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Interview Analysis

Transcribe and code interviews. MAXQDA has powerful functions to support the analysis and visualization of your results.

Literature Review

Organize and analyze literature. MAXQDA comes with many features to make your literature review faster and easier.

Mixed Methods

MAXQDA is the best choice for your mixed methods analysis. It works with a wide range of data types and offers powerful tools.

Content Analysis

Use MAXQDA to manage your entire research project. Easily import and organize your data. Link relevant quotes to each other, and share your work.

Questionnaire Analysis

Whether your survey contains standardized or open-ended questions, with MAXQDA you can easily import and analyze both types.

Why MAXQDA ?

World-leading mixed methods software.

Do you want to include quantitative analysis methods in your qualitative data analysis? MAXQDA offers you an unbeatable variety of mixed methods functions for this purpose.

Intuitive and easy to learn

Thanks to the self-explaining interface, you will quickly find your way around. Numerous tutorials, guides, and webinars, as well as an active community, help you dive deeper into MAXQDA.

Efficient teamwork

It has always been easy to collaborate with MAXQDA. The new TeamCloud makes it even easier. It takes care of file management and team communication for you.

Comprehensive customer support

If you have any questions, our customer service is happy to help – by phone, e-mail or chat. In addition, helpful FAQs and practical online manuals are available.

Identical on Windows & macOS

One license, two operating systems. The identical interface and functions make teamwork and teaching with MAXQDA easy. Decide flexibly what you want to work with.

Take it from researchers who work with MAXQDA

We consult with our worldwide stakeholders in free-form letter and survey format and analyze feedback to inform our standard setting processes. We found the software and expert services from MAXQDA invaluable in conducting a smooth and efficient analysis process, even where the volume of data to be analyzed was significant.

Chad Chandramohan

Chief Technology Officer, IFRS Foundation

Having used several qualitative data analysis software programs, there is no doubt in my mind that MAXQDA has advantages over all the others. In addition to its remarkable analytical features for harnessing data, MAXQDA’s stellar customer service, online tutorials, and global learning community make it a user friendly and top-notch product.

Sally S. Cohen

NYU Rory Meyers College of Nursing

I spent several months researching the options, and ultimately decided to trial MAXQDA. We brought in a MAXQDA certified trainer, and bought a network license so that our large team at Microsoft could use the tool. We were not disappointed[…] I was so convinced in its efficacy in the applied qualitative field that I bought MAXQDA for my team when I joined Amazon. I was especially delighted when they added the Stats package, which allows us to avoid the extra expense of buying SPSS.

Sam Ladner, Ph.D.

Former Senior Principal Researcher at Workday

I have been fascinated by qualitative research as it makes us reconsider reality from a new perspective. For such reconsideration, it is essential to read data from various viewpoints and write your ideas in notes and memos, continuing to renew your perspective. I have found MAXQDA to be an excellent tool for readily recording and organizing ideas that come up with at various stages of research […]. More than that, however, I feel the potential of MAXQDA is to promote dialogue within the researcher and facilitate new discoveries.

Masahiro Nochi

Graduate School of Education, The University of Tokyo

Understanding and analyzing production and work processes is an important part of my work at the Fraunhofer Institute for Factory Operation and Automation IFF, and MAXQDA supports me in this. MAXQDA has convinced me in every respect with its versatility, intuitive design, and the ability to work together as a team. In our interdisciplinary projects with innovative companies, this allows us to work effectively and efficiently.

Sebastian Häberer, M.Sc.

Expert Engineer, Fraunhofer IFF

With MAXQDA I saved a lot of time coding my research interviews, and with the Visual Tools I was able to show the results in a clear and simple way.MAXQDA has the advantage that it is very intuitive and therefore easy to learn and handle. In addition, they listen to users and provide continuous updates to improve the experience.

Luis Daniel Vazquez Cancino

PhD candidate in Architecture from the National Autonomous University of Mexico

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10 best qualitative data analysis tools

A lot of teams spend a lot of time collecting qualitative customer experience data—but how do you make sense of it, and how do you turn insights into action?

Qualitative data analysis tools help you make sense of customer feedback so you can focus on improving the user and product experience and creating customer delight.

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This chapter of Hotjar's qualitative data analysis (QDA) guide covers the ten best QDA tools that will help you make sense of your customer insights and better understand your users.

Collect qualitative customer data with Hotjar

Use Hotjar’s Surveys and Feedback widget to collect user insights and better understand your customers.

10 tools for qualitative data analysis 

Qualitative data analysis involves gathering, structuring, and interpreting contextual data to identify key patterns and themes in text, audio, and video.

Qualitative data analysis software automates this process, allowing you to focus on interpreting the results—and make informed decisions about how to improve your product—rather than wading through pages of often subjective, text-based data.

Pro tip: before you can analyze qualitative data, you need to gather it. 

One way to collect qualitative customer insights is to place Hotjar Surveys on key pages of your site . Surveys make it easy to capture voice-of-the-customer (VoC) feedback about product features, updated designs, and customer satisfaction—or to perform user and market research.

Need some ideas for your next qualitative research survey? Check out our Hotjar Survey Templates for inspiration.

Example product discovery questions from Hotjar’s bank of survey templates

1. Cauliflower

Cauliflower is a no-code qualitative data analysis tool that gives researchers, product marketers, and developers access to AI-based analytics without dealing with complex interfaces.

How Cauliflower analyzes qualitative data

Cauliflower’s AI-powered analytics help you understand the differences and similarities between different pieces of customer feedback. Ready-made visualizations help identify themes in customers’ words without reading through every review, and make it easy to:

Analyze customer survey data and answers to open-ended questions

Process and understand customer reviews

Examine your social media channels

Identify and prioritize product testing initiatives

Visualize results and share them with your team

One of Cauliflower’s customers says, “[Cauliflower is] great for visualizing the output, particularly finding relevant patterns in comparing breakouts and focussing our qualitative analysis on the big themes emerging.”

NVivo is one of the most popular qualitative data analysis tools on the market—and probably the most expensive. It’s a more technical solution than Cauliflower, and requires more training. NVivo is best for tech-savvy customer experience and product development teams at mid-sized companies and enterprises.

How NVivo analyzes qualitative data

NVivo’s Transcription tool transcribes and analyzes audio and video files from recorded calls—like sales calls, customer interviews, and product demos—and lets you automatically transfer text files into NVivo for further analysis to:

Find recurring themes in customer feedback

Analyze different types of qualitative data, like text, audio, and video

Code and visualize customer input

Identify market gaps based on qualitative and consumer-focused research

Dylan Hazlett from Adial Pharmaceuticals says, “ We needed a reliable software to perform qualitative text analysis. The complexity and features of [Nvivo] have created great value for our team.”

3. ​​Quirkos

Quirkos is a simple and affordable qualitative data analysis tool. Its text analyzer identifies common keywords within text documents to help businesses quickly and easily interpret customer reviews and interviews.

How Quirkos analyzes qualitative data

Quirkos displays side-by-side comparison views to help you understand the difference between feedback shared by different audience groups (by age group, location, gender, etc.). You can also use it to:

Identify keywords and phrases in survey responses and customer interviews

Visualize customer insights

Collaborate on projects

Color code texts effortlessly

One of Quirkos's users says, “ The interface is intuitive, easy to use, and follows quite an intuitive method of assigning codes to documents.”

4. Qualtrics

Qualtrics is a sophisticated experience management platform. The platform offers a range of tools, but we’ll focus on Qualtrics CoreXM here.  

Qualtrics CoreXM lets you collect and analyze insights to remove uncertainty from product development. It helps validate product ideas, spot gaps in the market, and identify broken product experiences, and the tool uses predictive intelligence and analytics to put your customer opinion at the heart of your decision-making.

How Qualtrics analyzes qualitative data

Qualtrics helps teams streamline multiple processes in one interface. You can gather and analyze qualitative data, then immediately share results and hypotheses with stakeholders. The platform also allows you to:

Collect customer feedback through various channels

Understand emotions and sentiment behind customers’ words

Predict what your customers will do next

Act immediately based on the results provided through various integrations

A user in project management shares, “The most useful part of Qualtrics is the depth of analytics you receive on your surveys, questionnaires, and other tools. In real-time, as you develop your surveys, you are given insights into how your data can be analyzed. It is designed to help you get the data you need without asking unnecessary questions.”

5. Dovetail

Dovetail is a customer research platform for growing businesses. It offers three core tools: Playback, Markup, and Backstage. For qualitative data analysis, you’ll need Markup.

Markup offers tools for transcription and analysis of all kinds of qualitative data, and is a great way to consolidate insights.

How Dovetail analyzes qualitative data

Dovetail’s charts help you easily quantify qualitative data. If you need to present your findings to the team, the platform makes it easy to loop in your teammates, manage access rights, and collaborate through the interface. You can:

Transcribe recordings automatically

Discover meaningful patterns in textual data

Highlight and tag customer interviews

Run sentiment analysis

Collaborate on customer research through one interface

Kathryn Rounding , Senior Product Designer at You Need A Budget, says, “Dovetail is a fantastic tool for conducting and managing qualitative research. It helps bring all your research planning, source data, analysis, and reporting together, so you can not only share the final results but all the supporting work that helped you get there.”

6. Thematic

Thematic's AI-driven text feedback analysis platform helps you understand what your customers are saying—and why they’re saying it.

How Thematic analyzes qualitative data

Thematic helps you connect feedback from different channels, uncover themes in customer experience data, and run sentiment analysis—all to make better product decisions. Thematic is helpful when you need to:

Analyze unstructured feedback data from across channels

Discover relationships and patterns in feedback

Reveal emerging trends in customer feedback

Split insights by customer segment

Use resulting data in predictive analytics

Emma Glazer , Director of Marketing at DoorDash, says, “Thematic empowers us with information to help make the right decisions, and I love seeing themes as they emerge. We get real-time signals on issues our customers are experiencing and early feedback on new features they love. I love looking at the week-over-week breakdowns and comparing segments of our audience (market, tenure, etc.) Thematic helps me understand what’s driving our metrics and what steps we need to take next.” 

Delve is cloud-based qualitative data analysis software perfect for coding large volumes of textual data, and is best for analyzing long-form customer interviews.

How Delve analyzes qualitative data

Delve helps reveal the core themes and narratives behind transcripts from sales calls and customer interviews. It also helps to:

Find, group, and refine themes in customer feedback

Analyze long-form customer interviews

Categorize your data by code, pattern, and demographic information

Perform thematic analysis, narrative analysis, and grounded theory analysis

One Delve user says, “Using Delve, it is easier to focus just on coding to start, without getting sidetracked analyzing what I am reading. Once coding is finished, the selected excerpts are already organized based on my own custom outline and I can begin analyzing right away, rather than spending time organizing my notes before I can begin the analysis and writing process.”

8. ATLAS.ti

ATLAS.ti is a qualitative data analysis tool that brings together customer and product research data. It has a range of helpful features for marketers, product analysts, UX professionals, and product designers.

How ATLAS.ti analyzes qualitative data

ATLAS.ti helps product teams collect, structure, and evaluate user feedback before realizing new product ideas. To enhance your product design process with ATLAS.ti, you can:

Generate qualitative insights from surveys

Apply any method of qualitative research

Analyze open-ended questions and standardized surveys

Perform prototype testing

Visualize research results with charts

Collaborate with your team through a single platform

One of the ATLAS.ti customers shares,“ATLAS.ti is innovating in the handling of qualitative data. It gives the user total freedom and the possibility of connecting with other software, as it has many export options.” 

MAXQDA is a data analysis software that can analyze and organize a wide range of data, from handwritten texts, to video recordings, to Tweets.

How MAXQDA analyzes qualitative data

MAWQDA organizes your customer interviews and turns the data into digestible statistics by enabling you to:

Easily transcribe audio or video interviews

Structure standardized and open-ended survey responses

Categorize survey data

Combine qualitative and quantitative methods to get deeper insights into customer data

Share your work with team members

One enterprise-level customer says MAXQDA has “lots of useful features for analyzing and reporting interview and survey data. I really appreciated how easy it was to integrate SPSS data and conduct mixed-method research. The reporting features are high-quality and I loved using Word Clouds for quick and easy data representation.”

10. MonkeyLearn

MonkeyLearn is no-code analytics software for CX and product teams.

How MonkeyLearn analyzes qualitative data

MonkeyLearn automatically sorts, visualizes, and prioritizes customer feedback with its AI-powered algorithms. Along with organizing your data into themes, the tool will split it by intent—allowing you to promptly distinguish positive reviews from issues and requests and address them immediately.

One MonkeyLearn user says, “I like that MonkeyLearn helps us pull data from our tickets automatically and allows us to engage with our customers properly. As our tickets come in, the AI classifies data through keywords and high-end text analysis. It highlights specific text and categorizes it for easy sorting and processing.”

The next step in automating qualitative data analysis 

Qualitative data analysis tools help you uncover actionable insights from customer feedback, reviews, interviews, and survey responses—without getting lost in data.

But there's no one tool to rule them all: each solution has specific functionality, and your team might need to use the tools together depending on your objectives.

With the right qualitative data analysis software, you can make sense of what your customers really want and create better products for them, achieving customer delight and loyalty.

FAQs about qualitative data analysis software

What is qualitative data analysis software.

Qualitative data analysis software is technology that compiles and organizes contextual, non-quantifiable data, making it easy to interpret qualitative customer insights and information.

Which software is used for qualitative data analysis?

The best software used for qualitative data analysis is:

Cauliflower

MonkeyLearn

Is NVivo the only tool for qualitative data analysis?

NVivo isn’t the only tool for qualitative data analysis, but it’s one of the best (and most popular) software providers for qualitative and mixed-methods research.

QDA examples

Previous chapter

Guide index

• University of Oregon Libraries
• Research Guides

How to Choose Data Analysis Software

Qualitative analysis software.

• UO Available Software
• Open Source Software
• Data Resources

Librarian for Research Data Management and Reproducibility

Atlas.ti is a qualitative data analysis software with the purpose of revealing meanings and relationships within complex datasets. Typical users include anthropologists, universities, and profit/nonprofit corporations. Free trial available . 

Import and Export File Capabilities

Import: Text files (.csv, .doc, .docx, .pdf, .rtf, .txt), Graphic files (.jpeg, .gif) Video files (.mp4) & audio formats

Export: Excel files (.xls, .xlsx), Web-based files (.html, .xml) & additional formats (.sav)

View the Atlas.ti video tutorial below to learn more about basic coding techniques and how to create and assign codes. Closed captions are available. 

Dedoose is a data analysis web-based software, that allows for easy integration of both qualitative and quantitative datasets. Typical users include psychologists, sociologists, social scientists, health researchers, market researchers, and policy researchers. Free trial available . 

Import and Export File Compatibility 

Import: Excel files (.xls, .xlsx), Text files (.doc, .docx, .txt, .rtf, .csv, .pdf), Video files (.pm4) & Audio files (.mp3, .wav, .m4a, .wma)

Export: Excel files (.xls, .xslx) & Text files (.doc, .docx, .txt, .csv, .pdf)

NVivo is a qualitative and mixed methods research software to organize and analyze data. Typical users include anthropologists and policy researchers. Free trial available . 

Import and Export File Compatibility

Import: Excel files (.xls, .xlsx), Text files (.doc, .docx, .pdf, .rtf, .txt), Graphic files (.bmp, .gif, .jpg, .jpeg, .png, .tif, .tiff), Video files (.mpg, .mpeg, .mpe, .mp4, .avi, .wmv, .mov, .qt, .3gp, .mts, .m2ts), Audio files (.mps, .m4a, .wma, .wav) & Web-based files

Export: Excel files (.xls, .xlsx) & Text files (.doc, .docx, .pdf, .rtf)

View the NVivo video tutorial below to learn more about the beginning steps of using NVivo.

Taguette is a free and open-source tool for qualitative research. You can import your research materials, highlight and tag quotes, and export the results. Use the free version hosted by NYU or download and host your own copy.

Import: .pdf , .docx , .txt , .odt , .md , or .html .

Export: QDC (REFI-QDA standard), CSV (spreadsheet), XLSX (Microsoft Excel), DOCX (Microsoft Word), HTML , and PDF .

Qualitative Analysis Resources

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• Last Updated: Apr 5, 2024 1:24 PM
• URL: https://researchguides.uoregon.edu/How-to-Choose-Data-Analysis-Software

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The Sheridan Libraries

• Qualitative Data Analysis Software (nVivo, Atlas.TI, and more)
• Sheridan Libraries

Qualitative Data Analysis Software (QDAS) overview

Choosing qda software, core qdas functions.

• Other QDAS Software
• Qualitative Data Sources

For direct assistance

Contact us , JHU Data Services   for assistance with access to nVivo and ATLAS.ti at the Data Services offices on A level, JHU Eisenhower Library.

Visit our website for more info and our upcoming training workshops !

Qualitative research has benefited from a range of software tools facilitating most qualitative methodological techniques, particularly those involving multimedia digital data. These guides focus on two major QDAS products, nVivo and ATLAS.ti.  Both programs can be found on the workstations at the Data Services computer lab on A-level, Eisenhower Library, and nVivo is available through JHU's SAFE Desktop . This guide also lists other QDA software and linked resources.

Many university libraries have produced comprehensive guides on nVivo, ATLAS.ti, and other QDA software, to which we will provide links with our gratitude

Schmider, Christian. n.d. What Qualitative Data Analysis Software Can and Can’t Do for You – an Intro Video . MERIT Library at the School of Education: School of Education, University of Wisconsin-Madison. Accessed January 7, 2020. https://www.youtube.com/watch?v=tLKfaCiHVic .

• Supported Methods
• Decision Factors
• Compare QDA Software

Qualitative Data Analysis (QDA) Software supports a variety of qualitative techniques and methodologies

Qualitative techniques supported by  QDAS

• Coding and Classifying
• Writing: analysis, description, memos
• Relating: finding and annotating connections, relationships, patterns
• Audio/Visual analysis: marking, clipping, transcribing, annotating
• Text mining: computer-aided discovery in large amounts of unstructured text
• Visualization: diagramming, relationship and network patterns, quantitative summary 

QDAS  supported methodologies

• Ethnography
• Case studies
• Grounded theory/ phenomenology
• Discourse/narrative analysis
• Sociolinguistic analysis
• Collaborative qualitative research
• Text analysis & text mining

Overview of qualitative methods from ATLAS.ti:  https://atlasti.com/qualitative-research-methods/

Decision factors for your research

• Methods to feature facilitation (in disciplinary context): How many features directly support your methodology?
• Interface for collection, analysis, reports: Do features accommodate most phases of your research workflow?
• Visualization and outputs: Does it produce and successfully export needed visualization without extensive modification?
• Cost and access to software: Is it worth the investment cost as well as in learning to use it? Look for education discounts.
• Software Comparisons: Commercial & Free. (George Mason University) Lists of flagship software, free software, and tools for converting codebooks among QDA software.
• QDA Software Comparison Chart (NYU Libraries) Comparison chart of QDA software from NYU Library's LibGuide
• Top 14 Qualitative Data Analysis Software Guide with descriptive summaries of the main QDA software, several with business focus.
• Dueling CAQDAS using ATLAS.ti and NVivo Webinar comparing features and use of ATLAS.ti and NVIvo for qualitative data analysis. Includes live demos.

Basic functions common to most QDA programs, and to NVivo and ATLAS.ti in particular:

• Application of a maintained set of terms and short phrases linked to segments of text or audio/video that can be queried and gathered for comparative analysis. 
• Longer narrative notes attached to text or a/v segments, or to codes
• Quick access to codes and segments that can be brought together in panel views for comparison, advanced Boolean search options, and flexible interlinking of segments, codes, and annotation
• Most QDAS facilitates transcribing audio and video, ideally maintaining the links between transcript and A/V segments. 
• Gathering codes, segments, and annotations facilitates pattern discovery and further description of relationships. Some QDAS support social network analysis techniques and visualization
• A range of reports using queries and filters to assemble data and annotations facilitates analysis and writing results.
• ​ Typically includes code tables, social network graphs, and annotated A/V clips.
• Shared access to data & analysis, facilitating comments and discussion, and tracking contributor actions and changes.
• Next: NVivo >>
• Last Updated: Jan 30, 2024 5:15 PM
• URL: https://guides.library.jhu.edu/QDAS

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29 Qualitative Data Analysis Strategies

Johnny Saldaña, School of Theatre and Film, Arizona State University

• Published: 02 September 2020
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This chapter provides an overview of selected qualitative data analysis strategies with a particular focus on codes and coding. Preparatory strategies for a qualitative research study and data management are first outlined. Six coding methods are then profiled using comparable interview data: process coding, in vivo coding, descriptive coding, values coding, dramaturgical coding, and versus coding. Strategies for constructing themes and assertions from the data follow. Analytic memo writing is woven throughout as a method for generating additional analytic insight. Next, display and arts-based strategies are provided, followed by recommended qualitative data analytic software programs and a discussion on verifying the researcher’s analytic findings.

Qualitative Data Analysis Strategies

Anthropologist Clifford Geertz ( 1983 ) charmingly mused, “Life is just a bowl of strategies” (p. 25). Strategy , as I use it here, refers to a carefully considered plan or method to achieve a particular goal. The goal in this case is to develop a write-up of your analytic work with the qualitative data you have been given and collected as part of a study. The plans and methods you might employ to achieve that goal are what this article profiles.

Some may perceive strategy as an inappropriate, if not manipulative, word, suggesting formulaic or regimented approaches to inquiry. I assure you that is not my intent. My use of strategy is dramaturgical in nature: Strategies are actions that characters in plays take to overcome obstacles to achieve their objectives. Actors portraying these characters rely on action verbs to generate belief within themselves and to motivate them as they interpret their lines and move appropriately on stage.

What I offer is a qualitative researcher’s array of actions from which to draw to overcome the obstacles to thinking to achieve an analysis of your data. But unlike the prescripted text of a play in which the obstacles, strategies, and outcomes have been predetermined by the playwright, your work must be improvisational—acting, reacting, and interacting with data on a moment-by-moment basis to determine what obstacles stand in your way and thus what strategies you should take to reach your goals.

Another intriguing quote to keep in mind comes from research methodologist Robert E. Stake ( 1995 ), who posited, “Good research is not about good methods as much as it is about good thinking” (p. 19). In other words, strategies can take you only so far. You can have a box full of tools, but if you do not know how to use them well or use them creatively, the collection seems rather purposeless. One of the best ways we learn is by doing . So, pick up one or more of these strategies (in the form of verbs) and take analytic action with your data. Also keep in mind that these are discussed in the order in which they may typically occur, although humans think cyclically, iteratively, and reverberatively, and each research project has its unique contexts and needs. Be prepared for your mind to jump purposefully and/or idiosyncratically from one strategy to another throughout the study.

