The visual life of qualitative data – PART 2
Luiz Rafael Andrade, Catarina Brandão and António Pedro Costa
In the second part, on “The visual life of qualitative data”, we will present examples of visual outputs explored through data that correspond to the analysis stage in qualitative research, as well as our final considerations on the topic.
The use of visual representations in the qualitative data analysis phase
After pointing out the importance of visualising the qualitative research organisation stage and the support of representations in the first part of this material, it is necessary to highlight the importance of this same support in the data analysis phase.
We know that a representation, if well used, can even contribute to identifying new knowledge about the displayed data. In this sense, the qualitative researcher must be empowered by visual strategies when analysing and, subsequently, communicating their data.
The use of visual representations in the analysis of qualitative data can be an option that enhances what is described in the text format, something that also allows the author to discover new interpretations about the object studied, as well as an artifice of synthesis of the information to be transmitted, being an ally element for scientific dissemination.
Below are examples of ways qualitative research data can be represented visually – through digital technologies – to the researcher and their readers. We start by listing a set of outputs that translate the researcher’s coding work, concluding with two possibilities of representation and synthesis of the results and conclusions of an investigation.
1. Pie Chart
Pie charts consist of circles representing 100% of something, divided into “slices” of proportions or percentages. They are a great way to illustrate the number of repetitions of a given survey and its source/data group percentage. We can also compare various categories in a given context. For example, it is possible to show how many people in a particular group have different behaviours, habits, or preferences. The exact representation relates to the totality of study participants.
Source: webqda.net
2. Dendrogram
The dendrogram visualises the family of diagrams, whose main structure is the tree diagram. The dendrogram can represent data in both horizontal and vertical formats. It can be used to visualise the codes structured in a tree in the vertical direction, facilitating how it will be exported and used, most of the time in works written in A4 format. Here, the primary and secondary categories can also be displayed and ranked by colour so that the reading and interpretation are increasingly faster and more objective for the reader. It can be seen in a single representation to what extent the categories can be close or distant from each other.
Source: minitab.com
3. Bar Chart
Bar charts are simple and somewhat easy to create. Bar charts can be vertical or horizontal, where each bar represents the values of each category/subcategory. Bar charts can represent any categories that the author can compare, using colours to represent the different categories. For example, you can compare interpretive categories with descriptive categories (age ranges, occupation). In this example, the representation displays the frequency (Y-axis) for each category (x-axis).
Source: observablehq.com
4. Double-entry Tables – Matrices
While not always the most visually appealing, double-entry tables allow you to provide multiple data points or categories to help readers understand relationships between items. In a table, interpretive categories can be organised in rows and descriptive categories of what is to be compared in columns. Each table cell (commonly known as matrix) crosses at least two categories. To keep the tables valuable and straightforward and prevent them from becoming confusing, you should highlight the sections that need attention, using colour.
Source: webqda.net
5. Conceptual Maps
Conceptual maps are hierarchical diagrams in which concepts are related in propositions through words or expressions. A Conceptual Map has two fundamental components:
- Ideas (translated by one or several words);
- Linking phrases or words.
Two or more concepts linked by a linking expression form a phrase consisting of a “statement with meaning”, also called a “semantic unit”. Some maps have the option of personalisation with the insertion of colours and basic formatting, such as bold and italics, so that the user can highlight the concepts related to each other.
Source: https://cmc.ihmc.us/Papers/cmc2004-060.pdf
6. Mental Map
The mind map is identical to a spider diagram that organises information around a central concept, connecting its branches. What distinguishes it from the diagram family is its ability to represent communication on the same primary object. It starts with different languages, such as text, image, video, links, etc. Mind maps seek to represent the conceptual relationship between data/information that is usually diffuse and fragmented. It is a visual option to illustrate ideas and concepts, give them shape and context, trace the relationships of cause, effect, symmetry, and similarity, and make them more tangible and measurable. One can plan actions and strategies to achieve specific goals.
The layout is controlled by moving codes closer to the tree’s parent code. When one of these codes is moved horizontally to the other side of the root, all of its sub-codes are sent to the layout in a new direction, causing the text to be carried out of the parent code. When a code is deleted, all of its sub-codes are deleted. When a code is dragged, all its sub-codes are also dragged. Depending on functionality, both are configured during Mind Map/Diagram creation.
Source: goconqr.com
Final considerations
Data visualisation can be used from organising qualitative research data to analysing and presenting results. It also can contribute to researchers and their readers to discover new interpretations and knowledge about the phenomenon studied. It can help businesses, individuals, and consumers make informed decisions by analysing otherwise difficult data trends.
When opting for visual data outputs, it is essential to consider the author’s knowledge of what these outputs translate. Ensuring that the author’s mastered information is transmitted to readers is crucial. Within the scope of articles submitted to the Ibero-American Congress on Qualitative Research (ciaiq.ludomedia.org) and to the World Conference on Qualitative Research (wcqr.ludomedia.org), the works often use a particular representation, but without any explanatory text on what is presented. A picture can be worth a thousand words, but it is necessary to express them.
Bibliography
Costa, A. P., Moreira, A., & Souza, F. N. de. (2019). webQDA – Qualitative Data Analysis (3.1). University of Aveiro and MicroIO. www.webqda.net
Eyenike, T. (2022). Data Visualization Using Chart.js and Gatsby. https://hackernoon.com/data-visualization-using-chartjs-and-gatsby
Google. (2021). Using Google Charts. https://developers.google.com/chart/interactive/docs
Long, A. (2017). Popular Techniques for Visualizing Qualitative Data. LinkedIn. https://www.linkedin.com/pulse/popular-techniques-visualizing-qualitative-data-adam-long/
Payette, N., & Watts, C. (2016). Quality versus Sexiness: The rival qualities of papers in the competition for academics’ attention. Share by PEERE. http://www.peere.org/wp-content/uploads/2016/03/Quality-versus-Sexiness_CW.pdf
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