qualitative research tool open source

FEEDING YOUR QUALITATIVE NEEDS

Have you ever searched "free qualitative research software" only to be disappointed that nothing lets you tag your materials? Search no more! Taguette is a free and open-source tool for qualitative research. You can import your research materials, highlight and tag quotes, and export the results!

Learn more » Try out Taguette on our server

Work Locally

Taguette works both on your local computer (macOS, Windows, Linux) and on a server. When running Taguette locally, your data is as secure as your computer . That means that if you can have your data on your computer, you can run Taguette with no worries.

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Highlight & Tag

Taguette allows you to upload your research materials and tag them, just as you would use different color highlighters with printed paper. You can add new tags, then just select some text, click 'new highlight', and add whatever tag you find to be most relevant!

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Export Results

After you've done your work highlighting materials in Taguette, you can export in a variety of ways -- your whole project, codebook, all your highlighted quotes (or ones for a specific tag!), and highlighted documents. It's a good practice to keep an archival copy of your work!

View details »

TAGUETTE is an open-source web-based document tagging tool for qualitative data analysis.

Using this tool, you can upload a collection of documents, create a hierarchy of tags, and annotate portions of documents with tags and notes that you can recall and organize later.

Register now for free and get started!

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.

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taguette 1.4.1

pip install taguette Copy PIP instructions

Released: Feb 4, 2023

Free and open source qualitative research tool

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View statistics for this project via Libraries.io , or by using our public dataset on Google BigQuery

License: BSD License (BSD-3-Clause)

Author: Remi Rampin

Tags qualitative, document, text, tagging, tags, highlights, notes

Requires: Python >=3.7, <4

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• 5 - Production/Stable
• Web Environment
• End Users/Desktop
• Science/Research
• OSI Approved :: BSD License
• OS Independent
• Python :: 3
• Python :: 3 :: Only
• Python :: 3.7
• Python :: 3.8
• Python :: 3.9
• Python :: 3.10
• Python :: 3.11
• Scientific/Engineering :: Information Analysis
• Scientific/Engineering :: Visualization
• Text Processing

Project description

A spin on the phrase “tag it!”, Taguette is a free and open source qualitative research tool that allows users to:

Check out our website to learn more about how to install and get started.

Motivation and goal

Qualitative methods generate rich, detailed research materials that leave individuals’ perspectives intact as well as provide multiple contexts for understanding the phenomenon under study. Qualitative methods are used by a wide range of fields, such as anthropology, education, nursing, psychology, sociology, and marketing. Qualitative data has a similarly wide range: observations, interviews, documents, audiovisual materials, and more.

However - the software options for qualitative researchers are either far too expensive , don’t allow for the seminal method of highlighting and tagging materials, or actually perform quantitative analysis , just on text.

It’s not right or fair that qualitative researchers without massive research funds cannot afford the basic software to do their research.

So, to bolster a fair and equitable entry into qualitative methods, we’ve made Taguette!

Installation

You can find complete installation instructions on our website , including installers for Windows and MacOS.

Development setup from the repository

You can install from a local clone of this repository, which will allow you to easily change the sources to suit your needs:

Licensed under a BSD 3-clause “New” or “Revised” License . See the LICENSE.txt file for details.

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8 Great Tools To Perform Qualitative Data Analysis in 2022

Collecting qualitative customer data unlocks a potential goldmine of growth for your organization.

That is, if you know what to do with it. 

Qualitative data tells you how your customers feel and what they want from you. Examining your customer’s experience (CX) and putting the customer at the center of everything you do is likely to lead to an increase in your bottom line. 

However, in order to extract meaningful insights, you have to effectively analyze the data you collect. For this you’ll need the right qualitative data analysis tools.

Traditionally these tools were used exclusively by data specialists or analysts. Nowadays, however, data is so ubiquitous that even those outside of those remits can find themselves needing to make sense of large amounts of data. 

There are a lot of options out there and choosing the ideal software for your needs is not always easy. Here we’ll explain exactly what qualitative data analysis software is, then talk you through some of the best tools on the market. . 

What is Qualitative Data Analysis Software?

The 8 best qualitative data analysis software.

In order to understand what qualitative software can do for us, we need to start with what qualitative data actually is. 

Essentially, qualitative data is data that is non-numerical. It is descriptive and conceptual. Qualitative data is collected from a number of different sources. Some popular types include interviews, focus groups, surveys, e-mails, customer feedback, customer service tickets, observation notes, and phone calls.

This data, when it comes back to you, can be immense. Qualitative data analysis tools can help you organize, process, and analyze data for actionable insights. 

Qualitative data analysis software is used across a wide range of sectors and industries such as healthcare, the legal industry, e-commerce businesses, marketing departments - and everything in between. If your company has large amounts of data, you most likely need QDA software. 

The functionality of these tools varies greatly. At one end of the spectrum, you have software which allows you to tag and highlight important parts of your research. At the other end you have the fastest, most efficient kinds of software which employ the help of artificial intelligence to help you tag, analyze and visualize your research at record speed. 

Let’s jump straight into the ins and outs of 8 of the best qualitative data analysis tools out there.

Here is our list of the 8 top qualitative data analysis software. 

• MAXQDA - A well-established, reliable QDA Software 
• NVivo - Intuitive software offering some automation 
• ATLAS.ti - A powerful QA tool that offers some AI-improved functions 
• QDA Miner - Offers both a free and paid version 
• Quirkos - An easy to use, simplified tool 
• Dedoose - A tool that enables collaboration and team work
• Taguette - A free, open-source, data organization option 
• MonkeyLearn - AI-powered, qualitative analysis and visualization tool 

MAXQDA is a qualitative, quantitative, and mixed method data analysis tool. It lets you input data from a range of sources such as surveys, interviews, and focus groups to name a few. You can then tag and categorize this data for analysis. 

Best for:  In their words, MAXQDA was “created by researchers, for researchers.” This is across the education, non-profit and commercial sectors. 

Strengths: It’s easy to use and can support a number of different languages. It also uses AI to help users with audio transcription. 

This software was founded in 1989, so they have been around for a while and you can trust that their offerings are reliable. 

Weaknesses: Using it collaboratively in a team is not easy as individual users have to save their work and then merge the versions. This can be cumbersome. It’s also not the most attractive to look at compared with other software. 

Pricing: They have three different pricing plans that come with both an annual and perpetual price. They also offer a free trial. You can find more information here .

Like MAXQDA, NVivo is a software tool that allows its users to organize and store their qualitative data ready for analysis. You can also import word docs, PDFs, audio, images, and video. 

Best for: Researchers or academics looking for software with autocoding. 

Strengths: The interface is easy to use and is quite like Microsoft - this makes it instantly familiar and intuitive for many users. It’s much more powerful than some other offerings and offers automated transcription and autocoding. 

Weakness: NVivo struggles with languages that have characters, this isn’t a problem, for instance, for MAXQDA. 

While it is powerful compared to some of its competitors like Taguette, it doesn’t have the power to work with large data sets. After you’ve coded your data, you will still have to analyze your data manually, which could take a long time. 

Pricing: The price will vary according to a few factors, for example, whether you are a student or whether you are purchasing for an organization. More information can be found here.

3. ATLAS.ti

ATLAS.ti is a powerful QDA software tool, it supports large bodies of textual, graphical, audio and video data. Unlike other software in this category such as Quirkos, it has incorporated AI technology as it has evolved. 

Best for: This is best for research organizations, corporations, and academic institutions due to the extra AI features and the added cost.   

Strengths: Its interface is cleaner and sleeker than both Nvivo and MAXQDA, and collaboration is easier than in MAXQDA. It is also more powerful, boating both sentiment analysis and autocoding. 

Weakness: It can get expensive for individual users (such as students). Some users have also complained that the coding features are not that intuitive. 

Pricing: They offer a free trial and an extensive amount of licensing options based on sector and individual needs. More information can be found here.

4. QDA Miner

QDA Miner is a qualitative and mixed-method software that helps you to organize, code and analyze your data. They offer both a paid and free version called QDA Miner Lite.

Best for: Those looking for advanced visualizations and who are working alone.

Strengths: The latest version, QDA Miner 6, which was released in 2020, can link up with Tableau, a top data visualization tool , to give you an array of visualization options. 

Weakness:   QDA Miner doesn’t allow for collaboration which would be a big drawback if you are working on data with a team. Within the free version, the import and export functions are limited, as are the analysis functions. However, it might be enough to get you started or if you’re looking to test the waters. 

Pricing: They offer a number of packages according to sector and needs. Find details here.

Quirkos describes itself as a simple software tool that can help in the analysis of qualitative data. It is affordable and is popular within the education sector.  

Best for: Students and academics. 

Strengths: Quirkos offers a free trial which can be great if you are not sure what software is right for you. It’s also more affordable than some of the more advanced options like Atlas.ti. The drag and drop text functions make it simple and easy to use. You can also work collaboratively in Quirkos, and in real time.

Weakness: Its simplicity means that it offers less functionality. You have to code manually and this could be a deal-breaker if you have a lot of data. It also has fewer import options than other software. 

Pricing: There are three different options. Student, academic, commercial. Prices vary according to version and whether you want it cloud or offline. You can find more information here.

Dedoose is a 100% web-based tool for qualitative analysis. It was created by academics from UCLA and was designed to analyze both qualitative and quantitative data. It’s capable of importing data from a range of different formats, including documents, images, audio, video, and spreadsheets. 

Best for: Those who want a fully web based option where they can easily collaborate with team members.

Strengths: This software is team-oriented and user-friendly. It’s easy to import both text and visual data. It’s also compatible with mobile. 

Weakness: While they boast affordability, Dedoose can work out to be more expensive than other software as they only charge a monthly fee rather than a yearly license. The fact that it’s 100% web based may also be a negative for some. Like Quirkos, there is no AI or machine learning used in this tool. 

Pricing: Dedoose offers different prices depending on whether you’re an individual, student or group. More information can be found here.

7. Taguette

Taguette is a free open source qualitative data analysis tool that allows you to tag your data so you can then export it for analysis. 

Best for: Those looking for a basic, free option to organize their data for analysis. 

Strengths: It is very simple and easy to use. The fact that it’s open-source is also beneficial for many. It offers both an online and local version. 

Weakness: The fact that it is so simple means some drawbacks. Taguette doesn’t support images or video like some of the others. It also comes with zero automation compared to some of the bigger players and can’t analyze your data for you. 