Qualitative Data Analysis Strategy: To Foresee

To foresee in qualitative data analysis (QDA) is to reflect beforehand on what forms of data you will most likely need and collect, which thus informs what types of data analytic strategies you anticipate using. Analysis, in a way, begins even before you collect data (Saldaña & Omasta, 2018 ). As you design your research study in your mind and on a text editing page, one strategy is to consider what types of data you may need to help inform and answer your central and related research questions. Interview transcripts, participant observation field notes, documents, artifacts, photographs, video recordings, and so on are not only forms of data but also foundations for how you may plan to analyze them. A participant interview, for example, suggests that you will transcribe all or relevant portions of the recording and use both the transcription and the recording itself as sources for data analysis. Any analytic memos (discussed later) you make about your impressions of the interview also become data to analyze. Even the computing software you plan to employ will be relevant to data analysis because it may help or hinder your efforts.

As your research design formulates, compose one to two paragraphs that outline how your QDA may proceed. This will necessitate that you have some background knowledge of the vast array of methods available to you. Thus, surveying the literature is vital preparatory work.

Qualitative Data Analysis Strategy: To Survey

To survey in QDA is to look for and consider the applicability of the QDA literature in your field that may provide useful guidance for your forthcoming data analytic work. General sources in QDA will provide a good starting point for acquainting you with the data analysis strategies available for the variety of methodologies or genres in qualitative inquiry (e.g., ethnography, phenomenology, case study, arts-based research, mixed methods). One of the most accessible (and humorous) is Galman’s ( 2013 ) The Good, the Bad, and the Data , and one of the most richly detailed is Frederick J. Wertz et al.’s ( 2011 ) Five Ways of Doing Qualitative Analysis . The author’s core texts for this chapter come from The Coding Manual for Qualitative Researchers (Saldaña, 2016 ) and Qualitative Research: Analyzing Life (Saldaña & Omasta, 2018 ).

If your study’s methodology or approach is grounded theory, for example, then a survey of methods works by authors such as Barney G. Glaser, Anselm L. Strauss, Juliet Corbin, and, in particular, the prolific Kathy Charmaz ( 2014 ) may be expected. But there has been a recent outpouring of additional book publications in grounded theory by Birks and Mills ( 2015 ), Bryant ( 2017 ), Bryant and Charmaz ( 2019 ), and Stern and Porr ( 2011 ), plus the legacy of thousands of articles and chapters across many disciplines that have addressed grounded theory in their studies.

Fields such as education, psychology, social work, healthcare, and others also have their own QDA methods literature in the form of texts and journals, as well as international conferences and workshops for members of the profession. It is important to have had some university coursework and/or mentorship in qualitative research to suitably prepare you for the intricacies of QDA, and you must acknowledge that the emergent nature of qualitative inquiry may require you to adopt analysis strategies that differ from what you originally planned.

Qualitative Data Analysis Strategy: To Collect

To collect in QDA is to receive the data given to you by participants and those data you actively gather to inform your study. Qualitative data analysis is concurrent with data collection and management. As interviews are transcribed, field notes are fleshed out, and documents are filed, the researcher uses opportunities to carefully read the corpus and make preliminary notations directly on the data documents by highlighting, bolding, italicizing, or noting in some way any particularly interesting or salient portions. As these data are initially reviewed, the researcher also composes supplemental analytic memos that include first impressions, reminders for follow-up, preliminary connections, and other thinking matters about the phenomena at work.

Some of the most common fieldwork tools you might use to collect data are notepads, pens and pencils; file folders for hard-copy documents; a laptop, tablet, or desktop with text editing software (Microsoft Word and Excel are most useful) and Internet access; and a digital camera and voice recorder (functions available on many electronic devices such as smartphones). Some fieldworkers may even employ a digital video camera to record social action, as long as participant permissions have been secured. But everything originates from the researcher. Your senses are immersed in the cultural milieu you study, taking in and holding onto relevant details, or significant trivia , as I call them. You become a human camera, zooming out to capture the broad landscape of your field site one day and then zooming in on a particularly interesting individual or phenomenon the next. Your analysis is only as good as the data you collect.

Fieldwork can be an overwhelming experience because so many details of social life are happening in front of you. Take a holistic approach to your entrée, but as you become more familiar with the setting and participants, actively focus on things that relate to your research topic and questions. Keep yourself open to the intriguing, surprising, and disturbing (Sunstein & Chiseri-Strater, 2012 , p. 115), because these facets enrich your study by making you aware of the unexpected.

Qualitative Data Analysis Strategy: To Feel

To feel in QDA is to gain deep emotional insight into the social worlds you study and what it means to be human. Virtually everything we do has an accompanying emotion(s), and feelings are both reactions and stimuli for action. Others’ emotions clue you to their motives, values, attitudes, beliefs, worldviews, identities, and other subjective perceptions and interpretations. Acknowledge that emotional detachment is not possible in field research. Attunement to the emotional experiences of your participants plus sympathetic and empathetic responses to the actions around you are necessary in qualitative endeavors. Your own emotional responses during fieldwork are also data because they document the tacit and visceral. It is important during such analytic reflection to assess why your emotional reactions were as they were. But it is equally important not to let emotions alone steer the course of your study. A proper balance must be found between feelings and facts.

Qualitative Data Analysis Strategy: To Organize

To organize in QDA is to maintain an orderly repository of data for easy access and analysis. Even in the smallest of qualitative studies, a large amount of data will be collected across time. Prepare both a hard drive and hard-copy folders for digital data and paperwork, and back up all materials for security from loss. I recommend that each data unit (e.g., one interview transcript, one document, one day’s worth of field notes) have its own file, with subfolders specifying the data forms and research study logistics (e.g., interviews, field notes, documents, institutional review board correspondence, calendar).

For small-scale qualitative studies, I have found it quite useful to maintain one large master file with all participant and field site data copied and combined with the literature review and accompanying researcher analytic memos. This master file is used to cut and paste related passages together, deleting what seems unnecessary as the study proceeds and eventually transforming the document into the final report itself. Cosmetic devices such as font style, font size, rich text (italicizing, bolding, underlining, etc.), and color can help you distinguish between different data forms and highlight significant passages. For example, descriptive, narrative passages of field notes are logged in regular font. “Quotations, things spoken by participants, are logged in bold font.”   Observer’s comments, such as the researcher’s subjective impressions or analytic jottings, are set in italics.

Qualitative Data Analysis Strategy: To Jot

To jot in QDA is to write occasional, brief notes about your thinking or reminders for follow-up. A jot is a phrase or brief sentence that will fit on a standard-size sticky note. As data are brought and documented together, take some initial time to review their contents and jot some notes about preliminary patterns, participant quotes that seem vivid, anomalies in the data, and so forth.

As you work on a project, keep something to write with or to voice record with you at all times to capture your fleeting thoughts. You will most likely find yourself thinking about your research when you are not working exclusively on the project, and a “mental jot” may occur to you as you ruminate on logistical or analytic matters. Document the thought in some way for later retrieval and elaboration as an analytic memo.

Qualitative Data Analysis Strategy: To Prioritize

To prioritize in QDA is to determine which data are most significant in your corpus and which tasks are most necessary. During fieldwork, massive amounts of data in various forms may be collected, and your mind can be easily overwhelmed by the magnitude of the quantity, its richness, and its management. Decisions will need to be made about the most pertinent data because they help answer your research questions or emerge as salient pieces of evidence. As a sweeping generalization, approximately one half to two thirds of what you collect may become unnecessary as you proceed toward the more formal stages of QDA.

To prioritize in QDA is also to determine what matters most in your assembly of codes, categories, patterns, themes, assertions, propositions, and concepts. Return to your research purpose and questions to keep you framed for what the focus should be.

Qualitative Data Analysis Strategy: To Analyze

To analyze in QDA is to observe and discern patterns within data and to construct meanings that seem to capture their essences and essentials. Just as there are a variety of genres, elements, and styles of qualitative research, so too are there a variety of methods available for QDA. Analytic choices are most often based on what methods will harmonize with your genre selection and conceptual framework, what will generate the most sufficient answers to your research questions, and what will best represent and present the project’s findings.

Analysis can range from the factual to the conceptual to the interpretive. Analysis can also range from a straightforward descriptive account to an emergently constructed grounded theory to an evocatively composed short story. A qualitative research project’s outcomes may range from rigorously achieved, insightful answers to open-ended, evocative questions; from rich descriptive detail to a bullet-point list of themes; and from third-person, objective reportage to first-person, emotion-laden poetry. Just as there are multiple destinations in qualitative research, there are multiple pathways and journeys along the way.

Analysis is accelerated as you take cognitive ownership of your data. By reading and rereading the corpus, you gain intimate familiarity with its contents and begin to notice significant details as well as make new connections and insights about their meanings. Patterns, categories, themes, and their interrelationships become more evident the more you know the subtleties of the database.

Since qualitative research’s design, fieldwork, and data collection are most often provisional, emergent, and evolutionary processes, you reflect on and analyze the data as you gather them and proceed through the project. If preplanned methods are not working, you change them to secure the data you need. There is generally a postfieldwork period when continued reflection and more systematic data analysis occur, concurrent with or followed by additional data collection, if needed, and the more formal write-up of the study, which is in itself an analytic act. Through field note writing, interview transcribing, analytic memo writing, and other documentation processes, you gain cognitive ownership of your data; and the intuitive, tacit, synthesizing capabilities of your brain begin sensing patterns, making connections, and seeing the bigger picture. The purpose and outcome of data analysis is to reveal to others through fresh insights what we have observed and discovered about the human condition. Fortunately, there are heuristics for reorganizing and reflecting on your qualitative data to help you achieve that goal.

Qualitative Data Analysis Strategy: To Pattern

To pattern in QDA is to detect similarities within and regularities among the data you have collected. The natural world is filled with patterns because we, as humans, have constructed them as such. Stars in the night sky are not just a random assembly; our ancestors pieced them together to form constellations like the Big Dipper. A collection of flowers growing wild in a field has a pattern, as does an individual flower’s patterns of leaves and petals. Look at the physical objects humans have created and notice how pattern oriented we are in our construction, organization, and decoration. Look around you in your environment and notice how many patterns are evident on your clothing, in a room, and on most objects themselves. Even our sometimes mundane daily and long-term human actions are reproduced patterns in the form of routines, rituals, rules, roles, and relationships (Saldaña & Omasta, 2018 ).

This human propensity for pattern-making follows us into QDA. From the vast array of interview transcripts, field notes, documents, and other forms of data, there is this instinctive, hardwired need to bring order to the collection—not just to reorganize it but to look for and construct patterns out of it. The discernment of patterns is one of the first steps in the data analytic process, and the methods described next are recommended ways to construct them.

Qualitative Data Analysis Strategy: To Code

To code in QDA is to assign a truncated, symbolic meaning to each datum for purposes of qualitative analysis—primarily patterning and categorizing. Coding is a heuristic—a method of discovery—to the meanings of individual sections of data. These codes function as a way of patterning, classifying, and later reorganizing them into emergent categories for further analysis. Different types of codes exist for different types of research genres and qualitative data analytic approaches, but this chapter will focus on only a few selected methods. First, a code can be defined as follows:

A code in qualitative data analysis is most often a word or short phrase that symbolically assigns a summative, salient, essence-capturing, and/or evocative attribute for a portion of language-based or visual data. The data can consist of interview transcripts, participant observation field notes, journals, documents, open-ended survey responses, drawings, artifacts, photographs, video, Internet sites, e-mail correspondence, academic and fictional literature, and so on. The portion of data coded … can range in magnitude from a single word to a full paragraph, an entire page of text or a stream of moving images.… Just as a title represents and captures a book or film or poem’s primary content and essence, so does a code represent and capture a datum’s primary content and essence. (Saldaña, 2016 , p. 4)

One helpful precoding task is to divide or parse long selections of field note or interview transcript data into shorter stanzas . Stanza division unitizes or “chunks” the corpus into more manageable paragraph-like units for coding assignments and analysis. The transcript sample that follows illustrates one possible way of inserting line breaks between self-standing passages of interview text for easier readability.

Process Coding

As a first coding example, the following interview excerpt about an employed, single, lower middle-class adult male’s spending habits during a difficult economic period in the United States is coded in the right-hand margin in capital letters. The superscript numbers match the beginning of the datum unit with its corresponding code. This method is called process coding (Charmaz, 2014 ), and it uses gerunds (“-ing” words) exclusively to represent action suggested by the data. Processes can consist of observable human actions (e.g., BUYING BARGAINS), mental or internal processes (e.g., THINKING TWICE), and more conceptual ideas (e.g., APPRECIATING WHAT YOU’VE GOT). Notice that the interviewer’s (I) portions are not coded, just the participant’s (P). A code is applied each time the subtopic of the interview shifts—even within a stanza—and the same codes can (and should) be used more than once if the subtopics are similar. The central research question driving this qualitative study is, “In what ways are middle-class Americans influenced and affected by an economic recession?”

Different researchers analyzing this same piece of data may develop completely different codes, depending on their personal lenses, filters, and angles. The previous codes are only one person’s interpretation of what is happening in the data, not a definitive list. The process codes have transformed the raw data units into new symbolic representations for analysis. A listing of the codes applied to this interview transcript, in the order they appear, reads:

BUYING BARGAINS

QUESTIONING A PURCHASE

THINKING TWICE

STOCKING UP

REFUSING SACRIFICE

PRIORITIZING

FINDING ALTERNATIVES

LIVING CHEAPLY

NOTICING CHANGES

STAYING INFORMED

MAINTAINING HEALTH

PICKING UP THE TAB

APPRECIATING WHAT YOU’VE GOT

Coding the data is the first step in this approach to QDA, and categorization is just one of the next possible steps.

Qualitative Data Analysis Strategy: To Categorize

To categorize in QDA is to cluster similar or comparable codes into groups for pattern construction and further analysis. Humans categorize things in innumerable ways. Think of an average apartment or house’s layout. The rooms of a dwelling have been constructed or categorized by their builders and occupants according to function. A kitchen is designated as an area to store and prepare food and to store the cooking and dining materials, such as pots, pans, and utensils. A bedroom is designated for sleeping, a closet for clothing storage, a bathroom for bodily functions and hygiene, and so on. Each room is like a category in which related and relevant patterns of human action occur. There are exceptions now and then, such as eating breakfast in bed rather than in a dining area or living in a small studio apartment in which most possessions are contained within one large room (but nonetheless are most often organized and clustered into subcategories according to function and optimal use of space).

The point is that the patterns of social action we designate into categories during QDA are not perfectly bounded. Category construction is our best attempt to cluster the most seemingly alike things into the most seemingly appropriate groups. Categorizing is reorganizing and reordering the vast array of data from a study because it is from these smaller, larger, and meaning-rich units that we can better grasp the particular features of each one and the categories’ possible interrelationships with one another.

One analytic strategy with a list of codes is to classify them into similar clusters. The same codes share the same category, but it is also possible that a single code can merit its own group if you feel it is unique enough. After the codes have been classified, a category label is applied to each grouping. Sometimes a code can also double as a category name if you feel it best summarizes the totality of the cluster. Like coding, categorizing is an interpretive act, because there can be different ways of separating and collecting codes that seem to belong together. The cut-and-paste functions of text editing software are most useful for exploring which codes share something in common.

Below is my categorization of the 15 codes generated from the interview transcript presented earlier. Like the gerunds for process codes, the categories have also been labeled as “-ing” words to connote action. And there was no particular reason why 15 codes resulted in three categories—there could have been less or even more, but this is how the array came together after my reflections on which codes seemed to belong together. The category labels are ways of answering why they belong together. For at-a-glance differentiation, I place codes in CAPITAL LETTERS and categories in upper- and lowercase Bold Font :

Category 1: Thinking Strategically

Category 2: Spending Strategically

Category 3: Living Strategically

Notice that the three category labels share a common word: strategically . Where did this word come from? It came from analytic reflection on the original data, the codes, and the process of categorizing the codes and generating their category labels. It was the analyst’s choice based on the interpretation of what primary action was happening. Your categories generated from your coded data do not need to share a common word or phrase, but I find that this technique, when appropriate, helps build a sense of unity to the initial analytic scheme.

The three categories— Thinking Strategically, Spending Strategically , and Living Strategically —are then reflected on for how they might interact and interplay. This is where the next major facet of data analysis, analytic memos, enters the scheme. But a necessary section on the basic principles of interrelationship and analytic reasoning must precede that discussion.

Qualitative Data Analysis Strategy: To Interrelate

To interrelate in QDA is to propose connections within, between, and among the constituent elements of analyzed data. One task of QDA is to explore the ways our patterns and categories interact and interplay. I use these terms to suggest the qualitative equivalent of statistical correlation, but interaction and interplay are much more than a simple relationship. They imply interrelationship . Interaction refers to reverberative connections—for example, how one or more categories might influence and affect the others, how categories operate concurrently, or whether there is some kind of domino effect to them. Interplay refers to the structural and processual nature of categories—for example, whether some type of sequential order, hierarchy, or taxonomy exists; whether any overlaps occur; whether there is superordinate and subordinate arrangement; and what types of organizational frameworks or networks might exist among them. The positivist construct of cause and effect becomes influences and affects in QDA.

There can even be patterns of patterns and categories of categories if your mind thinks conceptually and abstractly enough. Our minds can intricately connect multiple phenomena, but only if the data and their analyses support the constructions. We can speculate about interaction and interplay all we want, but it is only through a more systematic investigation of the data—in other words, good thinking—that we can plausibly establish any possible interrelationships.

Qualitative Data Analysis Strategy: To Reason

To reason in QDA is to think in ways that lead to summative findings, causal probabilities, and evaluative conclusions. Unlike quantitative research, with its statistical formulas and established hypothesis-testing protocols, qualitative research has no standardized methods of data analysis. Rest assured, there are recommended guidelines from the field’s scholars and a legacy of analysis strategies from which to draw. But the primary heuristics (or methods of discovery) you apply during a study are retroductive, inductive, substructive, abductive , and deductive reasoning.

Retroduction is historic reconstruction, working backward to figure out how the current conditions came to exist. Induction is what we experientially explore and infer to be transferable from the particular to the general, based on an examination of the evidence and an accumulation of knowledge. Substruction takes things apart to more carefully examine the constituent elements of the whole. Abduction is surmising from a range of possibilities that which is most likely, those explanatory hunches of plausibility based on clues. Deduction is what we generally draw and conclude from established facts and evidence.

It is not always necessary to know the names of these five ways of reasoning as you proceed through analysis. In fact, you will more than likely reverberate quickly from one to another depending on the task at hand. But what is important to remember about reasoning is:

to examine the evidence carefully and make reasonable inferences;

to base your conclusions primarily on the participants’ experiences, not just your own;

not to take the obvious for granted, because sometimes the expected will not happen;

your hunches can be right and, at other times, quite wrong; and

to logically yet imaginatively think about what is going on and how it all comes together.

Futurists and inventors propose three questions when they think about creating new visions for the world: What is possible (induction)? What is plausible (abduction)? What is preferable (deduction)? These same three questions might be posed as you proceed through QDA and particularly through analytic memo writing, which is substructive and retroductive reflection on your analytic work thus far.

Qualitative Data Analysis Strategy: To Memo

To memo in QDA is to reflect in writing on the nuances, inferences, meanings, and transfer of coded and categorized data plus your analytic processes. Like field note writing, perspectives vary among practitioners as to the methods for documenting the researcher’s analytic insights and subjective experiences. Some advise that such reflections should be included in field notes as relevant to the data. Others advise that a separate researcher’s journal should be maintained for recording these impressions. And still others advise that these thoughts be documented as separate analytic memos. I prescribe the latter as a method because it is generated by and directly connected to the data themselves.

An analytic memo is a “think piece” of reflective free writing, a narrative that sets in words your interpretations of the data. Coding and categorizing are heuristics to detect some of the possible patterns and interrelationships at work within the corpus, and an analytic memo further articulates your retroductive, inductive, substructive, abductive, and deductive thinking processes on what things may mean. Though the metaphor is a bit flawed and limiting, think of codes and their consequent categories as separate jigsaw puzzle pieces and their integration into an analytic memo as the trial assembly of the complete picture.

What follows is an example of an analytic memo based on the earlier process coded and categorized interview transcript. It is intended not as the final write-up for a publication, but as an open-ended reflection on the phenomena and processes suggested by the data and their analysis thus far. As the study proceeds, however, initial and substantive analytic memos can be revisited and revised for eventual integration into the final report. Note how the memo is dated and given a title for future and further categorization, how participant quotes are occasionally included for evidentiary support, and how the category names are bolded and the codes kept in capital letters to show how they integrate or weave into the thinking:

April 14, 2017 EMERGENT CATEGORIES: A STRATEGIC AMALGAM There’s a popular saying: “Smart is the new rich.” This participant is Thinking Strategically about his spending through such tactics as THINKING TWICE and QUESTIONING A PURCHASE before he decides to invest in a product. There’s a heightened awareness of both immediate trends and forthcoming economic bad news that positively affects his Spending Strategically . However, he seems unaware that there are even more ways of LIVING CHEAPLY by FINDING ALTERNATIVES. He dines at all-you-can-eat restaurants as a way of STOCKING UP on meals, but doesn’t state that he could bring lunch from home to work, possibly saving even more money. One of his “bad habits” is cigarettes, which he refuses to give up; but he doesn’t seem to realize that by quitting smoking he could save even more money, not to mention possible health care costs. He balks at the idea of paying $2.00 for a soft drink, but doesn’t mind paying $6.00–$7.00 for a pack of cigarettes. Penny-wise and pound-foolish. Addictions skew priorities. Living Strategically , for this participant during “scary times,” appears to be a combination of PRIORITIZING those things which cannot be helped, such as pet care and personal dental care; REFUSING SACRIFICE for maintaining personal creature-comforts; and FINDING ALTERNATIVES to high costs and excessive spending. Living Strategically is an amalgam of thinking and action-oriented strategies.

There are several recommended topics for analytic memo writing throughout the qualitative study. Memos are opportunities to reflect on and write about:

A descriptive summary of the data;

How the researcher personally relates to the participants and/or the phenomenon;

The participants’ actions, reactions, and interactions;

The participants’ routines, rituals, rules, roles, and relationships;

What is surprising, intriguing, or disturbing (Sunstein & Chiseri-Strater, 2012 , p. 115);

Code choices and their operational definitions;

Emergent patterns, categories, themes, concepts, assertions, and propositions;

The possible networks and processes (links, connections, overlaps, flows) among the codes, patterns, categories, themes, concepts, assertions, and propositions;

An emergent or related existent theory;

Any problems with the study;

Any personal or ethical dilemmas with the study;

Future directions for the study;

The analytic memos generated thus far (i.e., metamemos);

Tentative answers to the study’s research questions; and

The final report for the study. (adapted from Saldaña & Omasta, 2018 , p. 54)

Since writing is analysis, analytic memos expand on the inferential meanings of the truncated codes, categories, and patterns as a transitional stage into a more coherent narrative with hopefully rich social insight.

