thematic analysis in qualitative research software

All-in-one Thematic Analysis Software

Start your free trial.

Free MAXQDA trial for Windows and Mac

Your trial will end automatically after 14 days and will not renew. There is no need for cancelation.

Take your thematic analysis to the next level with MAXQDA

MAXQDA is the best choice for your thematic analysis. It works with a wide range of data types and offers powerful tools to analyze textual data, such as coding, visualization, mixed methods, statistical, and quantitative content analysis tools.

Document viewer

Your analysis.

Thematic analysis software for Windows and Mac

Developed by and for researchers – since 1989.

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

Thematic Analysis is Faster and Smarter with MAXQDA

MAXQDA makes thematic analysis faster and easier than ever before. Analyze all kinds of data – from texts to images and audio/video files, websites, tweets, focus group discussions, survey responses, and much more. MAXQDA is at once powerful and easy-to-use, innovative and user-friendly, as well as the only leading QDA software that is 100% identical on Windows and Mac.

As your all-in-one Thematic Analysis Software, MAXQDA can be used to manage your entire research project. Easily import a wide range of data types such as text documents, interview transcripts, focus groups, online surveys, web pages, images, audio and video files, spreadsheets, bibliographic data, YouTube data, and even tweets. Organize your data in groups, link relevant quotes to each other, use MAXQDA’s powerful tools designed to facilitate your thematic analysis, and share and compare work with your team members. Your project file stays flexible and you can expand and refine your category system as you go to suit your research.

Code and retrieve

MAXQDA is a Thematic Analysis Software that is easy-to-use and can be used to mark important information in your data with different codes, colors, symbols, or emoticons. Because time is precious, you can create codes with just one click and apply them to your data quickly via drag & drop. MAXQDA as the #1 Thematic Analysis Software offers Text Search tools that allow you to explore your documents without coding or reading them first. Search for keywords and automatically code them with just a few clicks. Organize your thoughts and theories in memos that can be linked to any element of your project. Retrieve your coded segments with one click or use MAXQDA’s powerful summary tools to test and develop new theories.

Capture your ideas while analyzing

Great ideas will often occur to you while you perform your thematic analysis. Using MAXQDA as your thematic analysis software, you can create memos to store your ideas, such as research questions and objectives, or you can use memos for paraphrasing passages into your own words. Memos are also great for creating audit trails. By attaching memos like post-it notes to text passages, texts, document groups, images, audio/video clips, and of course codes, you can easily retrieve them at a later stage. The unique MAXQDA memo manager and lexical search function guarantee immediate access to every single memo at any time.

Visual exploration of themes

Whether you are analyzing a single speech or an entire bookshelf: the tools of MAXQDA allow you to explore the content and structure of your texts without needing to read or code a single sentence in advance. With MAXQDA as your Thematic Analysis Software you can, for example, use the Interactive Wordtree. It visualizes all the combinations that lead to or from any word of your choice, including a detailed display of frequencies. This incredibly powerful feature can provide new and fascinating perspectives even on texts you know well and allows for a comprehensive overview of those you don’t.

Explore keywords in context

Researchers all around the world use quantitative text methods to enrich their thematic analysis. Because MAXQDA is a Thematic Analysis Software designed by and for researchers, it offers an entire add-on module – called MAXDictio – with tools specifically designed to facilitate Text Analyses. Are you interested in the use of certain terms in your material? Use MAXQDA’s Keyword-in-Context tool to search for relevant terms and word combinations in your material. Besides finding all occurrences of your search terms, the tool allows you to delve deeper into the context of your keywords by displaying all word locations and their (freely definable) context in an interactive result table.

Quantitative evaluation of themes

Quantitative aspects can also be relevant when conducting a thematic analysis. Using MAXQDA as your Thematic Analysis software enables you to employ a vast range of procedures for the quantitative evaluation of your material. You can sort sources according to document variables, compare amounts with frequency tables and charts, and much more. Make sure that you don’t miss the word frequency tools of MAXQDA’s add-on module for quantitative text analysis which makes the quantitative analysis of terms and their semantic contexts even easier. Additionally, MAXQDA offers mixed methods tools that allow you to easily combine qualitative and quantitative methods to get an even deeper insight into your data.

Visualize your qualitative data

As an all-in-one Thematic Analysis Software, MAXQDA offers a variety of visual tools that are tailor-made for qualitative research and thematic analyses. Create stunning visualizations to analyze your material. Of course, you can export your visualizations in various formats to enrich your final report. Visualize the progression of themes with the Codeline, use the Word Cloud to explore key terms and the central themes, or make use of the graphical representation possibilities of MAXMaps, which in particular permit the creation of concept maps. Thanks to the interactive connection between your visualizations with your MAXQDA data, you’ll never lose sight of the big picture.

AI Assist: thematic analysis meets AI

AI Assist – your virtual research assistant – supports your thematic analysis with various tools. Besides automatic transcription of audio and video recordings in different languages, AI Assist simplifies your work by automatically analyzing and summarizing elements of your research project and by generating suggestions for subcodes. No matter which AI tool you use – you can customize your results to suit your needs.

Free tutorials and guides on thematic analysis

MAXQDA offers a variety of free learning resources for thematic analysis, making it easy for both beginners and advanced users to learn how to use the software. From free video tutorials and webinars to step-by-step guides and sample projects, these resources provide a wealth of information to help you understand the features and functionality of MAXQDA for thematic analysis. For beginners, the software’s user-friendly interface and comprehensive help center make it easy to get started with your data analysis, while advanced users will appreciate the detailed guides and tutorials that cover more complex features and techniques. Whether you’re just starting out or are an experienced researcher, MAXQDA’s free learning resources will help you get the most out of your thematic analysis.

Free MAXQDA Trial for Windows and Mac

Get your maxqda license, compare the features of maxqda and maxqda analytics pro, faq: thematic analysis software.

Thematic Analysis Software refers to tools designed for the systematic examination of qualitative data to identify, analyze, and report themes or patterns within the data. MAXQDA is a widely recognized Thematic Analysis Software known for its comprehensive features and user-friendly interface. Researchers choose MAXQDA for its ability to efficiently conduct thematic analysis across various data formats, including text, audio, and video.

Thematic Analysis Software, such as MAXQDA, comes equipped with essential features for effective thematic analysis, including:

• Compatibility with various data types, ensuring versatility in analysis.
• Advanced coding and categorization tools for theme identification.
• Data visualization capabilities to aid in interpreting thematic findings.
• Tools for exploring relationships and connections between identified themes.
• A user-friendly interface for efficient navigation and workflow management.

Thematic Analysis Software, including MAXQDA, plays a crucial role in qualitative data interpretation. It simplifies the process by providing tools for coding, annotating, and organizing data, making it easier to uncover patterns, themes, and insights within qualitative data. Researchers can rely on MAXQDA to facilitate thorough data interpretation in their thematic analysis.

Yes, Thematic Analysis Software like MAXQDA caters to a wide range of users, regardless of their experience level. MAXQDA’s user-friendly interface and extensive support resources make it accessible to beginners. Simultaneously, its advanced features and capabilities meet the needs of experienced researchers, ensuring a seamless thematic analysis process for all users.

To begin using Thematic Analysis Software like MAXQDA, follow these steps:

• Download and install MAXQDA from the official website.
• Explore the provided tutorials and documentation to become familiar with the software.
• Import your qualitative data files into MAXQDA for analysis.
• Initiate coding and categorization to identify and examine themes.
• Utilize MAXQDA’s visualization tools to gain insights and present thematic findings effectively.
• For any questions or assistance, refer to MAXQDA’s support and community resources.

For students, MAXQDA is an excellent choice for thematic analysis software. It offers a student-friendly pricing model and provides all the necessary features and tools for conducting thorough thematic analysis. MAXQDA’s user-friendly interface and extensive support resources make it an ideal choice for students looking to gain experience in qualitative data analysis.

MAXQDA is available for both Windows and Mac platforms, making it a top choice for Mac users in need of thematic analysis software. It offers a native Mac version, ensuring seamless compatibility and performance on Macintosh computers. So, if you’re a Mac user, MAXQDA is a great thematic analysis solution.

The best thematic analysis software, without a doubt, is MAXQDA. MAXQDA stands out as a comprehensive and versatile tool for conducting thematic analysis. Its feature-rich platform, including advanced coding, categorizing, and visualization capabilities, makes it the preferred choice for researchers worldwide.

Yes, thematic analysis can be conducted manually without software. However, utilizing a thematic analysis software like MAXQDA significantly expedites and enhances the process. Software automates tasks, speeds up data organization, and provides valuable tools for efficient coding and interpretation. MAXQDA makes thematic analysis faster, smarter, and more systematic, allowing researchers to focus on insights rather than tedious manual tasks.

When it comes to thematic analysis AI tools, MAXQDA’s AI Assist tool is a top contender. MAXQDA integrates AI technology to assist researchers in identifying and suggesting potential themes within qualitative data. This innovative feature streamlines the thematic analysis process, making it more efficient and accurate, and sets MAXQDA apart as a leader in AI-powered qualitative analysis.

The world’s most powerful AI-based qualitative data analysis solution.

QualAI utilizes advanced AI technology to increase researcher efficiency, enhance data reliability, and mitigate bias.

researchers

QualAI aids researchers with data codification, thematic analyses, and content summaries to increase data reliability and mitigate bias.

organizations

QualAI helps organizations with market research, consumer analysis, business development, data aggregation and interpretation.

See how QualAI helps students analyze large-scale qualitative data sets, codify transcripts, and generate themes to reduce bias and increase efficiency.

ERIK ALANSON, Ph.d.

Co-Founder, QualAI

Academic Researcher

University Professor

tonkia bridges, ed.d.

Powered by WordPress.com .

• - Google Chrome

Intended for healthcare professionals

• Access provided by Google Indexer
• My email alerts
• BMA member login
• Username * Password * Forgot your log in details? Need to activate BMA Member Log In Log in via OpenAthens Log in via your institution

Search form

• Advanced search
• Search responses
• Search blogs
• Practical thematic...

Practical thematic analysis: a guide for multidisciplinary health services research teams engaging in qualitative analysis

• Related content
• Peer review
• Catherine H Saunders , scientist and assistant professor 1 2 ,
• Ailyn Sierpe , research project coordinator 2 ,
• Christian von Plessen , senior physician 3 ,
• Alice M Kennedy , research project manager 2 4 ,
• Laura C Leviton , senior adviser 5 ,
• Steven L Bernstein , chief research officer 1 ,
• Jenaya Goldwag , resident physician 1 ,
• Joel R King , research assistant 2 ,
• Christine M Marx , patient associate 6 ,
• Jacqueline A Pogue , research project manager 2 ,
• Richard K Saunders , staff physician 1 ,
• Aricca Van Citters , senior research scientist 2 ,
• Renata W Yen , doctoral student 2 ,
• Glyn Elwyn , professor 2 ,
• JoAnna K Leyenaar , associate professor 1 2
• on behalf of the Coproduction Laboratory
• 1 Dartmouth Health, Lebanon, NH, USA
• 2 Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth College, Lebanon, NH, USA
• 3 Center for Primary Care and Public Health (Unisanté), Lausanne, Switzerland
• 4 Jönköping Academy for Improvement of Health and Welfare, School of Health and Welfare, Jönköping University, Jönköping, Sweden
• 5 Highland Park, NJ, USA
• 6 Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St Louis, MO, USA
• Correspondence to: C H Saunders catherine.hylas.saunders{at}dartmouth.edu
• Accepted 26 April 2023

Qualitative research methods explore and provide deep contextual understanding of real world issues, including people’s beliefs, perspectives, and experiences. Whether through analysis of interviews, focus groups, structured observation, or multimedia data, qualitative methods offer unique insights in applied health services research that other approaches cannot deliver. However, many clinicians and researchers hesitate to use these methods, or might not use them effectively, which can leave relevant areas of inquiry inadequately explored. Thematic analysis is one of the most common and flexible methods to examine qualitative data collected in health services research. This article offers practical thematic analysis as a step-by-step approach to qualitative analysis for health services researchers, with a focus on accessibility for patients, care partners, clinicians, and others new to thematic analysis. Along with detailed instructions covering three steps of reading, coding, and theming, the article includes additional novel and practical guidance on how to draft effective codes, conduct a thematic analysis session, and develop meaningful themes. This approach aims to improve consistency and rigor in thematic analysis, while also making this method more accessible for multidisciplinary research teams.

Through qualitative methods, researchers can provide deep contextual understanding of real world issues, and generate new knowledge to inform hypotheses, theories, research, and clinical care. Approaches to data collection are varied, including interviews, focus groups, structured observation, and analysis of multimedia data, with qualitative research questions aimed at understanding the how and why of human experience. 1 2 Qualitative methods produce unique insights in applied health services research that other approaches cannot deliver. In particular, researchers acknowledge that thematic analysis is a flexible and powerful method of systematically generating robust qualitative research findings by identifying, analysing, and reporting patterns (themes) within data. 3 4 5 6 Although qualitative methods are increasingly valued for answering clinical research questions, many researchers are unsure how to apply them or consider them too time consuming to be useful in responding to practical challenges 7 or pressing situations such as public health emergencies. 8 Consequently, researchers might hesitate to use them, or use them improperly. 9 10 11

Although much has been written about how to perform thematic analysis, practical guidance for non-specialists is sparse. 3 5 6 12 13 In the multidisciplinary field of health services research, qualitative data analysis can confound experienced researchers and novices alike, which can stoke concerns about rigor, particularly for those more familiar with quantitative approaches. 14 Since qualitative methods are an area of specialisation, support from experts is beneficial. However, because non-specialist perspectives can enhance data interpretation and enrich findings, there is a case for making thematic analysis easier, more rapid, and more efficient, 8 particularly for patients, care partners, clinicians, and other stakeholders. A practical guide to thematic analysis might encourage those on the ground to use these methods in their work, unearthing insights that would otherwise remain undiscovered.

Given the need for more accessible qualitative analysis approaches, we present a simple, rigorous, and efficient three step guide for practical thematic analysis. We include new guidance on the mechanics of thematic analysis, including developing codes, constructing meaningful themes, and hosting a thematic analysis session. We also discuss common pitfalls in thematic analysis and how to avoid them.

Summary points

Qualitative methods are increasingly valued in applied health services research, but multidisciplinary research teams often lack accessible step-by-step guidance and might struggle to use these approaches

A newly developed approach, practical thematic analysis, uses three simple steps: reading, coding, and theming

Based on Braun and Clarke’s reflexive thematic analysis, our streamlined yet rigorous approach is designed for multidisciplinary health services research teams, including patients, care partners, and clinicians

This article also provides companion materials including a slide presentation for teaching practical thematic analysis to research teams, a sample thematic analysis session agenda, a theme coproduction template for use during the session, and guidance on using standardised reporting criteria for qualitative research

In their seminal work, Braun and Clarke developed a six phase approach to reflexive thematic analysis. 4 12 We built on their method to develop practical thematic analysis ( box 1 , fig 1 ), which is a simplified and instructive approach that retains the substantive elements of their six phases. Braun and Clarke’s phase 1 (familiarising yourself with the dataset) is represented in our first step of reading. Phase 2 (coding) remains as our second step of coding. Phases 3 (generating initial themes), 4 (developing and reviewing themes), and 5 (refining, defining, and naming themes) are represented in our third step of theming. Phase 6 (writing up) also occurs during this third step of theming, but after a thematic analysis session. 4 12

Key features and applications of practical thematic analysis

Step 1: reading.

All manuscript authors read the data

All manuscript authors write summary memos

Step 2: Coding

Coders perform both data management and early data analysis

Codes are complete thoughts or sentences, not categories

Step 3: Theming

Researchers host a thematic analysis session and share different perspectives

Themes are complete thoughts or sentences, not categories

Applications

For use by practicing clinicians, patients and care partners, students, interdisciplinary teams, and those new to qualitative research

When important insights from healthcare professionals are inaccessible because they do not have qualitative methods training

When time and resources are limited

Steps in practical thematic analysis

• Download figure
• Open in new tab
• Download powerpoint

We present linear steps, but as qualitative research is usually iterative, so too is thematic analysis. 15 Qualitative researchers circle back to earlier work to check whether their interpretations still make sense in the light of additional insights, adapting as necessary. While we focus here on the practical application of thematic analysis in health services research, we recognise our approach exists in the context of the broader literature on thematic analysis and the theoretical underpinnings of qualitative methods as a whole. For a more detailed discussion of these theoretical points, as well as other methods widely used in health services research, we recommend reviewing the sources outlined in supplemental material 1. A strong and nuanced understanding of the context and underlying principles of thematic analysis will allow for higher quality research. 16

Practical thematic analysis is a highly flexible approach that can draw out valuable findings and generate new hypotheses, including in cases with a lack of previous research to build on. The approach can also be used with a variety of data, such as transcripts from interviews or focus groups, patient encounter transcripts, professional publications, observational field notes, and online activity logs. Importantly, successful practical thematic analysis is predicated on having high quality data collected with rigorous methods. We do not describe qualitative research design or data collection here. 11 17

In supplemental material 1, we summarise the foundational methods, concepts, and terminology in qualitative research. Along with our guide below, we include a companion slide presentation for teaching practical thematic analysis to research teams in supplemental material 2. We provide a theme coproduction template for teams to use during thematic analysis sessions in supplemental material 3. Our method aligns with the major qualitative reporting frameworks, including the Consolidated Criteria for Reporting Qualitative Research (COREQ). 18 We indicate the corresponding step in practical thematic analysis for each COREQ item in supplemental material 4.

Familiarisation and memoing

We encourage all manuscript authors to review the full dataset (eg, interview transcripts) to familiarise themselves with it. This task is most critical for those who will later be engaged in the coding and theming steps. Although time consuming, it is the best way to involve team members in the intellectual work of data interpretation, so that they can contribute to the analysis and contextualise the results. If this task is not feasible given time limitations or large quantities of data, the data can be divided across team members. In this case, each piece of data should be read by at least two individuals who ideally represent different professional roles or perspectives.