Pricing: Taguette is free to use.

8. MonkeyLearn

MonkeyLearn is a powerful qualitative analysis software. It differs from most of the tools we have listed here in that it harnesses the power of AI and machine learning to make your data analysis process as efficient as possible. 

It offers an intuitive no-code interface that gets you to the stage of analyzing and visualizing your data in much less time. It’s also useful if you are not a data expert and you don’t have hours to spend coding manually or if, due to the size of your datasets, it’s simply impossible to do so. 

The MonkeyLearn Studio comes with pre-trained text analysis models, or, for more accurate insights, you can go ahead and build your own with your data and criteria. 

Once you’ve chosen your model you can start uploading your data from a range of options. Then you’ll be able to analyze this data with different tools like keyword extractor , feedback classifier , or sentiment analyzer . 

With that done, you can then view all your analysis in the interactive Studio dashboard (pictured below).

You can also learn more about our pricing and plans here . 

There are a number of qualitative data analysis software out there which will suit different needs. However, many of these tools require you to manually code your data in order to analyze it. 

If you are not that comfortable with coding or if you are working with datasets so large that this level of manual work is not feasible, you’ll need a tool like MonkeyLearn to help you process your qualitative data. 

MonkeyLearn provides a high level of automation, while still allowing you control of your data. This can make all the difference in terms of speed and cost. You can use your regained time to really understand the insights that crop up. 

Sign up for a free trial today to see how you can use MonkeyLearn Studio to best analyze your qualitative data.

Rachel Wolff

September 29th, 2021

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Topic selection & proposal development, enquire now, software implementation using matlab, questionnaire designing & data analysis, chapters writing & journal papers, 12 unexplored data analysis tools for qualitative research.

Welcome to our guide on 5 lesser-known tools for studying information in a different way – specifically designed for understanding and interpreting data in qualitative research. Data analysis tools for qualitative research are specialized instruments designed to interpret non-numerical data, offering insights into patterns, themes, and relationships.

These tools enable researchers to uncover meaning from qualitative information, enhancing the depth and understanding of complex phenomena in fields such as social sciences, psychology, and humanities.

In the world of research, there are tools tailored for qualitative data analysis that can reveal hidden insights. This blog explores these tools, showcasing their unique features and advantages compared to the more commonly used quantitative analysis tools.

Whether you’re a seasoned researcher or just starting out, we aim to make these tools accessible and highlight how they can add depth and accuracy to your analysis. Join us as we uncover these innovative approaches, offering practical solutions to enhance your experience with qualitative research.

Tool 1:MAXQDA Analytics Pro

MAXQDA Analytics Pro emerges as a game-changing tool for qualitative data analysis, offering a seamless experience that goes beyond the capabilities of traditional quantitative tools.

Here’s how MAXQDA stands out in the world of qualitative research:

Advanced Coding and Text Analysis: MAXQDA empowers researchers with advanced coding features and text analysis tools, enabling the exploration of qualitative data with unprecedented depth. Its intuitive interface allows for efficient categorization and interpretation of textual information.

Intuitive Interface for Effortless Exploration: The user-friendly design of MAXQDA makes it accessible for researchers of all levels. This tool streamlines the process of exploring qualitative data, facilitating a more efficient and insightful analysis compared to traditional quantitative tools.

Uncovering Hidden Narratives: MAXQDA excels in revealing hidden narratives within qualitative data, allowing researchers to identify patterns, themes, and relationships that might be overlooked by conventional quantitative approaches. This capability adds a valuable layer to the analysis of complex phenomena.

In the landscape of qualitative data analysis tools, MAXQDA Analytics Pro is a valuable asset, providing researchers with a unique set of features that enhance the depth and precision of their analysis. Its contribution extends beyond the confines of quantitative analysis tools, making it an indispensable tool for those seeking innovative approaches to qualitative research.

Tool 2: Quirkos

Quirkos , positioned as data analysis software, shines as a transformative tool within the world of qualitative research.

Here’s why Quirkos is considered among the best for quality data analysis: Visual Approach for Enhanced Understanding: Quirkos introduces a visual approach, setting it apart from conventional analysis software. This unique feature aids researchers in easily grasping and interpreting qualitative data, promoting a more comprehensive understanding of complex information.

User-Friendly Interface: One of Quirkos’ standout features is its user-friendly interface. This makes it accessible to researchers of various skill levels, ensuring that the tool’s benefits are not limited to experienced users. Its simplicity adds to the appeal for those seeking the best quality data analysis software.

Effortless Pattern Identification: Quirkos simplifies the process of identifying patterns within qualitative data. This capability is crucial for researchers aiming to conduct in-depth analysis efficiently.

The tool’s intuitive design fosters a seamless exploration of data, making it an indispensable asset in the world of analysis software. Quirkos, recognized among the best quality data analysis software, offers a visual and user-friendly approach to qualitative research. Its ability to facilitate effortless pattern identification positions it as a valuable asset for researchers seeking optimal outcomes in their data analysis endeavors.

Tool 3: Provalis Research WordStat

Provalis Research WordStat stands out as a powerful tool within the world of qualitative data analysis tools, offering unique advantages for researchers engaged in qualitative analysis:

WordStat excels in text mining, providing researchers with a robust platform to delve into vast amounts of textual data. This capability enhances the depth of qualitative analysis, setting it apart in the landscape of tools for qualitative research.

Specializing in content analysis, WordStat facilitates the systematic examination of textual information. Researchers can uncover themes, trends, and patterns within qualitative data, contributing to a more comprehensive understanding of complex phenomena.

WordStat seamlessly integrates with qualitative research methodologies, providing a bridge between quantitative and qualitative analysis. This integration allows researchers to harness the strengths of both approaches, expanding the possibilities for nuanced insights.

In the domain of tools for qualitative research, Provalis Research WordStat emerges as a valuable asset. Its text mining capabilities, content analysis expertise, and integration with qualitative research methodologies collectively contribute to elevating the qualitative analysis experience for researchers.

Tool 4: ATLAS.ti

ATLAS.ti proves to be a cornerstone in the world of qualitative data analysis tools, offering distinctive advantages that enhance the qualitative analysis process:

Multi-Faceted Data Exploration: ATLAS.ti facilitates in-depth exploration of textual, graphical, and multimedia data. This versatility enables researchers to engage with diverse types of qualitative information, broadening the scope of analysis beyond traditional boundaries.

Collaboration and Project Management: The tool excels in fostering collaboration among researchers and project management. This collaborative aspect sets ATLAS.ti apart, making it a comprehensive solution for teams engaged in qualitative research endeavors.

User-Friendly Interface: ATLAS.ti provides a user-friendly interface, ensuring accessibility for researchers of various skill levels. This simplicity in navigation enhances the overall qualitative analysis experience, making it an effective tool for both seasoned researchers and those new to data analysis tools. In the landscape of tools for qualitative research, ATLAS.ti emerges as a valuable ally. Its multi-faceted data exploration, collaboration features, and user-friendly interface collectively contribute to enriching the qualitative analysis journey for researchers seeking a comprehensive and efficient solution.

Tool 5: NVivo Transcription

NVivo Transcription emerges as a valuable asset in the world of data analysis tools, seamlessly integrating transcription services with qualitative research methodologies:

Efficient Transcription Services: NVivo Transcription offers efficient and accurate transcription services, streamlining the process of converting spoken words into written text. This feature is essential for researchers engaged in qualitative analysis, ensuring a solid foundation for subsequent exploration.

Integration with NVivo Software: The tool seamlessly integrates with NVivo software, creating a synergistic relationship between transcription and qualitative analysis. Researchers benefit from a unified platform that simplifies the organization and analysis of qualitative data, enhancing the overall research workflow.

Comprehensive Qualitative Analysis: NVivo Transcription contributes to comprehensive qualitative analysis by providing a robust foundation for understanding and interpreting audio and video data. Researchers can uncover valuable insights within the transcribed content, enriching the qualitative analysis process.

In the landscape of tools for qualitative research, NVivo Transcription plays a crucial role in bridging the gap between transcription services and qualitative analysis. Its efficient transcription capabilities, integration with NVivo software, and support for comprehensive qualitative analysis make it a valuable tool for researchers seeking a streamlined and effective approach to handling qualitative data.

Tool 6: Dedoose

Web-Based Accessibility: Dedoose’s online platform allows PhD researchers to conduct qualitative data analysis from anywhere, promoting flexibility and collaboration.

Mixed-Methods Support: Dedoose accommodates mixed-methods research, enabling the integration of both quantitative and qualitative data for a comprehensive analysis.

Multi-Media Compatibility: The tool supports various data formats, including text, audio, and video, facilitating the analysis of diverse qualitative data types.

Collaborative Features: Dedoose fosters collaboration among researchers, providing tools for shared coding, annotation, and exploration of qualitative data.

Organized Data Management: PhD researchers benefit from Dedoose’s organizational features, streamlining the coding and retrieval of data for a more efficient analysis process.

Tool 7: HyperRESEARCH

HyperRESEARCH caters to various qualitative research methods, including content analysis and grounded theory, offering a flexible platform for PhD researchers.

The software simplifies the coding and retrieval of data, aiding researchers in organizing and analyzing qualitative information systematically.

HyperRESEARCH allows for detailed annotation of text, enhancing the depth of qualitative analysis and providing a comprehensive understanding of the data.

The tool provides features for visualizing relationships within data, aiding researchers in uncovering patterns and connections in qualitative content.

HyperRESEARCH facilitates collaborative research efforts, promoting teamwork and shared insights among PhD researchers.

Tool 8: MAXQDA Analytics Plus

Advanced Collaboration:  

MAXQDA Analytics Plus enhances collaboration for PhD researchers with teamwork support, enabling multiple researchers to work seamlessly on qualitative data analysis.

Extended Visualization Tools:  

The software offers advanced data visualization features, allowing researchers to create visual representations of qualitative data patterns for a more comprehensive understanding.

Efficient Workflow:  

MAXQDA Analytics Plus streamlines the qualitative analysis workflow, providing tools that facilitate efficient coding, categorization, and interpretation of complex textual information.

Deeper Insight Integration:  

Building upon MAXQDA Analytics Pro, MAXQDA Analytics Plus integrates additional features for a more nuanced qualitative analysis, empowering PhD researchers to gain deeper insights into their research data.