Qualitative Data Analysis Strategy: To Code—A Different Way

The first example of coding illustrated process coding, a way of exploring general social action among humans. But sometimes a researcher works with an individual case study in which the language is unique or with someone the researcher wishes to honor by maintaining the authenticity of his or her speech in the analysis. These reasons suggest that a more participant-centered form of coding may be more appropriate.

In Vivo Coding

A second frequently applied method of coding is called in vivo coding. The root meaning of in vivo is “in that which is alive”; it refers to a code based on the actual language used by the participant (Strauss, 1987 ). The words or phrases in the data record you select as codes are those that seem to stand out as significant or summative of what is being said.

Using the same transcript of the male participant living in difficult economic times, in vivo codes are listed in the right-hand column. I recommend that in vivo codes be placed in quotation marks as a way of designating that the code is extracted directly from the data record. Note that instead of 15 codes generated from process coding, the total number of in vivo codes is 30. This is not to suggest that there should be specific numbers or ranges of codes used for particular methods. In vivo codes, however, tend to be applied more frequently to data. Again, the interviewer’s questions and prompts are not coded, just the participant’s responses:

The 30 in vivo codes are then extracted from the transcript and could be listed in the order they appear, but this time they are placed in alphabetical order as a heuristic to prepare them for analytic action and reflection:

“ALL-YOU-CAN-EAT”

“ANOTHER DING IN MY WALLET”

“BAD HABITS”

“CHEAP AND FILLING”

“COUPLE OF THOUSAND”

“DON’T REALLY NEED”

“HAVEN’T CHANGED MY HABITS”

“HIGH MAINTENANCE”

“INSURANCE IS JUST WORTHLESS”

“IT ALL ADDS UP”

“LIVED KIND OF CHEAP”

“NOT A BIG SPENDER”

“NOT AS BAD OFF”

“NOT PUTTING AS MUCH INTO SAVINGS”

“PICK UP THE TAB”

“SCARY TIMES”

“SKYROCKETED”

“SPENDING MORE”

“THE LITTLE THINGS”

“THINK TWICE”

“TWO-FOR-ONE”

Even though no systematic categorization has been conducted with the codes thus far, an analytic memo of first impressions can still be composed:

March 19, 2017 CODE CHOICES: THE EVERYDAY LANGUAGE OF ECONOMICS After eyeballing the in vivo codes list, I noticed that variants of “CHEAP” appear most often. I recall a running joke between me and a friend of mine when we were shopping for sales. We’d say, “We’re not ‘cheap,’ we’re frugal .” There’s no formal economic or business language in this transcript—no terms such as “recession” or “downsizing”—just the everyday language of one person trying to cope during “SCARY TIMES” with “ANOTHER DING IN MY WALLET.” The participant notes that he’s always “LIVED KIND OF CHEAP” and is “NOT A BIG SPENDER” and, due to his employment, “NOT AS BAD OFF” as others in the country. Yet even with his middle class status, he’s still feeling the monetary pinch, dining at inexpensive “ALL-YOU-CAN-EAT” restaurants and worried about the rising price of peanut butter, observing that he’s “NOT PUTTING AS MUCH INTO SAVINGS” as he used to. Of all the codes, “ANOTHER DING IN MY WALLET” stands out to me, particularly because on the audio recording he sounded bitter and frustrated. It seems that he’s so concerned about “THE LITTLE THINGS” because of high veterinary and dental charges. The only way to cope with a “COUPLE OF THOUSAND” dollars worth of medical expenses is to find ways of trimming the excess in everyday facets of living: “IT ALL ADDS UP.”

Like process coding, in vivo codes could be clustered into similar categories, but another simple data analytic strategy is also possible.

Qualitative Data Analysis Strategy: To Outline

To outline in QDA is to hierarchically, processually, and/or temporally assemble such things as codes, categories, themes, assertions, propositions, and concepts into a coherent, text-based display. Traditional outlining formats and content provide not only templates for writing a report but also templates for analytic organization. This principle can be found in several computer-assisted qualitative data analysis software (CAQDAS) programs through their use of such functions as “hierarchies,” “trees,” and “nodes,” for example. Basic outlining is simply a way of arranging primary, secondary, and subsecondary items into a patterned display. For example, an organized listing of things in a home might consist of the following:

Large appliances

Refrigerator

Stove-top oven

Microwave oven

Small appliances

Coffee maker

Dining room

In QDA, outlining may include descriptive nouns or topics but, depending on the study, it may also involve processes or phenomena in extended passages, such as in vivo codes or themes.

The complexity of what we learn in the field can be overwhelming, and outlining is a way of organizing and ordering that complexity so that it does not become complicated. The cut-and-paste and tab functions of a text editing page enable you to arrange and rearrange the salient items from your preliminary coded analytic work into a more streamlined flow. By no means do I suggest that the intricate messiness of life can always be organized into neatly formatted arrangements, but outlining is an analytic act that stimulates deep reflection on both the interconnectedness and the interrelationships of what we study. As an example, here are the 30 in vivo codes generated from the initial transcript analysis, arranged in such a way as to construct five major categories:

Now that the codes have been rearranged into an outline format, an analytic memo is composed to expand on the rationale and constructed meanings in progress:

March 19, 2017 NETWORKS: EMERGENT CATEGORIES The five major categories I constructed from the in vivo codes are: “SCARY TIMES,” “PRIORTY,” “ANOTHER DING IN MY WALLET,” “THE LITTLE THINGS,” and “LIVED KIND OF CHEAP.” One of the things that hit me today was that the reason he may be pinching pennies on smaller purchases is that he cannot control the larger ones he has to deal with. Perhaps the only way we can cope with or seem to have some sense of agency over major expenses is to cut back on the smaller ones that we can control. $1,000 for a dental bill? Skip lunch for a few days a week. Insulin medication to buy for a pet? Don’t buy a soft drink from a vending machine. Using this reasoning, let me try to interrelate and weave the categories together as they relate to this particular participant: During these scary economic times, he prioritizes his spending because there seems to be just one ding after another to his wallet. A general lifestyle of living cheaply and keeping an eye out for how to save money on the little things compensates for those major expenses beyond his control.

Qualitative Data Analysis Strategy: To Code—In Even More Ways

The process and in vivo coding examples thus far have demonstrated only two specific methods of 33 documented approaches (Saldaña, 2016 ). Which one(s) you choose for your analysis depends on such factors as your conceptual framework, the genre of qualitative research for your project, the types of data you collect, and so on. The following sections present four additional approaches available for coding qualitative data that you may find useful as starting points.

Descriptive Coding

Descriptive codes are primarily nouns that simply summarize the topic of a datum. This coding approach is particularly useful when you have different types of data gathered for one study, such as interview transcripts, field notes, open-ended survey responses, documents, and visual materials such as photographs. Descriptive codes not only help categorize but also index the data corpus’s basic contents for further analytic work. An example of an interview portion coded descriptively, taken from the participant living in tough economic times, follows to illustrate how the same data can be coded in multiple ways:

For initial analysis, descriptive codes are clustered into similar categories to detect such patterns as frequency (i.e., categories with the largest number of codes) and interrelationship (i.e., categories that seem to connect in some way). Keep in mind that descriptive coding should be used sparingly with interview transcript data because other coding methods will reveal richer participant dynamics.

Values Coding

Values coding identifies the values, attitudes, and beliefs of a participant, as shared by the individual and/or interpreted by the analyst. This coding method infers the “heart and mind” of an individual or group’s worldview as to what is important, perceived as true, maintained as opinion, and felt strongly. The three constructs are coded separately but are part of a complex interconnected system.

Briefly, a value (V) is what we attribute as important, be it a person, thing, or idea. An attitude (A) is the evaluative way we think and feel about ourselves, others, things, or ideas. A belief (B) is what we think and feel as true or necessary, formed from our “personal knowledge, experiences, opinions, prejudices, morals, and other interpretive perceptions of the social world” (Saldaña, 2016 , p. 132). Values coding explores intrapersonal, interpersonal, and cultural constructs, or ethos . It is an admittedly slippery task to code this way because it is sometimes difficult to discern what is a value, attitude, or belief since they are intricately interrelated. But the depth you can potentially obtain is rich. An example of values coding follows:

For analysis, categorize the codes for each of the three different constructs together (i.e., all values in one group, attitudes in a second group, and beliefs in a third group). Analytic memo writing about the patterns and possible interrelationships may reveal a more detailed and intricate worldview of the participant.

Dramaturgical Coding

Dramaturgical coding perceives life as performance and its participants as characters in a social drama. Codes are assigned to the data (i.e., a “play script”) that analyze the characters in action, reaction, and interaction. Dramaturgical coding of participants examines their objectives (OBJ) or wants, needs, and motives; the conflicts (CON) or obstacles they face as they try to achieve their objectives; the tactics (TAC) or strategies they employ to reach their objectives; their attitudes (ATT) toward others and their given circumstances; the particular emotions (EMO) they experience throughout; and their subtexts (SUB), or underlying and unspoken thoughts. The following is an example of dramaturgically coded data:

Not included in this particular interview excerpt are the emotions the participant may have experienced or talked about. His later line, “that’s another ding in my wallet,” would have been coded EMO: BITTER. A reader may not have inferred that specific emotion from seeing the line in print. But the interviewer, present during the event and listening carefully to the audio recording during transcription, noted that feeling in his tone of voice.

For analysis, group similar codes together (e.g., all objectives in one group, all conflicts in another group, all tactics in a third group) or string together chains of how participants deal with their circumstances to overcome their obstacles through tactics:

OBJ: SAVING MEAL MONEY → TAC: SKIPPING MEALS + COUPONS

Dramaturgical coding is particularly useful as preliminary work for narrative inquiry story development or arts-based research representations such as performance ethnography. The method explores how the individuals or groups manage problem solving in their daily lives.

Versus Coding

Versus (VS) coding identifies the conflicts, struggles, and power issues observed in social action, reaction, and interaction as an X VS Y code, such as MEN VS WOMEN, CONSERVATIVES VS LIBERALS, FAITH VS LOGIC, and so on. Conflicts are rarely this dichotomous; they are typically nuanced and much more complex. But humans tend to perceive these struggles with an US VS THEM mindset. The codes can range from the observable to the conceptual and can be applied to data that show humans in tension with others, themselves, or ideologies.

What follows are examples of versus codes applied to the case study participant’s descriptions of his major medical expenses:

As an initial analytic tactic, group the versus codes into one of three categories: the Stakeholders , their Perceptions and/or Actions , and the Issues at stake. Examine how the three interrelate and identify the central ideological conflict at work as an X VS Y category. Analytic memos and the final write-up can detail the nuances of the issues.

Remember that what has been profiled in this section is a broad brushstroke description of just a few basic coding processes, several of which can be compatibly mixed and matched within a single analysis (see Saldaña’s 2016   The Coding Manual for Qualitative Researchers for a complete discussion). Certainly with additional data, more in-depth analysis can occur, but coding is only one approach to extracting and constructing preliminary meanings from the data corpus. What now follows are additional methods for qualitative analysis.

Qualitative Data Analysis Strategy: To Theme

To theme in QDA is to construct summative, phenomenological meanings from data through extended passages of text. Unlike codes, which are most often single words or short phrases that symbolically represent a datum, themes are extended phrases or sentences that summarize the manifest (apparent) and latent (underlying) meanings of data (Auerbach & Silverstein, 2003 ; Boyatzis, 1998 ). Themes, intended to represent the essences and essentials of humans’ lived experiences, can also be categorized or listed in superordinate and subordinate outline formats as an analytic tactic.

Below is the interview transcript example used in the previous coding sections. (Hopefully you are not too fatigued at this point with the transcript, but it is important to know how inquiry with the same data set can be approached in several different ways.) During the investigation of the ways middle-class Americans are influenced and affected by an economic recession, the researcher noticed that participants’ stories exhibited facets of what he labeled economic intelligence , or EI (based on the formerly developed theories of Howard Gardner’s multiple intelligences and Daniel Goleman’s emotional intelligence). Notice how theming interprets what is happening through the use of two distinct phrases—ECONOMIC INTELLIGENCE IS (i.e., manifest or apparent meanings) and ECONOMIC INTELLIGENCE MEANS (i.e., latent or underlying meanings):

Unlike the 15 process codes and 30 in vivo codes in the previous examples, there are now 14 themes to work with. They could be listed in the order they appear, but one initial heuristic is to group them separately by “is” and “means” statements to detect any possible patterns (discussed later):

EI IS TAKING ADVANTAGE OF UNEXPECTED OPPORTUNITY

EI IS BUYING CHEAP

EI IS SAVING A FEW DOLLARS NOW AND THEN

EI IS SETTING PRIORITIES

EI IS FINDING CHEAPER FORMS OF ENTERTAINMENT

EI IS NOTICING PERSONAL AND NATIONAL ECONOMIC TRENDS

EI IS TAKING CARE OF ONE’S OWN HEALTH

EI MEANS THINKING BEFORE YOU ACT

EI MEANS SACRIFICE

EI MEANS KNOWING YOUR FLAWS

EI MEANS LIVING AN INEXPENSIVE LIFESTYLE

EI MEANS YOU CANNOT CONTROL EVERYTHING

EI MEANS KNOWING YOUR LUCK

There are several ways to categorize the themes as preparation for analytic memo writing. The first is to arrange them in outline format with superordinate and subordinate levels, based on how the themes seem to take organizational shape and structure. Simply cutting and pasting the themes in multiple arrangements on a text editing page eventually develops a sense of order to them. For example:

A second approach is to categorize the themes into similar clusters and to develop different category labels or theoretical constructs . A theoretical construct is an abstraction that transforms the central phenomenon’s themes into broader applications but can still use “is” and “means” as prompts to capture the bigger picture at work:

Theoretical Construct 1: EI Means Knowing the Unfortunate Present

Supporting Themes:

Theoretical Construct 2: EI Is Cultivating a Small Fortune

Theoretical Construct 3: EI Means a Fortunate Future

What follows is an analytic memo generated from the cut-and-paste arrangement of themes into “is” and “means” statements, into an outline, and into theoretical constructs:

March 19, 2017 EMERGENT THEMES: FORTUNE/FORTUNATELY/UNFORTUNATELY I first reorganized the themes by listing them in two groups: “is” and “means.” The “is” statements seemed to contain positive actions and constructive strategies for economic intelligence. The “means” statements held primarily a sense of caution and restriction with a touch of negativity thrown in. The first outline with two major themes, LIVING AN INEXPENSIVE LIFESTYLE and YOU CANNOT CONTROL EVERYTHING also had this same tone. This reminded me of the old children’s picture book, Fortunately/Unfortunately , and the themes of “fortune” as a motif for the three theoretical constructs came to mind. Knowing the Unfortunate Present means knowing what’s (most) important and what’s (mostly) uncontrollable in one’s personal economic life. Cultivating a Small Fortune consists of those small money-saving actions that, over time, become part of one’s lifestyle. A Fortunate Future consists of heightened awareness of trends and opportunities at micro and macro levels, with the understanding that health matters can idiosyncratically affect one’s fortune. These three constructs comprise this particular individual’s EI—economic intelligence.

Again, keep in mind that the examples for coding and theming were from one small interview transcript excerpt. The number of codes and their categorization would increase, given a longer interview and/or multiple interviews to analyze. But the same basic principles apply: codes and themes relegated into patterned and categorized forms are heuristics—stimuli for good thinking through the analytic memo-writing process on how everything plausibly interrelates. Methodologists vary in the number of recommended final categories that result from analysis, ranging anywhere from three to seven, with traditional grounded theorists prescribing one central or core category from coded work.

Qualitative Data Analysis Strategy: To Assert

To assert in QDA is to put forward statements that summarize particular fieldwork and analytic observations that the researcher believes credibly represent and transcend the experiences. Educational anthropologist Frederick Erickson ( 1986 ) wrote a significant and influential chapter on qualitative methods that outlined heuristics for assertion development . Assertions are declarative statements of summative synthesis, supported by confirming evidence from the data and revised when disconfirming evidence or discrepant cases require modification of the assertions. These summative statements are generated from an interpretive review of the data corpus and then supported and illustrated through narrative vignettes—reconstructed stories from field notes, interview transcripts, or other data sources that provide a vivid profile as part of the evidentiary warrant.

Coding or theming data can certainly precede assertion development as a way of gaining intimate familiarity with the data, but Erickson’s ( 1986 ) methods are a more admittedly intuitive yet systematic heuristic for analysis. Erickson promotes analytic induction and exploration of and inferences about the data, based on an examination of the evidence and an accumulation of knowledge. The goal is not to look for “proof” to support the assertions, but to look for plausibility of inference-laden observations about the local and particular social world under investigation.

Assertion development is the writing of general statements, plus subordinate yet related ones called subassertions and a major statement called a key assertion that represents the totality of the data. One also looks for key linkages between them, meaning that the key assertion links to its related assertions, which then link to their respective subassertions. Subassertions can include particulars about any discrepant related cases or specify components of their parent assertions.

Excerpts from the interview transcript of our case study will be used to illustrate assertion development at work. By now, you should be quite familiar with the contents, so I will proceed directly to the analytic example. First, there is a series of thematically related statements the participant makes:

“Buy one package of chicken, get the second one free. Now that was a bargain. And I got some.”

“With Sweet Tomatoes I get those coupons for a few bucks off for lunch, so that really helps.”

“I don’t go to movies anymore. I rent DVDs from Netflix or Redbox or watch movies online—so much cheaper than paying over ten or twelve bucks for a movie ticket.”

Assertions can be categorized into low-level and high-level inferences . Low-level inferences address and summarize what is happening within the particulars of the case or field site—the micro . High-level inferences extend beyond the particulars to speculate on what it means in the more general social scheme of things—the meso or macro . A reasonable low-level assertion about the three statements above collectively might read, The participant finds several small ways to save money during a difficult economic period . A high-level inference that transcends the case to the meso level might read, Selected businesses provide alternatives and opportunities to buy products and services at reduced rates during a recession to maintain consumer spending.

Assertions are instantiated (i.e., supported) by concrete instances of action or participant testimony, whose patterns lead to more general description outside the specific field site. The author’s interpretive commentary can be interspersed throughout the report, but the assertions should be supported with the evidentiary warrant . A few assertions and subassertions based on the case interview transcript might read as follows (and notice how high-level assertions serve as the paragraphs’ topic sentences):

Selected businesses provide alternatives and opportunities to buy products and services at reduced rates during a recession to maintain consumer spending. Restaurants, for example, need to find ways during difficult economic periods when potential customers may be opting to eat inexpensively at home rather than spending more money by dining out. Special offers can motivate cash-strapped clientele to patronize restaurants more frequently. An adult male dealing with such major expenses as underinsured dental care offers: “With Sweet Tomatoes I get those coupons for a few bucks off for lunch, so that really helps.” The film and video industries also seem to be suffering from a double-whammy during the current recession: less consumer spending on higher-priced entertainment, resulting in a reduced rate of movie theater attendance (recently 39 percent of the American population, according to a CNN report); coupled with a media technology and business revolution that provides consumers less costly alternatives through video rentals and Internet viewing: “I don’t go to movies anymore. I rent DVDs from Netflix or Redbox or watch movies online—so much cheaper than paying over ten or twelve bucks for a movie ticket.”

To clarify terminology, any assertion that has an if–then or predictive structure to it is called a proposition since it proposes a conditional event. For example, this assertion is also a proposition: “Special offers can motivate cash-strapped clientele to patronize restaurants more frequently.” Propositions are the building blocks of hypothesis testing in the field and for later theory construction. Research not only documents human action but also can sometimes formulate statements that predict it. This provides a transferable and generalizable quality in our findings to other comparable settings and contexts. And to clarify terminology further, all propositions are assertions, but not all assertions are propositions.

Particularizability —the search for specific and unique dimensions of action at a site and/or the specific and unique perspectives of an individual participant—is not intended to filter out trivial excess but to magnify the salient characteristics of local meaning. Although generalizable knowledge is difficult to formulate in qualitative inquiry since each naturalistic setting will contain its own unique set of social and cultural conditions, there will be some aspects of social action that are plausibly universal or “generic” across settings and perhaps even across time.

To work toward this, Erickson advocates that the interpretive researcher look for “concrete universals” by studying actions at a particular site in detail and then comparing those actions to actions at other sites that have also been studied in detail. The exhibit or display of these generalizable features is to provide a synoptic representation, or a view of the whole. What the researcher attempts to uncover is what is both particular and general at the site of interest, preferably from the perspective of the participants. It is from the detailed analysis of actions at a specific site that these universals can be concretely discerned, rather than abstractly constructed as in grounded theory.

In sum, assertion development is a qualitative data analytic strategy that relies on the researcher’s intense review of interview transcripts, field notes, documents, and other data to inductively formulate, with reasonable certainty, composite statements that credibly summarize and interpret participant actions and meanings and their possible representation of and transfer into broader social contexts and issues.

Qualitative Data Analysis Strategy: To Display

To display in QDA is to visually present the processes and dynamics of human or conceptual action represented in the data. Qualitative researchers use not only language but also illustrations to both analyze and display the phenomena and processes at work in the data. Tables, charts, matrices, flow diagrams, and other models and graphics help both you and your readers cognitively and conceptually grasp the essence and essentials of your findings. As you have seen thus far, even simple outlining of codes, categories, and themes is one visual tactic for organizing the scope of the data. Rich text, font, and format features such as italicizing, bolding, capitalizing, indenting, and bullet pointing provide simple emphasis to selected words and phrases within the longer narrative.

Think display was a phrase coined by methodologists Miles and Huberman ( 1994 ) to encourage the researcher to think visually as data were collected and analyzed. The magnitude of text can be essentialized into graphics for at-a-glance review. Bins in various shapes and lines of various thicknesses, along with arrows suggesting pathways and direction, render the study a portrait of action. Bins can include the names of codes, categories, concepts, processes, key participants, and/or groups.

As a simple example, Figure 29.1 illustrates the three categories’ interrelationship derived from process coding. It displays what could be the apex of this interaction, LIVING STRATEGICALLY, and its connections to THINKING STRATEGICALLY, which influences and affects SPENDING STRATEGICALLY.

Three categories’ interrelationship derived from process coding.