We recommend that researchers reflect on the data and independently write memos, defined as brief notes on thoughts and questions that arise during reading, and a summary of their impressions of the dataset. 2 19 Memoing is an opportunity to gain insights from varying perspectives, particularly from patients, care partners, clinicians, and others. It also gives researchers the opportunity to begin to scope which elements of and concepts in the dataset are relevant to the research question.

Data saturation

The concept of data saturation ( box 2 ) is a foundation of qualitative research. It is defined as the point in analysis at which new data tend to be redundant of data already collected. 21 Qualitative researchers are expected to report their approach to data saturation. 18 Because thematic analysis is iterative, the team should discuss saturation throughout the entire process, beginning with data collection and continuing through all steps of the analysis. 22 During step 1 (reading), team members might discuss data saturation in the context of summary memos. Conversations about saturation continue during step 2 (coding), with confirmation that saturation has been achieved during step 3 (theming). As a rule of thumb, researchers can often achieve saturation in 9-17 interviews or 4-8 focus groups, but this will vary depending on the specific characteristics of the study. 23

Data saturation in context

Braun and Clarke discourage the use of data saturation to determine sample size (eg, number of interviews), because it assumes that there is an objective truth to be captured in the data (sometimes known as a positivist perspective). 20 Qualitative researchers often try to avoid positivist approaches, arguing that there is no one true way of seeing the world, and will instead aim to gather multiple perspectives. 5 Although this theoretical debate with qualitative methods is important, we recognise that a priori estimates of saturation are often needed, particularly for investigators newer to qualitative research who might want a more pragmatic and applied approach. In addition, saturation based, sample size estimation can be particularly helpful in grant proposals. However, researchers should still follow a priori sample size estimation with a discussion to confirm saturation has been achieved.

Definition of coding

We describe codes as labels for concepts in the data that are directly relevant to the study objective. Historically, the purpose of coding was to distil the large amount of data collected into conceptually similar buckets so that researchers could review it in aggregate and identify key themes. 5 24 We advocate for a more analytical approach than is typical with thematic analysis. With our method, coding is both the foundation for and the beginning of thematic analysis—that is, early data analysis, management, and reduction occur simultaneously rather than as different steps. This approach moves the team more efficiently towards being able to describe themes.

Building the coding team

Coders are the research team members who directly assign codes to the data, reading all material and systematically labelling relevant data with appropriate codes. Ideally, at least two researchers would code every discrete data document, such as one interview transcript. 25 If this task is not possible, individual coders can each code a subset of the data that is carefully selected for key characteristics (sometimes known as purposive selection). 26 When using this approach, we recommend that at least 10% of data be coded by two or more coders to ensure consistency in codebook application. We also recommend coding teams of no more than four to five people, for practical reasons concerning maintaining consistency.

Clinicians, patients, and care partners bring unique perspectives to coding and enrich the analytical process. 27 Therefore, we recommend choosing coders with a mix of relevant experiences so that they can challenge and contextualise each other’s interpretations based on their own perspectives and opinions ( box 3 ). We recommend including both coders who collected the data and those who are naive to it, if possible, given their different perspectives. We also recommend all coders review the summary memos from the reading step so that key concepts identified by those not involved in coding can be integrated into the analytical process. In practice, this review means coding the memos themselves and discussing them during the code development process. This approach ensures that the team considers a diversity of perspectives.

Coding teams in context

The recommendation to use multiple coders is a departure from Braun and Clarke. 28 29 When the views, experiences, and training of each coder (sometimes known as positionality) 30 are carefully considered, having multiple coders can enhance interpretation and enrich findings. When these perspectives are combined in a team setting, researchers can create shared meaning from the data. Along with the practical consideration of distributing the workload, 31 inclusion of these multiple perspectives increases the overall quality of the analysis by mitigating the impact of any one coder’s perspective. 30

Coding tools

Qualitative analysis software facilitates coding and managing large datasets but does not perform the analytical work. The researchers must perform the analysis themselves. Most programs support queries and collaborative coding by multiple users. 32 Important factors to consider when choosing software can include accessibility, cost, interoperability, the look and feel of code reports, and the ease of colour coding and merging codes. Coders can also use low tech solutions, including highlighters, word processors, or spreadsheets.

Drafting effective codes

To draft effective codes, we recommend that the coders review each document line by line. 33 As they progress, they can assign codes to segments of data representing passages of interest. 34 Coders can also assign multiple codes to the same passage. Consensus among coders on what constitutes a minimum or maximum amount of text for assigning a code is helpful. As a general rule, meaningful segments of text for coding are shorter than one paragraph, but longer than a few words. Coders should keep the study objective in mind when determining which data are relevant ( box 4 ).

Code types in context

Similar to Braun and Clarke’s approach, practical thematic analysis does not specify whether codes are based on what is evident from the data (sometimes known as semantic) or whether they are based on what can be inferred at a deeper level from the data (sometimes known as latent). 4 12 35 It also does not specify whether they are derived from the data (sometimes known as inductive) or determined ahead of time (sometimes known as deductive). 11 35 Instead, it should be noted that health services researchers conducting qualitative studies often adopt all these approaches to coding (sometimes known as hybrid analysis). 3

In practical thematic analysis, codes should be more descriptive than general categorical labels that simply group data with shared characteristics. At a minimum, codes should form a complete (or full) thought. An easy way to conceptualise full thought codes is as complete sentences with subjects and verbs ( table 1 ), although full sentence coding is not always necessary. With full thought codes, researchers think about the data more deeply and capture this insight in the codes. This coding facilitates the entire analytical process and is especially valuable when moving from codes to broader themes. Experienced qualitative researchers often intuitively use full thought or sentence codes, but this practice has not been explicitly articulated as a path to higher quality coding elsewhere in the literature. 6

Example transcript with codes used in practical thematic analysis 36

• View inline

Depending on the nature of the data, codes might either fall into flat categories or be arranged hierarchically. Flat categories are most common when the data deal with topics on the same conceptual level. In other words, one topic is not a subset of another topic. By contrast, hierarchical codes are more appropriate for concepts that naturally fall above or below each other. Hierarchical coding can also be a useful form of data management and might be necessary when working with a large or complex dataset. 5 Codes grouped into these categories can also make it easier to naturally transition into generating themes from the initial codes. 5 These decisions between flat versus hierarchical coding are part of the work of the coding team. In both cases, coders should ensure that their code structures are guided by their research questions.

Developing the codebook

A codebook is a shared document that lists code labels and comprehensive descriptions for each code, as well as examples observed within the data. Good code descriptions are precise and specific so that coders can consistently assign the same codes to relevant data or articulate why another coder would do so. Codebook development is iterative and involves input from the entire coding team. However, as those closest to the data, coders must resist undue influence, real or perceived, from other team members with conflicting opinions—it is important to mitigate the risk that more senior researchers, like principal investigators, exert undue influence on the coders’ perspectives.

In practical thematic analysis, coders begin codebook development by independently coding a small portion of the data, such as two to three transcripts or other units of analysis. Coders then individually produce their initial codebooks. This task will require them to reflect on, organise, and clarify codes. The coders then meet to reconcile the draft codebooks, which can often be difficult, as some coders tend to lump several concepts together while others will split them into more specific codes. Discussing disagreements and negotiating consensus are necessary parts of early data analysis. Once the codebook is relatively stable, we recommend soliciting input on the codes from all manuscript authors. Yet, coders must ultimately be empowered to finalise the details so that they are comfortable working with the codebook across a large quantity of data.

Assigning codes to the data

After developing the codebook, coders will use it to assign codes to the remaining data. While the codebook’s overall structure should remain constant, coders might continue to add codes corresponding to any new concepts observed in the data. If new codes are added, coders should review the data they have already coded and determine whether the new codes apply. Qualitative data analysis software can be useful for editing or merging codes.

We recommend that coders periodically compare their code occurrences ( box 5 ), with more frequent check-ins if substantial disagreements occur. In the event of large discrepancies in the codes assigned, coders should revise the codebook to ensure that code descriptions are sufficiently clear and comprehensive to support coding alignment going forward. Because coding is an iterative process, the team can adjust the codebook as needed. 5 28 29

Quantitative coding in context

Researchers should generally avoid reporting code counts in thematic analysis. However, counts can be a useful proxy in maintaining alignment between coders on key concepts. 26 In practice, therefore, researchers should make sure that all coders working on the same piece of data assign the same codes with a similar pattern and that their memoing and overall assessment of the data are aligned. 37 However, the frequency of a code alone is not an indicator of its importance. It is more important that coders agree on the most salient points in the data; reviewing and discussing summary memos can be helpful here. 5

Researchers might disagree on whether or not to calculate and report inter-rater reliability. We note that quantitative tests for agreement, such as kappa statistics or intraclass correlation coefficients, can be distracting and might not provide meaningful results in qualitative analyses. Similarly, Braun and Clarke argue that expecting perfect alignment on coding is inconsistent with the goal of co-constructing meaning. 28 29 Overall consensus on codes’ salience and contributions to themes is the most important factor.

Definition of themes

Themes are meta-constructs that rise above codes and unite the dataset ( box 6 , fig 2 ). They should be clearly evident, repeated throughout the dataset, and relevant to the research questions. 38 While codes are often explicit descriptions of the content in the dataset, themes are usually more conceptual and knit the codes together. 39 Some researchers hypothesise that theme development is loosely described in the literature because qualitative researchers simply intuit themes during the analytical process. 39 In practical thematic analysis, we offer a concrete process that should make developing meaningful themes straightforward.

Themes in context

According to Braun and Clarke, a theme “captures something important about the data in relation to the research question and represents some level of patterned response or meaning within the data set.” 4 Similarly, Braun and Clarke advise against themes as domain summaries. While different approaches can draw out themes from codes, the process begins by identifying patterns. 28 35 Like Braun and Clarke and others, we recommend that researchers consider the salience of certain themes, their prevalence in the dataset, and their keyness (ie, how relevant the themes are to the overarching research questions). 4 12 34

Use of themes in practical thematic analysis

Constructing meaningful themes

After coding all the data, each coder should independently reflect on the team’s summary memos (step 1), the codebook (step 2), and the coded data itself to develop draft themes (step 3). It can be illuminating for coders to review all excerpts associated with each code, so that they derive themes directly from the data. Researchers should remain focused on the research question during this step, so that themes have a clear relation with the overall project aim. Use of qualitative analysis software will make it easy to view each segment of data tagged with each code. Themes might neatly correspond to groups of codes. Or—more likely—they will unite codes and data in unexpected ways. A whiteboard or presentation slides might be helpful to organise, craft, and revise themes. We also provide a template for coproducing themes (supplemental material 3). As with codebook justification, team members will ideally produce individual drafts of the themes that they have identified in the data. They can then discuss these with the group and reach alignment or consensus on the final themes.

The team should ensure that all themes are salient, meaning that they are: supported by the data, relevant to the study objectives, and important. Similar to codes, themes are framed as complete thoughts or sentences, not categories. While codes and themes might appear to be similar to each other, the key distinction is that the themes represent a broader concept. Table 2 shows examples of codes and their corresponding themes from a previously published project that used practical thematic analysis. 36 Identifying three to four key themes that comprise a broader overarching theme is a useful approach. Themes can also have subthemes, if appropriate. 40 41 42 43 44

Example codes with themes in practical thematic analysis 36

Thematic analysis session

After each coder has independently produced draft themes, a carefully selected subset of the manuscript team meets for a thematic analysis session ( table 3 ). The purpose of this session is to discuss and reach alignment or consensus on the final themes. We recommend a session of three to five hours, either in-person or virtually.

Example agenda of thematic analysis session

The composition of the thematic analysis session team is important, as each person’s perspectives will shape the results. This group is usually a small subset of the broader research team, with three to seven individuals. We recommend that primary and senior authors work together to include people with diverse experiences related to the research topic. They should aim for a range of personalities and professional identities, particularly those of clinicians, trainees, patients, and care partners. At a minimum, all coders and primary and senior authors should participate in the thematic analysis session.

The session begins with each coder presenting their draft themes with supporting quotes from the data. 5 Through respectful and collaborative deliberation, the group will develop a shared set of final themes.

One team member facilitates the session. A firm, confident, and consistent facilitation style with good listening skills is critical. For practical reasons, this person is not usually one of the primary coders. Hierarchies in teams cannot be entirely flattened, but acknowledging them and appointing an external facilitator can reduce their impact. The facilitator can ensure that all voices are heard. For example, they might ask for perspectives from patient partners or more junior researchers, and follow up on comments from senior researchers to say, “We have heard your perspective and it is important; we want to make sure all perspectives in the room are equally considered.” Or, “I hear [senior person] is offering [x] idea, I’d like to hear other perspectives in the room.” The role of the facilitator is critical in the thematic analysis session. The facilitator might also privately discuss with more senior researchers, such as principal investigators and senior authors, the importance of being aware of their influence over others and respecting and eliciting the perspectives of more junior researchers, such as patients, care partners, and students.

To our knowledge, this discrete thematic analysis session is a novel contribution of practical thematic analysis. It helps efficiently incorporate diverse perspectives using the session agenda and theme coproduction template (supplemental material 3) and makes the process of constructing themes transparent to the entire research team.

Writing the report

We recommend beginning the results narrative with a summary of all relevant themes emerging from the analysis, followed by a subheading for each theme. Each subsection begins with a brief description of the theme and is illustrated with relevant quotes, which are contextualised and explained. The write-up should not simply be a list, but should contain meaningful analysis and insight from the researchers, including descriptions of how different stakeholders might have experienced a particular situation differently or unexpectedly.

In addition to weaving quotes into the results narrative, quotes can be presented in a table. This strategy is a particularly helpful when submitting to clinical journals with tight word count limitations. Quote tables might also be effective in illustrating areas of agreement and disagreement across stakeholder groups, with columns representing different groups and rows representing each theme or subtheme. Quotes should include an anonymous label for each participant and any relevant characteristics, such as role or gender. The aim is to produce rich descriptions. 5 We recommend against repeating quotations across multiple themes in the report, so as to avoid confusion. The template for coproducing themes (supplemental material 3) allows documentation of quotes supporting each theme, which might also be useful during report writing.

Visual illustrations such as a thematic map or figure of the findings can help communicate themes efficiently. 4 36 42 44 If a figure is not possible, a simple list can suffice. 36 Both must clearly present the main themes with subthemes. Thematic figures can facilitate confirmation that the researchers’ interpretations reflect the study populations’ perspectives (sometimes known as member checking), because authors can invite discussions about the figure and descriptions of findings and supporting quotes. 46 This process can enhance the validity of the results. 46

In supplemental material 4, we provide additional guidance on reporting thematic analysis consistent with COREQ. 18 Commonly used in health services research, COREQ outlines a standardised list of items to be included in qualitative research reports ( box 7 ).

Reporting in context

We note that use of COREQ or any other reporting guidelines does not in itself produce high quality work and should not be used as a substitute for general methodological rigor. Rather, researchers must consider rigor throughout the entire research process. As the issue of how to conceptualise and achieve rigorous qualitative research continues to be debated, 47 48 we encourage researchers to explicitly discuss how they have looked at methodological rigor in their reports. Specifically, we point researchers to Braun and Clarke’s 2021 tool for evaluating thematic analysis manuscripts for publication (“Twenty questions to guide assessment of TA [thematic analysis] research quality”). 16

Avoiding common pitfalls

Awareness of common mistakes can help researchers avoid improper use of qualitative methods. Improper use can, for example, prevent researchers from developing meaningful themes and can risk drawing inappropriate conclusions from the data. Braun and Clarke also warn of poor quality in qualitative research, noting that “coherence and integrity of published research does not always hold.” 16

Weak themes

An important distinction between high and low quality themes is that high quality themes are descriptive and complete thoughts. As such, they often contain subjects and verbs, and can be expressed as full sentences ( table 2 ). Themes that are simply descriptive categories or topics could fail to impart meaningful knowledge beyond categorisation. 16 49 50

Researchers will often move from coding directly to writing up themes, without performing the work of theming or hosting a thematic analysis session. Skipping concerted theming often results in themes that look more like categories than unifying threads across the data.

Unfocused analysis

Because data collection for qualitative research is often semi-structured (eg, interviews, focus groups), not all data will be directly relevant to the research question at hand. To avoid unfocused analysis and a correspondingly unfocused manuscript, we recommend that all team members keep the research objective in front of them at every stage, from reading to coding to theming. During the thematic analysis session, we recommend that the research question be written on a whiteboard so that all team members can refer back to it, and so that the facilitator can ensure that conversations about themes occur in the context of this question. Consistently focusing on the research question can help to ensure that the final report directly answers it, as opposed to the many other interesting insights that might emerge during the qualitative research process. Such insights can be picked up in a secondary analysis if desired.

Inappropriate quantification

Presenting findings quantitatively (eg, “We found 18 instances of participants mentioning safety concerns about the vaccines”) is generally undesirable in practical thematic analysis reporting. 51 Descriptive terms are more appropriate (eg, “participants had substantial concerns about the vaccines,” or “several participants were concerned about this”). This descriptive presentation is critical because qualitative data might not be consistently elicited across participants, meaning that some individuals might share certain information while others do not, simply based on how conversations evolve. Additionally, qualitative research does not aim to draw inferences outside its specific sample. Emphasising numbers in thematic analysis can lead to readers incorrectly generalising the findings. Although peer reviewers unfamiliar with thematic analysis often request this type of quantification, practitioners of practical thematic analysis can confidently defend their decision to avoid it. If quantification is methodologically important, we recommend simultaneously conducting a survey or incorporating standardised interview techniques into the interview guide. 11

Neglecting group dynamics

Researchers should concertedly consider group dynamics in the research team. Particular attention should be paid to power relations and the personality of team members, which can include aspects such as who most often speaks, who defines concepts, and who resolves disagreements that might arise within the group. 52

The perspectives of patient and care partners are particularly important to cultivate. Ideally, patient partners are meaningfully embedded in studies from start to finish, not just for practical thematic analysis. 53 Meaningful engagement can build trust, which makes it easier for patient partners to ask questions, request clarification, and share their perspectives. Professional team members should actively encourage patient partners by emphasising that their expertise is critically important and valued. Noting when a patient partner might be best positioned to offer their perspective can be particularly powerful.