User-Friendly Interface:  

The tool maintains a user-friendly interface, ensuring accessibility for researchers of various skill levels, contributing to an effective and efficient data analysis experience.

Tool 9: QDA Miner

Versatile Data Analysis: QDA Miner supports a wide range of qualitative research methodologies, accommodating diverse data types, including text, images, and multimedia, catering to the varied needs of PhD researchers.

Coding and Annotation Tools: The software provides robust coding and annotation features, facilitating a systematic organization and analysis of qualitative data for in-depth exploration.

Visual Data Exploration: QDA Miner includes visualization tools for researchers to analyze data patterns visually, aiding in the identification of themes and relationships within qualitative content.

User-Friendly Interface: With a user-friendly interface, QDA Miner ensures accessibility for researchers at different skill levels, contributing to a seamless and efficient qualitative data analysis experience.

Comprehensive Analysis Support: QDA Miner’s features contribute to a comprehensive analysis, offering PhD researchers a tool that integrates seamlessly into their qualitative research endeavors.

Tool 10: NVivo

NVivo supports diverse qualitative research methodologies, allowing PhD researchers to analyze text, images, audio, and video data for a comprehensive understanding.

The software aids researchers in organizing and categorizing qualitative data systematically, streamlining the coding and analysis process.

NVivo seamlessly integrates with various data formats, providing a unified platform for transcription services and qualitative analysis, simplifying the overall research workflow.

NVivo offers tools for visual representation, enabling researchers to create visual models that enhance the interpretation of qualitative data patterns and relationships.

NVivo Transcription integration ensures efficient handling of audio and video data, offering PhD researchers a comprehensive solution for qualitative data analysis.

Tool 11: Weft QDA

Open-Source Affordability: Weft QDA’s open-source nature makes it an affordable option for PhD researchers on a budget, providing cost-effective access to qualitative data analysis tools.

Simplicity for Beginners: With a straightforward interface, Weft QDA is user-friendly and ideal for researchers new to qualitative data analysis, offering basic coding and text analysis features.

Ease of Use: The tool simplifies the process of coding and analyzing qualitative data, making it accessible to researchers of varying skill levels and ensuring a smooth and efficient analysis experience.

Entry-Level Solution: Weft QDA serves as a suitable entry-level option, introducing PhD researchers to the fundamentals of qualitative data analysis without overwhelming complexity.

Basic Coding Features: While being simple, Weft QDA provides essential coding features, enabling researchers to organize and explore qualitative data effectively.

Tool 12: Transana

Transana specializes in the analysis of audio and video data, making it a valuable tool for PhD researchers engaged in qualitative studies with rich multimedia content.

The software streamlines the transcription process, aiding researchers in converting spoken words into written text, providing a foundation for subsequent qualitative analysis.

Transana allows for in-depth exploration of multimedia data, facilitating coding and analysis of visual and auditory aspects crucial to certain qualitative research projects.

With tools for transcribing and coding, Transana assists PhD researchers in organizing and categorizing qualitative data, promoting a structured and systematic approach to analysis.

Researchers benefit from Transana’s capabilities to uncover valuable insights within transcribed content, enriching the qualitative analysis process with a focus on visual and auditory dimensions.

Final Thoughts

In wrapping up our journey through 5 lesser-known data analysis tools for qualitative research, it’s clear these tools bring a breath of fresh air to the world of analysis. MAXQDA Analytics Pro, Quirkos, Provalis Research WordStat, ATLAS.ti, and NVivo Transcription each offer something unique, steering away from the usual quantitative analysis tools.

They go beyond, with MAXQDA’s advanced coding, Quirkos’ visual approach, WordStat’s text mining, ATLAS.ti’s multi-faceted data exploration, and NVivo Transcription’s seamless integration.

These tools aren’t just alternatives; they are untapped resources for qualitative research. As we bid adieu to the traditional quantitative tools, these unexplored gems beckon researchers to a world where hidden narratives and patterns are waiting to be discovered.

They don’t just add to the toolbox; they redefine how we approach and understand complex phenomena. In a world where research is evolving rapidly, these tools for qualitative research stand out as beacons of innovation and efficiency.

PhDGuidance is a website that provides customized solutions for PhD researchers in the field of qualitative analysis. They offer comprehensive guidance for research topics, thesis writing, and publishing. Their team of expert consultants helps researchers conduct copious research in areas such as social sciences, humanities, and more, aiming to provide a comprehensive understanding of the research problem.

PhDGuidance offers qualitative data analysis services to help researchers study the behavior of participants and observe them to analyze for the research work. They provide both manual thematic analysis and using NVivo for data collection. They also offer customized solutions for research design, data collection, literature review, language correction, analytical tools, and techniques for both qualitative and quantitative research projects.

Frequently Asked Questions

• What is the best free qualitative data analysis software?

When it comes to free qualitative data analysis software, one standout option is RQDA. RQDA, an open-source tool, provides a user-friendly platform for coding and analyzing textual data. Its compatibility with R, a statistical computing language, adds a layer of flexibility for those familiar with programming. Another notable mention is QDA Miner Lite, offering basic qualitative analysis features at no cost. While these free tools may not match the advanced capabilities of premium software, they serve as excellent starting points for individuals or small projects with budget constraints.

2. Which software is used to Analyse qualitative data?

For a more comprehensive qualitative data analysis experience, many researchers turn to premium tools like NVivo, MAXQDA, or ATLAS.ti. NVivo, in particular, stands out due to its user-friendly interface, robust coding capabilities, and integration with various data types, including audio and visual content. MAXQDA and ATLAS.ti also offer advanced features for qualitative data analysis, providing researchers with tools to explore, code, and interpret complex qualitative information effectively.

3. How can I Analyse my qualitative data?

Analyzing qualitative data involves a systematic approach to make sense of textual, visual, or audio information. Here’s a general guide:

Data Familiarization: Understand the context and content of your data through thorough reading or viewing.

Open Coding: Begin with open coding, identifying and labeling key concepts without preconceived categories.

Axial Coding: Organize codes into broader categories, establishing connections and relationships between them.

Selective Coding: Focus on the most significant codes, creating a narrative that tells the story of your data.

Constant Comparison: Continuously compare new data with existing codes to refine categories and ensure consistency.

Use of Software: Employ qualitative data analysis software, such as NVivo or MAXQDA, to facilitate coding, organization, and interpretation.

4. Is it worth using NVivo for qualitative data analysis?

The use of NVivo for qualitative data analysis depends on the specific needs of the researcher and the scale of the project. NVivo is worth considering for its versatility, user-friendly interface, and ability to handle diverse data types. It streamlines the coding process, facilitates collaboration, and offers in-depth analytical tools. However, its cost may be a consideration for individuals or smaller research projects. Researchers with complex data sets, especially those involving multimedia content, may find NVivo’s advanced features justify the investment.

5. What are the tools used in quantitative data analysis?

Quantitative data analysis relies on tools specifically designed to handle numerical data. Some widely used tools include:

SPSS (Statistical Package for the Social Sciences): A statistical software suite that facilitates data analysis through descriptive statistics, regression analysis, and more. Excel: Widely used for basic quantitative analysis, offering functions for calculations, charts, and statistical analysis.

R and RStudio: An open-source programming language and integrated development environment used for statistical computing and graphics.

Python with Pandas and NumPy: Python is a versatile programming language, and Pandas and NumPy are libraries that provide powerful tools for data manipulation and analysis.

STATA: A software suite for data management and statistical analysis, widely used in various fields.

Hence, the choice of qualitative data analysis software depends on factors like project scale, budget, and specific requirements. Free tools like RQDA and QDA Miner Lite offer viable options for smaller projects, while premium software such as NVivo, MAXQDA, and ATLAS.ti provide advanced features for more extensive research endeavors. When it comes to quantitative data analysis, SPSS, Excel, R, Python, and STATA are among the widely used tools, each offering unique strengths for numerical data interpretation. Ultimately, the selection should align with the researcher’s goals and the nature of the data being analyzed.

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Free and open source qualitative research tool -- MIRROR OF GITLAB REPOSITORY

remram44/taguette

Folders and files, repository files navigation.

A spin on the phrase "tag it!", Taguette is a free and open source qualitative research tool that allows users to:

• Import PDFs, Word Docs ( .docx ), Text files ( .txt ), HTML, EPUB, MOBI, Open Documents ( .odt ), and Rich Text Files ( .rtf ).
• Highlight words, sentences, or paragraphs and tag them with the codes you create.
• (not yet) Group imported documents together (e.g. as 'Interview' or 'Lit Review').
• Export tagged documents, highlights for a specific tag, a list of tags with descriptions and colors, and whole projects.

Check out our website to learn more about how to install and get started.

Motivation and goal

Qualitative methods generate rich, detailed research materials that leave individuals' perspectives intact as well as provide multiple contexts for understanding the phenomenon under study. Qualitative methods are used by a wide range of fields, such as anthropology, education, nursing, psychology, sociology, and marketing. Qualitative data has a similarly wide range: observations, interviews, documents, audiovisual materials, and more.

However - the software options for qualitative researchers are either far too expensive , don't allow for the seminal method of highlighting and tagging materials, or actually perform quantitative analysis , just on text.

It's not right or fair that qualitative researchers without massive research funds cannot afford the basic software to do their research.

So, to bolster a fair and equitable entry into qualitative methods, we've made Taguette!

Installation

You can find complete installation instructions on our website , including installers for Windows and MacOS.

Development setup from the repository

You can install from a local clone of this repository, which will allow you to easily change the sources to suit your needs:

• Clone this git repository from the terminal: git clone https://gitlab.com/remram44/taguette.git
• Navigate on the command line to the repository you've just cloned locally, using the cd command. To get help using cd , use this tutorial .
• Taguette uses Poetry for its packaging and dependency management. You will need to install Poetry .
• Install Taguette and its dependencies by running poetry install . Poetry will create a virtual environment for you by default, activate it by running poetry shell .
• Build translation files using scripts/update_translations.sh .
• You can start taguette in development mode using taguette --debug (or taguette --debug server <config_file> ). This will start Tornado in debug mode, which means in particular that it will auto-restart every time you make changes.
• Navigate to localhost:7465 to use Taguette!
• Copyright (C) 2018, Rémi Rampin and Taguette contributors

Licensed under a BSD 3-clause "New" or "Revised" License . See the LICENSE.txt file for details.

Code of conduct

Contributors 12.