Figure 29.2 represents a slightly more complex (if not playful) model, based on the five major in vivo codes/categories generated from analysis. The graphic is used as a way of initially exploring the interrelationship and flow from one category to another. The use of different font styles, font sizes, and line and arrow thicknesses is intended to suggest the visual qualities of the participant’s language and his dilemmas—a way of heightening in vivo coding even further.

In vivo categories in rich text display

Accompanying graphics are not always necessary for a qualitative report. They can be very helpful for the researcher during the analytic stage as a heuristic for exploring how major ideas interrelate, but illustrations are generally included in published work when they will help supplement and clarify complex processes for readers. Photographs of the field setting or the participants (and only with their written permission) also provide evidentiary reality to the write-up and help your readers get a sense of being there.

Qualitative Data Analysis Strategy: To Narrate

To narrate in QDA is to create an evocative literary representation and presentation of the data in the form of creative nonfiction. All research reports are stories of one kind or another. But there is yet another approach to QDA that intentionally documents the research experience as story, in its traditional literary sense. Narrative inquiry serves to plot and story-line the participant’s experiences into what might be initially perceived as a fictional short story or novel. But the story is carefully crafted and creatively written to provide readers with an almost omniscient perspective about the participants’ worldview. The transformation of the corpus from database to creative nonfiction ranges from systematic transcript analysis to open-ended literary composition. The narrative, however, should be solidly grounded in and emerge from the data as a plausible rendering of social life.

The following is a narrative vignette based on interview transcript selections from the participant living through tough economic times:

Jack stood in front of the soft drink vending machine at work and looked almost worriedly at the selections. With both hands in his pants pockets, his fingers jingled the few coins he had inside them as he contemplated whether he could afford the purchase. Two dollars for a twenty-ounce bottle of Diet Coke. Two dollars. “I can practically get a two-liter bottle for that same price at the grocery store,” he thought. Then Jack remembered the upcoming dental surgery he needed—that would cost one thousand dollars—and the bottle of insulin and syringes he needed to buy for his diabetic, high maintenance cat—almost two hundred dollars. He sighed, took his hands out of his pockets, and walked away from the vending machine. He was skipping lunch that day anyway so he could stock up on dinner later at the cheap-but-filling all-you-can-eat Chinese buffet. He could get his Diet Coke there.

Narrative inquiry representations, like literature, vary in tone, style, and point of view. The common goal, however, is to create an evocative portrait of participants through the aesthetic power of literary form. A story does not always have to have a moral explicitly stated by its author. The reader reflects on personal meanings derived from the piece and how the specific tale relates to one’s self and the social world.

Qualitative Data Analysis Strategy: To Poeticize

To poeticize in QDA is to create an evocative literary representation and presentation of the data in poetic form. One approach to analyzing or documenting analytic findings is to strategically truncate interview transcripts, field notes, and other pertinent data into poetic structures. Like coding, poetic constructions capture the essence and essentials of data in a creative, evocative way. The elegance of the format attests to the power of carefully chosen language to represent and convey complex human experience.

In vivo codes (codes based on the actual words used by participants themselves) can provide imagery, symbols, and metaphors for rich category, theme, concept, and assertion development, in addition to evocative content for arts-based interpretations of the data. Poetic inquiry takes note of what words and phrases seem to stand out from the data corpus as rich material for reinterpretation. Using some of the participant’s own language from the interview transcript illustrated previously, a poetic reconstruction or “found poetry” might read as follows:

Scary Times Scary times … spending more   (another ding in my wallet) a couple of thousand   (another ding in my wallet) insurance is just worthless   (another ding in my wallet) pick up the tab   (another ding in my wallet) not putting as much into savings   (another ding in my wallet) It all adds up. Think twice:   don’t really need    skip Think twice, think cheap:   coupons   bargains   two-for-one    free Think twice, think cheaper:   stock up   all-you-can-eat    (cheap—and filling) It all adds up.

Anna Deavere Smith, a verbatim theatre performer, attests that people speak in forms of “organic poetry” in everyday life. Thus, in vivo codes can provide core material for poetic representation and presentation of lived experiences, potentially transforming the routine and mundane into the epic. Some researchers also find the genre of poetry to be the most effective way to compose original work that reflects their own fieldwork experiences and autoethnographic stories.

Qualitative Data Analysis Strategy: To Compute

To compute in QDA is to employ specialized software programs for qualitative data management and analysis. The acronym for computer-assisted qualitative data analysis software is CAQDAS. There are diverse opinions among practitioners in the field about the utility of such specialized programs for qualitative data management and analysis. The software, unlike statistical computation, does not actually analyze data for you at higher conceptual levels. These CAQDAS software packages serve primarily as a repository for your data (both textual and visual) that enables you to code them, and they can perform such functions as calculating the number of times a particular word or phrase appears in the data corpus (a particularly useful function for content analysis) and can display selected facets after coding, such as possible interrelationships. Basic software such as Microsoft Word and Excel provides utilities that can store and, with some preformatting and strategic entry, organize qualitative data to enable the researcher’s analytic review. The following Internet addresses are listed to help in exploring selected CAQDAS packages and obtaining demonstration/trial software; video tutorials are available on the companies’ websites and on YouTube:

ATLAS.ti: http://www.atlasti.com

Dedoose: http://www.dedoose.com

HyperRESEARCH: http://www.researchware.com

MAXQDA: http://www.maxqda.com

NVivo: http://www.qsrinternational.com

QDA Miner: http://www.provalisresearch.com

Quirkos: http://www.quirkos.com

Transana: http://www.transana.com

V-Note: http://www.v-note.org

Some qualitative researchers attest that the software is indispensable for qualitative data management, especially for large-scale studies. Others feel that the learning curve of most CAQDAS programs is too lengthy to be of pragmatic value, especially for small-scale studies. From my own experience, if you have an aptitude for picking up quickly on the scripts and syntax of software programs, explore one or more of the packages listed. If you are a novice to qualitative research, though, I recommend working manually or “by hand” for your first project so you can focus exclusively on the data and not on the software.

Qualitative Data Analysis Strategy: To Verify

To verify in QDA is to administer an audit of “quality control” to your analysis. After your data analysis and the development of key findings, you may be thinking to yourself, “Did I get it right?” “Did I learn anything new?” Reliability and validity are terms and constructs of the positivist quantitative paradigm that refer to the replicability and accuracy of measures. But in the qualitative paradigm, other constructs are more appropriate.

Credibility and trustworthiness (Lincoln & Guba, 1985 ) are two factors to consider when collecting and analyzing the data and presenting your findings. In our qualitative research projects, we must present a convincing story to our audiences that we “got it right” methodologically. In other words, the amount of time we spent in the field, the number of participants we interviewed, the analytic methods we used, the thinking processes evident to reach our conclusions, and so on should be “just right” to assure the reader that we have conducted our jobs soundly. But remember that we can never conclusively prove something; we can only, at best, convincingly suggest. Research is an act of persuasion.

Credibility in a qualitative research report can be established in several ways. First, citing the key writers of related works in your literature review is essential. Seasoned researchers will sometimes assess whether a novice has “done her homework” by reviewing the bibliography or references. You need not list everything that seminal writers have published about a topic, but their names should appear at least once as evidence that you know the field’s key figures and their work.

Credibility can also be established by specifying the particular data analysis methods you employed (e.g., “Interview transcripts were taken through two cycles of process coding, resulting in three primary categories”), through corroboration of data analysis with the participants themselves (e.g., “I asked my participants to read and respond to a draft of this report for their confirmation of accuracy and recommendations for revision”), or through your description of how data and findings were substantiated (e.g., “Data sources included interview transcripts, participant observation field notes, and participant response journals to gather multiple perspectives about the phenomenon”).

Data scientist W. Edwards Deming is attributed with offering this cautionary advice about making a convincing argument: “Without data, you’re just another person with an opinion.” Thus, researchers can also support their findings with relevant, specific evidence by quoting participants directly and/or including field note excerpts from the data corpus. These serve both as illustrative examples for readers and to present more credible testimony of what happened in the field.

Trustworthiness, or providing credibility to the writing, is when we inform the reader of our research processes. Some make the case by stating the duration of fieldwork (e.g., “Forty-five clock hours were spent in the field”; “The study extended over a 10-month period”). Others put forth the amounts of data they gathered (e.g., “Sixteen individuals were interviewed”; “My field notes totaled 157 pages”). Sometimes trustworthiness is established when we are up front or confessional with the analytic or ethical dilemmas we encountered (e.g., “It was difficult to watch the participant’s teaching effectiveness erode during fieldwork”; “Analysis was stalled until I recoded the entire data corpus with a new perspective”).

The bottom line is that credibility and trustworthiness are matters of researcher honesty and integrity . Anyone can write that he worked ethically, rigorously, and reflexively, but only the writer will ever know the truth. There is no shame if something goes wrong with your research. In fact, it is more than likely the rule, not the exception. Work and write transparently to achieve credibility and trustworthiness with your readers.

The length of this chapter does not enable me to expand on other QDA strategies such as to conceptualize, theorize, and write. Yet there are even more subtle thinking strategies to employ throughout the research enterprise, such as to synthesize, problematize, and create. Each researcher has his or her own ways of working, and deep reflexivity (another strategy) on your own methodology and methods as a qualitative inquirer throughout fieldwork and writing provides you with metacognitive awareness of data analysis processes and possibilities.

Data analysis is one of the most elusive practices in qualitative research, perhaps because it is a backstage, behind-the-scenes, in-your-head enterprise. It is not that there are no models to follow. It is just that each project is contextual and case specific. The unique data you collect from your unique research design must be approached with your unique analytic signature. It truly is a learning-by-doing process, so accept that and leave yourself open to discovery and insight as you carefully scrutinize the data corpus for patterns, categories, themes, concepts, assertions, propositions, and possibly new theories through strategic analysis.

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Starting out with qualitative analysis software

by Dr. Daniel Turner, Founder and Director, Quirkos Software . After gaining a PhD using qualitative research in global health, Daniel spent a decade on qualitative research projects at various UK universities. Daniel left academia to develop Quirkos to fill a need for a simple qualitative analysis tool. He still tries to keep up with qualitative research, running workshops on Quirkos and qualitative research across the world. He has written several textbook chapters and a regular blog on qualitative methods with more than 160 articles.

For more about Quirkos, see their blog .

When new researchers look at qualitative software for the first time, it can be a bit overwhelming: both the software itself and the raft of different packages. But there are some basic tips that can help break down the planning process, and get you coding and exploring your data.

The first step is to understand what is required by your analytic process. This will depend on the type of qualitative data you are using, your research questions and most importantly your epistemological stance. During your literature review, you probably will have decided on the best data collection methods to answer your research question, but this should also suggest the best way to analyse the data.

Contrary to popular belief, there isn’t one software package that is ‘best’ for a particular approach like grounded theory or IPA , they are all pretty flexible and can work with all types of analysis. It’s more likely that what works best for you depends on how you prefer to organise information: Do you think in lots of subheadings? Are you a visual thinker? Do you prefer to plan a structure at the start, or improvise?

Try and think about what you would like the process to be first, before trying too many qualitative packages. It’s easy to be seduced by features, but you should use them for what you want to do, and not try to force fancy features to work with your analysis. The 5 Level QDA approach advocates just this: encouraging you to think of the strategies and tactics you will need, and mapping these onto the components of the software packages. But regardless of your plan of action, there will likely be certain key things you need to do:

1. Bring in your data

Whether your data is interviews, focus groups, or art based, you will want to bring it into the software to work with. Not all software can import audio and video data, but you should think about transcribing your data as well. All software packages will also allow you to organise or describe attributes or properties in the data (such as demographics of your respondents), and this can help with comparative analysis and exploring differences in the data. Think of qualitative software as being part of a management system that helps organise your data (as well as your analysis), which is why it is also useful to...

2. Include your literature!

I’m a strong advocate for treating your academic literature as a source of data – bringing journal articles and textbook chapters into qualitative analysis software can really help when doing a literature or systematic review , but also in the writing up process. Thematically code research papers or chapters in the same way you explore your dataset, and it’s easy to compare the literature to your findings when you come to write up.

3. Create codes, themes or topics

Regardless of whether you are using grounded theory and doing inductive coding, or a framework analysis approach with a-priori codes, at some point you will create codes and/or themes in your data. These two types of approach will decide whether you create these before getting into the data (framework) or create them as you go (theory building / grounded theory). All qualitative software packages allow you to do both approaches, a good thing since many people end up doing a bit of each. Then you assign sections of the data to one (or more) codes or topics, building up patterns across your data.

Software also can help you organise basic codes into more insightful themes, and manage approaches with multiple stages like open and axial coding . At some point, you should have developed a codebook, essentially a list of codes/themes with relationships to each other, and detailed descriptions for what they mean. Note that the definitions and even names of these codes can change through the analysis process, and this is quite normal!

4. Use reflexive writing

Many people doing qualitative analysis can become too focused on codes and themes, but there are other important ways to understand qualitative data. Many people think best by writing – trying to articulate to themselves or others what they see in the data. This can take the form of memos or notes about a particular extract of the data, suggested by some approaches like IPA that use line by line coding. But it can also be helpful to keep a reflexive journal or research diary that records notes and insights into the whole research project.

All qualitative software packages allow you to write directly into them, in a variety of formats – long-form or short. Keeping your notes and writing together in your project allows you to keep the flow when analysing, and also cross reference them when writing up.

5. Read your data

New qualitative researchers sometimes come to the end of the coding process, and struggle with what to do next, and moving to wider conclusions . Remember that the aim with most qualitative analysis is to keep reading the data in different ways, to uncover non-obvious trends or new theory. So reading by the contents of the code rather than source can help you see trends or contradictions across different participants, and tying to get your themes to connect with each other can show a wider story, or deeper causations. Qualitative software can make this much easier than going through many sources on paper, looking for bits highlighted in a particular colour.

And if one analysis method didn’t work, try another ! Sometimes trying discourse or action analysis rather than simple thematic analysis can show things in a different light.

6. Write up your data

For many, this is a main aim of a research project: creating an output like a thesis, paper or report that communicates your findings. It’s worth remembering this while doing all the above, so that you keep some focus on how and what you are going to write. Using qualitative software makes it much easier to find quotes to illustrate and support your arguments. It often helps to write some sections around key themes, and all the sections of data you’ve assigned to these themes are easy to see (and copy and paste) to bring in.

This is where all the hard work you’ve put into reading and coding the data comes pays off – all the quotes that illuminate your data are now at hand to fill in your chapters, and structure your writing. In Quirkos for example, every quote has a copy button next to it, ready to put into wherever you are writing up. It may also be helpful to show the codebook as an appendix or figure, and to share with co-writers and supervisors that want to see how you have interpreted the data – again qualitative software gives you reports and exports that show and communicate the process.

These are the main stages, there may be more or less depending on your approach. However any qualitative research software will help you with these parts, and all have free trials so you can see which works best for you. Quirkos gives a simple and visual approach, and you can try this tool for qualitative analysis free offline for 21 days, or for 14 days with unlimited cloud storage and collaboration.

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webQDA – Qualitative Data Analysis Software

webQDA is a qualitative, web-based data analysis software intended for all researchers and professionals conducting qualitative research. webQDA allows you to analyze text, image, video, audio, tables, PDF files, Youtube videos, etc. in a collaborative, synchronous or asynchronous manner.

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Handbook of the Arts in Qualitative Research Perspectives, Methodologies, Examples, and Issues

• J. Gary Knowles - Ontario Institute of Education Studies, University of Toronto, Canada
• Ardra L. Cole - Ontario Institute of Education Studies, University of Toronto, Canada
• Description
• Defines and explores the role of the arts in qualitative social science research: The Handbook presents an analysis of classic and emerging methodologies and approaches that employs the arts in the qualitative research process.
• Brings together a unique group of scholars: Offering diverse perspectives, contributors to this volume represent a wide range of disciplines including the humanities, media and communication, anthropology, sociology, psychology, women's studies, education, social work, nursing, and health and medicine.
• Offers comprehensive coverage of the genres employed by qualitative researchers: Scholars use multiple ways to advance knowledge including literary forms, performance, visual art, various types of media, narrative, folk art, and more.
• Articulates challenges inherent in alternative methodologies: This volume discusses the issues and challenges faced when employing art in research including ethical issues, academic merit issues, and even funding issues.

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• An overview of the current or potential role of the arts in specific disciplines, including the humanities, media and communications, anthropology, sociology, psychology, women's studies, education, social work, nursing, and health and medicine

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Arts-Based Research

• First Online: 29 September 2022

Cite this chapter

• Robert E. White   ORCID: orcid.org/0000-0002-8045-164X 3 &
• Karyn Cooper 4  

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In its purest form, art may be simultaneously immediate and eternal: immediate in its ability to grasp one’s attention, to provoke or inspire; eternal in its ability to create deep and permanent impressions. Responses to art may be visceral, emotional or psychological by turns or even together. As such, a work of art may possess almost unlimited potential to educate (Leavy, 2017). Although a pursuit of matters artistic may be a worthy pursuit for its own sake, the arts also represent invaluable opportunities across all research disciplines. As such, arts-based research exists at intersections between art and science. According to McNiff ( 2008 ), both arts-based research and science involve the use of systematic experimentation with the goal of gaining knowledge about life.

Aristotle once said or, at least, was said to have said, man by nature seeks to know. Research, in the broadest sense, is an effort to know and I believe that the forms of knowing vary enormously…. – Elliot Eisner, Stanford Graduate School of Education

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Robert E. White

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Researching Creations: Applying Arts-Based Research to Bedouin Women’s Drawings

Ephrat Huss

Julie Cwikel

Ben Gurion University of the Negev, Beer-Sheva, Israel

Huss, E. & Cwikel, J. (2005). Researching creations: Applying arts-based research to Bedouin women’s drawings. The International Journal of Qualitative Methods 4 (4), 44-62.

All problem solving has to cope with an overcoming of the fossilized shape … the discovery that squares are only one kind of shape among infinitely many. —Rudolf Arnheim, 1996, p. 35

In this article, the author examines the combination of arts-based research and art therapy within Bedouin women ’ s empowerment groups. The art fulfills a double role within the group of both helping to illuminate the women ’ s self-defined concerns and goals, and simultaneously enriching and moving these goals forward. This creates a research tool that adheres to the feminist principles of finding new ways to learn from lower income women from a different culture, together with creating a research context that is of direct potential benefit and enrichment for the women. The author, through examples of the use of art within lower income Bedouin women ’ s groups, examines the theoretical connection between arts-based research and art therapy, two areas that often overlap but whose connection has not been addressed theoretically.

Keywords: art-based research, art therapy, researching women from a nondominant culture

Introduction: Why use the arts in research?

While I am talking with Bedouin women about their drawings, the tin hut in the desert that is the community center in which we work sometimes reverberates with lively stories and emotional closeness, and sometimes I, as a Jewish Israeli art therapist and researcher, and they, as a Bedouin Israeli women’s empowerment group, are lost to each other: When I suggest that we summarize the meaning of the art therapy sessions for the women, they nod their heads politely and thank me, and ignore my questions.

My aim in this article is to see how art-based research literature and art therapy literature can jointly contribute to both working with and understanding women from a different culture.

Art as communication (rather than as therapy) can be defined as the association between words, behavior, and drawing created in a group setting. McNiff (1995), a prominent art therapist and one of the pioneers of art-based research, suggested that art therapy research should move from justification (of art therapy) to creative inquiry into the roles of the art itself.

I will first review arts-based research in an effort to understand the use of art as research. I will then survey art therapy’s practice-based knowledge concerning working with art with women from a different culture, and third, I will apply both of these knowledge bases to Bedouin women’s drawings and words from within my case study.

Art as a form of inquiry

The aim in arts-based research is to use the arts as a method, a form of analysis, a subject, or all of the above, within qualitative research; as such, it falls under the heading of alternative forms of research gathering. It is used in education, social science, the humanities, and art therapy research. Within the qualitative literature, there is an “explosion” in arts-based forms of research (Mullen, 2003).

How does arts-based research help us to understand women from a different culture? It seems that classic verbal methods of interviewing or questionnaire answering are not effective forms of inquiry with these women. Bowler (1997) described the difficulties she found in using questionnaires and interviewing, both of which stress Western-style verbal articulation, as research methods with lower income Asian women. She found that the women try to give the “right” answer or to be polite. In-depth interviewing was also conceived of as a strange and foreign way of constructing and exploring the world for these women (Bowler, 1997; Lawler, 2002; Ried, 1993). The women are often mistakenly conceived of as “mute” because they do not verbalize information along Western lines of inquiry (Goldberger & Veroff, 1995).

The search for a method that “gives voice” to silenced women is a central concern for feminist methodologies. De-Vault (1999) analyzed Western discourse as constructed along male content areas and suggested that we “need to interview in ways that allow the exploration of un-articulated aspects of women’s experiences … and explore new methodologies” (p. 65). Using art as a way of initiating self-expression can be seen as such a methodological innovation.

The arts-based paradigm states that by handing over creativity (the contents of the research) and its interpretation (an explanation of the contents) to the research participant, the participant is empowered, the relationship between researcher and research participant is intensified and made more equal, and the contents are more culturally exact and explicit, using emotional as well as cognitive ways of knowing. Mason (2002) and Sclater (2003) have suggested that drawing or storytelling, or the use of vignettes or pictures as a trigger within an interview, already common in work with children, could also help adults connect ideological abstractions to specific situations, using both personal and collective elements of cultural experience.

Thus, culture and gender unite in making Western research methods insufficient for understanding women from a different culture. Using visual data-gathering methods, then, can be seen as a movement offering alternate avenues of self-expression for women from traditional cultures.

The arts are considered “soft,” female ways of knowing; they tend to be used as a counterpoint to the seriousness of words (Mason, 2002). Alternatively (and mistakenly), as in photography, arts are considered a depiction of absolute reality (Pink, 2001).

Silverman (2000) argued that research must access what people do, and not only what people say.

Art brings “doing” into the research situation. However, the inclusion of arts in research poses many methodological difficulties, described by Eisner (1997) in the title of his article as “The Promises and Perils of Alternative Research Gathering methods.” Denzin and Lincoln (1998) described personal experience methods as going “inwards and outwards, backwards and forwards” (p. 152). The art product by definition creates more “gaps” and entrances than closed statements or conclusions (this is what enables so many different people to connect to one picture!). The art process also includes moves between silences, times of doing, listening, talking, watching, thinking, and different gaps and connections between the above. For example, Mason (2002), a qualitative researcher, described how research participants agonize about where to put whom when drawing a genogram or family diagram. She claimed that this process of “agonizing,” or creating the genogram, is an important component of the finished genogram and should not be left out.