Insufficient time allocation

Researchers must allocate enough time to complete thematic analysis. Working with qualitative data takes time, especially because it is often not a linear process. As the strength of thematic analysis lies in its ability to make use of the rich details and complexities of the data, we recommend careful planning for the time required to read and code each document.

Estimating the necessary time can be challenging. For step 1 (reading), researchers can roughly calculate the time required based on the time needed to read and reflect on one piece of data. For step 2 (coding), the total amount of time needed can be extrapolated from the time needed to code one document during codebook development. We also recommend three to five hours for the thematic analysis session itself, although coders will need to independently develop their draft themes beforehand. Although the time required for practical thematic analysis is variable, teams should be able to estimate their own required effort with these guidelines.

Practical thematic analysis builds on the foundational work of Braun and Clarke. 4 16 We have reframed their six phase process into three condensed steps of reading, coding, and theming. While we have maintained important elements of Braun and Clarke’s reflexive thematic analysis, we believe that practical thematic analysis is conceptually simpler and easier to teach to less experienced researchers and non-researcher stakeholders. For teams with different levels of familiarity with qualitative methods, this approach presents a clear roadmap to the reading, coding, and theming of qualitative data. Our practical thematic analysis approach promotes efficient learning by doing—experiential learning. 12 29 Practical thematic analysis avoids the risk of relying on complex descriptions of methods and theory and places more emphasis on obtaining meaningful insights from those close to real world clinical environments. Although practical thematic analysis can be used to perform intensive theory based analyses, it lends itself more readily to accelerated, pragmatic approaches.

Strengths and limitations

Our approach is designed to smooth the qualitative analysis process and yield high quality themes. Yet, researchers should note that poorly performed analyses will still produce low quality results. Practical thematic analysis is a qualitative analytical approach; it does not look at study design, data collection, or other important elements of qualitative research. It also might not be the right choice for every qualitative research project. We recommend it for applied health services research questions, where diverse perspectives and simplicity might be valuable.

We also urge researchers to improve internal validity through triangulation methods, such as member checking (supplemental material 1). 46 Member checking could include soliciting input on high level themes, theme definitions, and quotations from participants. This approach might increase rigor.

Implications

We hope that by providing clear and simple instructions for practical thematic analysis, a broader range of researchers will be more inclined to use these methods. Increased transparency and familiarity with qualitative approaches can enhance researchers’ ability to both interpret qualitative studies and offer up new findings themselves. In addition, it can have usefulness in training and reporting. A major strength of this approach is to facilitate meaningful inclusion of patient and care partner perspectives, because their lived experiences can be particularly valuable in data interpretation and the resulting findings. 11 30 As clinicians are especially pressed for time, they might also appreciate a practical set of instructions that can be immediately used to leverage their insights and access to patients and clinical settings, and increase the impact of qualitative research through timely results. 8

Practical thematic analysis is a simplified approach to performing thematic analysis in health services research, a field where the experiences of patients, care partners, and clinicians are of inherent interest. We hope that it will be accessible to those individuals new to qualitative methods, including patients, care partners, clinicians, and other health services researchers. We intend to empower multidisciplinary research teams to explore unanswered questions and make new, important, and rigorous contributions to our understanding of important clinical and health systems research.

Acknowledgments

All members of the Coproduction Laboratory provided input that shaped this manuscript during laboratory meetings. We acknowledge advice from Elizabeth Carpenter-Song, an expert in qualitative methods.

Coproduction Laboratory group contributors: Stephanie C Acquilano ( http://orcid.org/0000-0002-1215-5531 ), Julie Doherty ( http://orcid.org/0000-0002-5279-6536 ), Rachel C Forcino ( http://orcid.org/0000-0001-9938-4830 ), Tina Foster ( http://orcid.org/0000-0001-6239-4031 ), Megan Holthoff, Christopher R Jacobs ( http://orcid.org/0000-0001-5324-8657 ), Lisa C Johnson ( http://orcid.org/0000-0001-7448-4931 ), Elaine T Kiriakopoulos, Kathryn Kirkland ( http://orcid.org/0000-0002-9851-926X ), Meredith A MacMartin ( http://orcid.org/0000-0002-6614-6091 ), Emily A Morgan, Eugene Nelson, Elizabeth O’Donnell, Brant Oliver ( http://orcid.org/0000-0002-7399-622X ), Danielle Schubbe ( http://orcid.org/0000-0002-9858-1805 ), Gabrielle Stevens ( http://orcid.org/0000-0001-9001-178X ), Rachael P Thomeer ( http://orcid.org/0000-0002-5974-3840 ).

Contributors: Practical thematic analysis, an approach designed for multidisciplinary health services teams new to qualitative research, was based on CHS’s experiences teaching thematic analysis to clinical teams and students. We have drawn heavily from qualitative methods literature. CHS is the guarantor of the article. CHS, AS, CvP, AMK, JRK, and JAP contributed to drafting the manuscript. AS, JG, CMM, JAP, and RWY provided feedback on their experiences using practical thematic analysis. CvP, LCL, SLB, AVC, GE, and JKL advised on qualitative methods in health services research, given extensive experience. All authors meaningfully edited the manuscript content, including AVC and RKS. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding: This manuscript did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Competing interests: All authors have completed the ICMJE uniform disclosure form at https://www.icmje.org/disclosure-of-interest/ and declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

Provenance and peer review: Not commissioned; externally peer reviewed.

• Ziebland S ,
• ↵ A Hybrid Approach to Thematic Analysis in Qualitative Research: Using a Practical Example. 2018. https://methods.sagepub.com/case/hybrid-approach-thematic-analysis-qualitative-research-a-practical-example .
• Maguire M ,
• Vindrola-Padros C ,
• Vindrola-Padros B
• ↵ Vindrola-Padros C. Rapid Ethnographies: A Practical Guide . Cambridge University Press 2021. https://play.google.com/store/books/details?id=n80HEAAAQBAJ
• Schroter S ,
• Merino JG ,
• Barbeau A ,
• ↵ Padgett DK. Qualitative and Mixed Methods in Public Health . SAGE Publications 2011. https://play.google.com/store/books/details?id=LcYgAQAAQBAJ
• Scharp KM ,
• Korstjens I
• Barnett-Page E ,
• ↵ Guest G, Namey EE, Mitchell ML. Collecting Qualitative Data: A Field Manual for Applied Research . SAGE 2013. https://play.google.com/store/books/details?id=-3rmWYKtloC
• Sainsbury P ,
• Emerson RM ,
• Saunders B ,
• Kingstone T ,
• Hennink MM ,
• Kaiser BN ,
• Hennink M ,
• O’Connor C ,
• ↵ Yen RW, Schubbe D, Walling L, et al. Patient engagement in the What Matters Most trial: experiences and future implications for research. Poster presented at International Shared Decision Making conference, Quebec City, Canada. July 2019.
• ↵ Got questions about Thematic Analysis? We have prepared some answers to common ones. https://www.thematicanalysis.net/faqs/ (accessed 9 Nov 2022).
• ↵ Braun V, Clarke V. Thematic Analysis. SAGE Publications. 2022. https://uk.sagepub.com/en-gb/eur/thematic-analysis/book248481 .
• Kalpokas N ,
• Radivojevic I
• Campbell KA ,
• Durepos P ,
• ↵ Understanding Thematic Analysis. https://www.thematicanalysis.net/understanding-ta/ .
• Saunders CH ,
• Stevens G ,
• CONFIDENT Study Long-Term Care Partners
• MacQueen K ,
• Vaismoradi M ,
• Turunen H ,
• Schott SL ,
• Berkowitz J ,
• Carpenter-Song EA ,
• Goldwag JL ,
• Durand MA ,
• Goldwag J ,
• Saunders C ,
• Mishra MK ,
• Rodriguez HP ,
• Shortell SM ,
• Verdinelli S ,
• Scagnoli NI
• Campbell C ,
• Sparkes AC ,
• McGannon KR
• Sandelowski M ,
• Connelly LM ,
• O’Malley AJ ,

• AI & NLP
• Churn & Loyalty
• Customer Experience
• Customer Journeys
• Customer Metrics
• Feedback Analysis
• Product Experience
• Product Updates
• Sentiment Analysis
• Surveys & Feedback Collection
• Try Thematic

Welcome to the community

Thematic Analysis Software: how it works and why you need It

Most likely, you landed in this blog because you have too much feedback to easily analyze. You’d think you could never get enough of a good thing, but once you get to a certain point, there’s just too much to analyze, especially if you're doing manual analysis .  

Maybe you sent out a survey or collected reviews. Maybe your customers are using your contact form to send in opinions as free-text feedback.  Now that you have this mountain of feedback available, what do you do with it? Is there any easy way to identify common themes in responses? How can you create a clear and meaningful report to turn feedback into action?

Many businesses avoid asking open-ended questions in surveys. Analysis of free text comments can be time-consuming and expensive. Traditional methods involve hours of sorting through a wall of text in a spreadsheet , coding each text response by hand. This takes precious headcount and a ton of manual effort.

Some end up spending thousands on old-school text analytics software without meaningful outcomes.

Is there a more efficient, less expensive way to derive insights from your customer feedback ? There is, and it's called thematic analysis software.

If you are only interested in manually analyzing your feedback, check out our guide: How to analyze your feedback in 10 minutes using word spotting. Or, download our toolkit which includes a spreadsheet template to help you get started.

We've also included a key takeaways section and a FAQs section at the end of the post - feel free to skip ahead !

What is thematic analysis?

Thematic analysis is a form of qualitative data analysis . The output of the analysis is a list of themes mentioned in text. These themes are discovered by analyzing word and the sentence structures.

For example, let's take these 3 sentences:

These comments include both positive and negative sentiment. There are two key themes expressed in different words:

• The helpfulness of flight attendants
• Customers needed help setting up a baby cot

Thematic analysis can be applied to any text. Interviews, conversations, product feature requests, and open-ended questions in surveys or reviews are all well suited to this analytical method and can be fed directly into thematic analysis software.

In this article, we'll focus on the thematic analysis of feedback collected at scale. Applying thematic analysis to large quantities of feedback helps quantify themes that impact business metrics. This is the important first step to any data-driven change and the continuous improvement we all want so much.

Thematic analysis vs. sentiment analysis

Thematic analysis and sentiment analysis is not an either-or. In fact, sentiment analysis is often a part of a thematic analysis solution.

Sentiment analysis captures how positive or negative the language is. It finds emotionally charged themes and helps separate them during a review.  In our three flight attendant reviews, we saw one positive and two negative mentions of a theme:

If you only had sentiment analysis, you would know that one person was happy and two unhappy. Thematic analysis tells you what they were happy or unhappy about. Combining thematic and semantic analysis in qualitative data analysis software results in better accuracy and nuance.

We've previously shared how thematic analysis compares to sentiment analysis . If you’d like to read up more on sentiment analysis, here is a comprehensive guide .

What is thematic analysis software?

Thematic analysis software automates thematic analysis. Some software combines human input with algorithmic analysis. More on this below.

Businesses use thematic analysis software for finding themes in customer feedback. Do your customers rate comfort over affordability? Would they pay more for faster service?

Thematic analysis software can help you find (and act on) the answers.

The best thematic analysis software is autonomous, meaning:

• You don’t need to set up themes or categories in advance,
• You don’t need to train the algorithm — it learns on its own,
• You can easily capture the “unknown unknowns” to identify themes you may not have spotted on your own.

Want to see an example? You can trial Thematic for free here.

How does thematic analysis software work?

Here’s how thematic analysis software automatically analyzes customer feedback to identify and extract themes.

Natural language processing (NLP)

Natural language processing (NLP) is a subcategory of Linguistics and AI. NLP enables computers to analyze large amounts of natural language, aka text.

Thematic analysis software uses natural language processing to find themes in a text. Often, this software also displays that analysis in analytic tools and dashboards.

When a computer attempts to model the meaning of words, sentences, and text, we call it natural language understanding , or NLU.

NLU is a sub-area of natural language processing (NLP). Some NLP tasks, e.g. figuring out a part of speech of a word, might not need to model word meanings for accurate results. But when it comes to thematic analysis, NLU is important. It helps derive the meaning of words used in customer feedback. For example, it can capture that "accommodating" and "helpful" mean the same thing.

What about text analytics ? This term is a more common way of referring to NLP and NLU in business settings.

How NLP is used in thematic analysis software

The goal of thematic analysis software is to automate theme discovery in text. Natural language understanding (NLU) is an important component of this process. This is different from applying text categorization, which simply puts text into buckets. NLU helps discover themes from the bottom up.

This helps us find “unknown unknowns”. These are recurrent points in feedback that you may not have considered. By finding these themes and tracking them over time, you can use your feedback to inform business decisions.

Algorithms can sometimes have a difficult time parsing negation. For example, imagine a customer responds to your survey with, “There’s nothing I did not like!” Many solutions will see “did not like” and categorize the feedback as negative.

The best thematic analysis software uses deep learning to recognize positive feedback, even if it’s couched in negative language.

Word embeddings

Word embeddings is a deep learning algorithm that finds similar words and phrases in text data. It analyzes occurrences of words across thousands of sentences and spits out a model.

One way of thinking about it would be that a word embeddings model translates our language (a vocabulary) to a computer’s language (vectors).

Thematic uses a custom word embeddings implementation to turn feedback into a hierarchy of themes :

Why you need thematic analysis software

Now that you know what thematic analysis software does, what about the why ?

Here’s how companies can benefit from adding thematic analysis software to their tech stack:

Save time and increase accuracy

When you’re running a business, time is a scarce resource. Thematic analysis software can save your team hundreds of hours a year and prevent them from making wrong decisions.

Many companies still analyze feedback using Excel. This is time-consuming and not scaleable, even for small businesses. Thematic analysis software will help you be more effective.

Thematic analysis software can also help avoid human errors. When people look at a dataset, they tend to view it through the lens of their own experiences and biases. They also might miss something unintentionally.

For example, we once tested Thematic against a human coder, Kate, when analyzing student feedback at a university.

Thematic found that students wanted better food/lunch options. Kate found the same issue, but at a much lower frequency. Why? When Kate looked at the student feedback, she tagged only one key issue per comment. Thematic tagged every issue mentioned in each student's comment. Once the university took improved food on campus, student satisfaction increased.

By using thematic analysis software, coders like Kate no longer have to code feedback. Instead, they can use their expertise to interpret the results and drive actions.

Quantify customer feedback

When we talk about quantitative customer feedback, metrics like Net Promoter Score (NPS) often come to mind. And while NPS scores can be useful snapshots of customer satisfaction, they don’t always tell the whole story.

Why did one of your most loyal customers rate you an 8 instead of a 10? Why did a detractor churn, while a promoter doubled their orders per month? What impact on NPS will we see by taking an action to address a specific customer pain point?

Thematic analysis software helps you find these answers. It turns text feedback into the hard data you need to report and measure the success of an initiative.

“This has made it much easier to get projects across the line, with hard data that we can use to measure success of an initiative." - said one Thematic user on G2 Crowd .

“Better yet, we can see how specific themes impact NPS scores!” - shared another.

Make data-driven decisions and track results

Thematic analysis software can turn feedback into hard data not only for making decisions but also for tracking progress.  

This allows you to see whether actions you’ve taken are making a difference in real-time, and allows you to adjust your response and fine-tune your solution. In many situations, this can save an incredible amount of time and money.

Let's go back to our university example above. To start with, a high percentage of students disliked campus food. The university put initiatives in place to address this, then they re-surveyed students.

Thematic analysis will show whether students noticed, and what other issues are now on the rise.  Here is an example of how Thematic visualizes this in its platform.

We see that by and large the university’s efforts were successful. If they’d like to continue working on student satisfaction, they'll need to dig in to what's causing issues with computer equipment, getting to campus, and social events.  

How to choose thematic analysis software

There are many qualitative data analysis software options on the market, but they don’t all perform to the same standards. Before you decide which text analysis solution to adopt, you’ll want to research both features and capabilities of each possibility on your shortlist.

Some software focuses more on audio files or interview transcripts, providing qualitative data analysis for focus groups and product research projects.  Others are more flexible, geared toward the continuous flow of customer reviews, support requests, and NPS surveys.

We've put together a free buyer's guide to help ensure you get what you need from a feedback analysis solution. Learn what to look for when trialing different solutions, so you can avoid buying a product that demos well but won't get you the depth of insights you need.

3 examples of thematic analysis software

Depending on your use case, you might want to use a different thematic analysis software. What's important is that it meets the individual needs of your company. Below, we describe our solution, Thematic, as well as two other highly rated solutions.

1. Thematic

We built Thematic specifically for automated feedback analysis. It's best suited for anyone who collects feedback from many different sources such as surveys, live chat, complaints, and reviews.

And this feedback-focused approach works: 87% of our customers increase their NPS by at least 8 points after using Thematic.

How Thematic works: combining AI with a human touch:

Since every business is different, Thematic is customizable on every level. Powerful AI combines with your unique expertise to create a personalized experienced that fits like a glove. There’s no one size fits all. All themes are discovered through thematic analysis and are custom for each dataset.

In the Themes Editor , you can adjust themes to make results more relevant to your business’s goals and priorities.

This combination of AI, NLP, and a human touch provides you with a list of themes that is:

• Contextually accurate,
• Varied enough to cover all of the topics in your dataset,
• Meaningful for you and your priorities.

Once you have your themes list, Thematic displays your analysis through customizable dashboards and analytical tools. These tools let you:

• Measure the importance of each theme,
• Compare each theme across different segments of your data, such as demographics or tenure,
• Calculate each theme’s impact is on metrics like satisfaction, loyalty, churn, and spend.

Here’s what our process looks like:

We give you the time and tools to focus on the more exciting parts of analyzing data and reporting on your findings.

2. DiscoverText

DiscoverText is another great example of thematic analysis in action. It's built for academic researchers who need to pull text from public data sources such as Twitter and analyze it quickly. Their data science methods originate in a decade of research with the National Science Foundation .