• Python 74.1%
• JavaScript 12.0%
• Dockerfile 0.3%

Qualitative Data Analysis

• Choosing QDA software
• Free QDA tools
• Transcription tools
• Social media research
• Mixed and multi-method research
• Network Diagrams
• Publishing qualitative data
• Student specialists

General Information

For assistance, please submit a request .  You can also reach us via the chat below, email [email protected] , or join Discord server .

If you've met with us before,                        tell us how we're doing .

Service Desk and Chat

Bobst Library , 5th floor

Staffed Hours: Spring 2024

Mondays:  12pm - 5pm         Tuesdays:  12pm - 5pm         Wednesdays:  12pm - 5pm         Thursdays:  12pm - 5pm         Fridays:  12pm - 5pm        

Data Services closes for winter break at the end of the day on Friday, Dec. 22, 2023. We will reopen on Wednesday, Jan. 3, 2024.

Open Source QDA Tools

• Introduction

Free/Libre Open Source Software for QDA

While NYU Data Services provides access to proprietary computer assisted QDA software, free tools are an excellent option for researchers who want to prevent getting stuck in a paid platform, who might have budgetary constraints, or simply want to have access to high quality free software. There are a variety of these types of software packages and although Data Services cannot provide support for all of them, these curated resources are intended to help with choosing and using them for your research. Currently, of these options we are supporting Taguette .

Taguette : A spin on the phrase "tag it!", Taguette is a free and open source qualitative research tool (which works on all operating systems!). With Taguette, users can import PDFs, Word Docs (.docx), Text files (.txt), HTML, EPUB, MOBI, Open Documents (.odt), and Rich Text Files (.rtf).

After uploading documents, users can highlight words, sentences, or paragraphs and tag them with the codes you create. All the work you do in Taguette is completely exportable, including tagged documents, codebooks, highlights for a specific tag, highlights for all tags, and a list of tags with their descriptions.

Users can upload documents in any language to Taguette. The interface is currently available in English (US), French, German, Italian, and Spanish. You can find more information about using Taguette via the getting started guide and FAQ .

You can install Taguette on your own computer with instructions and files from https://taguette.org/install.html .

If you need to collaborate on your qualitative project, you can use the free online server maintained by the developers of Taguette at https://app.taguette.org .

qcoder:  With particular strengths for mixed and multi-method qualitative research in R, q coder is an excellent choice for researchers looking for a light-weight qualitative package for text analysis. By clicking on the logo to the left, you will be able to access instructions on how to install and use this package.

• Using qcoder A how-to guide to creating a qcoder project.

QualCoder : is a free desktop application for QDA that can be used to code Text, Images, and Audio Visual materials. This program is most similar to our supported proprietary software, allowing for advanced case management, data management and analytical tools. To access the QualCoder homepage, please click on the logo to the left.

• Download QualCoder Access the QualCoder Github page to download and use this program.
• QualCoder Manual Advanced documentation for how to use QualCoder.

BORIS: is a free and open source software for coding audio visual source material. Developed by a group of collaborators at the University of Torino in order to code video materials for behavioral analysis. Below are two video tutorials that will help you get started with using BORIS. To access the BORIS homepage please click on the logo to the left.

• Download BORIS Use this to download and install BORIS
• BORIS Manual For more advanced documentation on how to use BORIS.
• << Previous: Choosing QDA software
• Next: Taguette >>
• Last Updated: May 13, 2024 6:28 PM
• URL: https://guides.nyu.edu/QDA

Data and Digital Humanities Software

• Getting Started
• Qualitative Data Analysis
• Quantitative Data Analysis
• Data Visualization
• Geospatial Analysis
• Publication
• Text Analysis
• Storytelling

Need help? Ask Us!

Selecting a qda software.

Several comparison charts are available to help you with selecting with qualitative data analysis software will work best for your project. 

• Choosing QDA Software (NYU Libraries)
• Choosing a Computer Assisted Qualitative Data Analysis Package (University of Surrey)
• Quantitative Software Overview and Comparison (George Mason University)
• QDA Dueling: Atlas.ti vs. NVivo iASSIST webinar highlights key features and approaches for using Atlas.ti and NVivo to analyze qualitative data, including live demos of these two proprietary software tools and interspersed commentary on their respective pros/con.

Open Source/Freely Available Software

• AQUAD Originally developed by Günter Huber at the University of Tübingen for analysis of textual, audio, video, and image data. The webpage includes a manual as well as examples of how to use it for video, audio and graphic analysis.
• Python A relatively easy data analysis software that is open access. Can be used for qualitative or quantitative analysis. Data wrangling, cleaning, and transforming is also possible using Python libraries.
• QCoder Free, open source option for textual analysis of qualitative data from interview transcripts, observation notes, memos, and more.
• QDA Miner Lite Like QDA Miner, but looking for a free software option? This is free version of QDA Miner developed by Provalis Research. It has limited functionality, but you could still perform the basic functions. Check out this comparison chart to see how they differ .
• QualCoder Developed by Colin Curtain of Australia as a fallout of his PhD work using RQDA. If you like RQDA, this may boost your research to include audio and video. Accepts Word documents, pdfs, images, and video, Takes a bit of work to get started, but easy to use once learned.
• RQDA RQDA is an R package for qualitative data analysis that works on Windows, Linux/FreeBSD and Mac OSX platforms for plain text formatted data.
• Taguette Includes features for text analysis and users can tag words, sentences, paragraphs with the codes you create.
• TAMS Analyzer TAMS stands for Text Analysis Markup System. It is used to identify themes in texts (web pages, interviews, field notes). It was designed for use in ethnographic and discourse research. **Note: Software only available for MAC.

Licensed Software

• ATLAS.ti A qualitative research tool that can be used for coding and analyzing transcripts & field notes, building literature reviews, creating network diagrams, and data visualizations. Can also be used for quantitative and mixed methods research.
• DeDoose (Low Cost) Designed for mixed methods projects with fewer but more complex codes (allows ratings), this full-featured online software is a good choice for distributed groups with somewhat complex projects, or those that would benefit from monthly pricing.
• MAXQDA Works with a range of data formats from text, Excel, PDFs, SPSS files, images and more. Offers transcription, annotation/memo management, visualization and reporting support. Provides free courses, tutorials and training on the software. William & Mary faculty, staff and students can download this by visiting software.wm.edu .
• NVivo A qualitative research tool that helps researchers collect, organize, and analyze qualitative and mixed methods research. Can be used to analyze unstructured text, audio, video, and image data. **Note: Mac version offers fewer features and different file type.
• Quirkos (Low Cost) A simple qualitative analysis software, designed to immerse you in your qualitative text data and help you to understand it quickly and easily, Offers student licenses $21 for 3 months with cloud storage. Best for shorter documents.
• << Previous: Getting Started
• Next: Quantitative Data Analysis >>
• Last Updated: Mar 8, 2024 10:41 AM
• URL: https://guides.libraries.wm.edu/software

University Library, University of Illinois at Urbana-Champaign

Qualitative Data Analysis: Free Tools for QDA

• Atlas.ti web
• R for text analysis
• Microsoft Excel & spreadsheets
• Other options
• Planning Qual Data Analysis
• Free Tools for QDA
• QDA with NVivo
• QDA with Atlas.ti
• QDA with MAXQDA
• PKM for QDA
• QDA with Quirkos
• Working Collaboratively
• Qualitative Methods Texts
• Transcription
• Data organization
• Example Publications

Most recent workshop slides

Most recent workshop recording, workshop information, workshop description .

In this workshop, we’ll discuss free tools that are available for conducting qualitative data analysis, with a focus on Taguette and spreadsheet tools.

This workshop does not assume any previous knowledge of Taguette or other QDA software, but does expect that you know how to work with data in spreadsheet format.

If you’d like to explore Taguette before the workshop, you can create an account on the Taguette website or explore their guide . 

Learning Outcomes 

By the end of the workshop, we expect that you’ll be able to:

• Create a project in Taguette and add research collaborators
• Upload text data to Taguette
• Building a code book with a hierarchical structure
• Coding text data using codes
• Export coded data from Tageutte to a spreadsheet
• Describe which spreadsheet tools can be used in qualitative data analysis
• Describe how Taguette and spreadsheet software can be used in your own research project
• List other free tools for qualitative data analysis and assess their use for your own work, including QualCoder and R.

Free QDA Software Comparison

Taguette how-to videos.

• Get Started
• Collaborating
• Google Sheets: Analysis
• Create Tags in Taguette Taguette uses the term tag to refer to codes. You can create single tags as well as a tag hierarchy using punctuation marks.
• Highlighting Select text with a document (a highlight) and apply tags to code data in Taguette.
• << Previous: Planning Qual Data Analysis
• Next: QDA with NVivo >>
• Last Updated: Apr 5, 2024 2:23 PM
• URL: https://guides.library.illinois.edu/qualitative

• Qualitative Data Analysis Resources
• Meet Our Scholars
• Research Roundup
• Research Areas
• Funding Opportunities
• Undergraduate Research Fellowships
• CI Research Spaces
• CI SONA Research Participation
• CI SONA Policy
• CI SONA Policy for Insertion in Course Syllabus
• Other Support Form Instructions

This list of resources provides information and guidance for researchers interested in conducting qualitative data analysis using open source software. These free tools are an excellent option for researchers who want to prevent getting stuck in a paid platform, who might have budgetary constraints or simply want to have access to high quality free software. There are a variety of these types of software packages, and these curated resources are intended to help with choosing and using them for your research.

AQUAD is a platform developed in Germany (you will have to translate the website, which is also in German) that supports text of any kind, audio, video and image files. There is a plugin available to use with R, the open source statistical analysis software. See the R for QDA section above for details on that.

CATMA (Computer Aided Textual Markup and Analysis) is a free, open source markup and analysis tool from the University of Hamburg's Department of Languages, Literature and Media. It incorporates three interactive modules, a tagger enabling textual markup and markup editing, an analyzer incorporating a query language and predefined functions and a query builder that allows users to construct queries from combinations of pre-defined questions while allowing for manual modification for more specific questions. It also interfaces with the Voyant toolset. 

Tutorials  

HyperResearch

The HyperRESEARCH cross-platform software for qualitative analysis is designed to aid you in any project involving analysis of qualitative data. It's easy to use, powerful and flexible - which means that no matter how you want to approach your data, the software will allow you to "do it your way." The intuitive interface and well-written documentation – and especially the step-by-step tutorials -- help get you up and running with your own data quickly and easily.

