Data Transcription in Qualitative Research: Everything You Need to Know
Learn about why you need data transcription in your qualitative research process and how to pick the right transcription services for you.
If your work involves data transcription in qualitative research, you might be overwhelmed by the amount of language data you record and transcribe on a daily basis.
Historically, transcription has always been the work of professional transcribers because of the high accuracy standards required for research validation.
These days, data transcription is increasingly going digital, with AI transcription looking to disrupt the space. This is primarily due to their lower costs, more efficient transcription workflow, and assistive features for professional transcribers.
Table of Contents
What is data transcription in qualitative research?
Data transcription refers to converting speech into written text for documentation or analysis purposes. Data transcription is also the first and most important step in a qualitative research project.
Before analyzing any recorded audio, you must first convert them into text. Once these transcripts are available, you can go through them multiple times and pick up on valuable unanticipated patterns.
Examples of dialogues that require transcription include:
- Focus group studies
- Patient consultations
- Court hearings
Since qualitative research is focused on exploring subjective characteristics and cannot be measured by numbers, a clean language data set is critical. Without a clean data set, your analysis could be incomplete or completely inaccurate.
Types of data transcription
There are two main types of data transcription – verbatim and intelligent transcription.
Verbatim/true verbatim transcription
What is verbatim transcription?
The first type of transcription is verbatim transcription, which means recording every part of the dialogue or sound on the audio file. This includes grammatical errors, pauses, and even non-verbal communication.
Verbatim transcription is often the preferred method for subjective qualitative research due to its 100% accuracy in staying true to the speaker’s intention. Rather than recording for readability, it focused on capturing how the person spoke.
Here’s an example:
Interviewer: So, you mentioned that you had some (coughs) experiences with this product. Could you please elaborate more on that?
Participant: Yeah, we bought the product and the entire family [laughs] loved it.
By capturing mannerisms in addition to the words spoken, readers can gain a more holistic understanding of the interview environment and rapport between the participants.
When is verbatim transcription necessary?
Verbatim transcription is necessary when your research’s goal is to capture the participant’s subjective feelings. By recording non-verbal cues and the research environment, you can interpret the speaker’s intentions more comprehensively.
Situations where you should use verbatim data transcription in qualitative research include:
- Focus groups or group discussions
- Patient consultations
- Patient diagnosis
- Court hearings
According to various studies, up to 70-93% of all communication is nonverbal. While this “rule” is difficult to prove with empirical results, the intent is clear — analyzing communication is more than just looking at the words used. You must also consider the non-verbal cues and tonality to analyze the speaker’s sentiments accurately.
One way to record verbatim transcription is through the help of professional transcribers, such as the human transcription services provided by Speak Ai.
Once you have a 99%+ accurate transcript, you can extract actionable insights using our software with more confidence, knowing that your language data set is as precise as possible
Intelligent verbatim/edited transcription
What is intelligent verbatim transcription?
Intelligent verbatim (also called clean or edit transcription) is transcription that omits fillers, repeating words and grammatical errors. The main focus of intelligent verbatim is to optimize the transcript for readability and clarity.
Intelligent verbatim transcription captures what the speaker is communicating rather than the how.
Here is an example:
Interviewer: So, you mentioned that you had some good experiences with this product. Could you please elaborate more on that?
Participant: Yeah, we bought the product and our entire family loved it!
As you can see, intelligent verbatim transcription improves the conciseness and clarity of the text. However, it can also unintentionally alter the original meaning of the sentences.
A professional transcriber usually does intelligent verbatim transcription. On the other hand, you can also use automated data transcription tools if your priority is to save time or just to get a mostly accurate first draft.
Automated data transcription is much more affordable for qualitative research. However, you may require some light editing to fix errors the speech-to-text AI makes.
When are edited transcripts necessary?
You should use intelligent verbatim transcription if you need transcripts that are easy to read or skim through. These transcripts may not record the non-verbal cues or sentiments of the speaker as well, but are perfect for capturing the dialogue content.
Moreover, you can easily share intelligent verbatim transcription because of its conciseness. Since the recordings have been optimized for reading, intelligent data transcription also has more practical applications and can be used for more general purposes.
Situations where intelligent verbatim could be more suitable include:
Publicly available transcripts
General business purposes
How to conduct data transcription for qualitative analysis?
There are two main ways to transcribe data for qualitative research purposes.
Professional human transcription is the best way to ensure the most accurate transcript possible — though it is more expensive and takes a lot more time to complete. This is usually reserved for important files and research jobs where data accuracy comes first.
For individuals or businesses with less stringent requirements for the accuracy of their data set, it is possible to supplement or entirely replace human transcription with automated transcription software.
Professional human transcription services for qualitative research
While automated speech recognition technologies are steadily improving, manual transcription is still superior for delivering accurate transcripts.
Anyone can transcribe an audio recording. However, it takes the average person anywhere from 4 – 5 hours to transcribe one hour of audio accurately. In comparison, a trained transcriptionist can complete the same job in as little as 2 hours.
If you work in research where you are appraised more on your research output than time put in, hiring a professional transcriptionist is probably a better use of your time. This way, you can focus your efforts on the work that matters — analyzing the transcripts for key moments that matter and converting them into actionable insights.