Like Thematic, DiscoverText understands the value in human and AI collaboration, recognizing that humans are good at some things and computers at others. DiscoverText writes that "a consistent back and forth between humans and machines increases the abilities of both to learn."

With DiscoverText, you can:

• Fetch live feeds,
• Filter by metadata,
• Redact and annotate sensitive information,
• Connect and work with peers in your browser.

3. Dovetail

Dovetail is a user research platform built for UX researchers who run small one-off research studies. Thematic analysis is one of its key features. It makes it easy to manually analyze text, tag specific parts of feedback with themes and then organize these themes. It's great for collaborating effectively with others and build up research repositories.

If you visit their website, you can see some animated examples of the software in action, transcribing audio files and pulling insights from  interview transcripts. Search for tags in an interview, and see an immediate count of all instances of what you’re looking for.

Key thematic analysis features include:

• Highlighting to tag text,
• Organizing taxonomies,
• Sentiment analysis,
• Graphical reporting.

Ready, set, go!

Now you are a master of thematic analysis software! You understand exactly what thematic analysis is and how it works. You also know how it can help you discover hidden insights in your feedback.

Researchers and insights professionals love the efficiencies thematic analysis software unlocks.

It saves time, money, and is just as accurate as human analysis! (and in some cases, even more accurate).

If you'd like to see how Thematic works on your data, book a demo with our team. They'll be able to set up a free trial on your dataset, so you can discover and review Thematic’s full functionality.

Key Takeaways

• Thematic Analysis Software: This technology plays a crucial role in extracting actionable insights from piles of customer feedback. It works by identifying recurring themes in feedback data using natural language processing (NLP) and understanding (NLU).
• Advantages: Thematic analysis saves time and increases accuracy in handling feedback. It offers a scalable solution compared to traditional manual techniques. The software also helps mitigate errors that can come from human bias.
• Turning Feedback into Hard Data: With thematic analysis software, feedback can be quantified (from qualitative into quantitative data). This enables data-driven decisions from unstructured feedback, and allows you to monitor the impact of these decisions in real time.
• Choosing Thematic Analysis Software: When considering various software options, look for the features and capabilities that best meet your needs. The software should be able to identify themes autonomously, meaning it learns on its own without needing to set up themes or categories in advance.
• Sentiment and Thematic Analysis: Thematic analysis and sentiment analysis are often used together to gain deep insights. While sentiment analysis provides a binary view (positive or negative) of the feedback, thematic analysis helps understand the 'why' behind the sentiment.

Frequently Asked Questions about Thematic Analysis Software

Thematic analysis software automates thematic analysis. It uses natural language processing and understanding to identify and extract recurring themes from large amounts of text data, such as customer feedback or open-ended survey responses.

How does thematic analysis software save time?

Traditional methods of analyzing text data, like using Excel spreadsheets, are time-consuming. They’re simply not scalable. Thematic analysis software automates this process, saving valuable time and effort.

How can thematic analysis software improve accuracy?

Human analysis is often affected by personal biases or oversight. Thematic analysis software minimizes these errors and is overall more consistent, enhancing the accuracy of the analysis.

What are some popular thematic analysis software platforms?

Notable examples of thematic analysis software include Thematic, DiscoverText, and Dovetail. Each of these platforms have unique features suited for specific use-cases.

How can I choose the best thematic analysis software for my needs?

When choosing thematic analysis software, consider factors like whether the software identifies themes autonomously, or requires training. Assess its capacity to handle the volume of data you're working with, how well it can turn feedback into quantitative data, and whether it suits your specific use-case and budget.

CEO and Co-Founder

Alyona has a PhD in NLP and Machine Learning. Her peer-reviewed articles have been cited by over 2600 academics. Her love of writing comes from years of PhD research.

We make it easy to discover the customer and product issues that matter.

Unlock the value of feedback at scale, in one platform. Try it for free now!

• Questions to ask your Feedback Analytics vendor
• How to end customer churn for good
• Scalable analysis of NPS verbatims
• 5 Text analytics approaches
• How to calculate the ROI of CX

Our experts will show you how Thematic works, how to discover pain points and track the ROI of decisions. To access your free trial, book a personal demo today.

Recent posts

When two major storms wreaked havoc on Auckland and Watercare’s infrastructurem the utility went through a CX crisis. With a massive influx of calls to their support center, Thematic helped them get inisghts from this data to forge a new approach to restore services and satisfaction levels.

Become a qualitative theming pro! Creating a perfect code frame is hard, but thematic analysis software makes the process much easier.

Everyone says they want customers to be satisfied, but what are you actually doing to make customers happy? How do you know if you’re on the right track? How do you know if your customer satisfaction efforts make a difference? Why even aim for customer satisfaction at all? We

Thematic analysis software

Automate theme discovery to deduce the meaning behind the words and reveal key insights from your data.

How thematic analysis software works

From customer interviews to product feedback, sophisticated natural language processing analyzes and automatically uncovers themes in text.

AI thematic clustering

Time is precious; uncover themes in seconds. Make sense of large amounts of data by automatically clustering highlights by theme in canvas.

Surface your insights

Visualize your findings in beautiful insights to share with your team and create your insight hub.

FAQs about thematic analysis software

The #1 Thematic Analysis Software developed by and for researchers. Easily identify and analyze patterns of themes with Dovetail.

What is thematic analysis software?

How does thematic analysis software work.

The goal of  thematic analysis  is to automate theme discovery in text. Thematic analysis software analyzes word and sentence structures to discover themes in the text. 

Natural language understanding (NLU) is a significant component of this process. The NLU discovers themes from the bottom up and assists in negation handling with   automated detection of polarity shifts in the opinion expressed in the text. Dovetail uses deep learning to recognize positive feedback, even if it's written in negative language.

What are the benefits of thematic analysis software?

Thematic analysis software offers several benefits to researchers. It provides a systematic and objective approach to analyzing qualitative data, ensuring researchers get all vital information. Additionally, it enables researchers to identify patterns and themes that may not be apparent to the human eye, leading to more accurate and comprehensive results.

What are applications of thematic analysis software?

Thematic analysis software has numerous applications in various fields. In product, it can be used to analyze customer feedback and reviews to gain insights into customer preferences and behaviors. In healthcare, it can be used to  analyze patient feedback  to improve the quality of care. Thematic analysis software is also widely used in social science research to analyze interviews, focus groups, and surveys.

What are thematic analysis software features?

Thematic analysis software has several features that make it a valuable tool for researchers. These features include:

Automatic coding: Thematic analysis software can automatically code data based on predefined criteria, saving researchers time and effort.

Data visualization: Thematic analysis software can generate visualizations like graphs and charts to help researchers better understand and interpret their data.

Collaboration: Thematic analysis software allows researchers to collaborate and share their findings with others, enabling a more comprehensive data analysis.

How does thematic analysis software differ from basic text analysis tools?

Thematic analysis software goes beyond basic text analysis by identifying recurring themes and patterns. It offers more advanced algorithms to extract meaningful insights.

Can thematic analysis software analyze multiple languages?

Yes, many thematic analysis software solutions support multilingual analysis. They are equipped to handle texts in various languages, expanding their usability.

What file formats does thematic analysis software support?

Most software supports standard file formats like PDF, Word documents, and plain text files. Dovetail also supports video and audio files along with text.

Join 10,000 teams building a customer insights hub

Get started for free, then upgrade from $50 USD / month.

Log in or sign up

Get started for free

Root out friction in every digital experience, super-charge conversion rates, and optimise digital self-service

Uncover insights from any interaction, deliver AI-powered agent coaching, and reduce cost to serve

Increase revenue and loyalty with real-time insights and recommendations delivered straight to teams on the ground

Know exactly how your people feel and empower managers to improve employee engagement, productivity, and retention

Take action in the moments that matter most along the employee journey and drive bottom line growth

Whatever they’re are saying, wherever they’re saying it, know exactly what’s going on with your people

Get faster, richer insights with qual and quant tools that make powerful market research available to everyone

Run concept tests, pricing studies, prototyping + more with fast, powerful studies designed by UX research experts

Track your brand performance 24/7 and act quickly to respond to opportunities and challenges in your market

Meet the operating system for experience management

• Free Account
• For Digital
• For Customer Care
• For Human Resources
• For Researchers
• Financial Services
• All Industries

Popular Use Cases

• Customer Experience
• Employee Experience
• Employee Exit Interviews
• Net Promoter Score
• Voice of Customer
• Customer Success Hub
• Product Documentation
• Training & Certification
• XM Institute
• Popular Resources
• Customer Stories
• Artificial Intelligence
• Market Research
• Partnerships
• Marketplace

The annual gathering of the experience leaders at the world’s iconic brands building breakthrough business results.

• English/AU & NZ
• Español/Europa
• Español/América Latina
• Português Brasileiro
• REQUEST DEMO
• Experience Management
• What is a Survey?
• Thematic Analysis

Try Qualtrics for free

Thematic analysis in qualitative research.

11 min read Your guide to thematic analysis, a form of qualitative research data analysis used to identify patterns in text, video and audio data.

What is thematic analysis?

Thematic analysis is used to analyse qualitative data – that is, data relating to opinions, thoughts, feelings and other descriptive information. It’s become increasingly popular in social sciences research, as it allows researchers to look at a data set containing multiple qualitative sources and pull out the broad themes running through the entire data set.

That data might consist of articles, diaries, blog posts, interview transcripts, academic research, web pages, social media and even audio and video files. They are put through data analysis as a group, with researchers seeking to identify patterns running through the corpus as a whole.

Free eBook: The qualitative research design handbook

Thematic analysis steps

Image source:  https://www.nngroup.com/articles/thematic-analysis/

While there are many types of thematic analysis, the thematic analysis process can be generalised into six steps. Thematic analysis involves initial analysis, coding data, identifying themes and reporting on the findings.

• Familiarisation – During the first stage of thematic analysis, the research teams or researchers become familiar with the dataset. This may involve reading and re-reading, and even transcribing the data. Researchers may note down initial thoughts about the potential themes they perceive in the data, which can be the starting point for assigning initial codes.
• Coding –  Codes in thematic analysis are the method researchers use to identify the ideas and topics in their data and refer to them quickly and easily. Codes can be assigned to snippets of text data or clips from videos and audio files. Depending on the type of thematic analysis used, this can be done with a systematic and rigorous approach, or in a more intuitive manner.
• Identifying theme –  Themes are the overarching ideas and subject areas within the corpus of research data. Researchers can identify themes by collating together the results of the coding process, generating themes that tie together the identified codes into groups according to their meaning or subject matter.
• Reviewing themes –  Once the themes have been defined, the researchers check back to see how well the themes support the coded data extracts. At this stage they may start to organise the themes into a map, or early theoretical framework.
• Defining and naming themes –  As researchers spend more time reviewing the themes, they begin to define them more precisely, giving them names. Themes are different from codes, because they capture patterns in the data rather than just topics, and they relate directly to the research question.
• Writing up –  At this stage, researchers begin to develop the final report, which offers a comprehensive summary of the codes and themes, extracts from the original data that illustrate the findings, and any other data relevant to the analysis. The final report may include a literature review citing other previous research and the observations that helped frame the research question. It can also suggest areas for future research the themes support, and which have come to light during the research process.

Another step which precedes all of these is data collection. Common to almost all forms of qualitative analysis, data collection means bringing together the materials that will be part of the data set, either by finding secondary data or generating first-party data through interviews, surveys and other qualitative methods.

Types of thematic analysis

There are various thematic analysis approaches currently in use. For the most part, they can be viewed as a continuum between two different ideologies. Reflexive thematic analysis (RTA) sits at one end of the continuum of thematic analysis methods. At the other end is code reliability analysis.

Code reliability analysis  emphasises the importance of the codes given to themes in the research data being as accurate as possible. It takes a technical or pragmatic view, and places value on codes being replicable between different researchers during the coding process. Codes are based on domain summaries, which often link back to the questions in a structured research interview.

Researchers using a code reliability approach may use a codebook. A codebook is a detailed list of codes and their definitions, with exclusions and examples of how the codes should be applied.

Reflexive thematic analysis  was developed by Braun & Clarke in 2006 for use in the psychology field. In contrast to code reliability analysis, it isn’t concerned with consistent codes that are agreed between researchers. Instead, it acknowledges and finds value in each researcher’s interpretation of the thematic content and how it influences the coding process. The codes they assign are specific to them and exist within a unique context that is made up of:

• The data set
• The assumptions made during the setup of the analysis process
• The researcher’s skills and resources

This doesn’t mean that reflexive thematic analysis should be unintelligible to anyone other than the researcher. It means that the researcher’s personal subjectivity and uniqueness is made part of the process, and is expected to have an influence on the findings. Reflexive thematic analysis is a flexible method, and initial codes may change during the process as the researcher’s understanding evolves.

Reflexive thematic analysis is an inductive approach to qualitative research. With an inductive approach, the final analysis is based entirely on the data set itself, rather than from any preconceived themes or structures from the research team.

Image source:  https://delvetool.com/blog/thematicanalysis

Thematic analysis vs other qualitative research methods

Thematic analysis sits within a whole range of qualitative analysis methods which can be applied to social sciences, psychology and market research data.

• Thematic analysis vs comparative analysis –  Comparative analysis and thematic analysis are closely related, since they both look at relationships between multiple data sources. Comparative analysis is a form of qualitative research that works with a smaller number of data sources. It focuses on causal relationships between events and outcomes in different cases, rather than on defining themes.
• Thematic analysis vs discourse analysis –  Unlike discourse analysis, which is a type of qualitative research that focuses on spoken or written conversational language, thematic analysis is much more broad in scope, covering many kinds of qualitative data.
• Thematic analysis vs narrative analysis –  Narrative analysis works with stories – it aims to keep information in a narrative structure, rather than allowing it to be fragmented, and often to study the stories from participants’ lives. Thematic analysis can break narratives up as it allocates codes to different parts of a data source, meaning that the narrative context might be lost and even that researchers might miss nuanced data.
• Thematic analysis vs content analysis –  Both content analysis and thematic analysis use data coding and themes to find patterns in data. However, thematic analysis is always qualitative, but researchers agree there can be quantitative and qualitative content analysis, with numerical approaches to the frequency of codes in content analysis data.

Thematic analysis advantages and disadvantages

Like any kind of qualitative analysis, thematic analysis has strengths and weaknesses. Whether it’s right for you and your research project will depend on your priorities and preferences.

Thematic analysis advantages

• Easy to learn –  Whether done manually or assisted by technology, the thematic analysis process is easy to understand and conduct, without the need for advanced statistical knowledge
• Flexible –  Thematic analysis allows qualitative researchers flexibility throughout the process, particularly if they opt for reflexive thematic analysis
• Broadly applicable –  Thematic analysis can be used to address a wide range of research questions.

Thematic analysis – the cons

As well as the benefits, there are some disadvantages thematic analysis brings up.

• Broad scope –  In identifying patterns on a broad scale, researchers may become overwhelmed with the volume of potential themes, and miss outlier topics and more nuanced data that is important to the research question.
• Themes or codes? –  It can be difficult for novice researchers to feel confident about the difference between themes and codes
• Language barriers –  Thematic analysis relies on language-based codes that may be difficult to apply in multilingual data sets, especially if the researcher and / or research team only speaks one language.

How can you use thematic analysis for business research?

Thematic analysis, and other forms of qualitative research, are highly valuable to businesses who want to develop a deeper understanding of the people they serve, as well as the people they employ. Thematic analysis can help your business get to the ‘why’ behind the numerical information you get from quantitative research.

An easy way to think about the interplay between qualitative data and quantitative data is to consider product reviews. These typically include quantitative data in the form of scores (like ratings of up to 5 stars) plus the explanation of the score written in a customer’s own words. The word part is the qualitative data. The scores can tell you what is happening – lots of 3 star reviews indicate there’s some room for improvement for example – but you need the addition of the qualitative data, the review itself, to find out what’s going on.

Qualitative data is rich in information but hard to process manually. To do qualitative research at scale, you need methods like thematic analysis to get to the essence of what people think and feel without having to read and remember every single comment.

Qualitative analysis is one of the ways businesses are borrowing from the world of academic research, notably social sciences, statistical data analysis and psychology, to gain an advantage in their markets.

Analysing themes across video, text, audio and more

Carrying out thematic analysis manually may be time-consuming and painstaking work, even with a large research team. Fortunately, machine learning and other technologies are now being applied to data analysis of all kinds, including thematic analysis, taking the manual work out of some of the more laborious thematic analysis steps.

The latest iterations of machine learning tools are able not only to analyse text data, but to perform efficient analysis of video and audio files, matching the qualitative coding and even helping build out the thematic map, while respecting the researcher’s theoretical commitments and research design.

Related resources

Post event survey questions 10 min read, choosing the best survey tools 16 min read, survey app 11 min read, close-ended questions 7 min read, survey vs questionnaire 12 min read, likert scales 14 min read, double barrelled question 11 min read, request demo.

Ready to learn more about Qualtrics?

• How it works

Thematic Analysis – A Guide with Examples

Published by Alvin Nicolas at August 16th, 2021 , Revised On August 29, 2023

Thematic analysis is one of the most important types of analysis used for qualitative data . When researchers have to analyse audio or video transcripts, they give preference to thematic analysis. A researcher needs to look keenly at the content to identify the context and the message conveyed by the speaker.

Moreover, with the help of this analysis, data can be simplified.  

Importance of Thematic Analysis

Thematic analysis has so many unique and dynamic features, some of which are given below:

Thematic analysis is used because:

• It is flexible.
• It is best for complex data sets.
• It is applied to qualitative data sets.
• It takes less complexity compared to other theories of analysis.

Intellectuals and researchers give preference to thematic analysis due to its effectiveness in the research.

How to Conduct a Thematic Analysis?

While doing any research , if your data and procedure are clear, it will be easier for your reader to understand how you concluded the results . This will add much clarity to your research.

Understand the Data

This is the first step of your thematic analysis. At this stage, you have to understand the data set. You need to read the entire data instead of reading the small portion. If you do not have the data in the textual form, you have to transcribe it.