• Introductory video
• Getting Started : A step-by-step guide for downloading and getting started using HyperResearch
• The Basics - Online Webinar Recordings : View a 4-part recorded webinar covering an introduction to the basics of using HyperRESEARCH for code and retrieve based qualitative research.

There is a Free Limited Edition available under the "Free Limited Edition of HyperRESEARCH Installers" section on this page .  The Free Limited Edition is suitable for teaching the basics of qualitative research without requiring students to purchase the full package. The Free Limited Edition also acts as a "free reader," able to open a HyperRESEARCH study of any size for review and analysis. You can also use the Free Edition to explore HyperRESEARCH's capabilities to determine if it is the right software for your needs. The Free Edition is fully functional, with the following limits:

• The Code Book is limited to 30 codes.
• A study is limited to 3 cases.
• Each case can have no more than 30 code references.

OTranscribe

OTranscribe is a free, open-source and web browser based tool for transcribing audio and video. You can upload media and use the tool to create citations. See the help pages for information.

QCAmap is an open access web application for systematic text analysis in scientific projects based on the techniques of qualitative content analysis (Mayring, 2022). QCAmap can be used within research projects to analyze small and large amounts of any text material and images coming from interviews, group discussions, observation protocols, documents, open-ended questionnaire items and others. To access more info on QCAMap

• Software Handbook
• QCAMap intro video and webinar video (be sure to translate the site from German to English)

With particular strengths for mixed and multi-method qualitative research in R, q coder is an excellent choice for researchers looking for a light-weight qualitative package for text analysis. By clicking here , you will be able to access instructions on how to install and use this package. Click here for a how-to guide to creating a qcoder project.  

QDA Miner Lite

QDA Miner Lite is a free easy-to-use version of a popular qualitative research software package.  It can be used for the analysis of textual data such as interviews, open-ended responses, transcripts, field notes, etc. as well as the analysis of still images.

• Import documents from a variety of formats including Word, HTML, PDF, plain text, CSV and Excel.
• Intuitive coding using codes organized in a tree structure.
• Add comments or memos to coded segments.
• Text search tool for retrieving and coding text segments
• Coding frequency analysis
• Export tables to Excel, Word or CSV.

Usage resources

• Video tutorial
• Overview video
• Step-by-step instructions and manual

Qigga is aQDA and reference manager software originally developed at Cambridge University and now continued as open source on GitHub . You can read about features on the Cambridge site but you'll need to download it from GitHub. It is perfect for annotating your PDFs, for reviewing your work and for creating bibliographies almost instantly. It helps you identify, tag and categorize them so that you do not have to go searching through and re-reading your research to find the piece you require. Secondly, it allows you to search through your research by using keywords, citations and authors. Thirdly, it can present all the pieces you need to add into your bibliography on a single page, from which you may create a pre-formatted and styled bibliography with the click of a button. Fourthly, the files you add and research are categorized in a way that shows you what you can and what you should research next. It is almost as if the tool is suggesting what you should research next based on your categories and what others you have researched. Not for the technically faint-hearted.

QualCoder: is a free desktop application for QDA that can be used to code Text, Images and Audio Visual materials. This program is most similar to our supported proprietary software, allowing for advanced case management, data management and analytical tools. 

QualCoder resources:

• QualCoder homepage
• Download QualCoder : Access the QualCoder Github page to download and use this program.
• QualCoder Manual : Advanced documentation for how to use QualCoder.
• Video: QualCoder 2.7 Settings Files Cases Journals
• Video: QualCoder 2.7 Text Coding

QualCoder 2.7 Settings Files Cases Journals

R is an open-source and flexible programming language that can be used for all kinds of data analysis, data visualization, text mining and even document creation. University of Illinois Research Data Librarian Sandi Caldrone offered an introduction to using R for text analysis in the Getting Started in R for QDA workshop . For anyone interested in using R for qualitative or liberal arts research, this workshop will show you how to get started in R with absolutely no coding experience necessary. Because R requires installing (totally free) software, please plan to attend this workshop with the computer you intend to use R on going forward. Click here to access and download R for QDA.

Social media methods

Open Sources Intelligence/ Investigations, refers broadly to any type of research or investigation that can legally be collected for readily available public information. For many researchers and practitioners this primarily means online sources such as social media, blogs, etc., however this can include any source of publicly available information. Often, OSINT requires the blending of a variety of disciplinary methods from computer science, journalism, sociology, law, etc. and is used widely to document abuses of human rights, monitor disinformation campaigns, study and promote social justice and accountability. Below are resources and tutorials to provide an introduction to OSINT tools and methods.

• Bellingcat's Online Investigation Guide : A google sheet with an expansive list of tools sorted by type of research.
• OSINT Bibliography - Bellingcat : A bibliography of suggested OSINT resources compiled by Giancarlo Fiorella of Bellingcat.

A spin on the phrase "tag it!", Taguette is a free and open source qualitative research tool (which works on all operating systems!). With Taguette, users can import PDFs, Word Docs (.docx), Text files (.txt), HTML, EPUB, MOBI, Open Documents (.odt), and Rich Text Files (.rtf).

After uploading documents, users can highlight words, sentences or paragraphs and tag them with the codes you create. All the work you do in Taguette is completely exportable, including tagged documents, codebooks, highlights for a specific tag, highlights for all tags and a list of tags with their descriptions.

Users can upload documents in any language to Taguette. The interface is currently available in English (US), French, German, Italian and Spanish. You can find more information about using Taguette via the getting started guide and FAQ .

You can install Taguette on your own computer with instructions and files from https://taguette.org/install.html .

If you need to collaborate on your qualitative project, you can use the free online server maintained by the developers of Taguette at https://app.taguette.org .

TAMS Analyzer

TAMS Analyzer is a free open source qualitative coding and analysis program for the MAC. TAMS stands for Text Analysis Markup System.  Use TAMS Analyzer to assign codes to passages of a text.  Just select the relevant text and double click a code you have added to a code list. You can then extract, analyze and save coded information. TAMS Analyzer is written to run on a MAC.  It does not run on a PC.

• Guided tour
• TAMS Analyzer website (including download information)
• Qualitative Analysis for Beginners (using TAMS Analyzer)
• Coding for Beginners with TAMS Analyzer
• TAMS Analyzer Video Coding and Analyzis (overview)

Connect with CI

Weft QDA is (or was) an easy-to-use, free and open-source tool for the analysis of textual data such as interview transcripts, fieldnotes and other documents. An excerpt from my MSc dissertation explains the thinking behind the software in more detail.

The software isn’t being maintained or updated, but the most recent version is available for interest. This version includes some standard CAQDAS features: (Follow the links to see screenshots)

• Import plain-text documents from text files or PDF
• Character-level coding using categories organised in a tree structure
• Retrieval of coded text and ‘coding-on’
• Simple coding statistics
• Fast free-text search
• Combine coding and searches using boolean queries AND, OR, AND NOT
• ‘ Code Review ’ to evaluate coding patterns across multiple documents
• Export to HTML and CSV formats

Using Weft QDA

The currrent version is 1.0.1 , which was released in April 2006. IMPORTANT : this software is offered without any warranty or support. Some people may still find it interesting or even useful, but:

• it is not for major projects like a PhD thesis.
• Version 1.0.1 does contain bugs that can occasion the loss of data in your analysis, or make the project file unreadable, so
• If you use Weft QDA in earnest, you should regularly make a back-up of your project (the .qdp file).
• (Jun 2014) Since Weft QDA was developed, free (but not open source) versions of some commercial software have emerged (e.g. QDA Miner Lite ).

For Windows

Weft QDA 1.0.1 was developed for Windows XP, but may work on newer versions. Windows users should download this installer which contains everything you need to use Weft. It’s available to download via Softpedia’s Weft QDA page .

Weft QDA download [2.66MB - version 1.0.1 - 26/04/2006]

Save the installer somewhere on your hard disk, then double-click the saved icon to run the installer. Follow the on-screen instructions.

Using 1.0.1 on Linux

Unfortunately, Weft QDA 1.0.1 depends on such old versions of system libraries (e.g. GTK) that it is very difficult or impossible to run Weft QDA 1.0.1 on a modern Linux distro. The source was hosted on RubyForge, but the site no longer exists. There is historic information on installation options and prerequisites .

The best way to run Weft QDA on Linux is to use WINE . C Lejeune offers further information on Weft QDA on (Debian) Linux .

Getting started and getting help

The Weft QDA Manual is the best place to start learning how to use the software. This manual is also available as a PDF for printing or on-screen viewing. Documentation in a variety of formats is included in the downloadable packages for off-line browsing.

There is also a spanish translation of the manual , courtesy of M. Cecilia Martínez.

Getting Help

I’m afraid there is no support for using Weft QDA; the mailing list previously on Rubyforge is no longer operational. Please don’t email me directly with requests for help using Weft QDA; I’m afraid I won’t reply.

History and Future

Weft QDA was originally written out of curiosity whilst completing a Masters in Social Research Methods at the University of Surrey in 2004. I was annoyed by over-priced and over-complex commercial CAQDAS . The software was tidied up and documented, and then first released in 2005.

Development / Abandonment

I did a lot of work towards a version 2.0 of Weft QDA, and got as far as a basic alpha release. Version 2.0 was going to add support for multilingual text, photos, audio and video documents, along with other new features and interface improvements, and was going to work well on OS X and modern Linux distros. However, I haven’t done any serious development on Weft QDA since early 2009, and it’s unlikely I’ll resume at any time soon. I’m just interested in thinking about and doing other things at the moment.

The project has thus been orphaned for several years. If you have a serious interest in reviving it, I do still have the source code somewhere. Weft QDA was written in the Ruby programming language, using WxRuby for the user interface and SQLite for the file storage.

Thank you to those who’ve tried the application, and especially to all those who’ve tried it and responded with their suggestions, views and kind words of thanks. Thanks to Rubyforge, who did for a long time hosted the project, including providing the downloads. Thanks also to the other open-source projects upon which it depended.

A few QDA Links

• TextAnalysis.info - Extensive listing and discussion of textual analysis applications, from Harald Klein
• CAQDAS Networking Project Training, advice and information about CAQDAS from a research centre the University of Surrey, UK
• Online QDA archive of Methods research project at Loughborough University

• Research Guides
• Topic Guides

Qualitative Data Analysis and QDA Tools

• Choose QDA software
• Access QDA software
• Find qualitative data
• Share qualitative data
• Teach with qualitative data
• Transcription

What is QDA?

