Diversity and inclusion in qualitative research is an important topic of discussion in Research Design Review. It is a topic closely related to the broader subject of sample design which has been discussed, directly or indirectly, in many RDR articles. One such article — “Sample Size in Qualitative Research & the Risk of Relying on Saturation” — talks about the many factors to be considered when determining sample size, including the diversity of participants.

“A TQF Approach to Choosing a Sample Design” discusses the Credibility component of the Total Quality Framework and specifically the area of Scope. This article emphasizes a systematic approach to sampling when recruiting from a large population to ensure an inclusive sample of participants who “share defining characteristics.”

In “Exploring Human Realities: A Quality & Fair Approach,” the focus is on the manner in which quality approaches to qualitative research design — including the scope of the sample design — enable researchers to “embrace diversity in our participants” by “giving participants a fair voice in the research.”

An all-important yet often overlooked consideration when building inclusive sample designs is quality data analysis. That is, the ability to account for and interpret the diversity embedded in the data. When the population of interest is large and diverse (e.g., parents and children participating in a state-wide youth program spanning five communities), the researcher needs to think carefully about the collected data and, specifically, about what can and cannot be interpreted from the final data set.

There are two broad scenarios when this consideration comes into play. In one instance, qualitative researchers may pride themselves with the inclusiveness and diversity of their research participants yet ignore the impact this diversity may have on the resulting data. If 30 in-depth interviews (IDIs) are conducted with parents and children representing a range of demographic characteristics (age, race, ethnicity, gender, income) across five communities, the researcher must consider how this diversity impacted what they learned. Did the diversity in their participants mask a more profound interpretation of participants’ contributions to the research question, and in this way weaken the overall usefulness of the findings? Or, is the researcher able to identify examples of exceptions in the data within segments of the sample that deserve further investigation?

The other scenario is when the researcher prioritizes the ability to analyze diverse segments of their population during the sample design phase. In this case, the researcher decides which participant characteristics are most relevant to their research objective and creates a sample design that will allow the researcher to analyze the data by one or more of these characteristics. Instead of 30 IDIs with a diverse group of parents and children, the researcher might decide to focus on parents for the initial study and conduct 10 dyads (two in each of the five communities) with White non-Hispanic parents, 10 dyads with non-Hispanic Black parents, and 10 dyads with Hispanic parents. At the conclusion of data collection, the analytic process will ultimately result in themes across the entire 30 dyads but importantly the researcher has the ability to look closely at the data associated with each of the three participant groups.

Making the most of diversity and inclusion in qualitative sample design requires the researcher to think carefully about the role diverse participants will play in defining the scope of the research, conducting the analysis, and the interpretation of the results.