Issues in arts-based research

Sclater (2003) explored the above-described complications of defining the “contours” of art-based research, as difficulties in defining issues related to the quality of art, to the relationship with the research participant, and to the relationship between art and words in arts based research.

Defining issues related to the quality of art

Mullen (2003) concluded that art-based research is focused on process as expressing the context of lived situations rather than the final products disconnected from the context of its creation. Mahon (2000) argued, through the concept of embedded aesthetics, that the aesthetic product is not inherent from within but is always part of broader social contexts, which both transform and are transformed by the art product and around which there is always a power struggle over different cultural meanings (see also Barone, 2003). At the same time, Mahon claimed that art includes elements and aesthetic languages that are specific to itself and that cannot be translated into action research or communication, or understood as direct translations of social interactions. The boundaries of quality are seen as marginalizing whoever does not conform to them, as in folk, vernacular, and outsider forms of art. In art-based research, elitism is replaced by art as communication, whereby reactions to the art work are more important than the quality of the art in terms of external aesthetic criteria. Within this paradigm, the criteria of communication and social responsibility predominate over craftsmanship (Finley, 2003; Mullen, 2003; Sclater, 2003).

Defining issue related to the relationship with the research participant

Another consideration for arts-based research is the setting of standards or limits around the roles of artist, researcher, and facilitator of creative activities. Mullen (2003) suggested,

We need to find ways not just to represent others creatively, but to enable them to represent themselves. The challenge is to go beyond insightful texts, to move ourselves and others into action, with the effect of improving lives. (p. 117)

Therefore, multiple or blurred roles are advantageous, as they reflect the complexity of reality within any research situation. By handing over creativity and its interpretation to the research participant, and including these elements within the research, the relationship between researcher and research participant is intensified, eliciting emotion and facilitating transformation. Thus, the blurring of the contours or roles of the researcher and research participant is seen as advantageous.

For example, cameras were given to lower income rural Chinese women, who, through photography, were able to communicate their concerns to policy makers with whom they would not engage in a direct verbal confrontation (Wang & Burris, 1994).

Defining issues related to the relationship between art and words in arts-based research

Art-based research literature addresses the problematic issue of how to work with the relationship between the verbal and nonverbal elements of the data, the art form, and its interpretation within a research context. Within research, the theoretical framework of understanding a work of art is harnessed to the reason art was used within the research puzzle (Mason, 2002). The use of verbal and nonverbal elements can be seen as a triangulation of data. It is important to understand why we are including art and to think about how the use of visual contents will help solve the “puzzle” of the research (Davis & Srinivasan, 1994; Finley, 2003; Mason, 2002). Save and Nuutinen (2003) defined the relationship between drawing\ and words (after researching a dialogue between the alternate use of pictures and words) as “creating a field of many understandings, creating a ‘third thing’ that is sensory, multi-interpretive, intuitive, and ever-changing, avoiding the final seal of truth” (p. 532).

Connections between art therapy and arts-based research

Art therapy, or any therapy, aims to connect, integrate, and transform experience and behavior. Art-based research also aims to transform, in that it can “use the imagination not only to examine how things are, but also how they could be” (Mullen, 2003, p. 117). It aims to connect and empower by creating something together with the research participants rather than the classic research orientation that takes information away from them (Finley, 2003; Sclater, 2003).

Sarasema (2003), a qualitative researcher, discussed the therapeutic advantages of storytelling for widowed research participants, claiming that art-based research is a way of creating knowledge that “connects head to heart” (p. 603).

Both art therapy and arts-based research involve the use of dialogue, observation, participant observation, and heuristic, hermeneutic, phenomenological, and grounded techniques of interpretation. Both relate to the ethical issues of art and interpretation ownership and a relational definition of art, including the skills of working simultaneously with both visual and verbal components (Burt, 1996; Mason, 2000; B. Moon, 2000; H. Moon, 2002; Talbot Green, 1989).

The difference between the two fields could be defined as art therapy implementing a theoretical psychological metaframework that organizes the therapeutic relationship while using the inherent qualities of different art materials and processes (Kramer, 1997). However, within art therapy, there are researchers who wish to discard these psychological metaframeworks and to focus more on “art-based” art therapy. For instance, in feminist, and studio or community art therapy, art is used both as an expression and a critique of society (Allen, 1995; B. Moon, 2000). Savneet (2000) claimed that art with women from the Developing World, such as the Bedouin women, can serve as a decolonizing tool by giving voice to women holding a polytheistic view of the world, as long as the interpreters of the art are the women and not an external interpreter. The nonverbal image should speak for itself, reducing the possibility of the artist-client’s being spoken over (Hogan, 1997). In addition, the image can be subversive, creating a narrative or counternarrative additional to the dominant one of words. The distancing or intermediating element of art can be helpful in interactions of inequality or of conflict (Dokter, 1998; Liebmann, 1996).

Art-based research, art therapy, and culture

Arts-based research literature focuses on art as a way to connect different people and to express different cultures, giving voice to nondominant narratives.

The culture of the viewer of the art will influence or interact with how the art is understood (Denzin & Lincoln, 1998). Another possibility is to accept that art does not define cultures from the outside but enables multiple and complex views of that culture (Eisner, 1997; Pink, 2001).

Art therapy literature also stresses the ability of art to help make cultural issues manifest within pictures by the fact that each picture shows differing understandings and conceptions of the content drawn, rendering new perspectives (Gerity, 2000). Quiet people can create “loud” art work. Art connects to individual-subjective rather than generalized and stereotyped levels of experience. Thus, we see that factors inherent in the art language help integrate the individual with the culture (Campanelli, 1991; Campbell, 1999; Hiscox & Calisch, 1998).

Art therapy literature also addresses the complexity of art as a culturally embedded vessel in itself. Hocoy (2002) has argued that art as self-expression is a deeply Western construct, not necessarily suited to people from different cultures. Acton (2001) warned against being a “color blind” art therapist, ignoring the cultural differences and approaches to healing of different people and their manifestations within art. Hogan (2003) stressed that art therapists can claim to be culturally sensitive but actually dominate the participants by offering an art process or interpretation that is alien and strange to them (Acton, 2001). Conversely, Hocoy (2002) pointed out that assuming that everything is a cultural difference can also create misunderstandings of pictures. Cultural possibilities for misunderstanding are, on the one hand, bridged by the third object—the artwork—but, on the other, intensified by it. Thus, art is not a “magic” way of overcoming cultural differences but has the potential to enable the multifaceted nature of different cultural identities. The analyses of the art, and the relationship, are harnessed to the therapeutic aims, taking culture into account. In general, art therapy literature supplies much practice-based knowledge of how to take culture into account while focusing on harnessing the artwork and relationship to the therapeutic goals of the interaction.

Having briefly summarized and created a connection between the central issues within arts-based research, and within art therapy with a different culture, I will now apply them to some drawings by the Bedouin women from my research, as a set of relevant data on which to continue examining the above concepts.

The context of the Bedouin women

My aim is to outline briefly the levels of change and stress that some women in this culture are currently experiencing.

Meir (1997) has suggested that under the influence of the dominant Israeli culture (and despite ongoing political friction between the Israeli government and the Bedouins’ claim to the right to continue a traditional nomadic lifestyle), Bedouin society is undergoing change from a collective to an individualistic culture, and from a nomadic lifestyle to fixed settlements. This has resulted in the devaluation of women and children, who no longer work in the fields and tend animals as part of the economic support system, as well as changes in the traditional role of elders. In addition, the loss of the traditional Bedouin tribal supportive roles with an externalization of these responsibilities to state authorities, who invest limited resources and cultural relevance, has resulted in the decline of collective family support and funds. These changes are creating high levels of stress (Abu-Rabia-Abu-Kuider, 1994; Meir, 1997).

The status of Arab women in Israel can thus be defined as doubly oppressed, both by their patriarchal society and by the Israeli political regime. Paradoxically, Bedouin women’s dependence on the males in their family has sometimes increased due to perceptions of women’s exposure to work, education, and individualism as a threat to tradition. Indeed, Bedouin women in the Negev were found to be intensely affected by poverty and the interconnected social and health problems that this entails (Cwikel, 2002; Cwikel, Wiesel, & Al-Krenawi, 2003).

Conversely, Arab feminists Hijab (1988) and Sabbagh (1997) have differentiated between issues of concern for Western women in Western society and those for Arab women. In the West, concerns focus on issues such as reproductive rights, legal equity, expression of self through work and art, and sexual freedom; for Arab women, concerns center on education, health, and employment opportunities as well as legal reform and political participation. Power is measured in relation to other women and not in relation to men (Hijab, 1988; Sabbagh, 1997).

We have found that there are many difficulties for Western female researchers who are not from within the Bedouin communities to understand the diverse concerns of Bedouin women. Bedouin middle- class women will also be from a different “culture” from that of Bedouin working-class women. We see that there is a paramount need to find alternative research methods that can enable outsiders to “hear” the concerns of the Bedouin women and that can enable the Bedouin women to communicate those concerns first to themselves and then to the dominant culture.

Using art as a research method: The Bedouin women’s drawings

The following examples of drawings are from three ongoing groups, in which the art activity was introduced for a few sessions, aiming to enrich, reflect on, or enhance the existing self-defined concerns of the group rather than to present an external study objective or research agenda. The three groups were all of poor Bedouin women living in a township in the Negev, including a group of single mothers meeting as a support group, a group of women undergoing vocational training to open early childhood centers within their homes for extra income, and a group of women without writing skills, wishing to learn arts and crafts as enrichment and eventually to make products to sell.

The art activity in all the groups and meetings divided into set stages, although the contents were in accordance to the group’s wishes. The meetings were undertaken by means of a Bedouin social worker learning art therapy, so as to enhance cultural suitability and to enable the women to talk in Arabic.

As stated, the aim of the art was two pronged.

The first direction is art as empowerment, enrichment, or self-expression. This is in accordance with feminist research that aims to be of direct benefit to the participants (especially as the aims of the group and the contents were defined by them).

The second direction is art as a research method, or a way to understand the concerns of the women (which is a preliminary step to any type of empowering or enriching intervention).

Following is a detailed explanation of the art stages and examples of each of the stages from the different case studies. The intent is not to present a full case study but to examine the interaction between arts-based research and art as empowerment, and lower income Bedouin women.

From a bird’s eye overview, the method of using art described within this article undergoes the following stages, which can be repeated, refining, redefining, deepening, or enriching the contents through doing, observing, and talking.

Participant interacts with art making (within the context of the group leader and group).

Participant interacts with art and group and group leader simultaneously.

Participant observes the pictures as a group exhibition.

Participant re-interacts with the above stages of art making, discussing, and observing, over an issue that arose in the former “wave.”

Step 1: The art-making stage

Each participant draws a picture in oil pastels, or makes a clay statue of a subject agreed on in the initial discussion and connected to the overall aim of the group:

Oil pastels with different sizes of paper, and clay are offered. Oil pastels enable both lines and areas to be created quickly with minimal mess. Clay might be a more familiar medium for Bedouin women.

Drawing can be used in a combination of directive and nondirective forms, similar to different levels of structuring an interview.

The type of art making is process rather than product oriented, termed diagrammic art within art therapy (Liebmann, 1996), which helps access and raise an issue rather than working on a product that exists independent of the creator, as in an art class. This means not that the art does not “lead” the artist but that the products are relational, used to communicate rather than to display talent (Hogan, 2003).

In the sketch shown in Figure 1 , the black circle (left) symbolizes the drawer, the red (vertical) oblong, her picture, and the arrows, the mutual influence of her on the picture and the picture, on her. The brown circle (right) is the context within which this reflective activity takes place, created by and observed by the group leader or researcher, symbolizing the dominant culture.

The question of whether to suggest a topic to draw can be seen as analogous to decisions concerning the level of structure of an interview. I chose to suggest a few topics, so as to make the drawing less threatening for people not used to drawing. Oil pastels include the elements of color and line, encouraging a “story” to be told. On the other hand, clay might be a more familiar medium for some women, and three-dimensionality evokes different types of storytelling. Time is then given to work individually or in pairs (according to what is preferred by the women) on the subject.

The assumption is that the engagement in the art process creates a novel interaction with the subject matter, showing differing perspectives and enhancing a connection between the emotive and the cognitive which in turn promotes a process of reflection and prioritizing elements to be included in the art. This creates a silent prestage of creative organization of personal data from inside onto the empty page, before or together with translating it to the group and to the researcher-observer.

Each type of art assignment embodies a different “culture” within the room in terms of collectivist or individualist interactions. Dosamantes-Beaudry (1999) showed how cultural self construal is depicted by working individually or in pairs in dance therapy. The use of time, space, materials, and so on are all expressions of power and will influence the type of discussion that emerges, enacted both physically and symbolically within the organization of the arts behavior.

An additional question arises if the group leader or researcher, beyond becoming an observer and student of the participant’s pictures, also draws so as to make transparent and clarify her position. According to arts-based research, the aim is to “blur the boundaries” of the (unequal) relationship between researcher and research participant. According to art therapy, this point is much disputed, with some advocating the above and others considering the danger of taking the client-drawer’s space, or intimidating or influencing the client.

All of these considerations become the research context. They need to be examined reflexively as they express the researcher’s cultural bias.

For example, I was certain that oil pastels were the most flexible medium, perhaps being the closest to a writing tool, which is the dominant medium within my culture, but the older Bedouin women responded immediately to clay. One single mother, an abandoned first wife and an older Bedouin woman did not draw but, when I included clay, immediately made a clay ashtray before bursting into tears. She explained that the ashtray was like an older woman, an empty and discarded container. A mundane clay ashtray thus becomes an object of intense meaning and communication illustrating the communicative rather than aesthetic quality of art. As Finley (2003) stated, within this paradigm, the reactions to the poem are more important than the poem itself. The above example also illustrates how the visual stimuli initiated associations that were not decided on in advance, and that were influenced by the material and by the context of the group.

An example of a woman’s interaction with her art was an older woman from the single mothers’ group, who did not speak at Figure 2 all at the beginning but repeated a schema of squares within each meeting. In one meeting, she stated that it was a house. It is not clear if the squares were an illustration of the house, the idea of a house emerged from the graphic shape of the squares, or the idea of a house emerged from within the context of the things other women said, or all of the different elements combined together. Arnheim (1996) stressed the inherent dynamics of an art gestalt that influences the observer (rather than just being a neutral vessel for projection (Figure 2 ).

The example in Figure 3 illustrates how the dialogue between art and the individual can be transforming in itself. One young third wife, whose husband is in jail for violence, said of her picture of a house with flowers, that her father did not allow her to plant flowers by the house and did not allow her to play with other children, and he chose her husband for her. About the picture, she said, “I want a house; I want to build a house of my own. Most important, I want to plant a garden by the house.” The picture contained past and future in a causal narrative, based on a specific instant that gained symbolic meaning. The narrative is poetically organized, with three elements from the past and three from the future, corresponding to the three pictures. The dialogue was transformative, in that it allowed the drawer “to use imagination to examine how things are, but also how they could be otherwise” (Finley, 2003, p. 292). This exemplifies the arts-based paradigm that has as an aim to “go beyond insightful texts, to move ourselves and others into action, with the effect of improving lives” (Mullen, 2003. p. 117).

Another example was when an older woman, who was silent in all the meetings, made a cow, saying that a women is like a cow: When she has no milk left, she is discarded. A younger woman made a horse, saying that a woman is like a horse, strong and able to carry many burdens. Here, the art “answered” the art.

Another woman made an ashtray, and while describing how tired she was of managing as a single mother with no money, she broke the ashtray into many tiny bits in nervous movements creating, a physical embodiment of her emotional state. When the women talked to her and suggested solutions, she started sticking all the pieces together again. She looked at her hands and laughed, noticing this.

One woman ignored the two directives and decided to draw, first in pencil Figure 2 , Figure 3 and then in paint, a stylized sunset picture she had once seen in a magazine. She worked quickly and carefully, begging for a few more minutes at the end. I framed the picture for her. She stated that she wanted to execute a picture like that to decorate her house, as she could not afford to buy one. She had worked hard and was proud of the result (Figure 4 ).

Although for me, as a Western-oriented art therapist, the discussion or individualized creativity of the product is most important (rather than copying a preexisting picture), for this woman, activating the will power and concentration to execute or copy a picture that she could not afford to buy, so as to have the product, was an empowering experience that connected her intensely to the art experience. It seems that the autonomy and intimacy inherent in the exclusive interaction between the drawer and her drawing enabled the woman to pursue her aims rather than to comply with our directives (Hogan, 1997). The woman’s self-directedness is a good example of a negotiation of power as against the dominant culture represented by our suggestions.

Another example of the complex interplay of power between the researcher and women follows. For example, although each of the women in the early childhood training group had 5 to 10 children and were very knowledgeable about early childhood, when I asked them what they would like to focus on in the drawings, they answered with questions conveying helplessness, such as what should be done with a crying child, what games to play, how to connect to the children, and what to feed them. Conversely, they were very clear and confident about the contents of their drawings in relation to early childhood. The art seemed to be express power and knowledge, whereas their words expressed helplessness. Perhaps the drawing enabled a simultaneous double transference: Words were used to express helplessness toward representatives of the dominant culture, but confidence and knowledge were expressed through their drawings. The multifaceted component of the drawing and then talking about it, simultaneously expressed and overcame the disempowerment of learning within the context of the dominant culture.

The discussion stage

After completing the artwork, we laid them out in a circle on the floor at the drawers’ feet, facing toward the group, both clearly connected to their creator, and also creating a group exhibition. The participants ask one another questions about their art work, and the women explain or connect to other’s art work in a free discussion.

The following sketch illustrates the complexity and multiple interactions that occur simultaneously in this situation.

Thus, the art work, group interaction, and so on cannot be analyzed separately, out of context with the other elements.

For example, one young woman was too shy to talk about her drawing of a black circle (Figure 5 ).

“I think you are drawing that you feel closed in a circle you can’t get out of because there are so many people in your small house.” (Friend)

Her friend sitting next to her said that she thought the girl was sad there were so many people in her small house that is like a closed circle that one cannot get out of. The woman nodded in agreement.

The interaction between the two friends is similar to Shvadren’s (1992) analogy of observing an art work as two people, (the creator and the observer) gazing into a lighted window and both seeing new things within the room. Within feminist theory, this emphatic understanding of another person has been termed a relational form of interaction that focuses on empathy and is characteristic of female interactions (Goldberger & Veroff, 1995). Feminist theory suggests that words, as power structures that define reality, are created by men and thus do not describe women’s experiences within this male-dominated world. For example, De-Vault (1999), a feminist theorist, claimed that we “need to interview in ways that allow the exploration of unarticulated aspects of woman’s experiences” (p. 65). The black circle described above and its ensuing dialogue might be such an “interview.” In terms of the art product, we see a simple black circle that is not rich in terms of crafts or in terms of Western art but is an art form used in art therapy, focusing on receptive or connective elements that emphasize thoughts, emotions, and relationships.

An intercultural term for this emotional understanding is Steinberg and Bar-On’s (2002) concept of a dialogic moment. Observing Arab-Jewish conflict resolution groups, they noted that these moments of empathy and understanding between Jewish and Arab students occur when a specific story or personal detail is expressed rather than when generalized ideologies are expressed. Drawing seems to encourage the description of a specific or personal instant and a specific way of “telling” or interpreting that instant, creating, in Abu-Lughod’s (1991) terms, “ethnographies of the particular … [that] capture the cultural and social ‘forces’ that are only embodied in the actions of individuals in time and space” (p. 156).

The visual stimuli themselves can also encourage engagement beyond the areas of conflict. For example, the Bedouin social worker who facilitated art with the group of single mothers stated in her summary of the experience that for the first time (with many years experience working with the women), she felt flooded and disturbed by their suffering. This might be what Finley (2003) defined as the purpose of arts-based inquiry, to contribute to deeper relationships between researcher and research participant.

Within the context of the group discussion, the picture creates a concrete anchor (to use yet another metaphor!) that can be related to on many different levels of language, with everyone seeing or reacting to the same trigger (the picture being discussed). It becomes a transitional space that is a useful mediator for people from different cultures, who formulate their stories along different types of narrative. The meanings of the picture can be negotiated and clarified through both people’s observing the same object. Drawing, and then discussing the drawings, serves as a form of self-interpretation, or validation, of the subject drawn, that is important with intercultural communication. In terms of art therapy, it is congruent with the feminist and phenomenological stands that stress the artist’s understandings of the art work.

For example, one woman drew a cupful of flowers (a traditional subject in Islamic art), then said that her life is empty and boring, not like the flowers, expressing an opposite relationship to the picture. Alternatively, another woman drew a fish in a stormy sea (Figure 6 ) to express her loneliness, far from her maternal family, using a metaphor from the natural world—expressing silence, loneliness, and the turbulence of her circumstances. Another woman used a metaphor of a black cloud, stating that that was the feeling of being a Bedouin woman without a husband.

One woman took this feeling as a confrontation, asking “Why did God give us [women] hands, if hen does not allow us to use them?” She then drew a picture of the modern and the traditional women holding hands and making a connection, stating that the modern women is pulling the traditional women in her direction, as can be seen in her picture (Figure 7 ). Another woman drew a television and said that all day she sits crying in front of the TV, bored and lonely, thus creating a metonym (Figure 8 ).

One woman, whose shack is going to be pulled down because she does not have a building permit, drew a steep slope, with a house at the end. She said that she feels the energy needed to keep her house is too steep a slope for her to climb, juxtaposing a concrete situation and a metaphor.

(top to bottom)

The above words describe different personal and cultural “entrances” to the pictures. Discussing the contents of the pictures thus helps clarify the participant’s stand toward her picture.

The art directive itself can also disclose cultural differences. For example, we asked all the participants to draw a symbol of themselves as an introduction (a common exercise in art therapy). However, they all drew a wish, something that they wanted, or something abstract. At first, it seemed that they had not understood or ignored the request for a symbol of self. However, a wish can also be understood as an abstract symbol of self extended into time and space outside or beyond the self. This might relate to collective identity, which extends beyond the individual, and to the aesthetics of Islamic art, aiming to cheer and express wishes for a better future. We see that basic concepts, such as symbols, constitute different formulations or “shapes” within different cultures. The concrete element of drawing makes the specific characteristics of concepts such as a symbol, wish, or moment less abstract and thus more overt. The dual activity of both concretely drawing or enacting these concepts, and then explaining them as they appear in the picture helps access these subtle differences that are lost in verbal interaction, where we can mistakenly assume that by using the same concept (such as a symbol) we mean the same thing. Bhaba’s (1994) statement that concepts, such as death, mothering, and aging, cannot be translated, having different values and meaning different things in different cultures. Thus, it is not possible to “translate” one culture into another.