Qualitative research takes many forms but often involves the collection, processing, and analysis of unstructured data, such as interviews, article text, online content, open-ended survey questions, recordings, focus group observations, etc.

Qualitative data analysis (QDA) can be supported by specialized tools and software that help manage, transcribe, organize, code, analyze, and visualize this data to yield insights.

Qualitative Data Support at Temple Libraries

We offer help via email or appointment. We can provide support for NVivo, ATLAS.ti, and some open-source qualitative data tools.

We can help with:

Learning about qualitative data analysis

Choosing QDA software

Conducting qualitative data and mixed methods analyses and using QDA software

Finding qualitative datasets and example studies

Managing and sharing qualitative data

Identifying transcription tools and services

An instruction session on any of these topics for your students or research group

Recommendations for teaching with qualitative data

QDA Software Access at Temple

Temple does not have a university-wide site license for NVivo, ATLAS.ti, MAXQDA, Dedoose, or any other commercial qualitative data analysis (QDA) software.

Options at Charles Library: The Duckworth Scholars Studio computer lab provides on-site access to NVivo and Qualcoder, and both on-site and remote access to ATLAS.ti. Contact the Scholars Studio for more information.

Options at The Tech Center: The computers in the Tech Center have ATLAS.ti installed. A license key is needed to open and use the program. Contact your professor, or your School or College's ITS ( tuhelp.temple.edu or call 215-204-8000) to inquire about getting an ATLAS.ti license.

Additional options: Students may take advantage of discounted pricing for some qualitative data analysis platforms directly from the platform websites. There are also open-source QDA platforms. Temple ITS' Educational Discounts page in TUPortal (login required) also links to third party vendor OnTheHub , which offers discounted pricing on NVivo and MAXQDA.

Olivia Given Castello Head of Business, Social Sciences and Education [email protected]

Will Dean Research and Data Services Librarian Ginsburg Health Sciences Library [email protected]

Van Tran Public Health and Social Sciences Librarian [email protected]

Kristina De Voe English & Communications Librarian [email protected]

Fred Rowland Humanities Librarian [email protected]

H. Alex Wermer-Colan , Ph.D. Digital Humanities Postdoctoral Fellow Loretta C. Duckworth Scholars Studio Charles Library [email protected]

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• Last Updated: Apr 10, 2024 5:25 PM
• URL: https://guides.temple.edu/qda

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• Can J Hosp Pharm
• v.68(3); May-Jun 2015

Qualitative Research: Data Collection, Analysis, and Management

Introduction.

In an earlier paper, 1 we presented an introduction to using qualitative research methods in pharmacy practice. In this article, we review some principles of the collection, analysis, and management of qualitative data to help pharmacists interested in doing research in their practice to continue their learning in this area. Qualitative research can help researchers to access the thoughts and feelings of research participants, which can enable development of an understanding of the meaning that people ascribe to their experiences. Whereas quantitative research methods can be used to determine how many people undertake particular behaviours, qualitative methods can help researchers to understand how and why such behaviours take place. Within the context of pharmacy practice research, qualitative approaches have been used to examine a diverse array of topics, including the perceptions of key stakeholders regarding prescribing by pharmacists and the postgraduation employment experiences of young pharmacists (see “Further Reading” section at the end of this article).

In the previous paper, 1 we outlined 3 commonly used methodologies: ethnography 2 , grounded theory 3 , and phenomenology. 4 Briefly, ethnography involves researchers using direct observation to study participants in their “real life” environment, sometimes over extended periods. Grounded theory and its later modified versions (e.g., Strauss and Corbin 5 ) use face-to-face interviews and interactions such as focus groups to explore a particular research phenomenon and may help in clarifying a less-well-understood problem, situation, or context. Phenomenology shares some features with grounded theory (such as an exploration of participants’ behaviour) and uses similar techniques to collect data, but it focuses on understanding how human beings experience their world. It gives researchers the opportunity to put themselves in another person’s shoes and to understand the subjective experiences of participants. 6 Some researchers use qualitative methodologies but adopt a different standpoint, and an example of this appears in the work of Thurston and others, 7 discussed later in this paper.

Qualitative work requires reflection on the part of researchers, both before and during the research process, as a way of providing context and understanding for readers. When being reflexive, researchers should not try to simply ignore or avoid their own biases (as this would likely be impossible); instead, reflexivity requires researchers to reflect upon and clearly articulate their position and subjectivities (world view, perspectives, biases), so that readers can better understand the filters through which questions were asked, data were gathered and analyzed, and findings were reported. From this perspective, bias and subjectivity are not inherently negative but they are unavoidable; as a result, it is best that they be articulated up-front in a manner that is clear and coherent for readers.

THE PARTICIPANT’S VIEWPOINT

What qualitative study seeks to convey is why people have thoughts and feelings that might affect the way they behave. Such study may occur in any number of contexts, but here, we focus on pharmacy practice and the way people behave with regard to medicines use (e.g., to understand patients’ reasons for nonadherence with medication therapy or to explore physicians’ resistance to pharmacists’ clinical suggestions). As we suggested in our earlier article, 1 an important point about qualitative research is that there is no attempt to generalize the findings to a wider population. Qualitative research is used to gain insights into people’s feelings and thoughts, which may provide the basis for a future stand-alone qualitative study or may help researchers to map out survey instruments for use in a quantitative study. It is also possible to use different types of research in the same study, an approach known as “mixed methods” research, and further reading on this topic may be found at the end of this paper.

The role of the researcher in qualitative research is to attempt to access the thoughts and feelings of study participants. This is not an easy task, as it involves asking people to talk about things that may be very personal to them. Sometimes the experiences being explored are fresh in the participant’s mind, whereas on other occasions reliving past experiences may be difficult. However the data are being collected, a primary responsibility of the researcher is to safeguard participants and their data. Mechanisms for such safeguarding must be clearly articulated to participants and must be approved by a relevant research ethics review board before the research begins. Researchers and practitioners new to qualitative research should seek advice from an experienced qualitative researcher before embarking on their project.

DATA COLLECTION

Whatever philosophical standpoint the researcher is taking and whatever the data collection method (e.g., focus group, one-to-one interviews), the process will involve the generation of large amounts of data. In addition to the variety of study methodologies available, there are also different ways of making a record of what is said and done during an interview or focus group, such as taking handwritten notes or video-recording. If the researcher is audio- or video-recording data collection, then the recordings must be transcribed verbatim before data analysis can begin. As a rough guide, it can take an experienced researcher/transcriber 8 hours to transcribe one 45-minute audio-recorded interview, a process than will generate 20–30 pages of written dialogue.

Many researchers will also maintain a folder of “field notes” to complement audio-taped interviews. Field notes allow the researcher to maintain and comment upon impressions, environmental contexts, behaviours, and nonverbal cues that may not be adequately captured through the audio-recording; they are typically handwritten in a small notebook at the same time the interview takes place. Field notes can provide important context to the interpretation of audio-taped data and can help remind the researcher of situational factors that may be important during data analysis. Such notes need not be formal, but they should be maintained and secured in a similar manner to audio tapes and transcripts, as they contain sensitive information and are relevant to the research. For more information about collecting qualitative data, please see the “Further Reading” section at the end of this paper.

DATA ANALYSIS AND MANAGEMENT

If, as suggested earlier, doing qualitative research is about putting oneself in another person’s shoes and seeing the world from that person’s perspective, the most important part of data analysis and management is to be true to the participants. It is their voices that the researcher is trying to hear, so that they can be interpreted and reported on for others to read and learn from. To illustrate this point, consider the anonymized transcript excerpt presented in Appendix 1 , which is taken from a research interview conducted by one of the authors (J.S.). We refer to this excerpt throughout the remainder of this paper to illustrate how data can be managed, analyzed, and presented.

Interpretation of Data

Interpretation of the data will depend on the theoretical standpoint taken by researchers. For example, the title of the research report by Thurston and others, 7 “Discordant indigenous and provider frames explain challenges in improving access to arthritis care: a qualitative study using constructivist grounded theory,” indicates at least 2 theoretical standpoints. The first is the culture of the indigenous population of Canada and the place of this population in society, and the second is the social constructivist theory used in the constructivist grounded theory method. With regard to the first standpoint, it can be surmised that, to have decided to conduct the research, the researchers must have felt that there was anecdotal evidence of differences in access to arthritis care for patients from indigenous and non-indigenous backgrounds. With regard to the second standpoint, it can be surmised that the researchers used social constructivist theory because it assumes that behaviour is socially constructed; in other words, people do things because of the expectations of those in their personal world or in the wider society in which they live. (Please see the “Further Reading” section for resources providing more information about social constructivist theory and reflexivity.) Thus, these 2 standpoints (and there may have been others relevant to the research of Thurston and others 7 ) will have affected the way in which these researchers interpreted the experiences of the indigenous population participants and those providing their care. Another standpoint is feminist standpoint theory which, among other things, focuses on marginalized groups in society. Such theories are helpful to researchers, as they enable us to think about things from a different perspective. Being aware of the standpoints you are taking in your own research is one of the foundations of qualitative work. Without such awareness, it is easy to slip into interpreting other people’s narratives from your own viewpoint, rather than that of the participants.

To analyze the example in Appendix 1 , we will adopt a phenomenological approach because we want to understand how the participant experienced the illness and we want to try to see the experience from that person’s perspective. It is important for the researcher to reflect upon and articulate his or her starting point for such analysis; for example, in the example, the coder could reflect upon her own experience as a female of a majority ethnocultural group who has lived within middle class and upper middle class settings. This personal history therefore forms the filter through which the data will be examined. This filter does not diminish the quality or significance of the analysis, since every researcher has his or her own filters; however, by explicitly stating and acknowledging what these filters are, the researcher makes it easer for readers to contextualize the work.

