RQDA: How an open source alternative to ATLAS.ti, MAXQDA, and NVivo opens new possibilities for qualitative data analysis

Coding CVs of members of the Bundestag using RQDA

At the WZB, as part of a project on the AfD (the new radical right party in Germany), I recently had to analyze CVs of Members of the Bundestag. The idea was to automatically download the MPs’ profiles from the Bundestag website using web scraping techniques and then to describe the social structure of the AfD faction using quantitative and qualitative methods. I was particularly interested in the prior political experience of the AfD representatives. Extracting this type of information automatically is difficult and I opted to code some of the material manually.

I was looking for a tool to work with the data collected. In social sciences, ATLAS.ti, MAXQDA, and NVivo are the most commonly used programs to analyze qualitative data. Yet, these programs are expensive and not everyone is able to afford a license. Also, I simply did not need all the bells and whistles offered by these tools (I suspect I am not the only one in this situation).

The essence of qualitative data analysis (QDA) is to annotate text using relevant codes. Think of a computer-assisted qualitative data analysis software (CAQDAS) as a super highlighter (your brain is still doing the hard stuff). The rest – extracting content from PDF files, combining codes, visualizing them, etc. – can be performed by other programs. These functions do not have to be necessarily bundled with QDA programs.

I took a look at RQDA, a free, light, and open-source CAQDAS built on top of R. RQDA was designed by Ronggui Huang from Fudan University, Shanghai, and has been used in a number of publications. The package is still in development (current version 0.3-1) and some bugs are apparent. Yet, RQDA does the essential right. It allows you to import text files (in many languages), code them using a graphical interface, and store your codes (and their meta-information) in a usable format.

What I find particularly exciting about RQDA is that it lets you use the powerful machinery of R. Compared to other programs that work with closed software environments, RQDA is highly expandable. Think of all the R packages available to manipulate, analyze, and visualize textual data. Combining qualitative and quantitative data is also really easy, which makes RQDA a very good tool for mixed methods.

Most importantly, since RQDA is free and open-source, anyone with an Internet access can download R and RQDA and reanalyze coded texts. Sometimes qualitative data contains sensitive information and it is not advisable to share it. Yet, often, scholars analyze data that is already public (as I do). In this case, it might be interesting to put your coding schemes online.

Researchers usually agree that quantitative methods should be reproducible. This means that it should be possible to reproduce the findings of a publication (tables and figures) by re-running code on the raw data. I argue that qualitative research, when it does not use sensitive data, should be traceable, in the sense that others should have the possibility to go back to the source texts, examine the context, and reinterpret the coding. Simply by being free and open source, RQDA facilitates the diffusion and reuse of qualitative material and makes qualitative research more traceable.

There are good RQDA tutorials online, especially the Youtube series prepared by Metin Caliskan, in French and English (see also, Chandra and Shang 2017; Estrada 2017). I learned a lot from these demonstrations and made good progress with the coding of the CVs of the members of the Bundestag. I am really satisfied with RQDA and, for the moment, do not feel the need to move to proprietary software.

Workshop on open access

As part of the Berlin Summer School in Social Sciences, I organized a workshop entitled “Open Access: Background and Tools for Early Career Researchers in Social Sciences.” The goal of the workshop was to introduce participants to open access publishing and present useful tools to make their publications available to a wider audience.

We addressed questions such as:

  • What are the limitations of the closed publication system?
  • What is OA publishing?
  • What are the different types of OA publications?
  • What are the available licenses for OA publications?
  • What is the share of OA publications in the scientific literature and how is this changing over time?
  • What sort of funding is available for OA publishing?

The workshop was structured around a 45 minute presentation punctuated by group discussions and exercises. 90 minutes were planned for the whole workshop.

This workshop was prepared as part of the Freies Wissen Fellowship sponsored by Wikimedia Deutschland, the Stifterverband, and the VolkswagenStiftung.

The contents of the workshop are under a CC BY 4.0 license. All the material of this workshop (the outline, the slides, and the bibliography) can be cloned or downloaded from GitHub.

Feel free to share and remix the material to create your own workshop.

Dear editor, what is your preprint policy?

Going through a peer-review process usually takes months if not years. At the end, if a paper makes it to publication, access will often be limited by publishers who impose a paywall on peer-reviewed articles.

Preprints allow authors to publish early research findings and to make them available to the entire world for free. The concept is simple: 1) you upload a paper to a public repository; 2) the paper goes through a moderation process that assesses the scientific character of the work; 3) the paper is made available online. These three steps are usually completed in a few hours. With preprints, authors can rapidly communicate valuable results and engage with a broader community of scholars.

Researchers are sometimes reluctant to publish their work as preprints for two reasons. First, they fear that their papers won’t be accepted by scholarly journals because preprints would violate the so-called Ingelfinger rule, i.e. their work would have been “published” before submission. Most journals however will accept to review and publish papers that are available as preprints. The SHERPA/RoMEO database catalogues journal policies regarding pre and postprints (accepted papers that incorporate reviewers’ comments). The vast majority of journals are listed as “yellow” or “green” in the database: they tolerate preprints (yellow) or pre and postprints (green).

Second, authors are worried that they might get scooped, that their work might be stolen by someone else who would get credits for their work. Yet, the experience of arXiv, the oldest preprint repository which publishes papers in mathematics, physics, and others, shows the exact opposite. Since its creation in 1991, the repository has helped prevent scooping by offering scholars the chance to put a publicly available timestamp on their work.[1]

My first preprint

I have decided to make a paper available as preprint in the coming days. My idea is to simultaneously submit the paper to a peer-reviewed journal and upload it on a public repository. I first shared the concerns of many of my colleagues regarding scooping and the possible rejection of my work by editors. However, the more I learned about preprints, the more I felt confident that this was the right way to proceed.

Here is what I did. I first selected the journal to which I would like to submit my paper. I then checked how the journal was rated in SHERPA/RoMEO. It turned out to be a “yellow” journal: so far so good. Finally, to be absolutely sure that the preprint would not be a problem I contacted the editor of the journal and asked:

Dear Professor XX,

I’m interested in publishing in journal YY. I would like to ask: what is your preprint policy? Would you review a paper that has been uploaded to a public repository like SocArXiv?

Best wishes,

Philippe Joly

And the response came a few minutes after:

Dear Mr Joly,

Yes, we would have no problem with that.


Professor XX

I now feel very comfortable uploading the preprint on a repository. I will try to store my paper on SocArxiv, which is one of the first online preprint servers for the social sciences. While economists have a long experience with publicly available working papers, sociologists and political scientists have been more reluctant to join the movement. SocArXiv has been active since 2017 and is modelled on arXiv. Interestingly, the team at SocArXiv has partnered with the Center for Open Science and their preprint service is hosted by the Open Science Framework, which I have covered in another post.

  1. Bourne PE, Polka JK, Vale RD, Kiley R (2017) Ten simple rules to consider regarding preprint submission. PLoS Comput Biol 13(5): e1005473.

Learning Git the wrong way

Git is a version control system. It keeps track of changes within files and allows for complex collaborative work. While it is mostly used by programmers for storing and sharing code, it can theoretically work with any type of file (text, images, etc.). GitHub is the most-used hosting service for Git. On Github, users store and make their Git repositories available to others.

The Git workflow is highly valued in the open science community for a few reasons. It is fast, secure, and well-suited for coordinating large collaborative projects. The characteristic feature of Git is its branch system which allows users to work on different lines of development in parallel. Branches can be easily merged or deleted with minimal risk of losing valuable material.

Inspired by other fellows and mentors of the Fellowship Freies Wissen, I started to use Git and GitHub about two months ago. However, I quickly faced difficulties.

The problem was that I took the wrong approach with Git. When learning a new computer skill (like R), I usually start experimenting early in the process and learn by solving the inevitable problems that come along the way. This “hands-on” approach proved to be more complicated with Git. While I was trying to keep track of the changes on my PhD project, I got rapidly confused by the concept of “staging area” and struggled moving from one branch to the other.

I realized that to start using Git efficiently I would need a more solid theoretical understanding of the system.

I stopped using Git with my project for a while and went back to the basics. I started reading Pro Git, 2nd edition, written by Scott Chacon and Ben Straub (Apress, 2018) and followed through the explanations with example repositories containing simple .txt files. I performed all the operations in Bash, the command-line system, instead of using a GUI.

Pro Git is a great resource: it is distributed under Creative Commons license and was translated in many languages. You can find the book in html, pdf, and other formats here.