Example: If you are visiting an adult dating website, you have to make a data corpus. You should read and re-read the data and consider several profiles. It will give you an idea of how adults represent themselves on dating sites. You may get the following results:

I am a tall, single(widowed), easy-going, honest, good listener with a good sense of humor. Being a handyperson, I keep busy working around the house, and I also like to follow my favourite hockey team on TV or spoil my two granddaughters when I get the chance!! Enjoy most music except Rap! I keep fit by jogging, walking, and bicycling (at least three times a week). I have travelled to many places and RVD the South-West U.S., but I would now like to find that special travel partner to do more travel to warm and interesting countries. I now feel it’s time to meet a nice, kind, honest woman who has some of the same interests as I do; to share the happy times, quiet times, and adventures together

I enjoy photography, lapidary & seeking collectibles in the form of classic movies & 33 1/3, 45 & 78 RPM recordings from the 1920s, ’30s & ’40s. I am retired & looking forward to travelling to Canada, the USA, the UK & Europe, China. I am unique since I do not judge a book by its cover. I accept people for who they are. I will not demand or request perfection from anyone until I am perfect, so I guess that means everyone is safe. My musical tastes range from Classical, big band era, early jazz, classic ’50s & 60’s rock & roll & country since its inception.

Development of Initial Coding:

At this stage, you have to do coding. It’s the essential step of your research . Here you have two options for coding. Either you can do the coding manually or take the help of any tool. A software named the NOVIC is considered the best tool for doing automatic coding.

For manual coding, you can follow the steps given below:

• Please write down the data in a proper format so that it can be easier to proceed.
• Use a highlighter to highlight all the essential points from data.
• Make as many points as possible.
• Take notes very carefully at this stage.
• Apply themes as much possible.
• Now check out the themes of the same pattern or concept.
• Turn all the same themes into the single one.

Example: For better understanding, the previously explained example of Step 1 is continued here. You can observe the coded profiles below:

Make Themes

At this stage, you have to make the themes. These themes should be categorised based on the codes. All the codes which have previously been generated should be turned into themes. Moreover, with the help of the codes, some themes and sub-themes can also be created. This process is usually done with the help of visuals so that a reader can take an in-depth look at first glance itself.

Extracted Data Review

Now you have to take an in-depth look at all the awarded themes again. You have to check whether all the given themes are organised properly or not. It would help if you were careful and focused because you have to note down the symmetry here. If you find that all the themes are not coherent, you can revise them. You can also reshape the data so that there will be symmetry between the themes and dataset here.

For better understanding, a mind-mapping example is given here:

Reviewing all the Themes Again

You need to review the themes after coding them. At this stage, you are allowed to play with your themes in a more detailed manner. You have to convert the bigger themes into smaller themes here. If you want to combine some similar themes into a single theme, then you can do it. This step involves two steps for better fragmentation. 

You need to observe the coded data separately so that you can have a precise view. If you find that the themes which are given are following the dataset, it’s okay. Otherwise, you may have to rearrange the data again to coherence in the coded data.

Corpus Data

Here you have to take into consideration all the corpus data again. It would help if you found how themes are arranged here. It would help if you used the visuals to check out the relationship between them. Suppose all the things are not done accordingly, so you should check out the previous steps for a refined process. Otherwise, you can move to the next step. However, make sure that all the themes are satisfactory and you are not confused.

When all the two steps are completed, you need to make a more précised mind map. An example following the previous cases has been given below:

Define all the Themes here

Now you have to define all the themes which you have given to your data set. You can recheck them carefully if you feel that some of them can fit into one concept, you can keep them, and eliminate the other irrelevant themes. Because it should be precise and clear, there should not be any ambiguity. Now you have to think about the main idea and check out that all the given themes are parallel to your main idea or not. This can change the concept for you.

The given names should be so that it can give any reader a clear idea about your findings. However, it should not oppose your thematic analysis; rather, everything should be organised accurately.

Does your Research Methodology Have the Following?

• Great Research/Sources
• Perfect Language
• Accurate Sources

If not, we can help. Our panel of experts makes sure to keep the 3 pillars of Research Methodology strong.

Also, read about discourse analysis , content analysis and survey conducting . we have provided comprehensive guides.

Make a Report

You need to make the final report of all the findings you have done at this stage. You should include the dataset, findings, and every aspect of your analysis in it.

While making the final report , do not forget to consider your audience. For instance, you are writing for the Newsletter, Journal, Public awareness, etc., your report should be according to your audience. It should be concise and have some logic; it should not be repetitive. You can use the references of other relevant sources as evidence to support your discussion.  

Frequently Asked Questions

What is meant by thematic analysis.

Thematic Analysis is a qualitative research method that involves identifying, analyzing, and interpreting recurring themes or patterns in data. It aims to uncover underlying meanings, ideas, and concepts within the dataset, providing insights into participants’ perspectives and experiences.

You May Also Like

This article presents the key advantages and disadvantages of secondary research so you can select the most appropriate research approach for your study.

A case study is a detailed analysis of a situation concerning organizations, industries, and markets. The case study generally aims at identifying the weak areas.

A variable is a characteristic that can change and have more than one value, such as age, height, and weight. But what are the different types of variables?

USEFUL LINKS

LEARNING RESOURCES

COMPANY DETAILS

• How It Works

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• v.21(12); 2021 Dec

General-purpose thematic analysis: a useful qualitative method for anaesthesia research

1 Centre for Medical and Health Sciences Education, School of Medicine, University of Auckland, Auckland, New Zealand

2 Department of Anaesthesia, Auckland City Hospital, Auckland, New Zealand

Learning objectives

By reading this article, you should be able to:

• • Explain when to use thematic analysis.
• • Describe the steps in thematic analysis of interview data.
• • Critique the quality of a study that uses the method of thematic analysis.
• • Thematic analysis is a popular method for systematically analysing qualitative data, such as interview and focus group transcripts.
• • It is one of a cluster of methods that focus on identifying patterns of meaning, or themes, across a data set.
• • It is relevant to many questions in perioperative medicine and a good starting point for those new to qualitative research.
• • Systematic approaches to thematically analysing data exist, with key components to demonstrate rigour, accountability, confirmability and reliability.
• • In one study, a useful six-step approach to analysing data is offered.

Anaesthesia research commonly uses quantitative methods, such as surveys, RCTs or observational studies. Such methods are often concerned with answering what questions and how many questions. Qualitative research is more concerned with why questions that enable us to understand social complexities. ‘Qualitative studies in the anaesthetic setting’, write Shelton and colleagues, ‘have been used to define excellence in anaesthesia, explore the reasons behind drug errors, investigate the acquisition of expertise and examine incentives for hand hygiene in the operating theatre’. 1

General-purpose thematic analysis (termed thematic analysis hereafter) is a qualitative research method commonly used with interview and focus group data to understand people's experiences, ideas and perceptions about a given topic. Thematic analysis is a good starting point for those new to qualitative research and is relevant to many questions in the perioperative context. It can be used to understand the experiences of healthcare professionals and patients and their families. Box 1 gives examples of questions amenable to thematic analysis in anaesthesia research.

Examples of questions amenable to thematic analysis.

• (i) How do operating theatre staff feel about speaking up with their concerns?
• (ii) What are trainee's conceptions of the balance between service and learning?
• (iii) What are patients' experiences of preoperative neurocognitive screening?

Alt-text: Box 1

Thematic analysis involves a process of assigning data to a number of codes, grouping codes into themes and then identifying patterns and interconnections between these themes. 2 Thematic analysis allows for a nuanced understanding of what people say and do within their particular social contexts. Of note, thematic analysis can be used with interviews and focus groups and other sources of data, such as documents or images.

Thematic analysis is not the same as content analysis. Content analysis involves counting the frequency with which words or phrases appear in data. Content analysis is a method used to code and categorise textual information systematically to determine trends, frequency and patterns of words used. 3 Conversely, thematic analysis focuses on the relative importance of ideas and how ideas connect and govern practices. Thematic analysis does not rely on frequency counts to indicate the importance of coded data. Content analysis can be coupled with thematic analysis, where both themes and frequencies of particular statements or words are reported.

Thematic analysis is a research method, not a methodology. A methodology is a method with a philosophical underpinning. If researchers report only on what they did, this is the method. If, in addition, they report on the philosophy that governed what they did, this is methodology. Common methodologies in qualitative research include phenomenology, grounded theory, hermeneutics, narrative enquiry and ethnography. 4 Each of these methodologies has associated methods for data analysis. Thematic analysis can be combined with many different qualitative methodologies.

There are also different types of thematic analysis, such as inductive (including general purpose), applied, deductive or semantic thematic analysis. Inductive analysis involves approaching the data with an open mind, inductively looking for patterns and themes and interpreting these for meaning. 2 , 4 Of note, researchers can never have a truly open mind on their topic of interest, so the process will be influenced by their particular perspectives, which need to be declared. In applied and deductive thematic analysis, the researcher will have a pre-existing framework (which may be informed by theory or philosophy) against which they will attempt to categorise the data. 4 , 5 , 6 For semantic thematic analysis, the data are coded on explicit content, and tend to be descriptive rather than interpretative. 6

In this review, we outline what thematic analysis entails and when to use it. We also list some markers to look for to appraise the quality of a published study.

Designing the data collection

Before embarking on qualitative research, as with quantitative research, it is important to seek ethical review of the proposed study. Ethical considerations include such issues as consent, data security and confidentiality, permission to use quotes, potential for identifying individuals or institutions, risk of psychological harm to participants with studies on sensitive issues (e.g. suicide or sexual harassment), power relationships between interviewer and interviewee or intrusion on other activities (such as teaching time or work commitments). 7

Qualitative research often involves asking people questions during interviews or focus groups. Merriam and Tisdell stated that, ‘The most common form of interview is the person-to-person encounter in which one person elicits information from the other’. 8 Information is elicited through careful and purposeful questioning and listening. 9 Research interviews in anaesthesia are generally purposeful conversations with a structure that allows the researcher to gather information about a participant's ideas, perceptions and experiences concerning a given topic.

A structured interview is when the researcher has already decided on a set of questions to ask. 9 If the researcher will ask a set of questions, but has flexibility to follow up responses with further questions, this is called a semi-structured interview. Semi-structured interviews are commonly used in research involving thematic analysis. The researcher can also use other forms of questioning, such as single-question interview. Semi-structured interviews are commonly used in anaesthesia, such as the studies from our own research group. 10 , 11 , 12

Interviews are usually recorded in audio form and then transcribed. For each interview or focus group, a single transcript is created. The transcripts become the written form of data and the collection of transcripts from the research participants becomes the data set.

Designing productive interview questions

The design of interview questions significantly shapes a participant's response. Interview questions should be designed using ‘sensitising concepts’ to encourage participants to share information that will increase a researcher's understanding of the participants' experiences, views, beliefs and behaviours. 13 ‘Sensitising concepts’ describe words in questions that bring the participants' attention to a concept of research interest. Examples of sensitising concepts include speaking up, teamwork and theoretical concepts (such as Kolb's experiential learning cycle or Foucauldian power theory in relation to trainee learning and operating theatre culture). 14 , 15 Specifically, the questions should be framed in such a way as to encourage participants to make sense of their own experience and in their own words. The researcher should try to minimise the influences of their own biases when they design questions. Using open-ended questions will increase the richness of data. Box 2 gives examples of question design.

How to design an interview question.

Alt-text: Box 2

Bias, positionality and reflexivity

Bias is an inclination or prejudice for or against someone or something, whereas positionality is a person's position in society or their stance towards someone or something. For example, Tanisha once had an inexperienced anaesthetist accidentally rupture one of her veins whilst they were siting an i.v. cannula in an emergency situation. Now, Tanisha has a bias against inexperienced anaesthetists. Tanisha's positionality —a medical anthropologist with no anaesthesia training, but working with many anaesthesia colleagues, including her director—may also inform that bias or the way that Tanisha interacts with anaesthetists. Reflexivity is a process whereby people/researchers proactively reflect on their biases and positionality. Biases shape positionality (i.e. the stance of the researcher in relation to the social, historical and political contexts of the study). In practical research terms, biases and positionality inform the way researchers design and undertake research, and the way they interpret data. It is important in qualitative research to both identify biases and positionality, and to take steps to minimise the impact of these on the research.

Some ways to minimise the influence of bias and positionality on findings include:

(i) Raise awareness amongst the research team of bias and positionality.

(ii) Design research/interview questions that minimise potential for these to distort which data are collected or how they are collected.

(iii) Researchers ask reflexive questions during data analysis, such as, ‘Is my bias about xxx informing my view of these data?’

(iv) Two or more researchers are involved in the analysis process.

(v) Data analysis member check (e.g. checking back with participants if the interpretation of their data is consistent with their experience and with what they said).

Before embarking on the study, researchers should consider their own experiences, knowledge and views; how this influences their own position in relation to the study question; and how this position could potentially introduce bias in how they collect and analyse the data. Taking time to reflect on the impact of the researchers' position is an important step towards being reflective and transparent throughout the research process. When writing up the study, researchers should include statements on bias and positionality. In quantitative research, we aim to eliminate bias. In qualitative research, we acknowledge that bias is inevitable (and sometimes even unconscious), and we take steps to make it explicit and to minimise its effect on study design and data interpretation.

Sampling and saturation

Qualitative research typically uses systematic, non-probability sampling. Unlike quantitative research, the goal of sampling is not to randomly select a representative sample from a population. Instead, researchers identify and select individuals or groups relevant to the research question. Commonly used sampling techniques in anaesthesia qualitative research are homogeneous (group) sampling and maximum variation sampling. In the former, researchers may be concerned with the experiences of participants from a distinct group or who share a certain characteristic (e.g. female anaesthesia trainees), so they recruit selectively from within the group with this shared characteristic to gain a rich, in-depth understanding of their experiences. Conversely, the aim with maximum variation sampling is to recruit participants with diverse characteristics to obtain a broad understanding of the question being studied (e.g. members of different professional groups within operating theatre teams, who have diverse ages, gender and ethnicities).

As with quantitative research, the purpose of sampling is to recruit sufficient numbers of participants to enable identification of patterns or richness in what they say or do to understand or explain the phenomenon of interest, and where collecting more data is unlikely to change this understanding.

In qualitative research, data collection and analysis often occur concurrently. This is because data collection is an iterative process both in recruitment and in questioning. The researchers may identify that more data are needed from a particular demographic group or on a particular theme to reach data saturation, so the next participants may be selected from a particular demographic, or be asked slightly different questions or probes to draw out that theme. Sample size is considered adequate when little or no new information emerges from interviews or focus groups; this is generally termed ‘data saturation’, although some qualitative researchers use the term ‘data sufficiency’. This could also be explained in terms of data reliability (i.e. the researcher is satisfied that collecting more data will not substantially change the results). Data saturation typically occurs with between 12 and 17 participants in a relatively homogeneous sampling, but larger numbers may be required, where the interviewees are from distinct groups or cultures. 16 , 17

Data management

For data sets that involve 10 or more transcripts or lengthy interviews (e.g. 90 min or more), researchers often use software to help them collate and manage the data. The most commonly used qualitative software packages are QSR NVivo, Atlas and Dedoose. 18 , 19 , 20 Many researchers use Microsoft Excel instead, or for small data sets the analysis can be done by hand, with pen, paper and scissors (i.e. researchers cut up printed transcripts and reorder the information according to code and theme). 21 NVivo and Atlas are simply repositories, in which you can input the transcripts and, using your coding scheme, sort the text into codes. They facilitate the task of analysis, rather than doing the analysis for you. Some advantages over coding by hand are that text can be allocated to more than one code, and you can easily identify the source of the segment of text you have coded.

Data analysis

Qualitative data analysis is ‘the classification and interpretation of linguistic (or visual) material to make statements about implicit and explicit dimensions and structures of meaning-making in the material and what is represented in it’. 22

Several social scientists have described this analytical process in depth. 2 , 6 , 22 , 23 , 24 , 25 For inductive studies, we recommend researchers follow Braun and Clarke's practical six-phase approach to thematic analysis. 26 The phases are (i) familiarising the researcher with the data, (ii) generating initial codes, (iii) searching for themes, (iv) reviewing themes, (v) defining and naming themes and (vi) producing the report. These six phases are described next.

Phase 1: familiarising the researcher with the data

In this step, the researchers read the transcripts to become familiar with them and take notes on potential recurring ideas or potential themes. They share and discuss their ideas and, in conjunction with any sensitising concepts, they start thinking about possible codes or themes.

Phase 2: generating initial codes

The first step in Phase 2 is ‘assigning some sort of short-hand designation to various aspects of your data so that you can easily retrieve specific pieces of the data’. 2 The designation might be a word or a short phrase that summarises or captures the essence of a particular piece of text. Coding makes it easier to summarise and compare, which is important because qualitative research is primarily about synthesis and comparison of data. 2 , 25 As the researcher reads through the data, they assign codes. If they are coding a transcript, they might highlight some words, for example, and attach to them a single word that summarises their meaning.

Researchers undertaking thematic analysis should iteratively develop a ‘coding scheme’, which is essentially a list of the codes they create as they read the data, and definitions for each code. 25 , 26 Code definitions are important, as they help the researcher make decisions on whether to assign this code or another one to a segment of data. In Table 1 , we have provided an example of text data in Column 1. TJ analysed these data. To do so, she asked, ‘What are these data about? How does it answer the research question? What is the essence of this statement?’ She underlined keywords and created codes and definitions (Columns 2 and 3). Then, TJ searched the remaining data to see if any more data met each code definition, and if so, coded that (see Table 1 ). As demonstrated in Table 1 , data can be coded to multiple codes.

Table 1

How to code qualitative data: an example

In thematic analysis of interview data, we recommend that code definitions begin with something objective, such as ‘participant describes’. This keeps the researcher's focus on what participants said rather than what the researcher thought or said.

There is no set rule for how many codes to create. 25 However, in our experience, effective manageable coding schemes tend to have between 15 and 50 codes. The coding scheme is iterative. This means that the coding scheme is developed over time, with new codes being created as more data are coded. For example, after a close reading of the first transcript, the researcher might create, say, 10 codes that convey the key points. Then, the researcher reads and codes the next transcript and may, for instance, create additional four codes. As additional transcripts are read and coded, more codes may be created. Not all codes are relevant to all transcripts. The researcher will notice patterns as they code more transcripts. Some codes may be too broad and will need to be refined into two or three smaller codes (and vice versa ). Once the coding scheme is deemed complete and all transcripts have been coded, the researcher should go back to the beginning and recode the first few transcripts to ensure coding rigour.

