Code Frequency Descriptor Bubble Plot

The Code Frequency by Descriptor Bubble Plot allows for a four-dimensional exploration and of your data based on the frequency with which particular codes were applied to excerpts across the selected descriptor field subgroups. 

Begin by selecting 1 code to place along the x-axis, 1 code along the y-axis, and 1 code that will be represented by the size of the bubble. 

Select a descriptor category from the drop-down menu. 

The chart will automatically populate based on your choices. You can switch and swap these selections at any time. 

This chart is both a great visual to communicate a finding in your paper and a great filtering tool. You can select any of the bubbles to bring up the qualitative data behind the number and further bolster your findings section with qualitative examples directly from participants.

Mixed Methods Example

In the example below from a study on the hotel characteristics reported as desirable across age and income level, the descriptor category selected is “Annual Income,” thus each bubble represents different annual income groups (the color-coded key is listed on the right side of the chart). The size of the bubbles represent the frequency with which the ‘Cost’ code was applied to excerpts within each sub-group. The X and Y axes represent the frequency with which the ‘Luxury’ and ‘Warmth’ codes were applied respectively. The highlighted bubble indicates that in comparison to other income groups, respondents reporting annual income of greater than $250K discuss issues of Luxury and Cost in hotel evaluations relatively more frequently and issues of Warmth relatively less frequently.

Qualitative Example with Demographics

The below example comes from a study exploring the racialized and gendered experiences of college athletes. The chart is visualizing 1 descriptor category (Race) and 3 codes:

• Injury and pain (x axis)
• Detrimental health outcomes (y axis)
• Negligence toward pain/injury (size of bubble)

The chart illustrates that Black participants spoke more often than any other group about their experiences with injury and pain (the x axis), had the highest rate of detrimental health outcomes from college sport (the y axis), and also experienced more instances of neglect from health professionals (the size of the bubble relative to the others).

The chart also shows that white athletes spoke about injury and pain more often than Latino participants and the two white and Latinx participants in the study.

When using code frequency charts, be sure to understand how you coded your data. For instance, in the above example, the researcher had to ensure that when someone was speaking about an instance of negligence toward an injury, it was coded only once per specific instance.