Data Saturation in Qualitative Research

In this blog post, learn what data saturation is, how it relates to qualitative research practices, and how to leverage quantilope's video research solution: inColor. 

Table of Contents: 

• What is data saturation in qualitative research?
• Does data saturation matter? 
• Data collection to reach data saturation 
• Methodologies used to reach data saturation
• qualitative data analysis with quantilope's inColor

What is data saturation in qualitative research?

Data saturation is the point in a research process where enough data has been collected to draw necessary conclusions, and any further data collection will not produce value-added insights. Data saturation is a term that originates from qualitative research’s grounded theory, a broad research method first coined in the 1960s by sociologists Glaser and Strauss.

Glaser and Strauss’ grounded theory describes the way in which social research reveals patterns in data that can then be used to generate theories and hypotheses on which further research can be done (this is in contrast to quantitative research, where pre-existing hypotheses form a framework for research).

In their 1967 book 'The Discovery of Grounded Theory,' Glaser and Strauss describe the concept of saturation like this:

 'The criterion for judging when to stop sampling the different groups pertinent to a category is the category’s theoretical saturation. Saturation means that no additional data are being found whereby the sociologist can develop properties of the [theoretical] category. As he sees similar instances over and over again, the researcher becomes empirically confident that a category is saturated.'

In other words, when the number of interviews, focus groups or other qualitative method is large enough, data analysis will start showing the same themes, with no new findings or variability, however thorough the analysis.

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Does data saturation matter?

There has been a lot of debate and disagreement amongst social sciences professionals and researchers around the importance of data saturation. One reason for this is that qualitative research studies vary in their end goals; some projects will require exploring all possible avenues in great detail, while others are looking for much less exhaustive studies.

It's true that in any qualitative research study, the researcher wants to be sure that the project obtains the information it sets out to discover. For some studies, this might mean a broad research question pertaining to the topic - for example, 'what are the main concerns that people in the US have about the world today?'. It's a broad subject, and for a CPG business wondering how best to position its product, it might be enough to know that personal finances and climate change are pretty high up in importance and that the pricing and eco-credentials of its product need to be in line with needs and expectations relating to those concerns (competitive pricing within the category and recyclable packaging, for example.)

However, if a media company asks the same research question, the depth of detail required might be greater. Within the broad themes that emerge, precise and detailed sub-categories of concern under those themes might be required to tailor news and commentary to the interest of the audience.

Another point of contention is that data saturation focuses on the number of research interviews (aka, sample size) rather than the quality of the data collection. A high number of depth interviews could be recruited and responses might start to repeat across the sample, but if the information extracted isn't rich enough then important insights can be missed. In an ideal study, a mix of both quantity and quality will be achieved.

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Data collection to reach data saturation

qualitative data collection offers a highly flexible way to explore topics of interest. The value in qualitative research lies in how well qualitative inquiries are collected, with in-depth probing and steering of the conversation towards the most useful insights. This, as with sampling, comes with experience and good training in qualitative data collection techniques.

To feel confident any qualitative research outcome will provide adequacy for all pertinent insights to be unearthed, researchers need to ensure two things in their data collection:

1. Adequate sample size
2. Research subjects and quality of responses are interrogated thoroughly 

The sample size of a qualitative research study really depends on the research questions at hand, and the nature of the information sought. For example, do researchers need just a few simple soundbite citations to support their research initiatives, or do they need in-depth quotes from case studies with specific recalled experiences?

Following qualitative research fieldwork, the analysis of responses is key to what is known as inductive thematic saturation: when the emergence of new themes and new codes has plateaued. When you're conducting thematic analysis and you're starting to hear the same responses come up again and again with nothing new emerging, then you're probably at the point of saturation.

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Methodologies used to reach saturation

Knowing what data saturation is, and best practices to keep in mind when collecting qualitative data, there are various methods a qualitative researcher can leverage during the study design process.

Qualitative research has traditionally been thought of as individual interviews but has expanded over time to include a whole host of other methodologies, including field methods, focus groups, video diaries, written diaries, ethnography observation exercises, and so on, all of which are valued for the unique angles they can deliver on a topic.

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qualitative data analysis with quantilope's inColor

quantilope's qualitative research solution, inColor, offers an instinctive platform that brings you face-to-face with your target market for video qualitative interviews. 

Setting up qualitative studies with inColor puts you in charge of the number of participants you would like to include, with the option to add more participants as the study progresses. You can watch and listen to videos that participants create, which in itself brings data collection to life to get a good sense of views and reactions that are emerging. However, the analysis isn't just left to the researcher; multiple AI-driven analyses ensure that keywords, sentiments, and emotions are identified so that new themes and their relative importance are always uncovered - helping to easily identify any point of saturation. This results in truly insightful, conceptual, qualitative data that can be applied to your business immediately. 

If you'd like to know more about qualitative market research with quantilope and how you can be sure you've got all bases covered with your qual insights get in touch with us below: