Harnessing ATLAS.ti Cloud for Collaborative Qualitative Analysis on Cyberbullying

January 13, 2021

By: Ani Munirah Mohamad

School of Law, Universiti Utara Malaysia, Kedah, Malaysia


Previously when I carried out any research involving qualitative data and analysis in ATLAS.ti, my team and I would work on our analysis on our own machines. We would then merge the projects to become one complete project for the purpose of further making sense of our data and reporting. It was an experiential process, and we loved every bit of it, particularly the part where we were able to compile all our works in a single project and see the connections between one researcher’s work against the other.

When ATLAS.ti launched its Cloud version a couple years back, and later added the collaborative analysis feature, I just knew this was the best thing that could ever happen! (At least for me and my research team who are always very fond of using ATLAS.ti for our qualitative data analysis).

This article aims to share our experiences on using ATLAS.ti Cloud in our recently completed project on cyberbullying.

Who’s in the research team?

We are a group of 6 researchers from School of Law (SOL) and School of Computing (SOC) of Universiti Utara Malaysia, Kedah, Malaysia. We have common research interests in cyber law, information technology law, computer crimes and networks.

A little bit about the project

The project aims to investigate the Malaysian laws on dealing with cyberbullying, which is a rising concern everywhere in the world. Our study looks at the Malaysian legal framework, as well as other nations’ laws on cyberbullying for lessons to be learned. The project was funded by the Malaysian Communications and Multimedia Commission, a national regulator that regulates the communications and multimedia industry in Malaysia. (Website: https://www.mcmc.gov.my/en/home)

Experts on cybersecurity, enforcement and industry players were interviewed, and open-ended survey responses were gathered. These data were added into ATLAS.ti Cloud for the purpose of analysis.

What did we do?

The process involves 6-stages, which is described in the following Figure 1.

Figure 1. Stages of collaborative analysis using ATLAS.ti Cloud

Stage 1:    As the project leader, I added the raw data we generated in the study into ATLAS.ti Cloud.

Stage 2:   I invited the research collaborators. I simply filled in the ‘Invite as collaborator’ setting and the system auto-generated an invite email to my team members.

Stage 3:   We discussed on the determination of a coding strategy, and we finally agreed to work on the analysis based on themes, which are guided by the objectives of the study.

Stage 4:   We worked on the coding and commenting process using a pure inductive approach according to the division of works agreed at Stage 3. (This process was seamless and created awesome vibes as we could monitor and follow each other’s works, in real-time!)

Stage 5:   The cleaning process involved reducing overlapping codes and redundancies created in the project.

Stage 6:   The project was then exported into ATLAS.ti Mac version 9 for the purpose of visualisation and reporting.


An example of the coding interface of the analysis project is shown in Figure 2.

Figure 2. Coding interface of our analysis project in ATLAS.ti Cloud

Our experiences and takeaways

It was an awesome experience having engaged in collaborative analysis using ATLAS.ti Cloud. When we started the project, we were worried that we might face difficulties in data collection and analysis given that our movements were restricted by local authorities during the COVID-19 pandemic. Due to safety and health issues, we feel blessed to be able to collaboratively work on our analysis – we owe this awesome experience to ATLAS.ti for coming up with such a useful and facilitative tool as the Cloud version of ATLAS.ti.

Among our takeaways from this entire process, some of our favourite features were:

  1. Ability to monitor and follow each other’s works, in real time
  2. The minimalistic interface of ATLAS.ti Cloud is user-friendly and easy to use
  3. The features available in ATLAS.ti Cloud are highly useful for the needs and purposes of our study
  4. Time-efficient in terms of coordinating the coding structure and progress of works
  5. Smart handling of our analysis works in ATLAS.ti Cloud as we are able to comment and rectify each other’s works

How would we do it differently?

We would not do anything differently than how we have done it, as we find that the features of ATLAS.ti Cloud matched 100% with our aims and needs. Thank you, ATLAS.ti!

Our way forward…

Our plans for the future, particularly for our upcoming research projects, is to continue to harness ATLAS.ti Cloud for our collaborative qualitative analysis. At the same time, we would become advocates for this awesome tool for our fellow researchers who are involved in research projects and wish to collaboratively analyse their qualitative data.



Ani Munirah Mohamad

About the author: Ani Munirah Mohamad is a senior lecturer at School of Law, Universiti Utara Malaysia, Kedah, Malaysia. She has been an avid user of ATLAS.ti since 2010. As a certified senior professional trainer, she has been teaching and coaching fellow researchers on how to maximise the usage of ATLAS.ti for literature reviews and qualitative analysis in Malaysia and neighbouring countries. She runs a blog at http://atlastimalaysia.com and may be contacted at [email protected]

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