If you are also starting to learn Git I would recommend going through the first three chapters (“Getting Started”, “Git Basics”, “Git Branching”) and the first sections of the chapter on GitHub. That’s about 120 pages.

Taking the time to learn Git properly proved very useful. After a few hours of reading, I was able to take advantage of most of the basic functions of Git. More and more, I am discovering the advantages of Git and wished I had learned it earlier.

You can now follow my work on GitHub here. I will continue to post vignettes in R and will distribute replication material for my papers.

Vignette: Improving the harmonization of repeated cross-national surveys with the R mice package

As part of the Freies Wissen Fellowship, I have prepared a vignette on:

Using multiple imputation to improve the harmonization of repeated cross-national surveys

The vignette introduces a technique for coping with the problem of systematically missing data in longitudinal social and political surveys.

The project combines many aspects of open science:

  • The multiple imputation procedure was implemented in R, a programming language and free software environment for statistical computing.
  • The text and the code of the vignette were prepared in R markdown, a form of literate programming. Literate programming makes research more transparent and reproducible. It is also a nice tool for the development of open educational resources (OER).
  • Finally, all the material of the vignette is on my GitHub page, where users can clone the repository, comment, and suggest modifications.

A look at the Open Science Framework (OSF)

Over the recent years, a number of tools have been proposed to help researchers make their research more accessible, from the first stages of project design to publication. While this development is welcome, the abundance of software solutions can sometimes feel overwhelming.

OSF: one tool across the entire research lifecycle

This week I have started experimenting with the Open Science Framework (OSF), a free and open source platform which aims at streamlining the research workflow in a single software environment. The OSF was developed by the Center for Open Science. It provides solutions for collaborative, open research throughout the research cycle.

After testing the platform for a few days, I am impressed by its flexibility and reliability together with its successful integration with other tools.

One of the great strengths of the OSF is that it gives users full control over what content is public and what content is private. By default, all projects are created as private (only the administrators and collaborators can see them), but as project evolves users can decide to make certain elements public. This transition from private to public is facilitated by the way the OSF organizes research material. Users develop “projects” which are divided into “components” (e.g. literature, data, analysis, communication), which can be further subdivided into “components” themselves, etc. This structure is highly flexible and allows researchers to use the OSF for almost any type of project (an article, an experiment, a class). Each project or component receives a globally unique identifier (GUID) and public projects can be attributed a DOI. Users define which license applies to which content.

This “OSF 101” video gives an overview of the many functions of the platform.

Video: “OSF 101”

My first OSF project

I decided to test the OSF by creating a new page which will group projects I am developing as part of the Freies Wissen Fellowship Program.

Figure: Project in public mode

In the public part of the project (see picture above), two components are currently available: “Literature” and “Data management plan.” The Literature component is a first attempt at making my bibliography open. There you will find a first bibTeX file with a list of references on “Political protest and political opportunity structures.” The Data management plan is a copy of the plan I posted on wikiversity. This version is written in markdown and can be modified and commented directly on the OSF platform.

Figure: Project in private mode

Of course, these two components are just a start. I plan on gradually adding more public content on my OSF project. In the private view of my project (picture above), you can see that I have linked three other projects: the two papers I am working on and the seminar on open social sciences.

Figure: Paper project

The paper projects are still in private mode, but, as shown in the picture above, they already contain a title, an abstract, tags, a manuscript, and a replication set. This already offers a good backup solution. There is currently no limit on the OSF storage. Only, individual files cannot have more than 5 GB. Files can be viewed, downloaded, deleted, and renamed. Plain text files can be edited collaboratively online. The OSF also has a multitude of well-integrated add-ons such as Dropbox and GitHub.

Overall, I am really satisfied with the OSF and will continue to explore it as I develop my projects further. If you want to learn more about the possibilities offered by the OSF, I recommend the John Hopkins’ Electronic Lab Notebook Template.

Creating a data management plan

As part of the Freies Wissen Fellowship Program, I was recently encouraged to prepare a data management plan (DMP) for my project. This was something new to me. In my study program (but also, I suspect, in most social science programs), issues related to data collection or generation, data analysis, data storing, and data sharing had usually been discussed on an ad hoc basis with supervisors or in small research teams.

Preparing an exhaustive DMP can be a tedious process at an early stage of a project, but it has obvious benefits down the road.

What is a data management plan?

