In summary, here are 5 easy steps to organizing your qualitative data
- Choose and follow a clear file naming system
- Develop a data tracking system
- Establish and document transcription/translation procedures
- Establish quality control procedures
- Establish a Realistic Timeline
Before we explore the 5 steps in-depth, what is qualitative data?
Qualitative data is information that characterizes but does not measure the attributes or characteristics or properties of an existence. Qualitative data provides a description and not a definition.
Major categories of qualitative data are in-depth interviews, focus group discussions, direct observation and written documents. In whichever category, qualitative data is quite wordy and may be more of a headache than a resource if not well organized.
Qualitative research data collection begins with by recording high quality audio (check out this great post on choosing the best voice recorder for research interviews).
Then, checking all your data to make sure everything is accurate and inclusive. For instance it’s important to review the transcripts of your research interviews before compiling and organizing them.
Finally, researchers analyze their data and use their finding to inform their thesis/dissertations, articles, reports, and books.
Here are the 5 steps to keeping your quantitative data well organized.
5 Steps to Organizing Your Qualitative Data
1. Choose and follow a clear file naming system
Identification is key.
Give your data an identity, name your data. According to the system you use, name your data categorically. You could also combine related themes into major categories and label them. And what better way to identify your data than to choose and follow a clear file naming system?
This way your information will be appropriately clustered, easy to find and work with.
For example, if the research was about drinking tendencies among the male population aged between 12 and 18, you could use age group as file name. So Age 12 to Age 18 will make 7 file names, inclusive of all findings of that particular age group.
Work with what is applicable to your data. All sections of data should have identifiers. Coding could be another option, where you could use tags to identify files with.
For large projects data collection and management entails coordination by many actors in a research team. From sources, to dates, interviewer, interviewee, coordinator, transcriber, translator (per diem) and even sites, depending on the study.
There is need for consistent flow of accounts and events so as not to have conflicting feedback for the sake of compilation.
You need to create a system that facilitates tracking of all data collected, right from fieldwork to compilation, all inclusive of sources and context. It is crucial to put in place a tracking system that is customized to the data with details of individuals, sites and dates.
3. Establish and document transcription/translation procedures
After tracking data, transcription procedures should be established as part of the organizing process. Systematically, recorded data needs to be transcribed. There are a couple of questions you would need to answer to get to the hang of it.
- What format will be used for transcription? Verbatim? Intelligent Verbatim? (Note that some research work calls for exclusively one or the other)
- Is there any translation required? Do you get multilingual transcribers or do you engage both translators and transcribers? How do you make sure information is not distorted during translation?
- What consistency formats will you establish? (symbols, layouts, spacing)
- Confidentiality procedures (What does your transcriber need to know? Do the participants in the interviews need to be protected? Do you need to sign Non Disclosure Agreements (NDAs))
- How much time is being allocated for transcription?
- Are you outsourcing for professional transcription services or will you hire your own full time or part time transcription staff? (Depending on nature and consistency of qualitative data flow, one might consider having your own staff. But for a one off compilation, outsourcing is the solution)
- Is there any training required? (What is your budget? What are your options?)
- What supervisory measures are required for quality purposes? (Is translation accurate? Were instructions followed? Was anything left out?)
- Are there necessary instructions? (are there segments that need to remain as is, no translations, no edits? Are there some that need to be left out?).
Once all the above questions are answered, a directive will be established on how to organize the research transcription procedures.
Just like transcription, translating takes up a lot of time, requires supervision for quality purposes and is quite costly. One of the major translation drawbacks is distortion. Organizing data using above questions as key pointers will make sure your maintain rigor in your research.
4. Establish quality control procedures
As demonstrated above there is quite a lot to put into consideration. As soon as transcription begins, a check system should be created for quality control purposes. A guide that would facilitate monitoring of the entire process. The recordings should be compared to the transcripts and constant communication with the transcriber would yield better results. This way it would be easy to catch mistakes on time and provide guidance as needed.
5. Establish a realistic timeline
Establish a realistic timeline. Plan your time, your strategy, and your needs. Have a plan of action. Have an all-inclusive plan of how you want to manage your data then work on it systematically.
If there is need to outsource, plan on all activities involved just to make sure you have all you need when you need it. Do make sure that you create realistic and workable timeframes. There is nothing as time saving, efficient and resourceful as organized qualitative data that has met deadlines through the entire collection and compilation process.
With all that said and done, it would be wise to make a copy of your work for safe keeping. Back up, back up and back up some more, the golden rule for research work. Copying and archiving your work may ultimately be a life saver; you just may never know when this simple act will be heaven sent.
Organizing your qualitative data should be a reflex to the collection and analysis process. Following the steps mentioned above prevents confusion right from data collection, to analysis and finally writing your dissertation.
Example of a Data Management Process
Here’s an example of a interview data management process.
- Back-up audio to hard drive.
- Rename file and create list on master spreadsheet.
- Edit audio file (if required).
- Save audio file in common work folder.
- Allocate transcription duties.
- Create text file and save with same name as audio file.
- First transcriber first draft.
- Revisit problem areas in first draft.
- Spell check, format check first draft.
- Second transcriber check.
- Return and or clarify (if required).
- Second transcriber repeats steps of first transcriber.
- Chief transcriber checks formatting, word count, length etc.
- Transcription file is locked for analysis.
Possible next stages:
• Investigator questions content and finds transcription does not match
• The transcription is returned to the chief transcriber for review.
• Draft versions are compared using WinMerge.
• Findings discussed and reviewed with relevant parties.
Source: Patkin, J. G. (2021). Transcribing in ESL: An Investigation into Role, Accuracy, and Skills. The Qualitative Report, 26(2), 588-609. https://doi.org/10.46743/2160-3715/2021.4338
Organized data produces high quality and accessible information, proper analysis documentation, preserved material and analysis correspondence after completing the study.
If there isn’t information coordination, you’ll get into trouble when you’re trying to make comparisons and rectify mixed up information. This may leads to inaccurate reporting, invalid and unreliable information and limits the applicability of your research findings. Beware that data integrity and reliability is a result of well-organized data.
Hope this short post helps you organize your qualitative data. If you have further questions please leave a comment and I’ll get back to you.