Art can contain different elements simultaneously.

One young woman said about the blue-and-white abstract silkscreen made in the arts and crafts group, that the brooch’s colors reminded her of the sea, with a boy standing in the distance. Everyone laughed and she said that she wanted to get married, although marriage is the end of freedom: You stay at home and do not go to the sea anymore. Thus, the picture enabled a dialogue of ambivalence. When people live in more than one culture and are undergoing acculturation, the ability to integrate different cultural or personal understandings, or even opposing feelings as part of a whole, is considered beneficial to the acculturation process. Talking in a linear sequence seems to invite a more unified dialogue, as each point has to come after the last, rather than being shown simultaneously. The art as a trigger for discussion enabled a complex version of reality that is not reduced to one truth.

Examples of the Magen David (A woman’s wishes). “ I wish for a house.” (Below) “ I wish for peace.”

Another example is of a young teenage girl from this group with no head cover wearing jeans and a large Jewish and national symbol that is currently part of the teen fashion in necklaces in Israel, who drew a picture of a Bedouin tent and said that she liked the traditional Bedouin culture best (perhaps also expressing a wish for less complicated times in terms of identity). This is similar to Abu-Lughod’s (1991) suggestion that specific, individual examples negate cultural stereotypes. For instance, she describes a woman swearing and citing from the Koran in the same sentence, thus refusing to be reduced to one truth (Abu-Lughod, 1991).

One woman drew a picture of a bus (driving accidents are a major problem within Israel in general and within the Bedouin villages and townships in particular). She described how, after many failures, she had just completed her driving theory test but must now find the money for driving lessons; otherwise, the theory would be out of date. She stated that, like the traffic light, when there is war, one needs to stop. She continued about how important her driving license was for her, as it would enable her to take the children to different places. She said her brothers were helping her to pay for the lessons, because she had left school at the age of 8 to look after them. She had written the words “ derech shalom-ve lo lemilhama ” above the bus, “a journey of peace and not war.” She explained, “I want there to be peace—inside me, between people, and between countries.” This is an example of the multiple levels of future and present, particularity and generalness, concreteness and abstractness, that can be contained within one picture, making it especially suitable for people undergoing cultural (and physical) transitions within their lives, incorporating different cultures.

To summarize, the reflective dialogue between drawer and drawing, and the interactive elements of the group dynamics combine to create a triangular situation with many different types of interactions, for instance between a drawer and her own drawing, between a drawer and other people’s drawings, and between a drawer and other people. In the following section, I illustrate the complexity and multiple interactions of this situation, showing the different types of interactions between the words and the art, and explaining the art creates a multifaceted level of content that refuses to be reduced to a simple entity.

Group stage, the whole picture

The third stage can be observing the art works as a unified exhibition or group statement. Recurring themes become overt both to the group itself and to an outsider, such as the researcher (Campbell, 1999; Hiscox & Calisch, 1998). Cultural stands or beliefs are often so embedded that we are usually not aware of them ourselves. Observing the meanings within the drawings of other people from the same culture strengthens and defines these messages, creating a type of critical pedagogy.

For example, when observing all the pictures of “what a child needs,” we noticed that the children always played outside and were depicted in rich color. The caretakers inside were depicted without color and in minimal pencil lines. Thus, outside was defined as the focus for exploration—having implications for creating a culturally sensitive early childhood curriculum for Bedouin children (Dosmantes-Beaudry, 1999).

This is also congruent with feminist group therapy, which defines problems as outside the individual, related to context, and experienced by anyone within that context (rather than defined as a personal pathology). In terms of art therapy, art work can become “embodied” with meanings that hold symbolic meaning for the whole group.

For example, houses were a strong theme with the single mothers, and we devoted a session to drawing more houses so as to understand their implications. This led to the following, last stage of this method.

Validating or deepening understandings through additional words or drawings

The fourth stage of the drawing process entails re-viewing pictures and re-drawing issues that it is felt need more clarification.

In terms of arts-based research, this serves as a type of validating mechanism, in that the group exhibition gives a chance for themes to be discussed and verified on the spot through the multiple voices or comments of the group. One of the advantages of drawings is that they are constant and permanent fixtures that can be re-viewed and additional meanings gained with each viewing. At the same time, the meanings can constantly shift, enabling different words or associations at different viewings (just as we enjoy observing a work of art again and again, giving it additional or different meanings).

Within art therapy, the observation of former pictures is used as a way to enhance self-reflection and emotive involvement with (or projection onto) the picture. Schaverien (1992) has discussed how a picture can become temporarily infused with much emotional meaning for the viewer, whereas at a later stage, the picture as a talisman is relinquished.

In this article, I attempted to combine the theories of art therapy and of art-based research concerned with working with a different culture. Canclini (1996) stated that we are used to the fusion of different cultural elements, such as modern art books sitting together with crafts books on our coffee tables, to multimedia reproductions of “high” culture, to foods that combine different cultural traditions, but that we mistakenly shy away from creating “hybrid” mixes of academics and of clinical practice.

This article can be seen as a double meeting between art as therapy or empowerment, and art as research, and between Bedouin women and Jewish Western art therapy. This combination was used to create an art activity that, I hope, is both informative as research and empowering as self-expression and enrichment.

It seems that art as research can enhance understanding between the Bedouin women and the dominant Israeli culture by offering a complex, multifaceted expression of the Bedouin women’s concerns, together with their understanding of these concerns. Feminist researchers have stated, “to hear women’s perspectives accurately, we have to learn to listen in sterio, receiving both the dominant and the muted channels clearly, and understanding the relationship between them” (Anderson & Jack, 1991, p. 11).

Similarly, art as therapy or empowerment can offer the transformative, enriching, and empowering elements of creating art, making it a worthwhile endeavor for the women. Both uses do not exclude the need for constant reflexivity in understanding the cultural meanings implied by different art interventions.

Thus, the research context becomes of direct potential benefit to the women, uniting research and therapy aims—observation and self-observation, action and reaction.

Spivak addresses the difficulty in “admitting non-Western cultural production into the Western academy without side-stepping its challenges to metropolitan canons and thus perpetuating the ‘subalterization’ of third world culture” (p. 254). This difficulty in accepting different forms of art—both Bedouin women’s art, such as crafts, and art within psychology, such as in art therapy (rather than art as diagnostics) and art within research (rather than words only)—challenges Western classic conceptions of art and its roles (and, thus, of Bedouin women, of psychology, and of research). The limitation of this article is that I did not fully explore the meanings of the art experience for the women. Another limitation is the paradox built into the method, and mentioned above, of trying to access non-Western experience, through Western methods.

When working with art materials, the narrative is developed through the interaction of doing and reflecting on one’s actions, in a constantly modifying activity. For example, wet paint makes the paper too wet, and so pencil can be tried, but then the shapes are too defined and have lost their essence and vitality. Oil pastels can be used as a compromise, although this might result in the loss of some of the essence of both vitality and definition, and so on, until a “good enough” solution is created. This constant negotiation and renegotiation of actions and their meanings seems an inherent part of any intercultural communication made concrete and visible through using art.

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White, R.E., Cooper, K. (2022). Arts-Based Research. In: Qualitative Research in the Post-Modern Era. Springer, Cham. https://doi.org/10.1007/978-3-030-85124-8_8

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Arts-Based Research:Definition  

"A set of methodological tools used by qualitative researchers across the disciplines during all phases of social research, including data collection, analysis, interpretation, and representation. These emerging tools adapt the tenets of the creative arts in order to address social research questions in holistic and engaged ways in which theory and practice are intertwined.” 

• Patricia Leavy, Method Meets Art: Arts-Based Research Practice (2009), p. 2-3
• Barone & Eisner, Arts Based Research, 2012, p. 3
• ABR approaches are particularly useful “for research projects that aim to describe, explore, or discover” (Leavy, 2009, p. 12).
• Multiple epistemological perspectives can be embedded within ABR
• There are multiple ways to see the world to gain understanding (Barone & Eisner, 2012)

Charlotte Symphony & Piedmont Middle School: Interpreting History Through Art Project

If you cannot access the above video, you can watch it here: https://www.youtube.com/watch?v=G7NH-bSkQPE .

Interpreting History through Art was a multi-disciplinary program presented by the Charlotte Symphony at Piedmont Open IB Middle School during the 2009-2010 school year. Program partners included the Levine Museum of the New South and the Light Factory. Teaching artists worked with students in each of the arts disciplines at the school focusing on the theme of "Changing Places". This theme is based on the exhibit of the same name at the Levine Museum of the New South which explores how people in the Charlotte region are dealing with the growing cultural diversity and change created by the influx of newcomers from across the U.S. and around the globe. Teaching artists were David Crowe (orchestra / band), Jen Crickenberger (visual art), Alyce Cristina Vallejo Moran (drama/dance), and Chris Stonnell (chorus) [ Read more... ]

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Arts-Based Data Collection Techniques

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Recently, Jennica Nichols and Maya Lefkowich (of AND Implementation ) hosted a Canadian Evaluation Society (CES) webinar about using art as a data collection method. The webinar was fun and interactive and included (you guessed it) hands-on examples of how to use arts-based techniques and how to modify them for an online audience. Without rehashing the entire webinar (CES members can re-watch it here: Using Art in Creative Data Collection and Evaluation ), I wanted to share the most salient points and how we, here at Eval Academy and Three Hive Consulting , have and will put them to use. 

Arts-based techniques can be used to get audiences to open-up or explore topics that can be hard to put into words. Jennica and Maya suggested using art-based methods for exploring relational meaning. In other words, they are important when: a) exploring concepts in context is important; b) needing to make connections between two distinct ideas (e.g. how the social determinants of health may mediate a program's impacts), or; c) exploring emotions or experiences that are hard to put into words. They also noted that arts-based methods allow for many ways of knowing, moving beyond text and words to think about how things are connected in space or time or can be represented in a tactile manner.

Arts-based methods also allow participants to make more spontaneous or out-of-the-box associations between ideas. They push us out of our comfort zones and encourage different forms of expression.

Arts-based data collection techniques are inherently participatory methods, involving the artist in the creation and interpretation of data. They are inductive techniques, meaning that they are meant to be used for exploring ideas or describing concepts. These techniques start with observations (the art!) then work with the participants to understand the meanings and conclusions that can be drawn from the art.

There are 5 main arts-based data collection techniques:

Literary (e.g. poetry)

Performative (e.g. interpretive dance, theatre)

Visual (e.g. pictures, collage)

Audiovisual (e.g. film, video)

Multimedia (e.g. graphic novel, art installation)

Multimethod techniques make use of two or more arts-based methods.

In the webinar, Maya and Jennica stressed that arts-based data collection techniques are not art nor art therapy, as they aim to answer specific questions and take the information outside of the data collection space to inform decisions. In arts-based data collection techniques the description or explanation of the art is used as data, rather than the art itself.

Like other data collection techniques, arts-based methods require consent from participants. Because participants won't know what they've created and how they feel about it being used before they've made it, Maya and Jennica suggest obtaining consent before the data collection begins and again once it is completed. They also suggest creating a clear consent checklist to provide participants options for how their art is used, including a discussion of if/how the participant want to be credited for what they've created (authorship). Check out Eval Academy’s information sheets and consent forms in our resources section - they can be downloaded and modified for this! Because the narrative behind the art is what is being evaluated, it is important to present the description alongside the art.

Before diving into creating art, it is important to develop a solid foundation with the participants. Give participants permission to be silly and creative. Maya and Jennica suggested setting the tone from the beginning of the session, tell a joke and set appropriate boundaries. Discuss the purpose of the session, let participants know what is expected of them, and how the art they create will be used. Before starting the activity, provide participants with clear prompts or questions they are to focus on when creating their art and set appropriate amounts of time for each of the activities. Too much time can cause participants to become stressed about adding more details or filling the space. Consider providing visual or auditory reminders of the prompt or question during the session to re-focus participants.

Once participants have created their artwork, the important work begins. Remember, when using arts-based data collection, the narrative or description behind the art is the data we are seeking to collect. Follow up with interviews or focus groups to understand the meaning or outcomes that came from the process of creating. Ask questions to illuminate underlying connections, assumptions, values, or ideas. 

After the process is complete, revisit consent with each of the participants. Check if and how they are ok with you sharing their art and the narrative that goes along with it. Be clear about how and where the information will be shared.

Organization Tips:

Make sure you have all the tools you need before your session

Don't assume that participants have access to items such as cameras, markers, glue, or other supplies

Prepare your questions and test the timing of your activities in advance

Tips for conducting online sessions:

Consider supplying the questions and supplies in advance of the session. Mail participants packages or provide the log in information for online platforms so that people can become familiar with them in advance

Build in extra time to orient people to using the online software.

Use Zoom polls or break out rooms to encourage reflection

Consider whether to follow up in groups or one-on-one; much like deciding between a focus group and an interview, the nature of the data you wish to collect should drive your decision

How We’ve Used Art-Based Techniques

Here at Eval Academy and Three Hive Consulting, we are big fans of using creative approaches in our evaluation practice. Our core values include being creative in our work, both to engage our clients and evaluation participants, and as a way to generate new ideas.

To get un-stuck and re-imagine the evaluation experience for our clients.

Recently, we opened our annual team retreat with an activity designed to help us channel our inner four-year-old to get silly and lower our creative inhibitions. Next, we doodled our way through a visioning exercise to help us re-imagine the evaluation experience for our clients. While a small flood prevented us from completing the second half of the exercise, we gained a pretty clear picture of the barriers to evaluation our clients might face.

As part of focus-groups and workshops.

We’re also a big fan of using the At My Best strengths cards which have pictures on the front and a single word on the back to do photo-elicitation techniques. We’ve used them in workshops to get participants to open up, to help jumpstart the outcome mapping process with program funders, and with health partners to develop an approach for complex patients. Interestingly, in our experiences using these cards, in every session at least one person can’t get the idea of using the photo and must flip the card over to use the words.

To understand the impacts of a program on children.

We used visual data collection methods in a feedback session with children and youth, allowing participants to give visual and verbal feedback. Children rotated through a series of flip charts with a question posed at the top. Facilitators helped the children interpret the questions and clarified the meanings of the images. One big thing we learned at these sessions were to use washable markers with children.

How we might be using these methods in the future

In a previous article, we explored using virtual reality tools to augment evaluation, including in data collection (check out: Visual Storytelling Though Augmented and Virtual Reality ) and I know we are just waiting for the right project to try this out with.

As we may not be meeting in person any time soon, we can use arts-based data collection techniques to better understand our participant’s experiences. Literary, visual and audiovisual methods can create a starting point to capture and understand participants’ stories in today’s virtual world. Because we can’t be in person to build rapport, arts-based techniques can create a common and safe starting point to explore ideas with participants.

And finally, in a recent team-building effort, we took a few hours off to play Pictionary and some other online drawing games as a team. After the fun and games, we noticed a few of us were in creative and out of the box mindsets and had a pile of new ideas. Using arts-based techniques to break out of our routines and explore new ways to approach our evaluation practice will be a trick we continue to use with our team.

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• Artist creation: the artist(s) create their own artwork, and the social change content is in the work itself (e.g., Pablo Picasso’s Guernica )
• Group problem solving: the artist(s) act as facilitators using art as part of a problem solving process, where group art creation is not the final goal of the project (e.g., image theatre to improve corporate teamwork)
• Group artwork creation: the artist(s) act as facilitators for group art creation for social good (e.g., see the Examples page)
• interactive performance
• spoken word
• oral storytelling
• digital storytelling
• art installation
• photography
• creative writing
• What is the theory behind the program? (What does the program hope to accomplish, and how?)
• Who is the evaluation for? (The internal staff? The community? The funder? All of the above?)
• What is the purpose of the evaluation? (Improve the program? Decide whether funding will be renewed? Other?)
• Program participants
• Facilitators
• Researchers
• Community groups
• Social activists
• What stage(s) of the evaluation to mix methods? (The design is considered much stronger if mixed methods are integrated into several or all stages of the evaluation.)
• Will methods be used:
• sequentially (the data from one source inform the collection of data from another source), or
• concurrently ( triangulation is used to compare information from different independent sources)?
• Will qualitative and quantitative methods will be given relatively equal weighting ?
• Participants have the opportunity to express themselves through different art forms which may reveal insights that they may not have otherwise been articulated
• May be more conducive to exploring and communicating subjective experiences that can be difficult to capture with traditional methods
• The arts can accommodate people who learn in different ways, people who have different cultural backgrounds and/or who are less articulate
• The arts can accommodate skills and abilities of vulnerable populations
• Encourage participation of those who may otherwise be reluctant
• Increase participant engagement and empowerment in producing arts-based work
• Address, challenge and rebalance traditional power dynamics
• Utilizes alternative ways to produce and communicate research findings, which are often more accessible to diverse audiences
• The arts may be better suited to capture complexity and multidimensionality that is otherwise difficult to capture comprehensively with traditional methods
• Some participants may be reluctant to engage with arts-based techniques
• Arts-based data may be more time consuming and/or resource intensive to collect (e.g., planning a performance, preparing an exhibit)
• Resources and costs required to produce art (e.g., cameras, art supplies, theatre space)
• Arts-based data may be more difficult to analyze and interpret
• May provoke painful or unexpected feelings for the participants and/or evaluators/researchers
• May be limited to small sample size and subsequent impact on generalizability
• Ethical issues regarding privacy and intellectual property surrounding group artwork creation
• Challenge to define, measure and collect longitudinal outcomes
• Difficulty in establishing validity in the case of multiple interpretations

“a complex multidimensional dialectic deeply rooted in social and political processes in which social groups have “ways of living” defined by their position in class/gender/ethno-cultural relations, in turn expressed in individual lifestyles and bio-psychological embodiments (Breilh, 2010, 2013; Krieger, 2011). The social determination of health approach, in contrast to the more traditional social determinants of health analytic framing, focuses attention not merely on the discrete factors or conditions that impact health and wellbeing (e.g. nutrition, housing, education, income, etc.), but rather on the structural processes at the societal level that lead to these social inequities, and the interrelationships among these (Breilh, 2008).”

“understanding the complex ways by which social policies, as well as their associated social interventions employing the arts (sociocultural interventions), intercede in the dominant modes of constructing ways of being and lifestyles at the individual and collective level.” (Spiegel et al., 2018)

• Authentic Partnering
• Shared Benefits
• Commitment to the Future
• Responsiveness to Causes of Inequities
• inappropriateness or difficulties in defining quantifiable outcome ; and/or
• small sample size ; and/or
• complexity of defining the target population ; and/or
• the nature of the program itself.
• The community
• Conducting evaluations or research
• Improve school readiness
• Enhance the quality of life of people living with a disease
• Build relationships across intercultural, intergenerational, and other differences in life experiences
• Include people with developmental disabilities in a university environment
• Empower social change within educational institutions
• Reduce reoffending rates in incarcerated youths
• Type of evaluation (arts-based, quantitative, qualitative)
• Art forms used in the evaluation
• Population that the program targets
• Issue that the program studied
• Country that the program took place in
• Defining the project outcome(s) of interest
• Planning the evaluation
• Selecting the method(s)
• Interpreting the observations
• Sharing the results
• Relationship development
• In-depth planning time
• Joint: partners collaborate with equal ownership and accountability; often involves shared funding and/or resources.
• Stratified: one partner assumes primary ownership while supporting the other partner(s).
• Co-location: use of shared studio and office spaces to avoid rising property costs, as well as opportunities for networking and the creation of new relationships and partnerships
• Integration of activities: institutions such as libraries and parks boards are integrating ASC activities into their own programs and facilities.
• Implementation
• Dissemination
• “Good endings and new beginnings”
• not predefine outcomes ; and/or
• be more interested in documenting the intrinsic value of the art-making rather than the evaluator’s concept of relevant indicators of success; and/or
• be more interested in understanding why a certain program seems to have the effect the organizers believe it has, rather than determining if there is indeed a measurable effect on something the evaluator thinks is important; and/or
• have small target groups that are not numerous enough to show statistically significant changes in specific indicators; and/or
• be designed such that it would be hard to gather complete or close to complete data from all the participants, which could result in biased results.
• have a clearly defined outcome
• be well organized and have concise questions
• be easy for the participants to understand
• Fewer resources are required to conduct surveys.
• There are many ways to distribute the survey (mail, email, website, etc.)
• Confidentiality can be ensured
• A lower proportion of the participants will complete the survey, leading to non-response bias
• Volunteer bias
• Recall bias
• Reporting bias
• Response bias
• Higher participation rate
• No technical roadblocks for the participant
• Opportunity for participant to ask questions and clarify
• More resource intensive
• Not anonymous - participants may not want to answer honestly to avoid embarrassment ( social desirability bias )
• Interviewer’s bias
• Recording bias
• Observer’s bias
• Beware of biases that can be inadvertently introduced
• Keep questions as simple as possible, and avoid asking about more than one concept in each question . For example, do not ask, “rate how you feel about your diet and the amount of exercise you get”; the participant may feel great about their diet and not good about their exercise, so will not know how to respond.
• More tips and guidelines for creating your own survey
• Label what each extent of the range means
• A unipolar range represents a continuum from zero to an extreme. For example: 1 = "not at all useful", 2 = "slightly useful", 3 = "moderately useful", 5 = "very useful", 5 = "extremely useful".
• A bipolar range has opposite end points with a neutral midpoint. For example: 1 = "strongly disagree", 2 = "disagree", 3 = "neither agree nor disagree", 4 = "agree", 5 = "strongly disagree".
• There is controversy as to whether all questions should go in the same direction , with 5 the best and 1 the worst outcome, or whether some questions should be phrased in the opposite way, where 5 is the worst outcome. It is easier to analyze and less confusing for the respondent if they are all in the same direction. However, when the directions switch, it requires the respondents to more carefully read each question, which arguably has advantages.
• View examples of Likert scales to help develop Likert questions.
• Visit the American Youth Circus Organization (AYCO) Program Evaluation Toolbox to access sample surveys and corresponding user guides.
• Primary Data: data you collect for the purpose of your evaluation. For example, the results of a survey that you conducted.
• Or, Secondary Data: data taken from existing sources. For example, obtaining school drop out rates from the school board.
• Data collected from interviews may be used to better understand the respondent’s unique perspectives, opinions, and worldviews .
• being a successful interviewer requires practice
• having someone with prior experience is beneficial
• the interviewer should be able to put the participants at ease and probe the relevant and useful responses
• informal and conversational
• no pre-determined questions are asked and the interviewer “goes with the flow”
• most commonly used among evaluators
• interviewer develops an interview guide consisting of a series of predetermined questions
• may also include several probes that the interviewer hopes to use to elicit useful information from respondents
• some flexibility in that the interviewer may not ask the same questions in the same ways to all respondents and may use their judgement go off script if their interest is piqued by something a respondent answer
• do not allow the interviewer any flexibility
• each respondent is asked the same exact questions in the same exact order
• essentially the administration of an open-ended questionnaire
• Document the interview time, date and place, as well as the names and positions of the interviewer and interviewee
• Allow space to take notes for each interview question, in case the recording does not work
• At the start of the interview, briefly describe the purpose of the interview and ask the interviewee if he/she has any questions
• Start with an easier question to warm-up
• Keep questions simple and brief
• Ask only one question at a time
• Use open-ended questions
• Avoid yes/no questions
• “Can you tell me about…?”
• “Do you remember an occasion when…?”
• “What happened when….?”
• “What did you do when…?”
• Interview questions are often derived from the research questions, phrased specifically for the interviewee
• Revise the interview questions through pilot testing
• At the end of the interview, ask the interviewee if he/she has anything to add and/or any questions
• Can elicit insight into each participant’s perceptions, opinions, beliefs, and attitudes
• More appropriate when there is anything sensitive about the feedback that participants may not feel comfortable sharing in front of other people
• Requires skilled interviewers and preparation (as do all methods)
• Costly and time consuming if many people are to be interviewed
• usually there are 6-12 people in a group
• having too many people can reduce the quality of the discussion
• too short, and participants may not have enough time to develop their thoughts
• too long, and it may be too inconvenient for participants
• prevent one or more people from dominating the group
• encourage quiet participants to contribute
• obtain responses from the entire group in order to ensure all perspectives are represented
• Allows input from more people and produce rich data in a short period of time
• Relatively inexpensive
• Allows brainstorming , with one person’s thoughts nurtured by someone else’s, helping people remember events and ideas
• It is essential that the power dynamics in a focus group be carefully considered. For example, it may be difficult for someone to speak openly if this person’s boss is also in the focus group
• May require an even more skilled facilitator than individual interviews because of the need to manage group dynamics
• The group culture may interfere with individual expression
• Journal records
• Story telling
• Letter writing
• Autobiographical and biographical writing
• Photography
• And many other art forms
• What happens?
• What matters?
• A quick process to help participants to “read” images and to reflect on their own daily experiences outside of circus. This first exercise focused on how the same image could be interpreted in many different ways.
• A discussion of the ethics of visual representation.
• Instruction on the use of the cameras, including a request to focus on content that was important to each of them, and to choose two photographs that they felt best about out of however many they took (of a suggested maximum of 30 images).
• What do you see?
• What does this image make you think about?
• How do you and your colleagues react to your photography?
• What meanings, feelings or interpretations do you and others give to photography?
• Why did you take this picture?
• What problems, conditions, experiences, environments or relationships do your photo represent?
• Joy derived from the social circus
• Satisfaction linked to the mastery of circus techniques
• The place of the circus within their lives
• Desire to "go further"
• Importance of artistic expression
• Value of friendship and teamwork
• Rejoicing and pride that come through the effort
• Reality, environment and social concerns
• Space of spirituality
• Family's place in society
• Search for the reaffirmation of identity and the rediscovery of indigenous roots
• Arts-Based Evaluation 101 | ArtsReach Toronto
• My-Peer Toolkit
• Visual Matrix | Creative & Credible
• Examples of Arts-Based Evaluation | Jumblies Theatre
• The Image as a Form of Sociological Data: A Methodological Approach to the Analysis of Photo-Elicited Interviews | Francesco Lapenta
• Use images : social media is very visual
• Engage with users: reach out to users with similar interests and actively respond to users who engage with your content
• Connect with communities : tap into existing conversations on similar topics by using relevant hashtags and monitoring ongoing conversations and influencers
• Write for social: like other media, social media has its own set of conventions around style, language and tone
• Monitor the analytics : platforms such as Facebook and Twitter provide metrics so you can track visitors and learn about your audience
• exhibition of the participants’ creation
• a dance performance by professional artists based on the data
• a mural depicting participants’ experience during the ASC process