Unlike automated transcriptions of qualitative data, human transcription is usually 99-100% accurate. Labor costs can also vary depending on where the professional is based, costing anywhere from $0.50 up to $2.00 per audio minute.
Almost 100% accurate transcription
Faster turnaround with trained transcribers
The best way to ensure high accuracy data sets
More consistent and accurate speaker labeling
Usually in line with compliance standards for research
Takes more time if done by an untrained professional (up to 5 hours per hour of audio)
Depending on the size of the workload and urgency of the job, Speak Ai’s expert team of transcribers can deliver your transcript in as little as 48 hours. You can also utilize our software’s advanced analysis tools, such as keyword extraction and sentiment analysis, to identify key moments in your text as part of your subscription.
Automated data transcription for qualitative research
Automated transcription services may not guarantee the same level of accuracy as professional transcribers, but they are a great way to scale your transcription efforts. Additionally, if the file audio quality is good, it is possible to get up to 95% accuracy with AI-powered transcripts – with only a small amount of transcript cleanup required.
There are various automated transcription tools available in the market. However, with Speak Ai, you get an all-in-one transcription software that significantly enhances your workflow. You can get automated transcriptions starting as low as $0.15 per audio minute, a 1:1 turnaround time, and an integrated option to order professional help within the platform to clean up your transcripts.
Not to mention a whole suite of machine-powered analyses to supplement your media transcripts as well as researcher and developer-friendly options, including JSON and CSV.
Quick turnaround times (1:1)
Easy to scale with larger media libraries
Can be embedded or integrated with other software stacks
Can be up to 95% accurate with high quality audio
Not as accurate as manual data transcription
May require some editing
Automated transcription services are a great starting point to build an accessible media database. Once you have a fully transcribed media library, you can decide which of your files are important enough to get professionally transcribed if you need cleaner data sets.
How to organize data transcription in qualitative research?
As you collect more data over time, your transcription management system can turn into a disorganized mess when using outdated workflows. Using an integrated transcription solution can save you many headaches down the line by letting you organize, transcribe, analyze, store and access your files by project, all in one place.
Here are some tips on how you can organize transcribed data neatly and extract key information more efficiently.
Keyword mapping is an effective method of identifying patterns both in your research and your competitors’ content. This insight on trending topics, keywords, and other relevant named entities will allow you to make conclusions with a more holistic overview.
Try Our AI Word Cloud Generator
Word clouds are a great way to highlight the most important words, topics and phrases in a text passage based on frequency and relevance. Generate word clouds from your text data to create an easily understood visual breakdown for deeper analysis. Try our free word cloud generator today to automatically visualize insights from your data.
Utilize data management systems
Extensive qualitative research entails coordination between different teams and departments across geographical regions. Simply storing your transcriptions in a cloud drive or email chains may work for a while but will clutter up quickly.
Robust data management systems such as Tableau allow you to share qualitative data results with anyone in your company, anywhere, at any time.
Using Tableau in conjunction with a transcription and QDA tool like Speak Ai lets you create a centralized, searchable media library to supplement your research process. By building this data management system, you can break down silos, enhance collaboration, and better validate your research findings in a streamlined manner.
Establish transcription procedures and requirements
Different transcribers may have different styles of converting audio speech into text. While this is not an issue in small teams, clashing transcription styles will cause confusion when more transcribers get involved.
Before you begin data transcription for qualitative research or any other purposes, ensure that you address:
- The type of transcription (verbatim or intelligent verbatim)
- Formatting consistencies
- Quality control procedures
- Any other specific transcription instructions
- Building a custom vocabulary list for industry specific terms
- Ethical and legal considerations
Clear transcription and quality control procedures may be a hassle to establish at first. However, good data and work organization always pays dividends in the long run and optimizes workflows.
Who can benefit from data transcription services?
If you handle large amounts of video and audio recordings and need to convert them into text, you will need a data transcription service.
Market researchers: Market researchers can use verbatim data transcription (word-for-word) for primary data sources such as group interviews. They can then access the transcripts as needed in their research process. Having an easily searchable, centralized library of all recorded interviews reduces administrative time and ensures better research results.
Medical: Medical professionals can benefit from data transcription in many ways. For example, patient records, surgery notes, and medical procedures can all be transcribed to ensure that no critical diagnostic information is missed.
Business: You can use automated data transcription services to make taking notes a breeze. Common occurrences of data transcription in business settings include recording stakeholder meetings, consultations, and client meetings for you to analyze after.
Legal: Legal transcripts are commonly used to record all parts of court proceedings, such as arguments, defense, and judge’s decisions.
Academic: Students and educators alike can use data transcription services to record lectures, consultations, presentations, dissertations, and other research work.
tl;dr - Key Takeaways
Data transcription in qualitative research is the first and most crucial step of the research process (and often the most expensive).
The best way to ensure a 99% accurate language data set for qualitative research studies is still through professional transcription services. However, automated transcription services are becoming more commonplace to make this process more efficient.
Medical professionals, market researchers, enterprises, lawyers, students, and educators can all benefit from an effective data transcription service when looking to optimize their research workflows.
If you are interested in finding out how to utilize data transcription in your qualitative research, sign up for Speak Ai’s 14-day trial today to get transcription management, qualitative data analysis, and shareable media databases all in one place.