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Data Exploration and Visualization with ATLAS.ti Networks

Written by Dr. Susanne Friese


The ATLAS.ti network function is a tool that allows you to explore your data visually. You started the journey by looking at a your data, exploring it and noticing interesting things (see From Raw Data to Qualitative Insights). After a while you were able to label what you noticed, and you gained a better understanding of the data landscape during the process of coding (see Building an Effective Coding System). At first this was a descriptive understanding. With a prolonged stay and further exploration, you were able to describe the various aspects of your data and their specifications in the form of a well-developed code system.


This enabled you to dig a bit deeper and to ask more specific questions utilizing several different tools provided by the ATLAS.ti workbench (see Unveiling Insights: Unleashing the Power of Co-occurrence Analysis with ATLAS.ti and  Further Analysis after Coding: Within and Across-Case Analysis with ATLAS.ti). 


As you go through your research questions and queries step by step in the process of further analysis, writing memos is a must. By writing, you need to put into words the results you see in the form of tables, numbers, and data, and add your thoughts, ideas, and interpretations. In this process 'happens' analysis. As Freeman (2017) puts is: It is important to understand that "writing is inseparable from analysis" (p.4).

It is also during this process that you realize how the different aspects of your data relate to each other. The visualization of these relationships in the form of the ATLAS.ti networks is a next logical step. It will advance your analysis on another level.

Graphical illustrations enable a different kind of exploration. Images in comparison to words activate different parts of the brain and lead to different processing modes (eg Khateb et al., 2002). The ATLAS.ti networks support creativity and help in the detailing of an idea or by developing a line of reasoning. They improve metacognition by encouraging a different way of thinking. At the receiving end, they help create a common understanding and help communicate complex ideas and arguments (Freeman, 2017, Novak and Cañas, 2006, Novak and Gowin, 2002).


In this article, I will give you some ideas how you can make good use of the ATLAS.ti network function.


Learning terminology of ATLAS.ti Networks

Basically, everything you do in ATLAS.ti – each link you create, be it a code–quotation link, a code–memo link, a memo–quotation link, a code group linked to its code members, and so on – can be visualized. The entire project consists of links and thus represents the total network. The process of coding has already generated a great number of links between individual codes and the data segments they encode.

The figure below shows the code ‘effects pos: improved relationships’, which codes six data segments and is linked to two other codes. Incidentally, each object that becomes part of a network is called a node.



As you can see in Figure 1, there are different types of relations – just a line linking two objects or a line plus a name for the link. The named links are referred to as first-class relations and the unnamed links as second-class relations. First-class relations can only be created between two codes or between two quotations. All other links are second class. In Figure 2 you see an overview of possible links.


First-class relations can be directed, that is pointing from A to B (A -> B), or undirected (A <--> B). The property of directed relations is asymmetric and those of undirected relations symmetric. You need to know these terms when creating new relations.


Second-class relations can be created between memos and quotations, codes and quotations, memos and codes, memos and documents, and between memos and memos. The links between groups and their members can be visualized as well. Indirect links exist between quotations and documents, codes and documents and codes and document groups. These are visualized as virtual dotted lines. All these links are second class, thus cannot be named.


When you open a network on a group, the lines between the group node and the member nodes are shown automatically. The same happens if you import quotations or codes for a document. In Figure 2 you can see an image document that has been added to a network and its virtual link to a code.



Next, I present a variety of ideas on how you can utilize networks effectively.


Exploring Code Co-occurrences in ATLAS.ti Networks


Let’s take a look at the following research question:

RQ1: Do blog respondents who have children define happiness differently from those without children?


The sample project, I am using here, contains comments as a reaction to a blog on parenting and happiness. Each comment has been coded with the information whether someone has children or not (if this information was available). Therefore, in order to find out whether they talked about the definition of happiness and how they defined it, we can use the code co-occurrence option.


Here is how to do it:

  • Create a new network and drag the two codes: ‘fam: have children’ and ‘fam: don’t have children’ into the network.

  • As we only want to import the codes that are relevant to the research question, you need to create a code group containing all relevant codes and set it as global filter. You can do this by right-clicking on a code group on the left-hand side in the Project Explorer.

  • Then you can proceed with importing the co-occuring codes. In the latest version, the menu option is called “Add Co-occuring Codes”.


Now ATLAS.ti runs the same query as it does when preparing a Code Co-occurrence Table (see Unleashing the Power of Co-occurrence Analysis with ATLAS.ti) or when you use the COOCCUR operator in the query tool.


If you do not set a filter, this query is run for all codes that cooccur with ‘#fam: have children’. However, this is not what you are interested in. You only want to see how parents define happiness (in comparison to those without children). Therefore, it was necessary to set the global filter. The result of the import is shown below.



Co-occurring codes are not automatically linked. In order not to lose the connection between the codes, I am going to show you how to link them:


  • Highlight all co-occurring codes (green on-screen) by drawing a frame around them with your mouse. As shown in Figure 7.5, all selected nodes display a red dot in the top left-hand corner.

  • Click on the Link button in the toolbar and move the mouse pointer on top of the ‘#fam: have children’ code. Left click. A list of relations opens. Select for instance the relation is associated with.

  • You could work a bit on the layout of the network, but this is not important at this stage. You can for instance move the nodes with your mouse pointer to a different position or use any of the layout options. But we will practice this later.

 

Then repeat the process for the node: #fam: don't have children.

  • Close the network once you are done. You will be asked whether you want to save this network. Select No. The links you have created are not deleted when you close the editor. You only need to save networks if you a) want to continue to work on them, or b) want to keep the network the way you arranged all nodes. Here we only needed it to import and link the co-occuring codes.

  • Open the minimized network that you created first on the ‘#fam: have children code’. Now drag and drop the ‘#fam: don’t have children’ code from the Project Explorer or Code Manager into this network. The two codes that you have linked before to the ‘#fam: don’t have children’ code will show automatically. Thus, you see that ATLAS.ti remembers established links even if you close a view.

  • Now arrange the codes in such a way that the story this network is telling starts to make sense. For instance, click on the Layout button and select the Organic layout from the drop-down menu.


  • Next, you can link the #fam: have children and #fam: don’t have children code nodes to each other by creating a new relation like: ‘difference between’.

  •  For the final layout, click on the Routing button and select Polyline Routing.

 

In Figure 5, you can see the frequency and density counts below the code labels. The density count shows how often a code is linked to other codes. To activate this option, select the View tab and from there Show Frequencies. Whether the network already tells the complete story can only be discovered after reading the data behind the code nodes. This can best be achieved in combination with the Code Co-occurrence Table.


Case-based Analysis in ATLAS.ti networks

Another option is the entity sensitive import. When combining this import option with global filters, it is possible to compare documents in terms of their coding. Let’s take a look at another research question and explore it in a network:


RQ3: Compare the comments written on Belkin’s blog to those written on the New York Times blog regarding for instance sources of happiness.


  • Set the ‘Sources of happiness’ code group as the global filter in the Project Explorer.

  • Create a new network and add the two documents that you want to compare.

  • Right-click on each document and select Preview.

  • To add the codes that have been applied in each document, right-click on a document and select Add Neighbors / Codes. Repeat this for each document.

  • Position the nodes either manually or use one of the layout options. Arrange the two documents so you can see whether there is a difference between them.



Using ATLAS.ti Networks to Develop the Story Line for a Research Report


When I developed a sample project to demonstrate how to analyse data according to the Grounded Theory approach described by Strauss & Corbin, I came to the point where I had to choose a core category. The study was about war experiences of veterans.


I decided that my analysis would be based on the concept of "coming home" after the war. At the time, I already had some ideas on how the "coming home" code could be linked to other concepts and categories. Therefore, I created a network and pulled in my "Coming Home" code.


I reflected on several key aspects influencing a soldier's sense of homecoming: What elements contribute to or impede a successful return? How do experiences differ between frontline combatants and support roles like paramedics? What coping mechanisms are employed during and after service? Additionally, I considered the evolution of soldiers' attitudes towards the war from its onset to the present.

To develop my storyline, I adopted a retrospective approach, tracing back from the present to the past. In doing so, I sought appropriate codes and incorporated them into a conceptual network. Figure 8 depicts this network, which, while seemingly disorganized, was not intended for presentation or reporting purposes.


Its primary function was to assist my thought process, enabling me to explore potential connections through the strategic placement and interlinking of nodes and the identification of relationships. This expansive network served as a foundation for focusing on specific story elements, leading to the creation of several smaller, more structured networks. Simultaneously, I diligently recorded my thoughts, ideas, and interpretations in memos.


Memo writing is a crucial component of this analytical phase, ensuring that fleeting insights are captured and preserved. These written reflections not only solidified my understanding but also facilitated the emergence of clearer connections. Over time, these interconnections gradually wove together, culminating in a cohesive and coherent narrative.



Using ATLAS.ti Networks to Discuss Findings with Your Adviser or Colleague(s)


As previously mentioned, visual images engage different areas of our brain, prompting diverse thought processes. To prepare for a meeting, consider sending a section of your analysis chapter to your adviser or colleagues in advance, giving them an overview of your work. Also, prepare one or two illustrative networks that encapsulate the main points you wish to discuss. Bring both a printout of these networks and your laptop to the meeting.


During the discussion, use the networks as a visual aid to explain your ideas, rather than relying solely on text. This approach can facilitate a more interactive and engaging conversation. If any questions arise about the data behind your networks, you can quickly access and display this information on your laptop using ATLAS.ti.


Often, discussing the findings with the help of the printed networks may suffice. The tactile experience of working with paper, complemented by the ability to annotate and sketch additional networks directly on the printouts, can be particularly effective for visualizing and expanding your ideas.