Evaluative Research Mini Course

• Validity: Information measures what it is intended to measure.
• Reliability: Information is measured and collected consistently according to standard definitions and methodologies; the results are the same when measurements are repeated.
• Completeness: All elements are included (as per the definitions and methodologies specified).
• Precision: Information has sufficient detail.
• Integrity: Information is protected from deliberate bias or manipulation for political or personal reasons.
• Timeliness: Information is up to date (current) and is available on time.
• Use standardized collection tools , which have already been tested in real life situations
• If you need to make changes to adapt tools to your local context, you should try to conduct a pilot test to improve the tool before using it more generally
• Use experienced collectors when possible
• Provide training for collectors on the specific tool and/or supervise collection to reduce bias (e.g., inappropriate prompting for answers during interviews) and errors (e.g., misunderstanding which program elements need to be observed)
• Consult key stakeholders (e.g., program staff and participants) throughout the evaluation process ( participatory evaluation )
• collecting and recording evaluation results
• storing information securely
• cleaning information
• transferring information (e.g., between software used for analysis and/or members of the research team)
• effectively presenting results and making information accessible
• What is the topic or issue?
• What is the context? For whom and under what circumstances?
• How will the analysis be used?
• The level of data analysis should be appropriate to the data gathered. For example, if the sample is small, sophisticated data analytic techniques may not be warranted, and statistical analysis may make no sense.
• Results should be interpreted with caution , particularly where a change is observed
• All data collected as part of the evaluation should be included in the analysis to reduce bias and improve the validity of the evaluation findings
• It is important to consider response rates and missing data
• It is recommended that the evaluator ask a peer to look over their analysis and to verify their interpretations of the data to reduce bias and improve the credibility of the findings
• It is particularly important to triangulate quantitative data with observations and knowledge from other means , to ensure everything makes sense. It is essential that the report be reviewed with people who know the context.
• For more in-depth analysis , it is recommended to consult an evaluation consultant or an academic partner with the necessary expertise
• the study concept it intends to operationalize,
• the level of measurement,
• the specific objective/hypothesis addressed,
• the scales from which questions were drawn and their reliability and validity, and
• the intended analysis.
• Nominal: Numbers assigned to categories do not necessarily have inherent meaning and the order of the categories may be arbitrary. For example, when asking about marital status, there are a limited set of possible responses and categories can be ordered in numerous ways (e.g. 1 = “married”, 2 = “not married”).
• Ordinal: Data are ordered, but the distances are not quantifiable (you cannot add or subtract). A question where the responses range from 1 = “strongly agree” to 5 = “strongly disagree” is an example of this type of categorical data.
• Interval: Data is like ordinal except we can say the intervals between each value are equidistant. This allows us to order the items that are measured and to quantify and compare the magnitudes of differences between them. For example, the difference between 20 and 21 degrees Fahrenheit is the same magnitude as the difference between 70 and 71. Data can take on positive or negative values.
• Ratio: Data is like interval data, but with a true zero point, meaning you can have nothing less than zero (no negative numbers). When the variable equals 0, there is none of that variable. For example, time is ratio since 0 time is meaningful. Variables like height and weight are ratio variables. Data are continuous positive measurements on a nonlinear scale.
• Infer from the sample to the population
• Determine probability of characteristics of the population based on the characteristics of your sample
• Help assess strength of the relationship between your independent (causal) variables, and you dependent (effect) variables.
• Many peer-reviewed academic journals will not publish articles that do not use inferential statistics
• Allows you to generalize your findings to the larger population
• Allows you to assess the relative impact of various program inputs on your program outcomes/objectives.
• How big/important is the association
• Is the association statistically significant, meaning is it due to chance, or is it likely to exist in the overall population to which we want to generalize? Statistical tests answer this question.
• What is the direction of the association? (look at graphs)
• What is its shape? Is it linear or non linear? (look at graphs)
• A small p-value (typically ≤ .05) indicates strong evidence against the null hypothesis, so you reject the null hypothesis and therefore reject that the observation is due to chance.
• A large p-value (> .05) indicates weak evidence against the null hypothesis, so you fail to reject the null hypothesis and assume that any observation is due to chance.
• p-values very close to the cutoff (.05) are considered to be marginal (could go either way). Always report the p-value so your readers can draw their own conclusions.
• How is a relationship between two variables changed if a third variable is controlled? (Multiple crosstabs, partial correlation, multiple regression, MANOVA)
• What is the overall variance of a dependent variable that can be explained by several independent variables. What are the relative strengths of different predictors (independent variables)? (Multiple regression)
• What groups of variables tend to correlate with each other, given a multitude of variables? (Factor analysis)
• Which individuals tend to be similar concerning selected variables? (Cluster analysis)
• Creative & Credible: Quantitative Evaluation
• CDC Evaluation Research Team: Analyzing Quantitative Data for Evaluation
• National Science Foundation: User-Friendly Handbook for Mixed Method Evaluations - Analyzing Qualitative Data
• Center for Applied Linguistices: Evaluator's Toolkit for Dual Language Programs - Step-by-Step Guide to Data Analysis & Presentation
• Australian Bureau of Statistics: A guide for using statistics for evidence based policy
• The interviewer or facilitator must be skilled at guiding the discussion without leading it to fit their own agenda.
• The interviewer or facilitator must be especially sensitive to the instances when participants may feel inhibited or find it difficult to discuss challenges and problems that they have experienced within the project.
• In addition to asking initial questions, the interviewer needs to be skilled at following up with prompts , ensuring that the interviewee is relaxed and that the process is not intrusive or upsetting.
• It might be preferable to undertake these in naturalistic settings where project activity takes place so that participants are familiar with the setting and associate it with the activity being discussed.
• Interviews that include sensitive topics should not be undertaken in settings where participants might be distracted by activity going on, or where there is no guarantee that the interview will not be interrupted.
• Interviewers need to have in place a range of strategies for responding appropriately to a range of disclosures that may need action, and opportunities to debrief in case they themselves find the process challenging.
• Analysis normally takes place on completion of the project . However, if the project is of a lengthy duration or a lot of data are gathered over the course of the project, it may be helpful to analyse data at intervals throughout the project to minimize the amount of work required post-project and also to ensure that any information gathered is still fresh in the evaluator’s mind.
• CDC Evaluation Research Team: Analyzing Qualitative Data for Evaluation
• NC State University: Qualitative program evaluation methods
• Familiarise yourself with the data by reading and rereading it.
• Generate initial codes. This entails working systematically to identify and name interesting items, especially if these are repeated. They could be words used by participants to describe their responses to a project. An inductive approach will stay close to participants’ language, while a more deductive approach may search for codes using a predetermined conceptual framework. Deductive approaches may seem more manageable in evaluation but they carry the drawback that the analysis might miss participants’ unanticipated responses.
• Group your codes into overarching themes. These might be different types of response, such as reported feelings, moods, creative challenges and other reflections.
• Review themes in order to gain a sense of what the different themes are, how they fit together, and the overall story they tell about the data.
• Define and name themes. This is an attempt to capture the essential character of each theme and show how it fits within the overall picture.
• Produce the report. The aim here is to tell the rich story of your data in a way that convinces the reader of the rigour of the analysis. This allows you to highlight out vibrant cases while showing how these fit within the overall body of information.
• Evaluators listen to or read transcripts of evaluation data and then move in the form of dance according to meaning being seen, heard or felt. Interpretations of the movement in terms of the raw data can then be shared and these interpretation can form the basis of themes that may be connected with other themes that form the evaluation story (Simons & McCormack, 2007) .
• Working with nurses in cancer service, movement, narratives, stories, poetry, collage, and creative writing were used, with data analysis performed by subgroups that played with transcripts, pictures, and poems to derive themes and categories to explain the quality of clinical practice, which were then explained to other subgroups that agreed, challenged, or extended interpretations. In this way, data are transformed from “cold data” into dynamic, creative, and embodied forms, and interpretation takes the form of artistic creation (Buck et al., 1999) .
• In an alternative approach to evaluating sexual health promotion, dramatized sexual scenes provide a context within which to analyse many of the behavioural and epidemiological factors associated with sexual practice and offered an entry point for dialogue. Participants’ analysis of narratives through a dramatized scene offer a testimony to sexual experience in their own terms. Evaluation is conducted collaboratively in examining changes in sexual scenes at multiple time points (e.g., 3-month, 6-month or 12-month intervals). Change is examined on an individual and structural level (Paiva, 2005) .
• An a/r/tographical framework is a method which links art, research and teaching, and privileges both text and image. (Garcia Lazo & Smith, 2014) .
• Photo-elicitation is the process of analyzing photos taken by the evaluator or the participant in the data collection process to gain insights into social phenomena that oral or written data cannot provide. (Lapenta, 2004) .
• Arts-based evaluation can open new ways of seeing and understanding , incorporating both emotion and intellect
• Arts-based data sources are often less tangible than numbers or transcripts and may therefore be less amenable to standard criteria
• Participants must overcome inhibitions and fear of being judged
• Arts-based evaluators may need a specialized skill set in both evaluation and artistic techniques
• Must be careful that focus on art-making and creative expression does not overshadow the evaluation
• We must broaden our concept of validity to embrace understandings gained from arts-based expression
• What stage of the evaluation to mix methods? (The design is considered much stronger if mixed methods are integrated into several or all stages of the evaluation.)
• concurrently ( triangulation is used to integrate information from different independent sources)
• Will qualitative and quantitative methods will be given relatively equal weighting?
• What are the main results or conclusions that can be drawn?
• What other interpretations could there be?
• Do conclusions make sense?
• Did the results differ from initial expectations? If so, how?
• Bamberger, Michael, ‘Introduction to Mixed Methods in Impact Evaluation’, Guidance Note No. 3, InterAction, Washington, D.C., August 2012.
• BetterEvaluation, ‘Analyze Data’, web page, BetterEvaluation.
• BetterEvaluation, ‘Collect and/or Retrieve Data’, web page, BetterEvaluation.
• BetterEvaluation, ‘Combine Qualitative and Quantitative Data’, web page, BetterEvaluation.
• BetterEvaluation, ‘Manage Data’, web page, BetterEvaluation.
• Evergreen, Stephanie, D.H., Presenting Data Effectively, Communicating Your Findings for Maximum Impact, Sage, Thousand Oaks, 2013.
• Measure Evaluation, ‘Data Quality Assurance’, web page, Measure Evaluation.
• Patton, Michael Quinn, Qualitative Research & Evaluation Methods, third edition, Sage, Thousand Oaks, 2001.
• Perrin, Burt, ‘Linking Monitoring and Evaluation to Impact Evaluation’, Guidance Note No. 2, InterAction, Washington, D.C., April 2012.
• http://managementhelp.org/evaluation/outcomes-evaluation-guide.htm
• Observation
• Focus groups
• Document review
• Narrative inquiry
• Respect (for each other and the space we work in)
• Relevance (to our lives)
• Reciprocity (exchange of information and skills)
• Responsibility (to ourselves, to each other, and to the communities we come from)

Additional Reading

• Community-university partnering
• Inclusion of arts-based research and research in the humanities
• Team issues
• Ethics of meaningful participation
• Ethics of consent
• Ethics of raising false expectations
• Ethics of stifling creativity in participatory action research: protocol rigidly hampering artistic process
• Ethics of authorship and ownership of arts-based intervention products
• Right of acknowledgement versus protection of anonymity
• Ethics of dangerous emotional terrain
• Ethics of representation
• Ethics of caring for team members, students and staff
• Ethics of researcher engagement and commitment
• Ethics of expanding the team after the project is in process
• Ethics of interdisciplinarity – different cultures of publication, collaboration, and notions of ethical research

There are constraints as to who can easily provide consent, for example:

• Only those over 18
• People with enough literacy and time to read detailed text-heavy materials imposed by Research Ethics Boards (REBs)
• People who are not concerned about signing documents for other reasons such as illegal presence in the country

These constraints can result in excluding important components of the populations , if taken too literally.

Conversely individuals declining consent is important to document as well, analyzing why this refusal is occurring.

Consent needs to be made context-sensitive and flexible to avoid excluding the voices of marginalized populations while respecting the rights of individuals and communities to refrain from engaging in the research process or evaluation.

• The time-frame
• Limitations of the study
• What questions would be answered
• What, if any, the likely impact of the research or evaluation will be
• Film an artistic process (with consent of participants)
• Conduct informal interviews at an arts show training session or other community event
• Authorship and ownership of the work
• Consent to use the work
• The truthfulness or adequacy of the work as a representation of participants’ experiences
• Take preliminary “results” back to artist participants for feedback
• Establish a monitor for the group during the interpretation process
• Make time commitments transparent to all at the beginning
• Review time commitments periodically
• Plan regular “check-ins” to take unforeseen circumstances into account
• Articulate at the beginning of the process clearly stated criteria for inviting colleagues and partners
• When bringing in new expertise, undertake dialogue with original team members to ensure that the new recruit will not undermine the complex research dynamics in any way
• Extending invitation to co-author
• How collaboration is conducted
• What constitutes ethical research practice
• Collaboration
• Implications of one approach to research or evaluation on the integrity for another
• Engage in considerable dialogue and reflection at all stages of the research, particularly in mixed methods studies attempting to embody multiple approaches
• Helping participants feel empowered in their daily lives; or
• Strengthening participants’ ability to humanize their social reality but not fundamentally challenge the logic of social relations; or
• Building awareness and skills in participants to revolutionize the social system to make it more equitable.
• Personal empowerment , e.g., asking participants about their personal empowerment before and after the program
• Social engagement , e.g., documenting participants’ changes in social engagement
• Hierarchical relations and social activism , e.g., observing changes in hierarchical relations within the group and examining the nature of social activism
• Personal empowerment
• Self-confidence
• Reframing of identity
• Fortified social support
• Increased social inclusion
• Policy change (e.g., through " legislative theatre ")
• Social media movements (e.g., #MeToo)
• Electoral outcomes (e.g., election results, voter turnout)
• Participation in civil disobedience protests

• Research Note
• Open access
• Published: 29 March 2024

From voice to ink (Vink): development and assessment of an automated, free-of-charge transcription tool

• Hannah Tolle   ORCID: orcid.org/0009-0008-6179-8278 1 ,
• Maria del Mar Castro   ORCID: orcid.org/0000-0002-0485-2919 1 ,
• Jonas Wachinger   ORCID: orcid.org/0000-0002-5480-3138 2 ,
• Agrin Zauyani Putri   ORCID: orcid.org/0009-0008-2095-5556 2 ,
• Dominic Kempf   ORCID: orcid.org/0000-0002-6140-2332 3 ,
• Claudia M. Denkinger   ORCID: orcid.org/0000-0002-7216-7067 1 , 5 &
• Shannon A. McMahon   ORCID: orcid.org/0000-0002-8634-9283 2 , 4  

BMC Research Notes volume  17 , Article number:  95 ( 2024 ) Cite this article

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Verbatim transcription of qualitative audio data is a cornerstone of analytic quality and rigor, yet the time and energy required for such transcription can drain resources, delay analysis, and hinder the timely dissemination of qualitative insights. In recent years, software programs have presented a promising mechanism to accelerate transcription, but the broad application of such programs has been constrained due to expensive licensing or “per-minute” fees, data protection concerns, and limited availability of such programs in many languages. In this article, we outline our process of adapting a free, open-source, speech-to-text algorithm (Whisper by OpenAI) into a usable and accessible tool for qualitative transcription. Our program, which we have dubbed “Vink” for voice to ink, is available under a permissive open-source license (and thus free of cost).

We conducted a proof-of-principle assessment of Vink’s performance in transcribing authentic interview audio data in 14 languages. A majority of pilot-testers evaluated the software performance positively and indicated that they were likely to use the tool in their future research. Our usability assessment indicates that Vink is easy-to-use, and we performed further refinements based on pilot-tester feedback to increase user-friendliness.

With Vink, we hope to contribute to facilitating rigorous qualitative research processes globally by reducing time and costs associated with transcription and by expanding free-of-cost transcription software availability to more languages. With Vink running on standalone computers, data privacy issues arising within many other solutions do not apply.

Peer Review reports

Introduction

Recent decades have witnessed an ever-increasing use of qualitative approaches in global health research [ 1 , 2 ], due at least in part to the recognition that in-depth, qualitative insights can add richness to existing data and can facilitate more person-centered, bottom-up solutions to health challenges [ 3 ]. However, one factor that limits broader and timelier use of qualitative data is transcription. Transcription refers to the process of converting recorded audio speech, for example from an interview or focus group discussion, into a written format. Transcription is an indispensable part of the qualitative process, and the selection of an adequate transcription approach (e.g. transcribing dialogue versus also capturing utterances such as “uh-huh” or “umm”, details of who is speaking, interruptions, pauses, or involuntary and non-lexical noises such as coughs or throat clearing) is seen as crucial to maintain quality and rigor of data [ 4 , 5 ]. Nevertheless, the processes and decisions made during transcription represent an often-neglected space within qualitative scholarship, receiving limited attention and reporting in the literature. A recent review about reporting of transcription processes found that 41% of articles employing interviews as a research method did not mention transcription, while 11% mentioned transcripts but not the process of transcription [ 6 ]. Given the extensive use of transcription in qualitative research, the limited discourse on the processes, strengths and limitations inherent to transcription is striking [ 7 ].

To date, transcription has mainly been accomplished in three ways: by a single researcher or research team who listens to the audio files and manually types text; by professional transcription services wherein recorded material is sent to a company that then returns transcripts; or by software-based transcription programs that entail payment to an external provider, where recorded material is uploaded, automatically transcribed (with or without additional accuracy checks), and transcripts can then be downloaded. Each of these existing approaches entails opportunities and challenges. Manual transcription by the lead researcher or team facilitates extensive engagement with the data, but it is time consuming for the individual(s) transcribing and for the project as a whole. One hour of recorded material typically requires six to seven hours of transcription time [ 8 ]. Despite being inherent to the process of manual transcription, delays can lead to collected data waning in relevance [ 9 ] or, as witnessed in COVID-19 research [ 10 ], becoming obsolete. Many qualitative teams have sought to mitigate transcription delays by forgoing verbatim transcription in favor of selective transcription or via capturing data in the form of field notes and summaries [ 11 , 12 ]. While selective transcription and related techniques can facilitate timely results, these approaches can increase the risk for researcher bias and information loss [ 13 ].

Increasing the number of individuals transcribing a dataset by outsourcing transcription can reduce time but may increase project expenses [ 14 ] and cause variability of transcript quality and content, as transcribers may have little familiarity with the research aims [ 15 ]. Additionally, in case of emotionally straining research topics or respondent narratives, outsourcing can induce mental stress for transcribers who otherwise would not have come in contact with the data [ 16 ]. Data safety and privacy are also a concern when sharing raw data with individuals outside the study team.