The second step in Phase 2, once the coding is complete, is to collate all the data relevant to each of these codes.

Phase 3: searching for themes

In this phase, the researchers look across the codes to identify connections between them, with the intention of collating the codes into possible themes. Once these possible themes have been identified, all the data relevant to each possible theme are pulled together under that theme.

Phase 4: reviewing the themes

After the initial collation of the data into themes, the researchers undertake a rigorous process of checking the integrity of these themes, through reading and re-reading their data. This process includes checking to see if the themes ‘fit’ in relation to the coded excerpts (i.e. Do all the data collected under that theme fit within that theme?). Next is checking if the themes fit in relation to the whole data set (i.e. Do the themes adequately reflect the data?) This step may result in the search for additional themes. As a final step in this phase, the researchers create a thematic ‘map’ of the analysis.

When viewed together, the themes should answer the research question and should summarise participant experiences, views or behaviours.

Phase 5: naming the themes

Once researchers have checked the themes and included any additional emerging themes they name the final set of themes identified. Each theme and any subthemes should be listed in turn.

Phase 6: producing the report

The report should summarise the themes and illustrate them by choosing vivid or persuasive extracts from the data. For data arising from interviews, extracts will be quotes from participants. In some studies, researchers also report strong associations between themes, or divide a theme into sub-themes.

Tight word limits on many academic journals can make it difficult to include multiple quotes in the text. 27 One way around a word limit is to provide quotes in a table or a supplementary file, although quotes within the text tend to make for more interesting and compelling reading.

Who should analyse the data?

Ideally, each researcher in the team should be involved in the data analysis. Contrasting researcher viewpoints on the same study subject enhance data quality and validity, and minimise research bias. Independent analysis is time and resource intensive. In clinical research, close independent analysis by each member of the research team may be impractical, and one or two members may undertake the analysis while the rest of the research team read sections of data (e.g. reading two or three transcripts rather than closely analysing the whole data set), thus contributing to Phase 1 and Phase 2 of Braun and Clarke's method. 2

The research team should regularly meet to discuss the analytical process, as described earlier, to workshop and reach agreement on the coding and emergent themes (Phase 4 and Phase 5). The research team members compare their perspectives on the data, analyse divergences and coincidences and reach agreement on codes and emerging themes. Contrasting researcher viewpoints on the same study subject enhance data quality and validity, and minimise research bias.

Judging the quality and rigour of published studies involving thematic analysis

There are a number of indicators of quality when reading and appraising studies. 28 , 29 , 30 , 31 In essence, the authors should clearly state their method of analysis (e.g. thematic analysis) and should reference the literature relevant to their qualitative method, for example Braun and Clarke. 2 This is to indicate that they are following established steps in thematic analysis. The authors should include in the methods a description of the research team, their biases and experience and the efforts made to ensure analytical rigour. Verbatim quotes should be included in the findings to provide evidence to support the themes.

A number of guides have been published to assist readers, researchers and reviewers to evaluate the quality of a qualitative study. 30 , 31 The Joanna Briggs Institute guide to critical appraisal of qualitative studies is a good start. 30 This guide includes a set of 10 criteria, which can be used to rate the study. The criteria are summarised in Box 3 . Within these criteria lie rigorous methodological approaches to how data are collected, analysed and interpreted.

Ten quality appraisal criteria for qualitative literature.31

• (i) Alignment between the stated philosophical perspective and the research methodology
• (ii) Alignment between the research methodology and the research question or objectives
• (iii) Alignment between the research methodology and the methods used to collect data
• (iv) Alignment between the research methodology and the representation and analysis of data
• (v) Alignment between the research methodology and the interpretation of results
• (vi) A statement locating the researcher culturally or theoretically (positionality and bias)
• (vii) The influence of the researcher on the research, and vice versa
• (viii) Adequate representation of participants and their voices
• (ix) Ethical research conduct and evidence of ethical approval by an appropriate body
• (x) Conclusions flow from the analysis, or interpretation, of the data

Alt-text: Box 3

Another approach to quality appraisal comes from Lincoln and Guba, who have published widely on the topic of judging qualitative quality. 28 They look for quality in terms of credibility, transferability, dependability, confirmability and authenticity. There are many qualitative checklists readily accessible online, such as the Standards for Reporting Qualitative Research checklist or the Consolidated Criteria for Reporting Qualitative Research checklist, which researchers can include in their work to demonstrate quality in these areas.

Conclusions

As with quantitative research, qualitative research has requirements for rigour and trustworthiness. Thematic analysis is an accessible qualitative method that can offer researchers insight into the shared experiences, views and behaviours of research participants.

Declaration of interests

The authors declare that they have no conflicts of interest.

The associated MCQs (to support CME/CPD activity) will be accessible at www.bjaed.org/cme/home by subscribers to BJA Education .

Biographies

Tanisha Jowsey PhD BA (Hons) MA PhD is a senior lecturer in the Centre for Medical and Health Sciences Education, School of Medicine, University of Auckland. She has a background in medical anthropology and has expertise as a qualitative researcher.

Carolyn Deng MPH FANZCA is a specialist anaesthetist at Auckland City Hospital. She has a Master of Public Health degree. She is embarking on qualitative research in perioperative medicine and hopes to use it as a tool to complement quantitative research findings in the future.

Jennifer Weller MD MClinEd FANZCA FRCA is head of the Centre for Medical and Health Sciences Education at the University of Auckland. Professor Weller is a specialist anaesthetist at Auckland City Hospital and often uses qualitative methods in her research in clinical education, teamwork and patients' safety.

Matrix codes: 1A01, 2A01, 3A01

No products in the cart.

#1 QUALITATIVE DATA ANALYSIS SOFTWARE FOR 30 YEARS

NVivo 14 - Leading Qualitative Data Analysis Software with AI Solution

Enhance Your Use of NVivo 14

Harness the Power of AI in NVivo

Harness the Power of AI in NVivo for Qualitative Analysis

The NVivo Getting Started Bundle includes all the essentials you need for your content analysis.

An NVivo license:  The most cited and powerful QDA software for data analysis. Choose a Windows or Mac individual license.

NVivo Core Skills Online Course: Includes videos, live coaching and a Q&A forum to help you analyze qualitative data fast.

Access the entire bundle for just the normal price of NVivo. That’s a saving of $279.99 USD! Available for a limited time only, don’t miss out.

Click more with your research team, less with your mouse, collaborate, discover all the ways nvivo 14 works for you, enhance team research, boost productivity, collaborate easily, uncover richer insights, make robust conclusions, deliver comprehensive findings, enjoy a more streamlined user experience.

Collaboration Cloud

Collaboration Server

Transcription

NVivo Academy

NVivo 14 Licenses

Student Licenses provide access to all the features of NVivo, limited for 12 months.

Individual and small group licenses (up to nine) can be bought online.

Organization licenses are available. If you want to purchase ten or more licenses, or enter an entreprise agreement, contact our sales team.

Enterprise Licensing: Better Research, Insights, and Outcomes for all

Lumivero’s team-based solutions allow you to:, need help choosing qda software, what is nvivo, what can i do with nvivo, who is nvivo for, how much does nvivo cost.

It's easy to buy student, individual and small group licenses (student license limited to one per account, individual and small group licenses up to nine) online.

To purchase ten or more NVivo licenses for your team or organization, Contact Us to reach our sales team or one of our international NVivo partners.

Are there free qualitative data analysis tutorials?

How do i upgrade nvivo.

For individual and small groups (less than 9 individuals), take advantage of the most recent updates and software enhancements by purchasing the latest version here .  For larger groups and institutional/enterprise users, subscription options at volume rates are offered that secure the latest features as they are released. To purchase ten or more NVivo licenses for your team or organization, Contact Us to reach our sales team or one of our international NVivo partners. Lumivero's Support team is committed to your success using our software and actively supports the two previous versions of the most current version.

Get Started with NVivo Qualitative Data Analysis Software (QDA) Today

Begin your journey towards deeper insights and more robust results. NVivo provides better research collaboration, deeper integration, and is easier to use than ever.

Qualitative data brought to life. Qualitative data brought to life.

Quirkos is simple qualitative analysis software, designed to immerse you in your qualitative text data and help you to understand it quickly and easily.

Helping qualitative researchers understand their data for 9+ years

Quirkos is the best tool for qualitative research - it really helps with visualising the data and identifying patterns within multiple datasets!

Oliver, PhD Candidate

Work together with Quirkos Cloud

Code qualitative text with an unlimited number of simultaneous users with no extra costs. Work from any device and see edits from colleagues pop up in real time.

With live chat and all your collaborative projects synced and backed up on our secure cloud servers, it's never been easier to stay on the same page.

Quick to learn, flexible to use

Quirkos is easy to master, with just the essential tools you need to help you focus on the richness of your small qualitative data sets. Organise your sources, canvas and codes in any way, and finely categorise and filter your data. Our flexible design is agnostic, so you can use grounded theory, thematic analysis, or IPA as you prefer.

Cloud collaboration and secure storage

With a Quirkos Cloud subscription, you can collaborate seamlessly with colleagues in real-time. Our automated transcription service makes it easy to bring in your recorded data and get analysing quickly. You'll also get unlimited cloud storage and the ability to work across all your devices, with your project data stored and backed up automatically on our secure servers.

Platform freedom

Enjoy the same intuitive experience and features across Windows, Mac, Linux, and any web browser - including tablets and smartphones with Quirkos Cloud.

Easy import

Cross-compatibility, data visualisation, side-by-side comparisons, robust export options, secure transcription, security by design, first-class support, make qualitative data analysis easier with quirkos.

Watch a video introduction to Quirkos, or view our free resources and tutorials for more in-depth guides.

Affordable qualitative software for everyone

I thoroughly enjoyed using your software to analyze my data. I've enjoyed it so much that I sing your praises at my university.

Kevin, PhD candidate

Our mantra is 'accessibility'; Quirkos is often half the price of alternative qualitative software, with great discounts for students, academics, non-profit organisations, and the public sector. For those in the global majority world, we also have a 25% discount to make qualitative analysis software accessible to everyone. Find out more on our pricing page .

Cloud or Offline?

We offer two options for purchasing Quirkos: a recurring annual or quarterly subscription, or a permanent, single-purchase software licence. Both provide full access to Quirkos Desktop , our desktop app for Windows, Mac and Linux, while Quirkos Cloud subscriptions allow you to access your synced data anywhere, including in the Web App with nothing to install.

As well as the below prices for individuals, we offer significantly discounted group rates for organisations and teams interested in using Quirkos.

Prices below are based on 1 year subscriptions; minimum subscription is 3 months.

Select a rate:

Quirkos Cloud

• Access to Quirkos Desktop on all devices you own for the duration of your subscription
• Unlimited, secure Cloud storage
• Access to Quirkos Web, our browser-based version which requires no download
• Live collaboration and project sharing

Offline Licence

$69 / lifetime.

• Lifetime access to Quirkos Desktop on one or two computers
• Store projects offline on your hard drive
• No registration or log-in required
• Collaborate with other Quirkos users offline, sending projects back and forth

Our next event

Ready to give Quirkos a try? Register for a free 14-day trial of Quirkos today, with no restrictions on features or projects. with no restrictions on features or projects. Want to learn more? Read more about our features or see Quirkos in action !

Harnessing ChatGPT for Thematic Analysis: Are We Ready?

Affiliations.

• 1 Division of Family Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
• 2 Department of Family Medicine, National University Health System, Singapore, Singapore.
• 3 Centre for Research in Health Systems Performance, National University of Singapore, Singapore, Singapore.
• PMID: 38819896
• DOI: 10.2196/54974

ChatGPT (OpenAI) is an advanced natural language processing tool with growing applications across various disciplines in medical research. Thematic analysis, a qualitative research method to identify and interpret patterns in data, is one application that stands to benefit from this technology. This viewpoint explores the use of ChatGPT in three core phases of thematic analysis within a medical context: (1) direct coding of transcripts, (2) generating themes from a predefined list of codes, and (3) preprocessing quotes for manuscript inclusion. Additionally, we explore the potential of ChatGPT to generate interview transcripts, which may be used for training purposes. We assess the strengths and limitations of using ChatGPT in these roles, highlighting areas where human intervention remains necessary. Overall, we argue that ChatGPT can function as a valuable tool during analysis, enhancing the efficiency of the thematic analysis and offering additional insights into the qualitative data. While ChatGPT may not adequately capture the full context of each participant, it can serve as an additional member of the analysis team, contributing to researcher triangulation through knowledge building and sensemaking.

Keywords: ChatGPT; NLP; efficiency; medical research; natural language processing; qualitative data; qualitative research; technology; thematic analysis; viewpoint.

©V Vien Lee, Stephanie C C van der Lubbe, Lay Hoon Goh, Jose Maria Valderas. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 31.05.2024.

• Natural Language Processing*
• Qualitative Research

The Guide to Thematic Analysis

• What is Thematic Analysis?
• Advantages of Thematic Analysis
• Disadvantages of Thematic Analysis
• Thematic Analysis Examples
• How to Do Thematic Analysis
• Introduction

What is the purpose of coding in thematic analysis?

What should a codebook include, how should i code data for a thematic analysis, how do you organize codes in a thematic analysis.

• Collaborative Thematic Analysis
• Thematic Analysis Software
• Thematic Analysis in Mixed Methods Approach
• Abductive Thematic Analysis
• Deductive Thematic Analysis
• Inductive Thematic Analysis
• Reflexive Thematic Analysis
• Thematic Analysis in Observations
• Thematic Analysis in Surveys
• Thematic Analysis for Interviews
• Thematic Analysis for Focus Groups
• Thematic Analysis for Case Studies
• Thematic Analysis of Secondary Data
• Thematic Analysis Literature Review
• Thematic Analysis vs. Phenomenology
• Thematic vs. Content Analysis
• Thematic Analysis vs. Grounded Theory
• Thematic Analysis vs. Narrative Analysis
• Thematic Analysis vs. Discourse Analysis
• Thematic Analysis vs. Framework Analysis
• Thematic Analysis in Social Work
• Thematic Analysis in Psychology
• Thematic Analysis in Educational Research
• Thematic Analysis in UX Research
• How to Present Thematic Analysis Results
• Increasing Rigor in Thematic Analysis
• Peer Review in Thematic Analysis

Thematic Coding

Thematic analysis is a qualitative research method widely used across various disciplines to identify, analyze, and report patterns within data . It plays a crucial role in providing a detailed and complex account of data. Similar to many other qualitative research methods like framework analysis , narrative analysis , and discourse analysis , the process of coding is fundamental to thematic analysis, serving as the bridge between raw data and the emergence of insightful themes.

This article will guide you through the essential steps of coding for thematic analysis, from understanding the purpose of coding to organizing codes efficiently. By offering a clear and concise overview of the coding process, we aim to equip qualitative researchers with the necessary tools for conducting their thematic analysis effectively.

Coding in thematic analysis serves several critical functions. First, it allows researchers to systematically sift through vast amounts of qualitative data —such as interview transcripts , observations , or written responses—to identify significant patterns or themes. By breaking down the data into manageable segments, coding transforms raw information into organized categories that are easier to analyze.

Second, coding facilitates the recognition of relationships between different data segments. As researchers assign codes to data, they might begin to notice connections, contrasts, and trends that were not apparent at first glance. This process is crucial for developing a deeper understanding of the data and for the subsequent identification of themes that capture the studied phenomenon.

Furthermore, coding facilitates the rigor and transparency of the analysis. A well-documented coding process allows other researchers to understand the steps taken to arrive at certain conclusions, thereby enhancing the credibility and rigor of the study. Coding is a methodical approach to qualitative analysis , providing a clear trail from the raw data to the final report.

Lastly, coding is not just about data reduction; it's also an interpretative act. Researchers engage with the data, applying their theoretical knowledge and analytical skills to discern subtle nuances and meanings. This interpretive aspect of coding is what allows thematic analysis to go beyond mere description to provide insightful interpretations of complex human experiences and social phenomena.

When it comes to a codebook , thematic analysis requires a set of elements to facilitate coding qualitative data . It encapsulates not only the definitions of each code but also integrates rules for application, examples, and provisions for theme development, making it an indispensable tool for researchers. Crafting a codebook is an iterative part of coding, setting a structured path for qualitative data analysis and ensuring a uniform approach across the data set.

Central to the codebook are the definitions of the codes themselves. These are crafted with precision, providing researchers with clear guidance on when and how to apply each code to the data. This clarity is crucial for enhancing consistency in the data analysis process, thereby enhancing the overall trustworthiness and quality of the research findings.

Alongside these definitions, the codebook delineates specific rules for coding. These rules address potential challenges in coding, such as handling ambiguous data, coding data that might fit into multiple categories, and distinguishing between codes that are similar in nature.

Equally important are the examples included for each code. By illustrating how codes are applied to actual pieces of data, these examples serve as practical guides that clarify the definitions and rules, ensuring that researchers can apply the codes accurately and consistently.

As the analysis evolves, the codebook itself is designed to accommodate the emergence and definition of themes. This includes grouping codes under broader thematic categories and providing preliminary definitions and examples for these themes, thereby facilitating a deeper and more organized analysis of the data.

The dynamic nature of thematic analysis necessitates that the codebook also includes a section for revision history. This part of the document tracks the evolution of the codebook, documenting any changes or updates made throughout the analysis. This not only provides transparency but also aids in understanding the development and refinement of the coding scheme over time.

Furthermore, additional notes may be included to cover any other pertinent information that does not fit neatly into the aforementioned categories but is nevertheless crucial for the coding process. This could encompass reflections on the coding strategy, details about the coding environment, or the thematic analysis software tools utilized, offering valuable insights for the ongoing analysis or for other researchers who might use the codebook as a reference.