Post-meeting, these handwritten notes and sketches can be integrated into ATLAS.ti. This digital platform allows for easy verification and refinement of your ideas against the data with just a few clicks, enabling you to enhance your networks and the analysis in your research question memo.


Presenting Findings through Networks


In the following section, I showcase sample networks from diverse studies, illustrating how networks effectively present research findings. In smaller projects, such as a Master’s thesis, a single comprehensive network might suffice to encapsulate all findings. However, in some cases, multiple networks may be necessary to adequately represent the complexity and breadth of the research


Illustrating results from the Schwarzenegger project

The network shown in Figure 8 illustrates a result from analysing the sample data set used for the first edition of my book Qualitative Data Analysis with ATLAS.ti. The data consisted of newspaper articles from Germany and the USA collected one day after Arnold Schwarzenegger was elected Governor in California in the 2003 recall election.



You can insert the network as a graphic into a PowerPoint or Prezi presentation and explain what you found. Prezi has the advantage of being able to zoom to the parts of the network you are currently talking about. The text that goes along with the network shown here could be something like this:


The network shows that there are differences in topics covered by the German local and the German national press. As can be seen from the network, the local press provided some information on the recall process and strongly focused on the election results. In comparison, the national press provided more general background information and focussed more on Schwarzenegger’s political program.


Illustrating results from a media analysis of the financial crisis

Figure 9 shows on the financial crisis and illustrates how you can make your data come alive in presentations.


The network below shows factors that have been mentioned by various sources (personal experiences, statistical figures, news agencies, and political opinion) as immediate, long-term and individual consequences. Also shown is an activated text quotation. The study was developed as a sample study based on a small data set. Therefore, the results are fictitious.



Using ATLAS.ti Networks in Publications

The next two networks, featured in scholarly publications, encapsulate key discoveries.


Figure 10 presents an outcome from my dissertation research, which delineates the stages of an addictive buying process. The ATLAS.ti network was originally published in my dissertation (Friese, 2000), albeit in a less colourful version as I was working with version 4 of ATLAS.ti.



In the following example, I'm grateful to Eddie Hartmann for granting permission to use his data (Hartmann, 2011). Hartmann carried out 20 interviews, creating a structured profile for each participant based on four key criteria: negation, affirmation, rejective negation, and positive substitution. Figure 11 illustrates one such case.



This figure clearly delineates which criteria were relevant for each case and the specific subcategories within them. For example, 'Affirmation' was not applicable to case 3. Additionally, to convey the logical flow of each interviewee's narrative, quotations were incorporated into the networks, highlighting the argument sequence


Summary

My goal in writing this article was to offer insights into utilizing the network function of ATLAS.ti. Through practical examples, from exploring code co-occurrences to delving into case-based analyses, I aimed to demonstrate the versatility and efficacy of this tool in transforming raw data into coherent, insightful narratives.


Moreover, I illustrated how ATLAS.ti networks can enhance discussions and presentations, highlighting their ability to clarify and effectively communicate complex research findings. Lastly, I provided instances of how these networks have been successfully incorporated into published works, showcasing their applicability and value in academic and professional contexts.



Cite as follows:

Friese, S. (2023). "Data Exploration and Visualization with ATLAS.ti Networks." Dr. Susanne Friese's Blog. Available at: https://www.drsfriese.com/post/data-exploration-and-visualization-with-atlasti-networks



Literature

Friese, Susanne (2000). Self-concept and Identity in a Consumer Society: Aspects of Symbolic Product Meaning. Marburg: Tectum.


Friese, Susanne (2011). Using ATLAS.ti for analyzing the financial crisis data [67 paragraphs]. Forum Qualitative Sozialforschung/Forum: Qualitative Social Research, 12(1), Art. 39, http://nbn-resolving.de/urn:nbn:de:0114-fqs1101397.


Friese, Susanne (2016). CAQDAS and Grounded Theory Analysis. MMG Working Paper 16-07October 2016.


Friese, Susanne (2019). Qualitative Data Analysis with ATLAS.ti (3rd edition). Londong: Sage.


Hartmann, Eddie (2011). Strategien des Gegenhandelns: Zur Soziodynamik symbolischer Kämpfe um Zugehörigkeit. Konstanz: UVK.


Khateb, A., Pegna, A.J., Michel, C.M., Landis, T. and Annoni, J.M. (2002). Dynamics of brain activation during an explicit word and image recognition task: an electrophysiological study. In Brain Topography, Spring 14(3): 197–213.


Novak, Josef D. and Cañas, Alberto J. (2006). The theory underlying concept maps and how to construct and use them. Institute for Human and Machine Cognition. http://en.wikipedia.org/wiki/Concept_map (accessed 5 January 2024).


Novak, Josef D. and Gowin, D. Bob (2002). Learning How to Learn. New York: Cambridge University Press. (First published in 1984.)

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