Software-based alternatives (e.g., NVivo, TranscribeMe, happyscribe, OneNote (Microsoft) or Smart Pen [ 17 ]) are new entrants into the transcription field whose broad utility in academic research has been limited by several factors [ 18 ]. In some cases, programs require training on a user’s voice, which is a time-consuming step that reduces the program’s sensitivity to other voices [ 19 ]. In other cases, software-based services are expensive and exclusionary, which hinders their use in projects with limited funding or in projects that use languages that transcription firms do not offer within their range of products [ 20 ]. Literature on the consistency and accuracy of speech-to-text software is currently limited, but at least one study showed that accuracy varied widely depending on the used algorithm and decreased overall with audio files that were low-quality or entailed multiple speakers [ 21 ]. This presents further challenges for researchers since qualitative data often stems from conversational speech (e.g., interviews, focus group discussions wherein multiple speakers and background noise are common). Since software developers often do not provide word-error-rates for this sort of non-naturalized audio recordings, further exploration in this field is necessary [ 22 ].

In response to the existing challenges of cost, timeliness, availability, exclusivity and reliability, and with the advent of stronger and less resource-intensive algorithms for everyday use, software engineers and computer scientists worldwide have begun debating feasibility, trade-offs, and opportunities related to transcription via open-source (i.e., free-of-cost) speech-to-text algorithms. Such a platform would mitigate several barriers inherent to manual and/or commercial transcription, but as of now we are not aware of a program that is adjusted to the needs of qualitative researchers, is user-friendly in terms of navigation and is available in an equitable format in terms of language, downloadability and cost.

In this article, we outline our process of developing and adapting a free, open-source, speech-to-text algorithm into a usable and accessible tool for qualitative transcription. We conduct a proof-of-principle assessment of our standalone application, in terms of usability and performance in transcribing non-naturalized audio data in several languages. We further provide a detailed step-by-step guide for researchers considering using this tool for their own data transcription. The headings are not all formatted the right way. Starting from "Developing and testing a free transcription software package" to the "Discussion" they are all one level to low. It should be:Section1Developing and testing a free transcription software packageSection2DevelopmentSection2Proof-of-principle of Vink’s performance on multilingual realistic audio dataSection2Reliability and perceived usefulness of the generated transcriptsSection1Usability of VinkSection2Testing the usability of our transcription package and user interfaceSection2Challenges and improvementsSection1Summative Evaluation of VinkWould it be possible to change this formatting?

Developing and testing a free transcription software package

Development.

As a first step in developing our transcription tool, we identified available open-source speech-to-text (STT) algorithms including VOSK by Alpha Cephei [ 23 ], Silero by SileroAI [ 24 ] and Whisper by Open AI [ 25 ]. These algorithms were pilot tested using non-naturalized interview data in German in an exploratory approach. We ultimately selected Whisper by OpenAI (see breakout box 1 ) based on the accuracy and readability of transcripts, the inclusion of punctuation and case sensitive lettering, robustness to background noise, and the program’s potential applicability in numerous languages.

Breakout Box 1: Whisper by OpenAI

Whisper by OpenAI is an open-source automatic speech recognition (ASR) system trained on multilingual audio data in an end-to-end approach. OpenAI emphasizes Whisper’s ability to navigate transcription that captures or mitigates challenges related to accents, background noise, and technical language. The algorithm uses one single speech model that automatically recognizes the audio file language and transcribes the data. Audio recordings with mixed languages can therefore also be transcribed easily. Since Whisper was not built via one specific dataset or voice, the system is applicable across qualitative research projects. Furthermore, Whisper runs locally on the user’s computer without requiring a data upload, thereby mitigating privacy concerns. While the program does not require an online connection, running Whisper requires good hardware as it uses between 1–10 GB of RAM, depending on which of the five available speech model sizes is selected. Using Whisper thus entails a trade-off: if a higher level of transcription accuracy is sought, the program’s runtime and RAM requirements will increase.

Like many currently available ASR algorithms, Whisper requires software programming knowledge (e.g. Python) in order to use it for transcribing audio files into text [ 26 ], placing it beyond reach for researchers who lack programming skills. Noticing this gap, we developed a standalone application to open the potential of Whisper to a broader pool of researchers. Our goal was to create a downloadable, ready-to-use transcription package that bundles the Python interpreter, the Whisper package, as well as all its dependencies into one standalone tool that allows anyone to run the Whisper algorithm on a personal computer without much effort. We also wanted a product that had an easily navigable user interface and was free to anyone interested in using it for their own research.

The final transcription tool, which we dubbed “Vink” due to its ability of transferring textual data from voice to ink, is available at https://heibox.uni-heidelberg.de/f/6b709d18b0d244cdb792/ . More technical information on this standalone application, which was created using PyInstaller, is available online at https://github.com/ssciwr/whisper-standalone/ . The tool currently is only available for Windows, the development of macOS and Linux versions is in progress.

When we talk about Vink, we mean our transcription tool which is using the open source STT-algorithm Whisper, and from this point forward we will only talk about Vink unless when explicitly talking about characteristics of the used STT-algorithm.

All assessments were done anonymously and did not include any personal or individually identifiable information. The institutional review board of the medical faculty, University of Heidelberg, Germany, therefore exempted this study from ethical review.

Proof-of-principle of Vink’s performance on multilingual realistic audio data

We conducted a proof-of-principle assessment of Vink’s performance when transcribing realistic (non-naturalized) audio data in 14 languages including: English (American), Arabic (Classical Arabic), Bahasa Indonesia, Burmese, Chinese (Mandarin), Filipino, French, German, Malagasy, Portuguese (Brazilian), Spanish (Colombian), Tamil, Turkish, and Yoruba.

Multilingual transcription pilot-testers with varying experience in manual audio data transcription each provided one audio file of a discussion in their mother tongue following detailed recording instructions (see Appendix S1 ). Pilot-testers were selected from the authors’ networks based on interest expressed, languages spoken, and time available. To mimic real-life qualitative data quality, audio files were recorded on either a phone or a regular recording device in a quiet setting. Transcripts of the audio files were generated using the medium size language model of Whisper (5GB RAM required) and were sent back to the pilot-testers for assessment. Pilot-testers were then asked to correct the automatically generated transcript in one sitting, and to record the time needed to correct the transcript and the word error rate (WER) including errors linked to the deletion of filler words (e.g. “uhh” or “umm”); this process facilitated our measure of transcript accuracy. For review instructions, see Appendix S1 . Pilot-testers were also asked to complete an anonymous questionnaire on the perceived usefulness of the transcript (see Appendix S2 ). Following this approach, a total of 19 audio files were provided, 14 of which were assessed. The remaining 5 pilot-testers did not provide an assessment of the transcript (3 contact reminders were sent).

Study data were collected between December 2022 and April 2023, and managed using REDCap electronic data capture tools hosted at the Universitätsklinikum Heidelberg [ 27 , 28 ].

Reliability and perceived usefulness of the generated transcripts

Table  1 summarizes the recordings assessed in our proof-of-principle of the algorithm’s transcription performance. Substitutions describe replaced words (e.g. transcribing “house” for “mouse”). Insertions represent added words that were not said, and deletions were cases in which words or non-verbal cues were left out of the transcript.

The performance of Vink varied widely across languages, with audio files in Chinese, Portuguese, Filipino, English, German, Bahasa and Turkish yielding the most accurate transcripts (WER < 10%), and Malagasy, Tamil and Burmese producing the least accurate transcripts (WER > 40%), according to pilot-testers. As in Radford’s [ 25 ] large-scale assessment, the algorithm’s performance did not seem to be language group specific with e.g., high accuracy in Chinese (Mandarin) and extremely low accuracy in Burmese. More likely, this is associated with the very low percentage of e.g. Burmese audio in the training dataset of the Whisper algorithm ( [ 25 ]; Appendix E). Among European languages, French required the most extensive transcription correction. The time needed to correct transcripts varied greatly and took between 1.7-fold (Portuguese) and 16-fold (Tamil) the duration of the original audio file.

Overall, most pilot-testers evaluated the generated transcripts positively in the short questionnaire (4 or 5 on a 5-point Likert-scale). The perceived readability of transcripts, which pilot-testers indicated on a 5-point Likert-scale in the short questionnaire, was associated with indication of a low WER category (0–10%, 11–20%, 31–40% or > 40%) of the respective transcript, with an overall high perceived readability across languages. All pilot-testers whose transcript had a WER below 20% ( n  = 9), and a total of 10 out of 12 pilot-testers who completed the short questionnaire, indicated that they were either likely or very likely to use Vink-based automated transcription in their future research. Results of the short questionnaire are presented in Fig.  1 .

User assessment of generated transcripts– Perceived usefulness, readability of transcripts and likeliness of future use

However, the results from the questionnaire revealed several areas for improvement. First, the algorithm seems to naturalize the text output and therefore rarely includes filler words in the transcript. Non-verbal vocalizations such as laughing, crying or hesitations are omitted as well. Repetitions are partly cleared in the final transcript, producing a denaturalized transcript version [ 29 ]. These deleted, non-verbal vocalizations account for a significant part of the WER in our assessment. For instance, the algorithm would naturalize the sentence “We, ehm, wanted to gi-… give an example.” to “We wanted to give an example.”, which would be counted as two deletions in our assessment. Respondents wished for hesitations and pauses to be included and captured with an ellipsis symbol (“…”) rather than a comma.

According to respondents, the algorithm (as described in previous papers on ASR [ 21 , 30 ]) struggled during crosstalk segments of the audio data. Some respondents suggested that highlighting longer pauses or the different speakers in the audio recording could be helpful, for instance line breaks between speakers. Speaker recognition was also deemed to potentially be helpful to distinguish the different voices, especially if Vink were to be applied for transcribing focus group discussions.

Usability of Vink

Testing the usability of our transcription package and user interface.

To gauge the usability of the downloadable package and interface of Vink, we gave 5 people [ 31 ] without previous experience in computational science access to the transcription package and provided them with an instruction sheet (see Appendix S3 ) on how to download and use the transcription tool. We then observed how well users were able to navigate our transcription tool using cognitive think-out-loud interviewing during first use. In addition, we asked users for feedback regarding how they perceived the tool in terms of usability and user-friendliness, and what changes they suggested to increase usability.

Challenges and improvements

Our usability assessment showed that users were able to independently install Vink and transcribe an audio file using the incorporated interface. Reported issues included difficulties finding the executable file for Vink in the downloaded folder and confusion about suitable text file formats, which were addressed in the latest version of Vink to enhance user friendliness. Inter alia an installer was added to facilitate the set-up process. Most struggles and uncertainties resulted from pilot-testers overlooking content in the instruction manual, highlighting the importance for our team to maximize the self-explanatory nature of the interface. See Table  2 for the complete list of reported usability issues and subsequent improvements.

Vink’s interface and the instructions for use were also further modified following a rapid, iterative approach drawing on human-centered design principles. The user manual of the newest version of Vink can be found in Appendix S4 .

Summative evaluation of Vink

Taken as a whole, existing standards for transcription present challenges that can be addressed by ASR algorithms such as Whisper, which can be made accessible via standalone applications such as Vink. Table  3 summarizes overarching challenges to traditional verbatim transcription, how Whisper as an ASR algorithm can address some of these challenges, how Vink influences the usability of Whisper for audio transcription, and what additional needs persist.

Vink is an easy-to-use, open-source speech-to-text tool that facilitates the use of the Whisper ASR-algorithm for non-programmers in qualitative research. It is free of cost, making it an accessible transcription solution for research projects. The usability for transcribing audio files in non-western and (in a research sense) rarer languages, as well as the limited computing power required to operate it, make our transcription tool usable for everyone with access to a standard computer or laptop. These characteristics may help mitigate global disparities in health research resources [ 32 ]. In addition, compared to uploading data to third-party transcription services, Vink runs locally, which allows protection of privacy and confidentiality of data, an established principle of qualitative research [ 33 , 34 ].

The accuracy of generated transcripts is central to the application’s value in qualitative research. Poland [ 35 ] defined transcription accuracy as faithfulness to the original speaker’s intention and fit with the research aims. In practice, transcripts are often considered accurate when they match the recorded audio, disregarding the original interaction. Although problematic as this takes a purely positivist view that there is one ‘correct’ version, this understanding allows for a comparison of transcripts and presents a feasible common ground for accuracy assessment in our case. Part of this consideration on transcript accuracy is the inclusion of behavioral annotations. Gestures and non-verbal vocalizations can be considered representative of e.g., the speakers’ engagement in the interview or topic, or their certainty in their expressed opinions. However, non-verbal cues are often excluded from transcripts, whether transcribed by hand or with algorithm support. This form of ‘selective transcription’ increases readability but loses data and risks researcher bias. By virtue of saving time on the pure documentation of words, Vink may allow researchers to invest more time in capturing and annotating the broader context of the interview or focus group discussion.

In Radford’s [ 25 ] large scale and our proof-of-principle assessment of Whisper’s accuracy on multilingual speech, the overall performance (or word-error-rate (WER)) of the algorithm is good. Variability in WERs show that despite the algorithm technically being applicable to a high number of languages, remarkable disparities in accuracy remain across languages, commonly favoring languages such as English, German, and Chinese. In a few languages that are linguistically more distant from English, or for which the amount of audio data used in training Whisper was comparatively low ( [ 25 ] Appendix E), the quality and therefore usefulness of the transcripts decreased. While the amount of respective audio data for training is strongly correlated with Whisper’s performance, an additional factor for those languages is a lack of transfer due to the linguistic distance from English, which was predominantly (65%) used for training Whisper.

The lack of transparency regarding metrics in machine learning literature [ 36 ], including the exact definition of the WER in the original publication on the Whisper algorithm [ 25 ], challenges comparisons across programs. For example, it is not clear whether filler words are considered in the WER assessment. Such deletions are relevant for qualitative research, as pauses for example can indicate divided attention or nervousness of the interviewee [ 37 ], and most word errors in our assessments were due to deletions of non-verbal vocalizations. However, the WER as a metric does not account for the causes of errors. Factors that can affect WER, independent of the capabilities of the ASR technology, include recording quality, technical terms or proper nouns, background noise, sex of the speaker, pronunciation, and speech fluency. These factors might explain the differences in WER between our own assessment and the large scale original assessment of Whisper’s WER [ 25 ]. With the limitations of the WER, other parameters (e.g., perceived usefulness or time-needed-to correct) provide valuable information for a realistic assessment of the transcript’s value for researchers. In our findings, the readability of transcripts was generally perceived as high, which implies an accelerated process of correction since the text can be followed and adjusted more easily. However, our preliminary assessment can only provide first insights into practical performance of Vink in real-life research scenarios; we would encourage scholars employing Whisper or Vink in their work to share their own experiences or further large-scale assessments.

Researchers have argued that computers may tempt qualitative scholars to perform ‘quick and dirty’ research [ 38 ] and could lead to a loss of closeness to the data [ 39 ]. In the context of automated transcription, we see the risk of generated text being superficially evaluated in terms of its readability and not by its nuanced representation of the original recording, including non-verbal cues. Additionally, the Whisper algorithm is trained to condition on the history of text of the transcript in order to use longer-range context to resolve ambiguous audio [ 25 ]. Sentences with non-understandable parts are reconstructed leading to overall higher accuracy and good readability but possibly a false sense of certainty of transcript correctness in hard-to-understand passages. We therefore advocate for researchers considering using speech-to-text tools (including Vink) to carefully choose its exact mode of application. Especially for researchers interested in nuances of human interaction, too much reliance on the automatically generated transcript might cause a significant loss of valuable data. The applicability of automated transcription is also challenged by scholars such as Lapadat [ 40 ] who view transcription as a process rather than a product, as it involves constant decisions regarding how to present the data and which additional information to include. This makes transcription an inherently interpretative act, influenced by the transcriber’s own biases and assumptions [ 41 ]. As algorithms are not able to make such decisions about meaning-making and interpretations, nor about ways in which these meanings may best be represented [ 21 ], we propose that ASR generated transcripts should merely be seen as a first step in the transcription process, and are to be revised and modified [ 42 ].

In terms of limitations, Vink as of now is only available for Windows computers, which restricts its potential user base. We are currently working on macOS and Linux versions. Additionally, our assessment of the time-needed-to-correct and WERs across languages was designed for proof-of-principle purposes. Despite efforts to provide as detailed descriptions for transcript correction and assessment as possible, pilot-testers’ varying levels of experience in transcribing or correcting qualitative data may have introduced variation in the time-needed-to-correct and WER assessments. Larger, systematic evaluations of the algorithm’s performance, building on the assessment by Radford et al. [ 25 ], and evaluations of Vink’s usefulness in facilitating qualitative research transcription processes would provide additional insights. Similarly, we did not assess the algorithm in several contexts relevant for qualitative research (e.g., focus group discussions, speech with strong accents, more background noises). As qualitative research often is performed in settings where the researcher only has limited control over environmental factors, such further assessment would allow a firmer establishment of the conditions required for the algorithm performance to be sufficiently useful in the particular context of qualitative research.

Going forward

A step-by-step guide on how to install and use Vink is available for use (Appendix S4 ). The code for the graphical user interface of Vink, as well as the combined work with the bundled dependencies are published under the MIT license. Vink’s installer will also install a number of bundled software packages under a variety of software licenses (Nvidia License Agreement for Nvidia SDKs, LLGPL v3, MPL v2, PSF License, Apache 2.0, BSD-3, BSD-2, MIT, Zlib license, Unlicense). For detailed information about these licenses, please read the license agreement. We ask users to credit OpenAI when using the algorithm, and to cite this publication when using Vink in their own work. As mentioned, Vink-generated transcripts should be seen as a first step in the transcription process, which are to be revised by research teams (and ideally, those who undertook the data collection activity and/or who will undertake data analysis).

We are happy to hear about other researchers’ experiences, successes, and challenges in applying this approach to automatic transcription in their own work and are open to feedback and suggestions. We intend to make a portal for feedback available, in the meantime please contact the corresponding author. Additional guidance and information on the Whisper algorithm are available online (not moderated by us), for example at https://openai.com/research/whisper or https://github.com/openai/whisper . Tutorials and forums to chat about possibilities and limitations of automated speech-to-text transcription are emerging, allowing for an exchange between interested individuals. To the best of our knowledge, such forums are primarily technical in nature.

We aim to improve and update the standalone package in the future. Improvements in language models, when published by Open AI, will be considered in newer versions. Being based on an open-source algorithm means that the way this program operates is more transparent than in commercial software and can be examined by the research community.

In this article, we have introduced and evaluated our novel transcription tool Vink for automated interview transcription in various languages, based on OpenAI’s Whisper. Our findings outline the possibilities of integrating open-source speech-to-text algorithms into qualitative research. With the current rapid developments in this field, we expect the accuracy, relevance, and ease of use of ASR to continue to increase, and we want to contribute to the emerging discourse on its resulting potentials and drawbacks for qualitative research. We hope that by providing a ready-to-use and free tool we will allow qualitative researchers, especially those with limited resources, to save time and money. These resources in turn can be reinvested in engaging more profoundly with data and in deepening other steps of the analytic process, thereby ultimately strengthening the quality of qualitative research across settings and disciplines.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

Automated Speech Recognition

Gender non-binary

Random-Access Memory

Speech-To-Text

Word Error Rate

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Acknowledgements

We thank all the pilot-testers that contributed to the assessment of the Vink app’s usability and evaluation of transcripts. They include Lukas Brümmer, Myo Chit, Abeer Fandy, Zavaniarivo Rampanjato, Mark Donald C. Reñosa, Sonjelle Shilton, Girish Srinivas, Anete Trajman, Stefan Weber, Rayan Younis, among others that preferred to remain anonymous.We would like to thank the Scientific Software Center of the Heidelberg University for their development work on this project. The Scientific Software Center is funded as part of the Excellence Strategy of the German Federal and State Governments.We thank Frank Tobian for the technical support of this work. Also, we would like to thank Rayan Younis and Ralf Tolle for proofreading this paper.We thank the team from FIND for their support. For the publication fee we acknowledge financial support by Heidelberg University.

Article processing charges are paid by Heidelberg University and Shannon McMahon.

The Scientific Software Center Heidelberg (funded as part of the Excellence Strategy of the German Federal and State Governments) paid Dominic Kempf during activities related to the creation of the application.

The Division of Infectious Diseases and Tropical Medicine, Center of Infectious Diseases, Heidelberg University Hospital, Heidelberg, Germany granted time to conduct activities related to this manuscript for Maria del Mar Castro and Hannah Tolle.

Maria del Mar Castro and Claudia Maria Denkinger were funded by the National Institute of Allergy and Infectious Diseases, NIH, USA [grant number: U01AI152087] for the Rapid Research in Diagnostics Development for TB Network (R2D2 TB Network) while working on this project.

Claudia Maria Denkinger and Hannah Tolle were funded by the Ministry of Science, Research and the Arts Baden-Wuerttemberg while working on this project.

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Open Access funding enabled and organized by Projekt DEAL.

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Division of Infectious Diseases and Tropical Medicine, Center of Infectious Diseases, Heidelberg University Hospital, Heidelberg, Germany

Hannah Tolle, Maria del Mar Castro & Claudia M. Denkinger

Heidelberg Institute of Global Health (HIGH), Heidelberg University Hospital, Heidelberg, Germany

Jonas Wachinger, Agrin Zauyani Putri & Shannon A. McMahon

Scientific Software Center, Heidelberg University, Heidelberg, Germany

Dominic Kempf

Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

Shannon A. McMahon

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Contributions

Formal Analysis: H.T., A.P.Investigation: A.P., H.T., S.M., C.D.Methodology: H.T., M.C., J.W., S.M.Project Administration: H.T., S.M.Resources: S.M., C.D.Software: D.K., H.T.Supervision: S.M., C.D.Visualization: A.P., M.C.Writing– Initial Draft Preparation: H.T.Writing– Review & Editing: all authors.

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Correspondence to Hannah Tolle .

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The text here seems to be slightly shifted to re right the pilot-testers of the automatically generated transcripts only submitted technical information about the transcript. They did not provide any personal information.

All assessment for perceived usefulness was done anonymously and did not include any personal or individually identifiable information.

The assessment for usability of the application was done within the research group.

The institutional review board of the medical faculty, University of Heidelberg, Germany, which was consulted when planning this project, therefore exempted this study from ethical review.

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The authors declare no competing interests.

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Tolle, H., Castro, M., Wachinger, J. et al. From voice to ink (Vink): development and assessment of an automated, free-of-charge transcription tool. BMC Res Notes 17 , 95 (2024). https://doi.org/10.1186/s13104-024-06749-0

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Received : 05 December 2023

Accepted : 18 March 2024

Published : 29 March 2024

DOI : https://doi.org/10.1186/s13104-024-06749-0

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