Turn your research question into valuable insights with ATLAS.ti

Organize, code, and analyze your data with our powerful analysis platform. Start with a free trial today.

Coding data in a thematic analysis process involves systematically identifying and labeling relevant parts of the data. This process is not just about tagging data with codes but also about engaging deeply with the content to discern underlying patterns and meanings. Coding lays the groundwork for the subsequent organization of codes and the generation of themes. It requires a meticulous and iterative approach, where data is reviewed multiple times to ensure that codes are accurately and comprehensively applied.

Below, we outline three key phases of the analysis process in thematic analysis: coding data, identifying patterns across data, and interpreting patterns across data.

Coding data

Coding the data refers to the process of reading through the data set (e.g., interview transcripts , field notes , documents , social media posts , etc.) to identify interesting excerpts and assigning codes that capture the essence of each data segment relevant to the research questions or objectives. At this stage, the aim is to code qualitative data as broadly and inclusively as possible, without worrying about the specificity or the potential overlap between different codes.

Coders should approach the data with an open mind, allowing the data itself to guide the creation of new codes. Codes can also emerge from more latent meanings present in the data, such that researchers can draw on their conceptual or theoretical understanding to create codes. This phase is exploratory in nature, with the goal of creating codes that capture the richness and diversity of the data.

Identifying patterns across data

After the expansive coding of the data, researchers can work with these codes to identify themes or patterns. This often also involves refining the codes and beginning to narrow down which are the most appropriate codes for the research questions or objectives. This phase requires the coder to make decisions about which codes to keep, combine, or discard.

The researcher can begin to identify patterns as they revise their codes: while a code might capture one idea, a theme brings together multiple ideas around a central organizing concept. At this stage, researchers begin creating provisional themes which will continue to be refined as the researcher progresses through their interpretive analysis.

Interpreting patterns across data

After engaging deeply with thematic coding and pattern identification, researchers can full develop their analysis by interpreting or making sense of the emerging patterns. It is important to revisit the data excerpts captured within each theme to ensure the theme effectively portrays the central organizing concept within the supporting data. This is also the point at which researchers name and define their themes, which can involve revising, combining, or even discarding themes; the objective is to have a set of themes that that tell a coherent and meaningful story about the data.

The researcher's subjective experience plays an important role in interpreting patterns as well, and researchers can critically reflect on how and why they make their interpretations. Fleshing out the interpretation of patterns and themes also relies heavily on writing up the analysis, as putting one's thoughts into words often clarifies new insights, exposes inconsistencies, and can effectively bridge key findings and supporting data.

Organizing codes is a critical step in thematic coding. It involves sorting, grouping, and categorizing codes into meaningful clusters that facilitate researchers' interpretation and development of themes.

This is where the researcher begins to see beyond individual data points and starts to understand the broader patterns and relationships within the data. It requires a thoughtful and iterative approach, constantly refining the organization of codes to ensure that they represent the data and align with the research objectives.

Here, we explore three essential strategies for organizing codes in thematic analysis: creating thematic maps, using code hierarchies, and iterative re-coding.

Creating thematic maps

Thematic maps are visual representations that illustrate the relationships between codes and potential themes. They help researchers move from a collection of codes to a structured understanding of how these codes interconnect and form broader themes. Creating a thematic map involves arranging codes based on their conceptual similarities and identifying the overarching themes to which they contribute. This visual tool is particularly useful for seeing how individual codes can combine to form a coherent narrative within the data. It also aids in identifying any gaps or overlaps in the coding.

Using code hierarchies

Code hierarchies involve organizing codes into a structured format, where broader categories encompass a set of related sub-codes. This approach helps in managing the complexity of the data by breaking down broad themes into more specific, manageable elements. Hierarchies can clarify the relationships between codes, indicating which are central themes and which are supporting or subsidiary. By establishing a hierarchical structure, researchers can more easily navigate their codes and refine their analysis, ensuring that each code is placed within a meaningful context.

Iterative re-coding

Organizing codes is not a one-off task but an iterative process that evolves as the analysis deepens. Iterative re-coding involves revisiting and potentially re-organizing the codes multiple times throughout the analysis. This may include merging similar codes, splitting broad codes into more specific ones, or discarding codes that no longer seem relevant. Each round of re-coding refines the organization of codes, making the eventual themes more robust and grounded in the data. This process ensures that the final themes reflect the complexities and richness of the data, contributing to a more nuanced and insightful qualitative research analysis.

Make ATLAS.ti your thematic analysis software solution

ATLAS.ti is the ideal qualitative data analysis software for qualitative coding. See why with a free trial.

• Open access
• Published: 30 May 2024

Challenges and advantages of electronic prescribing system: a survey study and thematic analysis

• Hamid Bouraghi 1 ,
• Behzad Imani 2 ,
• Abolfazl Saeedi 3 ,
• Ali Mohammadpour 1 ,
• Soheila Saeedi 1   na1 ,
• Taleb Khodaveisi 1   na1 &
• Tooba Mehrabi 4  

BMC Health Services Research volume  24 , Article number:  689 ( 2024 ) Cite this article

52 Accesses

Metrics details

Introduction

Electronic prescribing (e-prescribing) systems can bring many advantages and challenges. This system has been launched in Iran for more than two years. This study aimed to investigate the challenges and advantages of the e-prescribing system from the point of view of physicians.

In this survey study and thematic analysis, which was conducted in 2023, a researcher-made questionnaire was created based on the literature review and opinions of the research team members and provided to the physician. Quantitative data were analyzed using SPSS software, and qualitative data were analyzed using ATLAS.ti software. Rank and point biserial, Kendall’s tau b, and Phi were used to investigate the correlation between variables.

Eighty-four physicians participated in this study, and 71.4% preferred to use paper-based prescribing. According to the results, 53.6%, 38.1%, and 8.3% of physicians had low, medium, and high overall satisfaction with this system, respectively. There was a statistically significant correlation between the sex and overall satisfaction with the e-prescribing system ( p -value = 0.009) and the computer skill level and the prescribing methods ( P -value = 0.042). Physicians face many challenges with this system, which can be divided into five main categories: technical, patient-related, healthcare providers-related, human resources, and architectural and design issues. Also, the main advantages of the e-prescribing system were process improvement, economic efficiency, and enhanced prescribing accuracy.

The custodian and service provider organizations should upgrade the necessary information technology infrastructures, including hardware, software, and network infrastructures. Furthermore, it would be beneficial to incorporate the perspectives of end users in the system design process.

Peer Review reports

Medicine, a crucial commodity in healthcare due to its economic and strategic value, is a fundamental pillar in primary disease treatment. It constitutes significant health expenditures and budgets worldwide [ 1 ]. The prudent management of this valuable resource, through its appropriate prescription and usage, is essential. This is a key factor in ensuring the health security of communities [ 2 ]. Numerous studies indicate that errors in drug administration are prevalent. Although a significant proportion of these errors are preventable, they can leave serious complications for patients and even fatalities [ 3 ]. As the complexity of the drug prescribing process increases, resultant injuries and complications will likely escalate. Therefore, medication prescription is one of the main concerns and priorities of policymakers and trustees in the healthcare domain. In this regard, relentless endeavors are undertaken to enhance and optimize this process, and new supplementary solutions will be used as required. Employing electronic prescription (e-prescribing) systems as an alternative to manual prescription is a practical solution that can enhance and streamline this critical process [ 4 ].

In the traditional paper-based prescribing system, numerous issues arise, including illegible prescriptions, ambiguous orders, omissions, prescription forgery, and misidentification of patients. Studies indicate that these problems compromise patient safety and negatively impact the outcomes of drug treatments [ 5 , 6 ]. E-prescribing emerges as an effective and definitive solution to the inefficiencies, susceptibility to fraud, and administrative burdens associated with paper-based prescribing systems [ 7 ]. E-prescribing extends beyond merely utilizing a computer for prescription writing and storage. This technology encompasses all stages of the prescription process, including patient identification, prescription registration, prescription modification, duplication and renewal of prescriptions, and the transfer of prescriptions among stakeholders, all facilitated through specialized software and internet platforms [ 8 , 9 , 10 ].

As an information system, the e-prescribing system can integrate with other organizational systems, such as electronic health records and pharmacy information systems, within healthcare centers like hospitals [ 11 ]. Through the implementation and utilization of such a system, it is possible to overcome the problems and constraints of the traditional prescribing system due to the complexity of medical care and the increase in the number of drugs, thereby benefiting from its potential advantages. Some of the benefits of an e-prescribing system include reducing healthcare costs for stakeholders (patients, healthcare providers, insurers, and policymakers), reducing common prescribing errors, improving medication outcomes, increasing patient safety, increasing the readability and accuracy of prescriptions, enhancing coordination among stakeholders involved in the drug therapy process, and supporting clinical decision-making at the time of drug administration [ 12 , 13 , 14 ].

Despite the potential benefits of e-prescribing systems in the healthcare industry and significant investments and efforts by stakeholders to support such systems, their usage and adoption remain low, resulting in the failure of numerous implemented projects [ 11 , 12 ]. Given that e-prescribing systems are designed according to the specific needs and internal standards of each country, numerous studies have been conducted worldwide to investigate the benefits, challenges, the reasons for the failure and lack of acceptance of such systems [ 15 , 16 ].

E-prescribing systems in countries like Denmark, the United States, Finland, Sweden, and the United Kingdom are commonly tested and implemented at state, local, or regional levels. These systems cover the entire or a significant portion of the prescribing process. Variations in healthcare and insurance systems across different countries lead to diverse approaches regarding e-prescribing and its evolution. Consequently, these countries exhibit distinct starting points, implementation procedures, and technical strategies. Moreover, e-prescribing systems and models vary not only across different countries but also within the same country [ 17 ]. While meticulously developed and successfully implemented in the United States of America, England, and Germany, this system has reached significant maturity and yielded substantial advantages for the health systems of these countries. However, in other nations, especially developing countries, e-prescribing still encounters significant challenges on its path to widespread acceptance and goal achievement [ 18 , 19 , 20 , 21 ].

Recognizing that the implementation of e-prescribing is a priority for the Iran Ministry of Health and Medical Education (MOH), the Iran Food and Drug Administration (IFDA) established a multi-stakeholder working group in 2015. This group, composed of medical informatics experts, aimed to develop recommendations for effective e-prescribing implementation [ 22 ]. In Iran, adopting e-prescribing in governmental and university hospitals has been proposed as a legal requirement since 2020. The Social Security Organization, a pioneering institution in this domain, has aligned with the implementation policies of this plan and has ceased issuing treatment booklets since early 2021 [ 23 ]. The Health Insurance Organization, as another government institution, independently developed and deployed its e-prescription system across all medical education centers affiliated with universities of medical sciences in Iran. Consequently, the two primary organizations (Social Security Organization and Health Insurance Organization) have successfully implemented the e-prescribing system. Their goals include efficient management of healthcare resources, reduction of common manual prescribing errors, and enhancement of patient safety [ 24 ].

In general, medical centers in Iran employ three distinct electronic prescription systems. “Electronic Prescription (EP)” and “Dinad” serve outpatients covered by the Social Security and Health Insurance Organization, while “Shafa” caters to all inpatients. For individuals without coverage from these insurances, physicians resort to paper prescriptions [ 25 ]. Electronic prescribing was not implemented simultaneously in all provinces of Iran. It was first used on a trial basis in a few provinces and then implemented throughout the country. Although these systems have provided significant benefits to their users in Iran, they have also encountered numerous challenges. Consequently, this comprehensive study was undertaken to explore both the advantages and obstacles associated with e-prescribing systems in Iran.

This survey study and thematic analysis was conducted to examine the challenges and advantages of the e-prescribing system in Iran in 2023. This study was conducted in three main steps: literature review and questionnaire design, data collection, and data analysis.

Literature review and questionnaire design

In the first step of this research, a questionnaire was designed based on the review of similar studies and the opinions of the research team members. To design the questionnaire, various databases, including PubMed, Google Scholar, and Scopus, were searched with related terms such as “electronic prescribing,” “electronic prescribing challenges,” and “electronic prescribing advantages.” Then, the most relevant articles retrieved from these databases were examined, and relevant data were extracted from these articles. Then, focus group sessions were held with the research team. The data extracted from the articles were presented in the sessions, and based on these data and the opinions of the research team, the questionnaire was finalized. This questionnaire had three sections: (1) demographic data (2), questions related to the advantages and challenges of e-prescribing, and (3) open-ended questions related to the challenges and advantages of the e-prescribing system. A five-point Likert scale from completely agree to completely disagree was used for the questions of the second part of the questionnaire. The face and content validity of the questionnaire was checked and confirmed with the cooperation of five experts in health information management, medical informatics, and information technology who were thoroughly familiar with prescribing systems. The content validity of the questionnaire was measured using the Content Validity Index (CVI) and Content Validity Ratio (CVR). To determine CVR, the experts were asked to classify each of the questions based on the three-point Likert scale as follows:

The question is necessary

The question is useful but not necessary

The question is not necessary

Then, the following formula was used to calculate CVR:

CVR = (Ne − N/2)/ (N/2), (N: total number of experts, Ne: the number of experts who have chosen the “necessary” option.).

Based on the Lawshe table for minimum values of CVR, items with CVR equal to or greater than 0.99 were kept. To calculate the CVI, the experts determined the degree of relevance of each question on a 4-point Likert scale from not relevant to completely relevant. The following formula was used to decide about the acceptance of each question:

CVI: The number of experts who chose options 3 and 4 / the total number of experts. It was decided to reject or accept each question as follows: < 0.7 = rejected, 0.7–0.79 = revised, > 0.79 = accepted. The reliability of the questionnaire was calculated using Cronbach’s alpha and Guttman coefficient. Values greater than 0.7, 0.5–0.7, and less than 0.5 indicate high, acceptable, and low reliability of the questionnaire, respectively.

The third part of the questionnaire included open-ended questions. Two following questions were placed at the end of the questionnaire and were asked to the physicians:

In your opinion, what other advantages does this electronic prescribing system have?

In your opinion, what other challenges does this electronic prescribing system have?

Data collection

After the questionnaire was finalized, it was prepared in both paper and electronic formats. The electronic version of the questionnaire was prepared on the Porsline platform. For the survey, first, a list of physicians working in the teaching hospitals was prepared, and then we tried to get the contact numbers of the physicians as well. The questionnaire link was sent to physicians through the local social networks whose contact numbers were available, and physicians whose contact numbers were not available were referred to them in person. Many physicians refused to receive the questionnaire and answers due to lack of time. Two reminder messages were also sent to the doctors who had received the questionnaire link through social networks. In the face-to-face group, the doctors who did not have enough time to complete the questionnaire at that moment, the researcher provided the questionnaire to the physicians and coordinated with them to receive it at a later time. A total of 122 physicians agreed to participate in the study. It should be noted that to avoid missing data, it was mandatory to answer all the questions in the electronic questionnaire, and in the paper-based questionnaire, the researchers checked the questionnaire immediately, and if any fields were not completed, they asked the physicians to complete the incomplete items of the questionnaire again.

Data analysis

Descriptive statistics including mean, standard deviation, frequency median, interquartile range and percentage were used for data analysis.

The relationship of “sex,” “specialty,” “physician’s computer skills,” “age,” and “duration” with “satisfaction” was investigated. Since “satisfaction” is a qualitative ordinal variable, the Rank-biserial index was used to examine the relationship between this variable and two-level nominal variables such as “gender” and “specialty.” Kendall’s tau b index was also used to examine the relationship between “satisfaction” (ordinal variable) with rank variables such as “physician’s computer skills” and continuous quantitative variables such as “age” and “duration.” To investigate the relationship between “willingness to use paper-based or e-prescribing” with “sex,” “specialty,” “physician’s computer skills,” “age,” and “duration,” Phi, Rank-biserial, and Point-biserial were used respectively. The p -values obtained from the chi-square test were also reported to check the presence or absence of a relationship between two variables. The type I error in this study was considered 5%. Data analysis was carried out using SPSS version 26.

The answers given by 84 physicians to two open-ended questions were typed in Word.

Thematic analysis was used to analyze the open-ended questions and identify themes within qualitative data. For thematic analysis, first, the answers typed in the Word were imported into the ATLAS.ti software, and then the pattern extraction process was carried out according to the following steps:

The imported text was read several times to get familiar with the data

After familiarizing with the data, initial coding was done

After coding, the extracted codes were checked and revised many times

Similar codes were merged and grouped, and subthemes were created

Finally, the sub-themes were reviewed and linked, and the main themes were created

The designed questionnaire was given to 122 physicians, of which 84 physicians completed the questionnaires (response rate: 68.85%). Demographic characteristics of physicians are given in Table  1 . Most of the participants were general practitioners (56%) and women (53.6%). 91.7% of the physicians believed that they have medium and high computer skills and the average duration of using the e-prescribing system was 15.50 ± 8.798 months.

The results showed that the questionnaire had acceptable reliability (Cronbach’s alpha = 0.605, Guttman’s coefficient = 0.718). The mean (std. deviation), median and interquartile range of each question in the questionnaire are given in Table  2 . The questions were categorized into two sections: advantages and challenges of the e-prescribing system. The total mean score of advantages for the e-prescribing system was 2.15 and this value for challenges of this system was 2.75. Out of the advantages of this technology, the highest mean score (2.79) was related to the “E-prescribing system has reduced the possibility of wrong drug delivery due to illegible prescriptions” and the lowest (1.24) was related to the “The e-prescribing system has led to improved physician performance”. The most important challenge that physicians had with the e-prescribing system was the insufficient bandwidth with an average of 3.49. Two other challenges mentioned by physicians about this system and received a high mean score (3.43) were the challenges related to lengthening the duration of each visit and increasing the waiting time of patients.

The results of investigating the correlation between the duration of e-prescribing system use, age, sex, specialty, and the physician’s computer skills with the overall satisfaction with the e-prescribing system are reported in Table  3 . According to the results, 45 (53.6%), 32 (38.1%), and 7 (8.3%) physicians had low, medium and high overall satisfaction with this system, respectively. There was a statistically significant correlation between the sex and overall satisfaction with the e-prescribing system ( p -value = 0.009).