Transcribing and Checking

For the purposes of this paper it is assumed that interviews or focus groups have been audio-recorded. As mentioned above, transcribing is an arduous process, even for the most experienced transcribers, but it must be done to convert the spoken word to the written word to facilitate analysis. For anyone new to conducting qualitative research, it is beneficial to transcribe at least one interview and one focus group. It is only by doing this that researchers realize how difficult the task is, and this realization affects their expectations when asking others to transcribe. If the research project has sufficient funding, then a professional transcriber can be hired to do the work. If this is the case, then it is a good idea to sit down with the transcriber, if possible, and talk through the research and what the participants were talking about. This background knowledge for the transcriber is especially important in research in which people are using jargon or medical terms (as in pharmacy practice). Involving your transcriber in this way makes the work both easier and more rewarding, as he or she will feel part of the team. Transcription editing software is also available, but it is expensive. For example, ELAN (more formally known as EUDICO Linguistic Annotator, developed at the Technical University of Berlin) 8 is a tool that can help keep data organized by linking media and data files (particularly valuable if, for example, video-taping of interviews is complemented by transcriptions). It can also be helpful in searching complex data sets. Products such as ELAN do not actually automatically transcribe interviews or complete analyses, and they do require some time and effort to learn; nonetheless, for some research applications, it may be a valuable to consider such software tools.

All audio recordings should be transcribed verbatim, regardless of how intelligible the transcript may be when it is read back. Lines of text should be numbered. Once the transcription is complete, the researcher should read it while listening to the recording and do the following: correct any spelling or other errors; anonymize the transcript so that the participant cannot be identified from anything that is said (e.g., names, places, significant events); insert notations for pauses, laughter, looks of discomfort; insert any punctuation, such as commas and full stops (periods) (see Appendix 1 for examples of inserted punctuation), and include any other contextual information that might have affected the participant (e.g., temperature or comfort of the room).

Dealing with the transcription of a focus group is slightly more difficult, as multiple voices are involved. One way of transcribing such data is to “tag” each voice (e.g., Voice A, Voice B). In addition, the focus group will usually have 2 facilitators, whose respective roles will help in making sense of the data. While one facilitator guides participants through the topic, the other can make notes about context and group dynamics. More information about group dynamics and focus groups can be found in resources listed in the “Further Reading” section.

Reading between the Lines

During the process outlined above, the researcher can begin to get a feel for the participant’s experience of the phenomenon in question and can start to think about things that could be pursued in subsequent interviews or focus groups (if appropriate). In this way, one participant’s narrative informs the next, and the researcher can continue to interview until nothing new is being heard or, as it says in the text books, “saturation is reached”. While continuing with the processes of coding and theming (described in the next 2 sections), it is important to consider not just what the person is saying but also what they are not saying. For example, is a lengthy pause an indication that the participant is finding the subject difficult, or is the person simply deciding what to say? The aim of the whole process from data collection to presentation is to tell the participants’ stories using exemplars from their own narratives, thus grounding the research findings in the participants’ lived experiences.

Smith 9 suggested a qualitative research method known as interpretative phenomenological analysis, which has 2 basic tenets: first, that it is rooted in phenomenology, attempting to understand the meaning that individuals ascribe to their lived experiences, and second, that the researcher must attempt to interpret this meaning in the context of the research. That the researcher has some knowledge and expertise in the subject of the research means that he or she can have considerable scope in interpreting the participant’s experiences. Larkin and others 10 discussed the importance of not just providing a description of what participants say. Rather, interpretative phenomenological analysis is about getting underneath what a person is saying to try to truly understand the world from his or her perspective.

Once all of the research interviews have been transcribed and checked, it is time to begin coding. Field notes compiled during an interview can be a useful complementary source of information to facilitate this process, as the gap in time between an interview, transcribing, and coding can result in memory bias regarding nonverbal or environmental context issues that may affect interpretation of data.

Coding refers to the identification of topics, issues, similarities, and differences that are revealed through the participants’ narratives and interpreted by the researcher. This process enables the researcher to begin to understand the world from each participant’s perspective. Coding can be done by hand on a hard copy of the transcript, by making notes in the margin or by highlighting and naming sections of text. More commonly, researchers use qualitative research software (e.g., NVivo, QSR International Pty Ltd; www.qsrinternational.com/products_nvivo.aspx ) to help manage their transcriptions. It is advised that researchers undertake a formal course in the use of such software or seek supervision from a researcher experienced in these tools.

Returning to Appendix 1 and reading from lines 8–11, a code for this section might be “diagnosis of mental health condition”, but this would just be a description of what the participant is talking about at that point. If we read a little more deeply, we can ask ourselves how the participant might have come to feel that the doctor assumed he or she was aware of the diagnosis or indeed that they had only just been told the diagnosis. There are a number of pauses in the narrative that might suggest the participant is finding it difficult to recall that experience. Later in the text, the participant says “nobody asked me any questions about my life” (line 19). This could be coded simply as “health care professionals’ consultation skills”, but that would not reflect how the participant must have felt never to be asked anything about his or her personal life, about the participant as a human being. At the end of this excerpt, the participant just trails off, recalling that no-one showed any interest, which makes for very moving reading. For practitioners in pharmacy, it might also be pertinent to explore the participant’s experience of akathisia and why this was left untreated for 20 years.

One of the questions that arises about qualitative research relates to the reliability of the interpretation and representation of the participants’ narratives. There are no statistical tests that can be used to check reliability and validity as there are in quantitative research. However, work by Lincoln and Guba 11 suggests that there are other ways to “establish confidence in the ‘truth’ of the findings” (p. 218). They call this confidence “trustworthiness” and suggest that there are 4 criteria of trustworthiness: credibility (confidence in the “truth” of the findings), transferability (showing that the findings have applicability in other contexts), dependability (showing that the findings are consistent and could be repeated), and confirmability (the extent to which the findings of a study are shaped by the respondents and not researcher bias, motivation, or interest).

One way of establishing the “credibility” of the coding is to ask another researcher to code the same transcript and then to discuss any similarities and differences in the 2 resulting sets of codes. This simple act can result in revisions to the codes and can help to clarify and confirm the research findings.

Theming refers to the drawing together of codes from one or more transcripts to present the findings of qualitative research in a coherent and meaningful way. For example, there may be examples across participants’ narratives of the way in which they were treated in hospital, such as “not being listened to” or “lack of interest in personal experiences” (see Appendix 1 ). These may be drawn together as a theme running through the narratives that could be named “the patient’s experience of hospital care”. The importance of going through this process is that at its conclusion, it will be possible to present the data from the interviews using quotations from the individual transcripts to illustrate the source of the researchers’ interpretations. Thus, when the findings are organized for presentation, each theme can become the heading of a section in the report or presentation. Underneath each theme will be the codes, examples from the transcripts, and the researcher’s own interpretation of what the themes mean. Implications for real life (e.g., the treatment of people with chronic mental health problems) should also be given.

DATA SYNTHESIS

In this final section of this paper, we describe some ways of drawing together or “synthesizing” research findings to represent, as faithfully as possible, the meaning that participants ascribe to their life experiences. This synthesis is the aim of the final stage of qualitative research. For most readers, the synthesis of data presented by the researcher is of crucial significance—this is usually where “the story” of the participants can be distilled, summarized, and told in a manner that is both respectful to those participants and meaningful to readers. There are a number of ways in which researchers can synthesize and present their findings, but any conclusions drawn by the researchers must be supported by direct quotations from the participants. In this way, it is made clear to the reader that the themes under discussion have emerged from the participants’ interviews and not the mind of the researcher. The work of Latif and others 12 gives an example of how qualitative research findings might be presented.

Planning and Writing the Report

As has been suggested above, if researchers code and theme their material appropriately, they will naturally find the headings for sections of their report. Qualitative researchers tend to report “findings” rather than “results”, as the latter term typically implies that the data have come from a quantitative source. The final presentation of the research will usually be in the form of a report or a paper and so should follow accepted academic guidelines. In particular, the article should begin with an introduction, including a literature review and rationale for the research. There should be a section on the chosen methodology and a brief discussion about why qualitative methodology was most appropriate for the study question and why one particular methodology (e.g., interpretative phenomenological analysis rather than grounded theory) was selected to guide the research. The method itself should then be described, including ethics approval, choice of participants, mode of recruitment, and method of data collection (e.g., semistructured interviews or focus groups), followed by the research findings, which will be the main body of the report or paper. The findings should be written as if a story is being told; as such, it is not necessary to have a lengthy discussion section at the end. This is because much of the discussion will take place around the participants’ quotes, such that all that is needed to close the report or paper is a summary, limitations of the research, and the implications that the research has for practice. As stated earlier, it is not the intention of qualitative research to allow the findings to be generalized, and therefore this is not, in itself, a limitation.

Planning out the way that findings are to be presented is helpful. It is useful to insert the headings of the sections (the themes) and then make a note of the codes that exemplify the thoughts and feelings of your participants. It is generally advisable to put in the quotations that you want to use for each theme, using each quotation only once. After all this is done, the telling of the story can begin as you give your voice to the experiences of the participants, writing around their quotations. Do not be afraid to draw assumptions from the participants’ narratives, as this is necessary to give an in-depth account of the phenomena in question. Discuss these assumptions, drawing on your participants’ words to support you as you move from one code to another and from one theme to the next. Finally, as appropriate, it is possible to include examples from literature or policy documents that add support for your findings. As an exercise, you may wish to code and theme the sample excerpt in Appendix 1 and tell the participant’s story in your own way. Further reading about “doing” qualitative research can be found at the end of this paper.

CONCLUSIONS

Qualitative research can help researchers to access the thoughts and feelings of research participants, which can enable development of an understanding of the meaning that people ascribe to their experiences. It can be used in pharmacy practice research to explore how patients feel about their health and their treatment. Qualitative research has been used by pharmacists to explore a variety of questions and problems (see the “Further Reading” section for examples). An understanding of these issues can help pharmacists and other health care professionals to tailor health care to match the individual needs of patients and to develop a concordant relationship. Doing qualitative research is not easy and may require a complete rethink of how research is conducted, particularly for researchers who are more familiar with quantitative approaches. There are many ways of conducting qualitative research, and this paper has covered some of the practical issues regarding data collection, analysis, and management. Further reading around the subject will be essential to truly understand this method of accessing peoples’ thoughts and feelings to enable researchers to tell participants’ stories.

