Exploring data saturation in qualitative research

Saturation is a familiar term in qualitative research. Not only is it an important principle, but it’s also one of the defining traits of qualitative research. This article defines data saturation and highlights how it relates to qualitative research practices. 

The concept of saturation relates to the number of interviews conducted in qualitative research. Data saturation occurs in the research process when you’ve collected sufficient data to draw the necessary conclusions, and collecting any further data won't produce value-added insights. 

The term data saturation originated from qualitative research's grounded theory. Grounded theory is a broad research approach first introduced by sociologists Glaser and Strauss in the 1960s. 

Sample sizes in qualitative research

It’s important to understand sample sizes in qualitative research before discussing data saturation in qualitative research. Qualitative research focuses on themes, unlike quantitative research, which involves the collection and analysis of numerical data.

You can gather qualitative data via observation, interviews, and task completion. A key element in qualitative research is small sample sizes that are homogeneous in nature. Qualitative research focuses on segmenting audiences into similar psychographic traits instead of interviewing a population with a broad range of traits. 

This guarantees that a research study focuses on exploring ideas or themes from a particular subset of a population. The idea is to ensure that segments have well-defined traits which are screened during recruitment. Since there will be well-defined segments, qualitative researchers focus on addressing a strictly defined number of participants to explore themes and ideas. 

How many participants should a qualitative study have?

The ideal sample size for a qualitative study depends on various factors, such as the research question, population, and objectives. Recent research by Monique Hennink and Bonnie N. Kaiser (2022) found that you can achieve saturation with fewer participants than previously believed. Their review of 23 peer-reviewed articles suggests that 9–17 interviews or 4–8 focus group discussions can be sufficient to reach saturation, especially for studies with homogenous populations and narrowly defined objectives. 

However, it's important to note that researchers should maintain a rigorous recruitment process to ensure the integrity of the study, regardless of the sample size. Overall, the goal of qualitative research should be to achieve saturation, which occurs when researchers begin to observe the same themes and patterns repeatedly, regardless of the number of participants interviewed or observed.

Data saturation in qualitative interviews

Data saturation means that researchers aren't finding any additional data from interviews. In most cases, researchers go out of their way to seek groups that stretch data diversity as far as possible just to ensure that saturation is based on the broadest possible range of data on the category. 

Parameters and factors that influence saturation in focus group data include:

• Study purpose

• Type of codes

• Type and degree of saturation

• Group stratification

• The number of groups per stratum 

Different experts have adopted several interpretations of saturation combining two or more models of saturation, making its conceptualization less distinct. Common modes of saturation include:

• Data saturation. Its principal focus is data collection, and it relates to the degree to which new data repeat what was mentioned in the previous data.

• A priori semantic saturation. Its principal focus is sampling, and it relates to the degree to which identified themes or codes are exemplified in the data.

• Inductive thematic saturation. Its principal focus is analysis, and it relates to the emergence of new themes or codes.

• Theoretical saturation. Its principal focus is sampling, and it relates to developing theoretical categories. It is also related to grounded theory methodology. 

Different experts' views on saturation seem to embody different elements of saturation. Experts such as Hennink MM have created hybrid saturation by combining elements of all four saturation models. 

The perspective that a researcher takes on what saturation means within a particular study will have implications for when they seek saturation. For instance, you can identify saturation at an earlier stage in the research process if a researcher considers the data saturation approach. This is because, from this perspective, you typically view saturation as separate from formal analysis. 

When researchers consider inductive thematic saturation, the fact that researchers focus on reaching saturation levels at the analysis level might suggest that they will achieve saturation at a later stage than in data saturation approaches. On the other hand, theoretical saturation indicates that the analysis process is often at a more advanced stage and at a higher level of theoretical generality. 

Ways to measure saturation in qualitative research include:

• Reliance on probability theory or the assumption of a random sample

• Retrospective assessment dependent on having a fully coded/analyzed data set

• Lack of comparability in metrics 

You can use different strategies to assess saturation in qualitative research. These include:

Code frequency counts 

This approach involves counting codes in successive transcripts or sets of transcripts until new code frequency diminishes, signaling the reach of saturation. 

Code meaning 

This approach focuses on reaching a full understanding of issues in data as the indicator that you’ve achieved saturation by assessing whether the issue, its nuances, and dimensions are completely identified and understood. 

Comparative method

This method adds a more structured comparison to the code frequency counts approach. It involves reviewing data in pre-determined batches and listing all new codes in a saturation table for each data batch. 

High-order groupings 

This approach involves counting higher-order groupings of codes like salient themes, meta themes, or categories. 

Stopping criterion 

Under this approach, you add a stopping criterion to the code frequency count approach. This approach involves reviewing initial interview samples or focus groups to identify new codes. It also involves using a pre-determined stopping criterion, which is typically the number of consecutive groups/interviews after the initial sample where you identified no new codes in the sample. You accomplish saturation when you identify no new codes after the stopping criteria of interviews after the initial sample. 

Achieving saturation shouldn't be an easy ground for ending your study right away. If you arrive at saturation quickly, first ask yourself whether you've covered the audience for the product/idea.

If not, recruit more participants who fit your segment and then test the ideas/product extensively. However, if you've interacted with many participants and don't discover any new themes consecutively, it may be wise to stop at the number of participants you’re at. 

Other variables may affect saturation, such as the quality of your recruiting, the overall focus of your objectives, and how you ask your study questions. Ensure you understand saturation before discovering its role in effective qualitative research.