A data management plan is a “written document that describes the data you expect to acquire or generate during the course of a research project, how you will manage, describe, analyze, and store those data, and what mechanisms you will use at the end of your project to share and preserve your data.” [1]

DMPs can vary in terms of format, but they typically aim at answering questions such as:

  • What type of data will be analyzed or created?
  • What file format will be used?
  • How will the data be stored and backed-up during the project?
  • How will sensitive data be handled?
  • How will the data be shared and archived after the project?
  • Who will be responsible for managing the data?

A good DMP should be a “living document” in the sense that it needs to evolve with the research project.

Why do you need a data management plan?

A DMP forces you to anticipate and address issues of data management that will arise in the course of the research project. There are a few reasons why such an approach can be beneficial, as highlighted by the University of Lausanne:

  • Taking a systematic and rigorous approach to data management saves time and money.
  • Developing a good backup strategy can avoid tragedies such as the loss of irrecoverable data.
  • More and more funders are asking for DMPs.
  • A DMP is a first logical step when planning to open your data to the public.
  • Generally, DMPs are an integral part of honest, responsible, and transparent research. [2]

A good example of the new direction public funders are taking with regards to DMPs is the EU Horizon 2020 Program. As part of the Open Research Data Pilot, the EU is now encouraging applicants to present a DMP where they detail how their research data will be findable, accessible, interoperable, and reusable (FAIR). [3]

Creating my own data management plan

For my project, I followed the DMP model proposed by the Bielefeld University. This concise model summarizes in 8 sections the essential elements of a DMP. You can find my plan here.


I thought the exercise would be rather straightforward as my dissertation relies mostly on existing data. My contribution lies mostly in the merging and harmonization of repeated cross-national surveys together with national-level indicators.

I nonetheless faced some challenges. The first was that not all data providers state precisely what their terms of use are. Very few use an explicit license. Most of them will have conditions similar to the World values survey: “The data are available without restrictions, provided that 1) they are used for non-profit purposes, 2) data files are not redistributed, 3) correct citations are provided wherever results based on the data are published.”[4]

The second challenge I faced was to elaborate a backup strategy. One advantage of DMPs is that they quickly highlight your weaknesses. In my case, it was clear that I had not reflected enough on how to safely save and archive the thousands of lines of code I had produced over years. (Code is also data in a broad sense!) That’s why I am exploring new avenues to protect my files and soon distribute my replication material.

I am currently exploring solutions such as the Open Science Framework platform. OSF seems a good option as it is free, open source, and well-integrated with other platforms such as Github and Dropbox. I will report on my progress in upcoming posts.


  1. Stanford Libraries. “Data management plans.” https://library.stanford.edu/research/data-management-services/data-management-plans (accessed November 13, 2017).
  2. Université de Lausanne. “Réaliser un Data Management Plan.” https://uniris.unil.ch/researchdata/sujet/realiser-un-data-management-plan/ (accessed November 13, 2017).
  3. European Commission. Directorate-General for Research & Innovation. “H2020 Programme: Guidelines on FAIR Data Management in Horizon 2020.” http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h2020-hi-oa-data-mgt_en.pdf (accessed November 13, 2017).
  4. World Values Survey. “Integrated EVS/WVS 1981-2008 Conditions of Use.” http://www.worldvaluessurvey.org/WVSContents.jsp (accessed November 13, 2017).

New blog

Several factors are pushing for the implementation of open science ideas and principles in social science. First, social science tends to rely more and more on large datasets assembling hundreds of thousands, if not millions, of observations. This has raised questions about the transparency and reproducibility of data collection processes. Second, social scientists are using ever more sophisticated statistical techniques to analyze their data. As these methods necessitate both specialized knowledge and large computing infrastructure, clear communication of empirical strategies, using open-source software, becomes increasingly important. Finally, social science also had infamous cases of scientific fraud, that could have been avoided if authors had made their data and methodology public (see LaCour and Green (2014), “When contact changes minds,” Science).

For all these reasons, I have felt the necessity, as a political scientist, to engage more with notions of open access, open data, open source, and open methodology.

Following my nomination for a Fellowship “Freies Wissen,” I have decided to use this website as a platform for discussing open science ideas in sociology and political science. In the coming months, I will also present very concretely the challenges and opportunities I encounter when communicating my own work in an open science framework.