Updated: Oct 20
Written by Dr. Susanne Friese
ATLAS.ti provides many tools that allow you to analyse your coded data. In this article, I explain how to conduct a code co-occurrence analysis. I first explain the operators you need to know about. Then I walk you through some examples, show you how to visualize findings, and write it all up in a report.
What remains for you to do is to apply it to your own research. As Freeman (2017) rightly noted: “As novice researchers take on the analytic task, they begin to gain a concrete understanding for three fundamental principles […]: First, the need to do analysis to understand analysis; second, the importance of understanding the relationship between analysis and interpretation; and third, the essential role of writing” (p. 122).
Understanding Code Co-occurrence Analysis with ATLAS.ti
Proximity operators analyse spatial relations (e.g., distance, embeddedness, overlapping, co-occurrence) between coded data segments. To better understand what this means, I explain some technical details you need to be familiar with when running a co-occurrence analysis with ATLAS.ti.
Coded data segments can be embedded in one another; one can enclose the other; one can overlap the other, or be overlapped by the other quotation.
Proximity operators differ from the other operators in one important aspect. When using the query tool, you need to observe the place where you in insert them in a query. While “A OR B” is equal to “B OR A,” this does not hold for any of the proximity operators: “A WITHIN B” is not equal to “B WITHIN A.” When building a query, always enter the expressions in the order in which they appear in their natural language manifestation.
The embedding operators describe quotations that are contained in one another.
A is enclosed by B - in other words, A is WITHIN B - retrieves all quotations coded with A contained within data segments coded with B.
A encloses B, on the other hand, retrieves all quotations coded with A that contain quotations coded with B.
Example for a WITHIN query
Let us assume you have coded a biographic interview. During the interview, respondents talked about different time periods in their lives. All these sections are coded. Within those sections, among other things, they talked about the role of friendship. This also has been coded.
Now you are interested in reading everything about ‘friendship’ in the time period that was coded with ‘childhood’. To find those segments, you can use the WITHIN operator:
Friendship WITHIN Childhood
If you enter Childhood WITHIN friendship, you do not find anything, as such a constellation does not exist.
Example for and ENCLOSES query
An example of the use of the ENCLOSES operator is: Find all blog posts that contain information about sources of happiness:
Finding Overlapping Quotations
The overlap operators describe quotations that overlap one another:
Overlaps (quotation overlapping at the start): A OVERLAPS B retrieves all quotations coded with A that overlap quotations coded with B
Overlapped by (quotations overlapping at the end): A OVERLAPPED BY B retrieves all quotations coded with A that are overlapped by quotations coded with B.
For example, the ability to ask exactly where code A, and code B overlap, or vice versa, is a viable option when working with video data in which the order of events is often more important than for interview data.
Consider a classroom situation. The teacher stands at the blackboard explaining something (A). The door opens, and a student comes in (B). Does the teacher continue with the lesson (A ENCLOSES B), or does he or she turn to the pupil who comes in (A is overlapped by B)?
Please note ATLAS.ti can only retrieve quotations and not the intersection of the overlapping segments as this is not a quotation! This is illustrated in the figure below.
Finding Co-occurring Quotations
Often when exploring the relationship between two or more codes, you do not really care whether something overlaps or is overlapped by or is within it or encloses it. If this is the case, you simply use the COOC operator.
The code co-occurrence operator is a shortcut for a combination of the four proximity operators discussed above plus the operator AND. AND is a Boolean operator, but it also finds cooccurrence, namely all coded segments that overlap 100%.
The more general co-occurrence operator is quite useful when working with transcripts. In interviews, people often jump back and forth in time or between contexts, and therefore it often does not make much sense to use specific embedding or overlap operators.
With other types of data, they are, however, quite useful. Think of video data where it might be important whether action A was already going on before action B started or vice versa.
Or if you have coded longer sections in your data, like biographical time periods in a person’s life, and then did some more fine-grained coding within these time periods. The WITHIN operator comes in very handy in such instances.
The same applies when working with survey or focus group data where ATLAS.ti automatically codes all questions/speakers. Using the WITHIN operator, you can ask, for instance, for all quotations coded with ‘Topic X’ WITHIN ‘question 5’ or ‘Topic X WITHIN speaker units of speaker Y’.
The co-occurrence operator, essentially the combination of the five operators, is also used when running the Code Co-occurrence Explorer or Code Co-occurrence Table.
Running a Code Co-occurrence Analysis
I will now show how to make use of the co-occurrence operators using the Code Co-occurrence Table. I will use the Children & Happiness sample project. I describe the sample data on my YouTube channel. You find the video here.
We will look at a few research questions and how to find answers to them. When reading through the examples, think about how to transfer this knowledge to investigate the data in your projects. Here is the first research question:
RQ1: Do parents with one child differ from parents with two or more children regarding the positive and negative effects of parenting they report?
If you look at the sample project, you will find two documents (D3 and D5) that contain comments from multiple people on a parenting blog and comments on an article published by the New York Time Magazine. As each document contains responses from multiple respondents, sociodemographic characteristics needed to be coded. Document groups could not be used here. You can find more information on project setup here.
