A reflection on navigating the process of refining and grouping data with ATLAS.ti
This blog article was written by one of our Certified ATLAS.ti Professional Trainers, Muller Spies (North-West University, South Africa)
I was first introduced to the highly versatile qualitative data analysis software program, ATLAS.ti, in 2017. Working as a Project Manager and later on as a Research Officer within a transdisciplinary research unit, my manager and I were tasked to write up a case-study report for a sustainable agriculture not-for-profit organisation. We knew that the amount and depth of qualitative data that will surface from the site visits and data collection were going to be vast and we needed a data analysis tool which will be much more efficient than old school conventional practices of using envelopes, shoe boxes and some sticky notes to colour code in order to organise and make sense of the data. With my then manager having some previous exposure and experience to the efficiency and value of ATLAS.ti, we immediately decided that this will be our tool to effectively organise, analyse and make sense of the data collected while being guided by the five capitals of the sustainable livelihoods approach providing us with some themes to start the process off with. Somewhere during that process, I decided that if I ever had to pursue my masters degree and make use of a qualitative study design which provided me with a good structure to work from and with, to organise, analyse and make sense of my data, that I will make use of ATLAS.ti.
In 2018/2019 the time came and I had the opportunity to start with my transdisciplinary masters degree, in which I had the task of elucidating the two complex universal concepts of transdisciplinarity and sustainability within a specific context. The process consisted of analysing a wide spectrum of documents and the diversity and amount of data that came from it were immense. Despite working with a study design which provided and guided me with precise steps to follow throughout the process to make sense of the data, eight different steps to be exact, a point was still reached where the data had to be further refined, filtered and grouped. With the assistance of ATLAS.ti, I was able to manage the vast amount of data systematically and ensure rigour through a three-phased process of 1) identification, 2) refining, and 3) filtering and grouping. This allowed me to effectively refine, filter and group my study’s data from roughly a cumulative total of 1,986 identified quotations, linked to four sub-themes as codes at first for each of the two concepts acting as themes, to 58 quotations; refined, filtered and grouped into core ideas pertaining to each of the sub-themes, towards the outcome of formulating operational definitions and graphically depicting the relational nature of the two concepts within the specific context through a conceptual framework.
Back to basics
First, a recap on the fundamental basics on which ATLAS.ti builds, a quotation in ATLAS.ti is a highlighted segment of data which is of interest to your research (Fig.1), and a code in ATLAS.ti is the smallest fundamental segment of information that is of interest to your research, linked to a quotation (Fig.2) and forms the basis of your coding system and analysis process (Fig 3).
Figure 1: A quotation in ATLAS.ti
Figure 2: A code in ATLAS.ti linked to a quotation
Your coding system (Fig 3) develops towards the formulation of categories being groups of coded data with similarities or conveying a core idea, and themes and at times sub-themes (depending on the nature of the analysis process) which are abstract entities that provides meaning to a group of categories. In the case of my study, the two concepts being analysed, transdisciplinarity and sustainability, functioned as themes and the steps provided by the study design as sub-themes.
Figure 3: Elements of a coding system developed throughout the analysis process
Functions in ATLAS.ti to aid the process of refining and grouping of quotations
I was in the fortunate or maybe unfortunate position of working with a research question and study design which provided me with potential themes and sub-themes upfront. This allowed me to immediately start to analyse documents and coding data until a point of saturation, when no new contributing data surfaced, was reached. A point in my analysis process was reached where the codes (mainly consisting of sub-themes) and quotations had to be refined and grouped to further make sense of the data at hand. At this point ATLAS.ti made it able for me to generate a Quotation Report, Excel Report in this case, according to the steps below (see Fig 5 to 6) to gain a birds-eye view of what the data was saying, and what my next step(s) should be.
Creating a Quotation Report:
- Open the Quotation Manager
Home > Quotations
Figure 4: Steps to open the Quotation Manager
- Create a Quotation Report (Excel Report) in the Quotation Manager
Quotation Manger > Excel Report > Select appropriate Filter option >
Select attributes to be exported to an Excel Spreadsheet > Export
Figure 5: Steps to export a Quotation Report (Excel Report)
Figure 6: Example of an Excel Report
With an Excel Report at hand like the one in Fig 6 (above) it allowed me to continue the process of analysis in the Excel spreadsheet by identifying and refining the quotations into core ideas, grouping them based on possible similarities according to colours and in the end condense them into categories for each of the existing sub-themes, see Fig 7 (Below).
Figure 7: Process of analysis in Excel Report
Insights gained towards best practices for using ATLAS.ti
After assisting students with using ATLAS.ti, reflecting and getting to know and understand the functionalities of the software more, I realise that I could’ve utilised it better and possibly facilitated my study’s whole analysis process within ATLAS.ti without having to export a report, although the report function allows one to gain a good overview of the data. A combination of study design and time constraints set me on the path I followed utilising a top-down approach regarding the suggested elements of a coding system instead of bottom-up, still being adequate but maybe not as efficient. ATLAS.ti allows for a similar process to the one I followed when following a bottom-up approach, starting with open coding to identify the core ideas, the grouping of these codes with potential similarities according to colour (with the change color function) and then merging of these codes into categories and making use of the comment function to elaborate descriptions, to ensure depth of the data to be secured for final write up.
Insights gained, and some best practices:
- Start with open-coding, get to know your data through a process and work from the bottom-up;
- Codes are your fundamental element to any analysis process;
- Follow and trust your process;
- Continuously develop and refine your coding system throughout the process;
- Use the merge, group or color code functions to filter and refine towards a good coding system;
- You only learn by doing.
There is not a right or a wrong way, but when it comes to research and time constraints, efficiency may sometimes take priority and influence your process.
Stay teachable and never stop learning.
About the author
Müller Ockert Spies obtained a BSc in Enviromental Science in 2015. Since 2017, he is providing support to students concerning ATLAS.ti 8. He is now one of our Certified ATLAS.ti Professional Trainers in South Africa.