The results of the correlation between duration, age, sex, specialty, and the physician’s computer skills with the willingness to use paper-based prescribing or the e-prescribing system are reported in Table  4 . According to the results, 60 (71.4%) and 24 (28.6%) physicians preferred to use paper-based and e-prescribing respectively. There was a statistically significant correlation between the computer skill level and the prescribing methods ( P -value = 0.042).

The themes and sub-themes extracted from the question related to the advantages of the e-prescribing system are shown in Fig.  1 . The main themes of the e-prescribing system’s advantages were the following:

Process improvement

Economic efficiency

Enhance the accuracy of prescribing

These three themes included a total of 10 sub-themes.

Among the advantages noted for electronic prescribing, the possibility of editing prescriptions, providing different dosages of drugs, and the impossibility of manipulating prescriptions by patients or other people were mentioned more than other advantages. Also another mentioned advantage was the possibility of providing pre-prepared prescriptions for common diseases, which led to the acceleration of prescribing for these diseases.

Thematic map of concepts extracted from qualitative data related to the advantages of the e-prescribing system

Concepts related to the challenges of the e-prescribing system were categorized into five main themes as follows (Fig.  2 ):

Technical issues

Patient-related issues

Healthcare providers-related issues

Human resources challenges

Architectural and design issues

These five themes included more than 30 sub-themes.

Many challenges for electronic prescribing were mentioned in the form of given themes. One of the most important challenges mentioned by many physicians was various technical problems including network disconnection. Also, another big challenge that caused the dissatisfaction of the patients was the lack of skill of many physicians in working with computer systems, which led to the low speed of typing the drugs in the system and as a result, increased the duration of the patients’ visits. Also, many physicians did not have computer systems in their clinics, which led to the lack of electronic prescriptions and, as a result, the lack of use of insurance services for patients. Also, considering that many physicians are used to the paper prescription method, they were not willing to accept the changes and resisted these changes, as a result, they needed personnel to register the prescriptions.

Thematic map of concepts related to the challenges of the e-prescribing system

E-prescribing systems have many advantages, but they also pose certain challenges. These systems can enhance medication safety by reducing prescription errors caused by illegible handwriting or oral miscommunication. They can also improve efficiency by streamlining the prescription process, reducing the time spent on phone calls and faxes between healthcare providers and pharmacies. Furthermore, e-prescribing can provide clinicians with up-to-date information about patients’ medications and allergies, thereby improving patient care.

Although e-prescribing systems have many advantages, their implementation is not without any challenges. These include the costs associated with system implementation and maintenance, issues related to system interoperability, and the necessity for user training and technical support. Moreover, while these systems can mitigate traditional medication errors, they may also introduce new types of errors, such as those caused by user interface design or software glitches. Maximizing the benefits and minimizing the challenges associated with e-prescribing systems requires meticulous system design, comprehensive user training, and continuous system evaluation.

As demonstrated in the results section, the e-prescribing system’s mean overall benefit score was 2.15. This score suggests a moderate level of perceived benefits. It implies that while certain advantages are acknowledged, the system still needs to be improved to enhance user satisfaction and the perception of benefits. In this context, among the factors associated with the system’s benefits from the users’ perspective, the statements “Improved workflow has resulted from e-prescribing” and “The e-prescribing system has led to improved physician performance” received average scores of 1.48 and 1.24, respectively. These relatively low scores suggest that respondents of the survey or study largely disagree that the electronic system has enhanced their workflow or improved their performance. Several studies [ 11 , 12 , 26 , 27 ] have demonstrated that users do not concur that the use of prescribing systems leads to workflow improvement or performance enhancement. There are multiple possible reasons for this, including:

Usability issues: The e-prescribing system might not be user-friendly or intuitive, leading to difficulties in adoption among healthcare professionals.

Training and support: There might be a lack of adequate training and support for the users, making it challenging for them to adapt to the new system.

System limitations: The system might not be flexible enough to accommodate the diverse needs of different healthcare settings, leading to workflow inefficiencies.

Resistance to change: Healthcare professionals, like any other group, might resist changes to established routines. This resistance could affect their perception of the system’s benefits.

Among the challenges identified in the use of e-prescribing systems, the statement “Doctors have faced challenges with e-prescribing due to insufficient bandwidth” received the highest score of 3.49. According to this relatively high score, the survey or study respondents strongly agree that insufficient bandwidth has been a significant obstacle to the use of e-prescribing. This issue results in prolonged patient waiting times, leading to extended queues and a decrease in physician productivity. There are multiple factors that can cause insufficient bandwidth, such as:

Network Infrastructure: In areas with poor network infrastructure, insufficient bandwidth can significantly slow down the operation of e-prescribing systems, making it difficult for doctors to use them effectively.

System Requirements: To function optimally, e-prescribing systems may need a certain level of bandwidth. System lags or downtime could result if the available bandwidth is below this level.

Data Transfer: E-prescribing systems often need to transfer large amounts of data, including patient records, prescriptions, and other related information. Insufficient bandwidth can slow down this data transfer, affecting the system’s efficiency.

Real-time Updates: Many e-prescribing systems provide real-time updates to ensure that all users have the most current information. If there is not enough bandwidth, these updates can be delayed, resulting in potential errors or miscommunications.

Generally, as indicated by various studies [ 28 , 29 , 30 ], the implementation of e-prescribing systems requires robust hardware, sophisticated software, and a reliable network infrastructure. These elements are integral to the successful deployment and operation of such systems. According to this study, the hardware, software, and network infrastructure in Iran are not suitable for the implementation of e-prescribing systems. This inadequacy has caused increased challenges and dissatisfaction among users. Furthermore, our evaluation of physicians’ overall satisfaction with the e-prescribing system revealed that the majority, 45 (53.6%), had low satisfaction. Conversely, only a small proportion, 7 (8.3%), reported high satisfaction. Subsequently, the e-prescribing system is not widely accepted by users, with the majority (71.4%) favoring paper-based prescribing. Many other studies have indicated higher levels of user satisfaction and a greater willingness to accept and use e-prescribing systems, contrary to our study’s findings [ 31 , 32 , 33 , 34 ]. The low level of satisfaction and users’ reluctance to adopt the e-prescribing system can be attributed to various challenges and problems identified by them. Users have been greatly impacted by these issues, which range from technical difficulties to system design and architecture issues, resulting in dissatisfaction, diminished motivation, and resistance towards the system.

Although e-prescribing systems represent a novel and transformative approach in healthcare, they offer numerous benefits, including improved efficiency, reduced medication errors, and enhanced patient safety. However, our study highlights the presence of significant challenges, such as technical issues and problems related to system design and architecture, which result in low user satisfaction and hinder system adoption. The custodian and service provider organizations should upgrade the necessary information technology infrastructures, including hardware, software, and network infrastructures, to address the technical challenges. Furthermore, given that the design and architectural issues of the e-prescribing systems have resulted in user dissatisfaction and diminished motivation to use the system, identifying and addressing these problems and shortcomings in future updates is recommended. Moreover, it is important to take into account the end users’ perspectives during the system design process.

Data availability

All data generated or analyzed during this study are included within this article.

Babaie J, Elmi S. Drug prescribing in family physician program involved health care centers and hospital of Hashtrood (Iran) in 2017. Iran J Health Insurance. 2019;2(3):162–71.

Google Scholar  

Borriharn S, Kaewvichit S, Pannavalee W, Thiankhanithikun K, Kanjanarat P. A systematic review: quality indicators for assessing Drug System Management. J Health Sci. 2014:934–42.

Pirmohamed M, James S, Meakin S, Green C, Scott AK, Walley TJ, et al. Adverse drug reactions as cause of admission to hospital: prospective analysis of 18 820 patients. Bmj. 2004;329(7456):15–9.

Cresswell K, Coleman J, Slee A, Williams R, Sheikh A, Team eP. Investigating and learning lessons from early experiences of implementing ePrescribing systems into NHS hospitals: a questionnaire study. PLoS ONE. 2013;8(1):e53369.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Elliott RA, Lee CY, Hussainy SY. Electronic prescribing and medication management at a residential aged care facility. Appl Clin Inf. 2016;7(01):116–27.

Article   Google Scholar  

Shawahna R, Rahman NU, Ahmad M, Debray M, Yliperttula M, Decleves X. Electronic prescribing reduces prescribing error in public hospitals. J Clin Nurs. 2011;20(21–22):3233–45.

Article   PubMed   Google Scholar  

Ahmadi M, Samadbeik M, Sadoughi F. Modeling of outpatient prescribing process in Iran: a gateway toward electronic prescribing system. Iran J Pharm Res. 2014;13(2):725.

PubMed   PubMed Central   Google Scholar  

Shi L-P, Liu C-H, Cao J-F, Lu Y, Xuan F-X, Jiang Y-T, et al. Development and application of a closed-loop medication administration system in University of Hongkong-Shenzhen Hospital. Front Nurs. 2018;5(2):105–9.

Grossman JM, Gerland A, Reed MC, Fahlman C. Physicians’ experiences using Commercial E-Prescribing systems: Physicians are optimistic about e-prescribing systems but face barriers to their adoption. Health Aff. 2007;26(Suppl2):393–404.

Bell DS, Cretin S, Marken RS, Landman AB. A conceptual framework for evaluating outpatient electronic prescribing systems based on their functional capabilities. J Am Med Inf. 2004;11(1):60–70.

Vejdani M, Varmaghani M, Meraji M, Jamali J, Hooshmand E, Vafaee-Najar A. Electronic prescription system requirements: a scoping review. BMC Med Inf Decis Mak. 2022;22(1):1–13.

Mohsin-Shaikh S, Furniss D, Blandford A, McLeod M, Ma T, Beykloo MY, et al. The impact of electronic prescribing systems on healthcare professionals’ working practices in the hospital setting: a systematic review and narrative synthesis. BMC Health Serv Res. 2019;19:1–8.

Gates PJ, Hardie R-A, Raban MZ, Li L, Westbrook JI. How effective are electronic medication systems in reducing medication error rates and associated harm among hospital inpatients? A systematic review and meta-analysis. J Am Med Inform Assoc. 2021;28(1):167–76.

Hailiye Teferi G, Wonde TE, Tadele MM, Assaye BT, Hordofa ZR, Ahmed MH, et al. Perception of physicians towards electronic prescription system and associated factors at resource limited setting 2021: Cross sectional study. PLoS ONE. 2022;17(3):e0262759.

Boonstra A. Interpretive perspectives on the acceptance of an electronic prescription system. J Inform Technol Case Application Res. 2003;5(2):27–50.

Boonstra A, Boddy D, Fischbacher M. The limited acceptance of an electronic prescription system by general practitioners: reasons and practical implications. New Technol Work Employ. 2004;19(2):128–44.

Samadbeik M, Ahmadi M, Sadoughi F, Garavand A. A copmarative review of electronic prescription systems: lessons learned from developed countries. J Res Pharm Pract. 2017;6(1):3–11.

Article   PubMed   PubMed Central   Google Scholar  

Chang H-Y, Kan HJ, Shermock KM, Alexander GC, Weiner JP, Kharrazi H. Integrating e-prescribing and pharmacy claims data for predictive modeling: comparing costs and utilization of health plan members who fill their initial medications with those who do not. J Managed Care Specialty Pharm. 2020;26(10):1282–90.

Cresswell KM, Lee L, Slee A, Coleman J, Bates DW, Sheikh A. Qualitative analysis of vendor discussions on the procurement of computerised physician order entry and clinical decision support systems in hospitals. BMJ open. 2015;5(10):e008313.

Fischer SH, Rudin RS, Shi Y, Shekelle P, Amill-Rosario A, Scanlon D, et al. Trends in the use of computerized physician order entry by health-system affiliated ambulatory clinics in the United States, 2014–2016. BMC Health Serv Res. 2020;20:1–6.

Gall W, Aly A-F, Sojer R, Spahni S, Ammenwerth E. The national e-medication approaches in Germany, Switzerland and Austria: a structured comparison. Int J Med Informatics. 2016;93:14–25.

Dehghan H, Eslami S, Ghasemi SH, Jahangiri M, Bahaadinbeigy K, Kimiafar K, et al. Development of a National Roadmap for electronic prescribing implementation. Stud Health Technol Inform. 2019;260:121–7.

PubMed   Google Scholar  

Jebraeily M, Rashidi A, Mohitmafi T, Muossazadeh R. Evaluation of Outpatient Electronic prescription system capabilities from the perspective of Physicians in Specialized Polyclinics of Urmia Social Security Organization. Payavard Salamat. 2021;14(6):557–68.

Raeesi A, Abbasi R, Khajouei R. Evaluating physicians’ perspectives on the efficiency and effectiveness of the electronic prescribing system. Int J Technol Assess Health Care. 2021;37(1):e42.

Hayavi-Haghighi MH, Davoodi S, Teshnizi SH, Jookar R. Usability evaluation of electronic prescribing systems from physicians’ perspective: a case study from southern Iran. Inf Med Unlocked. 2024;45:101460.

Williams J, Bates DW, Sheikh A. Optimising electronic prescribing in hospitals: a scoping review protocol. BMJ Health Care Inf. 2020;27(1).

Santiago BC, Bengoechea MM, Barrueta OI, Ibañez AS, Aramburu EA, Garcia EI, et al. OHP-005 advantages and disadvantages of an electronic prescribing system. Aspects to consider during pharmacist validation. Eur J Hosp Pharmacy: Sci Pract. 2013;20(Suppl 1):A137–A.

Tamblyn R, Huang A, Kawasumi Y, Bartlett G, Grad R, Jacques A, et al. The development and evaluation of an integrated electronic prescribing and drug management system for primary care. J Am Med Inform Assoc. 2006;13(2):148–59.

Elson B. Electronic prescribing in ambulatory care: a market primer and implications for managed care pharmacy. J Managed Care Pharm. 2001;7(2):115–20.

Farida S, Krisnamurti DGB, Hakim RW, Dwijayanti A, Purwaningsih EH. Implementation of electronic prescribing. eJournal Kedokteran Indonesia. 2017;5(3):16–211.

Abdel-Qader DH, Cantrill JA, Tully MP. Satisfaction predictors and attitudes towards electronic prescribing systems in three UK hospitals. Pharm World Sci. 2010;32:581–93.

Shams MEHES. Implementation of an e-prescribing service: users’ satisfaction and recommendations. Can Pharmacists J. 2011;144(4):186–91.

Bright HR, Peter J, Chandy S. Electronic prescribing system in a Teaching Hospital-user satisfaction and factors affecting successful implementation. Der Pharmacia Letter. 2019;11(2):10–24.

Jariwala KS, Holmes ER, Banahan DJ III. Adoption of and experience with e-prescribing by primary care physicians. Res Social Administrative Pharm. 2013;9(1):120–8.

Download references

Acknowledgements

This work was supported by a grant from Hamadan University of Medical Sciences Research Council (140206074578).

Author information

Soheila Saeedi and Taleb Khodaveisi contributed equally to this work.

Authors and Affiliations

Department of Health Information Technology, School of Allied Medical Sciences, Hamadan University of Medical Sciences, Shahid Fahmideh Blvd, Hamadan, Iran

Hamid Bouraghi, Ali Mohammadpour, Soheila Saeedi & Taleb Khodaveisi

Department of Operating Room, School of Paramedicine, Hamadan University of Medical Sciences, Hamadan, Iran

Behzad Imani

School of Medicine, Iran University of Medical Sciences, Tehran, Iran

Abolfazl Saeedi

Health Information Management Department, Besat Hospital, Hamadan University of Medical Sciences, Hamadan, Iran

Tooba Mehrabi

You can also search for this author in PubMed   Google Scholar

Contributions

SS, TKH and HB developed the concept for the study. SS, TM, and AS collected data. SS and TKH carried out the analysis and interpretation under the supervision of HB and BI. Finally, SS, AM, AS, and HB drafted the manuscript. All authors reviewed the content and approved it.

Corresponding authors

Correspondence to Soheila Saeedi or Taleb Khodaveisi .

Ethics declarations

Ethics approval and consent to participate.

The study was conducted in accordance with the Declaration of Helsinki and approved by a local ethics committee in Iran, namely Ethics Committee of the Hamadan University of Medical Sciences (IR.UMSHA.REC.1402.408). Verbal informed consent obtained from all the participants included in the study and was approved by the Ethics Committee of the Hamadan University of Medical Sciences.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Bouraghi, H., Imani, B., Saeedi, A. et al. Challenges and advantages of electronic prescribing system: a survey study and thematic analysis. BMC Health Serv Res 24 , 689 (2024). https://doi.org/10.1186/s12913-024-11144-3

Download citation

Received : 11 December 2023

Accepted : 23 May 2024

Published : 30 May 2024

DOI : https://doi.org/10.1186/s12913-024-11144-3

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Electronic prescribing system
• Thematic analysis
• Prescription

BMC Health Services Research

ISSN: 1472-6963

• General enquiries: [email protected]

Qualitative Research Methods : Roadmap To Thematic Analysis

Participants will gain insight into the fundamental principles in thematic analysis.

Date and time

Centre for Innovation in Education, University of Liverpool

About this event

Sumamry : Participants will gain insight into the fundamental principles and techniques involved in thematic analysis, enabling them to effectively identify, analyze, and interpret patterns within qualitative data.

Speaker: Dr Nashwa Ismail - Lecturer in Digital Education and Innovation

Date: 05/06/2024

Time: 2:00 pm

Online MS Teams : https://teams.microsoft.com/l/meetup-join/19%3ameeting_OWY5MWNjM2UtMWE2Yy00NGVlLWE1NzItMWI0OWU5OGIwMTM0%40thread.v2/0?context=%7b%22Tid%22%3a%2253255131-b129-4010-86e1-474bfd7e8076%22%2c%22Oid%22%3a%229d934999-8347-452d-ae7d-152ad0dbcf21%22%7d

Meeting ID: 352 017 025 888 - Passcode: yPTn6u

Looking forward to meeting you all.

• United Kingdom Events
• Liverpool Events
• Things to do in Liverpool
• Liverpool Classes
• Liverpool Science & Tech Classes
• #qualitative

Organized by