will help you identify responses from each participant in a group session when done in a particular way. Unfortunately Quirkos doesn’t support this at the moment, but with focus group data it is important to make sure that each transcription is formatted in the same way, and that the identifiers for each user are unique. So for example if you are using initials for each respondent such as:
JT: I’m not sure about that statement.
FA: It doesn’t really speak to me
Make sure that there aren’t people with the same initials in other sessions, and consider having unique participant numbers which will also help better anonymise the data.
A formatting standard is especially important if you have a team project where there are multiple interviewers and transcribers. Make sure they are using the same formatting for pauses, emphasis and identifying speakers. The guide to transcription in a previous blog post covers some of the things you will want to standardise. Some people prefer to read through the transcripts checking for typos and inaccuracies, possibly even while listening to the audio recording of the session. It can be tempting to assume you will pick these up when reading through the data for analysis, but you may find that correcting typos breaks your train of thought too often.
Also consider if your sources will need page, paragraph or sentence numbers in the transcript, and how these will be displayed in your software of choice. Not all software supports the display of line/paragraph numbers, and it is getting increasingly rare to use them to reference sources, since text search on a computer is so fast.
You’ll see a few guides that suggest preparing for your analysis by using a database or spreadsheet to keep track of your participant data. This can help manage who has been interviewed, set dates for interviews, note return of consent forms and keep contact and demographic information. However, all CAQDAS software (not just Quirkos) can store this kind of information about data sources in the project file with the data. It can actually be beneficial to set up your project before-hand in QDA software, and use it to document your data and even keep your research journal before you have collected the data.
Doing this in advance also makes sure you plan to collect all the extra data you will need on your sources, and not have to go back and ask someone’s occupation after the interview. There is more detail in this article on data collection and preparation techniques.
As we’ve mentioned before, qualitative analysis software can also be used for literature reviews, or even just keeping relevant journal articles and documents together and taggable. However, you can even go further and keep your participant data in the project file, saving time entering the data again once it is collated.
Finally, being well prepared will help at the end of your research as well. Having a consistent style defined before you start data entry and transcription can also make sure that any quotes you use in write-ups and outputs look the same, saving you time tidying up before publication.
If you have any extra tips or tricks on preparing data for analysis, please share them on our Twitter feed @quirkossoftware and we will add them to the debate. And don’t forget to download a free trial of Quirkos, or watch a quick overview video to see how it helps you turn well prepared data into well prepared qualitative analysis.