Appendix 1. Excerpt from a sample transcript

The participant (age late 50s) had suffered from a chronic mental health illness for 30 years. The participant had become a “revolving door patient,” someone who is frequently in and out of hospital. As the participant talked about past experiences, the researcher asked:

• What was treatment like 30 years ago?
• Umm—well it was pretty much they could do what they wanted with you because I was put into the er, the er kind of system er, I was just on
• endless section threes.
• Really…
• But what I didn’t realize until later was that if you haven’t actually posed a threat to someone or yourself they can’t really do that but I didn’t know
• that. So wh-when I first went into hospital they put me on the forensic ward ’cause they said, “We don’t think you’ll stay here we think you’ll just
• run-run away.” So they put me then onto the acute admissions ward and – er – I can remember one of the first things I recall when I got onto that
• ward was sitting down with a er a Dr XXX. He had a book this thick [gestures] and on each page it was like three questions and he went through
• all these questions and I answered all these questions. So we’re there for I don’t maybe two hours doing all that and he asked me he said “well
• when did somebody tell you then that you have schizophrenia” I said “well nobody’s told me that” so he seemed very surprised but nobody had
• actually [pause] whe-when I first went up there under police escort erm the senior kind of consultants people I’d been to where I was staying and
• ermm so er [pause] I . . . the, I can remember the very first night that I was there and given this injection in this muscle here [gestures] and just
• having dreadful side effects the next day I woke up [pause]
• . . . and I suffered that akathesia I swear to you, every minute of every day for about 20 years.
• Oh how awful.
• And that side of it just makes life impossible so the care on the wards [pause] umm I don’t know it’s kind of, it’s kind of hard to put into words
• [pause]. Because I’m not saying they were sort of like not friendly or interested but then nobody ever seemed to want to talk about your life [pause]
• nobody asked me any questions about my life. The only questions that came into was they asked me if I’d be a volunteer for these student exams
• and things and I said “yeah” so all the questions were like “oh what jobs have you done,” er about your relationships and things and er but
• nobody actually sat down and had a talk and showed some interest in you as a person you were just there basically [pause] um labelled and you
• know there was there was [pause] but umm [pause] yeah . . .

This article is the 10th in the CJHP Research Primer Series, an initiative of the CJHP Editorial Board and the CSHP Research Committee. The planned 2-year series is intended to appeal to relatively inexperienced researchers, with the goal of building research capacity among practising pharmacists. The articles, presenting simple but rigorous guidance to encourage and support novice researchers, are being solicited from authors with appropriate expertise.

Previous articles in this series:

Bond CM. The research jigsaw: how to get started. Can J Hosp Pharm . 2014;67(1):28–30.

Tully MP. Research: articulating questions, generating hypotheses, and choosing study designs. Can J Hosp Pharm . 2014;67(1):31–4.

Loewen P. Ethical issues in pharmacy practice research: an introductory guide. Can J Hosp Pharm. 2014;67(2):133–7.

Tsuyuki RT. Designing pharmacy practice research trials. Can J Hosp Pharm . 2014;67(3):226–9.

Bresee LC. An introduction to developing surveys for pharmacy practice research. Can J Hosp Pharm . 2014;67(4):286–91.

Gamble JM. An introduction to the fundamentals of cohort and case–control studies. Can J Hosp Pharm . 2014;67(5):366–72.

Austin Z, Sutton J. Qualitative research: getting started. C an J Hosp Pharm . 2014;67(6):436–40.

Houle S. An introduction to the fundamentals of randomized controlled trials in pharmacy research. Can J Hosp Pharm . 2014; 68(1):28–32.

Charrois TL. Systematic reviews: What do you need to know to get started? Can J Hosp Pharm . 2014;68(2):144–8.

Competing interests: None declared.

Further Reading

Examples of qualitative research in pharmacy practice.

• Farrell B, Pottie K, Woodend K, Yao V, Dolovich L, Kennie N, et al. Shifts in expectations: evaluating physicians’ perceptions as pharmacists integrated into family practice. J Interprof Care. 2010; 24 (1):80–9. [ PubMed ] [ Google Scholar ]
• Gregory P, Austin Z. Postgraduation employment experiences of new pharmacists in Ontario in 2012–2013. Can Pharm J. 2014; 147 (5):290–9. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Marks PZ, Jennnings B, Farrell B, Kennie-Kaulbach N, Jorgenson D, Pearson-Sharpe J, et al. “I gained a skill and a change in attitude”: a case study describing how an online continuing professional education course for pharmacists supported achievement of its transfer to practice outcomes. Can J Univ Contin Educ. 2014; 40 (2):1–18. [ Google Scholar ]
• Nair KM, Dolovich L, Brazil K, Raina P. It’s all about relationships: a qualitative study of health researchers’ perspectives on interdisciplinary research. BMC Health Serv Res. 2008; 8 :110. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Pojskic N, MacKeigan L, Boon H, Austin Z. Initial perceptions of key stakeholders in Ontario regarding independent prescriptive authority for pharmacists. Res Soc Adm Pharm. 2014; 10 (2):341–54. [ PubMed ] [ Google Scholar ]

Qualitative Research in General

• Breakwell GM, Hammond S, Fife-Schaw C. Research methods in psychology. Thousand Oaks (CA): Sage Publications; 1995. [ Google Scholar ]
• Given LM. 100 questions (and answers) about qualitative research. Thousand Oaks (CA): Sage Publications; 2015. [ Google Scholar ]
• Miles B, Huberman AM. Qualitative data analysis. Thousand Oaks (CA): Sage Publications; 2009. [ Google Scholar ]
• Patton M. Qualitative research and evaluation methods. Thousand Oaks (CA): Sage Publications; 2002. [ Google Scholar ]
• Willig C. Introducing qualitative research in psychology. Buckingham (UK): Open University Press; 2001. [ Google Scholar ]

Group Dynamics in Focus Groups

• Farnsworth J, Boon B. Analysing group dynamics within the focus group. Qual Res. 2010; 10 (5):605–24. [ Google Scholar ]

Social Constructivism

• Social constructivism. Berkeley (CA): University of California, Berkeley, Berkeley Graduate Division, Graduate Student Instruction Teaching & Resource Center; [cited 2015 June 4]. Available from: http://gsi.berkeley.edu/gsi-guide-contents/learning-theory-research/social-constructivism/ [ Google Scholar ]

Mixed Methods

• Creswell J. Research design: qualitative, quantitative, and mixed methods approaches. Thousand Oaks (CA): Sage Publications; 2009. [ Google Scholar ]

Collecting Qualitative Data

• Arksey H, Knight P. Interviewing for social scientists: an introductory resource with examples. Thousand Oaks (CA): Sage Publications; 1999. [ Google Scholar ]
• Guest G, Namey EE, Mitchel ML. Collecting qualitative data: a field manual for applied research. Thousand Oaks (CA): Sage Publications; 2013. [ Google Scholar ]

Constructivist Grounded Theory

• Charmaz K. Grounded theory: objectivist and constructivist methods. In: Denzin N, Lincoln Y, editors. Handbook of qualitative research. 2nd ed. Thousand Oaks (CA): Sage Publications; 2000. pp. 509–35. [ Google Scholar ]

Analyzing Text Data

• Overview of Text Analysis and Text Mining
• Text Analysis Methods
• Library Databases
• Social Media
• Open Source
• Language Corpora
• Web Scraping

Software for Text Analysis

• Text Data Citation

Data Services Librarian

There are a few software packages available to help you analyze your text data. 

• Software available on Gelman Library computers
• Software available on GW's Virtual Computer Lab

Which software should you use?

The following links provide useful comparisons of available tools for qualitative data analysis.

• GW's Research Software comparison
• Comparing QDA (Qualitative Data Analysis) tools at NYU
• Qualitative Research Tools at George Mason University
• Open-source and free programs for text mining from University of Illinois

What is NVivo?

NVivo is a qualitative data analysis software designed to assist researchers in organizing, analyzing, and gaining insights from unstructured data such as text, audio, video, and images. It provides tools for coding, categorizing, and visualizing data to support in-depth exploration and interpretation of qualitative research.

More information about NVivo

Accessing NVivo

You can access NVivo via the following methods:

• On-site use : NVivo is available on computers in Gelman Library note : we currently have access to NVivo 20, which came out in 2020. The most current version, NVivo 14 (released in 2023), will be available on library computers soon!
• NVivo Installation Guide for Windows  (also works on Mac). Pay special attention to Step 1, in which you have to download the latest NVivo software by going to the  Box NVivo Software folder
• Remote access : use the LAI desktop via  GW Virtual Computer Lab

NVivo Tutorials

• NVivo Online Tutorials
• Sage Research Methods: NVivo
• George Mason University offers a useful playlist of videos for those interested in learning NVivo

What is ATLAS.ti?

Atlas.ti is a qualitative data analysis software that aids researchers in systematically analyzing and interpreting textual, visual, and audio data. It offers features for coding, organizing, and exploring qualitative data

More information about ATLAS.ti

Accessing ATLAS.ti

• CCAS   students can request access to ATLAS.ti 

ATLAS.ti Tutorials

• ATLAS.ti Video Tutorials
• ATLAS.ti Academy  (free & premium ATLAS.ti online training)
• Sage Research Methods: ATLAS.ti

What is Taguette?

Taguette is an open-source qualitative data analysis tool designed for collaborative and systematic tagging of textual data. It allows users to annotate and categorize text, facilitating the organization and analysis of qualitative content in research projects.

More information about Taguette

Accessing Taguette

• Install Taguette

Taguette Tutorials

• Taguette, Getting Started
• Free Qualitative Data Analysis with Taguette and qcoder! an IASSIST webinar

What is Voyant?

Voyant is a free, web-based text analysis tool that enables users to visualize and explore patterns within textual data. It provides a range of tools for tasks such as word frequency analysis, topic modeling, and trend identification.

• More information about Voyant
• Voyant server (GitHub)

Accessing Voyant

• Access Voyant from their website

Voyant Tutorials

• Voyant tutorial by Voyant
• McGill University,  Voyant screencasts  (2012)
• Rice University, Using Voyant for text analysis

What is Qualtrics?

Qualtrics is an online survey platform that allows individuals and organizations to design, distribute, and analyze surveys for collecting and evaluating data. Known for its user-friendly interface and powerful analytics tools, Qualtrics is widely used in academic, business, and research settings to gather and derive insights from diverse types of feedback and responses. Qualtrics iQ uses machine learning and native language processing to discover patterns and trends survey responses.

More information about Qualtrics Text IQ

Accessing Qualtrics

• CCAS   students can request a Qualtrics account
• SEAS students can request a Qualtrics account

Qualtrics Tutorials

• Qualtrics Text Analysis tutorial
• Qualtrics Text iQ Functionality
• Sentiment Analysis in Qualtrics
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