As you can see from Figure 6, each response was coded with sociodemographic codes like having children (yes/no) and number of children, and with codes that describe other aspects like various positive and negative effects of parenting, attitudes, and perceptions.
The relationships between these various categories of codes can be explored using the Code Co-occurrence Table.
The cells of the table show the number of co-occurrences. If you click on a cell, you can retrieve the quotations for the codes in the rows and columns. In the figure above, the retrieved quotations are for the column code ‘number of children: 1 child’ and for the row code ‘negative effects: on relationships’.
What you can see in the table is that there is a shift to writing more about the positive effects of parenting when having two or more children. Personal growth is a positive effect for parents with one child. When we now begin to describe this in a memo for this research question (see more on memo writing here), we move from analysis to interpretation.
We could, for example, apply self-consistency theory to explain the findings arguing that parents with two or more children feel compelled to report positive effects; otherwise, they would need to question their own decision why to have more than one child. Another explanation could be that life as a parent gets easier with more experience. Reading the data behind the numbers will likely give you some clues regarding which explanation might be more appropriate.
If you may wonder about the low frequencies in the above table, it is worth noting that this is just a small sample project that is used here for illustrative purposes. Scientific conclusions cannot be drawn from it. However, it is still fun to explore this data further as you get meaningful results.
For instance, if you look at the relationship between reported effects of parenting and whether people believe children make you happy or not, you also see an interesting trend:
People who think that children make a person unhappier report more negative effects of parenting; with those who think that the level of happiness does not change with children, it is a mixed effect; those who believe children contribute to happiness report only positive effects.
This result might trigger ideas about which other relations to explore, for example, the relationship between attitude codes and the number of children:
We see the same trend. Those with two or more children report more often that they believe that children bring happiness, and they report more positive effects of parenting. So, piece by piece, the analysis comes together. Reading the quotations by clicking on a cell in the table will help you interpret the data.
Exporting results: If you want to continue to work with the resulting numbers, you can export the table as an Excel file.
Visualizing Findings and Writing It Up
The results of the code co-occurrence analysis have shown that there are a number of relations between the number of children, the way parenting has been described, and the perceived level of happiness.
The tables give you a 2-dimensional view; we can only relate two categories or dimensions simultaneously. We get a multidimensional picture if we move on to the networks trying to represent our findings there.
Bringing in all the codes that we had in the tables resulted in a network that was difficult to comprehend. Therefore, I created smart codes that already captured the relationship between the number of children and perceived happiness. Then I brought in the co-occurring effects of parenting codes by setting a global filter.
The network shows that all described negative effects are related to having one child and feeling less or equal levels of happiness. Negative effects on relationships can also occur even though one feels happier with the child. Those are related to quarrels in that most of the additional work is done by one partner, and the other partner is not “pulling” their weight as illustrated by the following quote:
“I would have to say it is not the child that makes you unhappy but maybe when your partner/spouse is not “pulling” their weight and you start adding up the lack of assistance/help they provide (dishes, laundry, meals etc…) I am happy to do those things for my child (and do not keep a running tally) but if I start comparing how much I do and how much my life has changed in comparison to my spouse (when we both work)-that makes me unhappy. Ha ha ha. I love my spouse, but it seems like the least he can get away with…the least he will do” (female, 3:163).
Personal growth is mentioned as a positive effect by parents with one or two or more children who perceive an equal level or greater level of happiness. If we now add all other parents into the network that have not written about their perception of happiness, we get the following picture:
In order not to clutter the network, I added code groups for responses by parents with different numbers of children. You can see that parents with one child perceive parenthood much more negatively overall. For parents with two or more children, the effect on careers can be both positive and negative. In the perception of some parents with several children, the sacrifices made for the children do not offset the gains:
“However, I have never felt that the time, money and effort I exerted to keep them healthy and happy and occupied offset all the sacrifices I have made in my personal life, despite the pride I feel when considering their achievements. My overriding sentiment is resentment” (female, 3:68).
There is a shift to more positivity the more children you have, but this picture is not free of ambiguity. If I were to continue this analysis, I could explore this further by looking at other topics like sources of happiness and reasons for having and not having children. How does this relate to how parenting and happiness are perceived?
Does this give you an idea of how you can proceed with analysis after coding?
Some Final Words
To come back to the quote at the beginning of this article – the relationship between analysis, interpretation and writing. You could see the link from description to interpretation when I offered some example explanations by drawing on existing theories. The first step is to begin to write down what you see. Then you interpret what you observe by linking it to other data, other research, theories, etc.
Networks can help you relate findings that otherwise might stand isolated side-by-side and make them more tangible to readers. Visualizations make complex relationships easier to understand. Networks also help you develop a storyline of what you want to tell about your research.
If you want to learn more about ATLAS.ti, visit Qualitative Research Training.
If you are looking for support: join the Qualitative Research Community.
Freeman, Melissa (2017). Modes of Thinking for Qualitative Data Analysis. NY: Routledge.
Friese, Susanne (2019). Qualitative Data Analysis With ATLAS.ti. London: Sage.