03 · What You Need to Know
How Participant Selection Works in Qualitative Descriptive Research
Start With the Phenomenon, Not the Available Participants
Qualitative descriptive research seeks to provide a detailed, context-sensitive account of experiences, perceptions, events, or practices. Participant selection should therefore begin by identifying whose accounts are necessary to describe the phenomenon adequately.
Suppose a researcher wants to understand the difficulties teachers encounter when using generative AI to prepare classroom assessments. Recruiting teachers merely because they are employed at a convenient school would not establish that they can provide relevant evidence.
The researcher first needs to determine whether participants have actually used generative AI for assessment preparation, what kinds of assessment activities are relevant, and whether differences in teaching contexts may influence their experiences.
This follows the logic of qualitative descriptive research: the study is designed to produce an informative account of a defined phenomenon rather than to achieve statistical representation of a population.
The relevant participants are not necessarily those with the most extensive experience. Depending on the question, novice users, people who discontinued a practice, or individuals who encountered unsuccessful implementation may provide particularly useful perspectives.
What Is Purposive Sampling?
Purposive sampling, also called purposeful sampling, involves deliberately selecting participants or cases because they possess characteristics, experiences, or knowledge relevant to the research question.
Unlike probability sampling, its primary objective is not to give every member of a population a known probability of selection. Instead, it seeks information-rich cases that can illuminate the phenomenon under investigation.
Palinkas and colleagues (2015) discuss purposeful sampling in qualitative research, particularly in implementation research, and explain how different strategies may be selected according to the purpose of inquiry.
For qualitative description, purposive sampling is often suitable because the researcher usually needs participants who have encountered a particular experience, service, process, or practice.
However, purposive sampling is a broad category rather than a complete sampling plan. Researchers should explain the specific logic used to identify and recruit participants.
Which Sampling Strategies Are Appropriate?
Several sampling strategies may support qualitative descriptive inquiry. The choice depends on whether the study requires a focused account of a particular group, an examination of contextual variation, or a more detailed understanding of selected experiences.
| Sampling strategy |
How it works |
When it may be useful |
| Criterion sampling |
Select participants who meet predefined conditions relevant to the phenomenon. |
When everyone included must have experienced a particular practice, event, or service. |
| Maximum variation sampling |
Deliberately include participants who differ across relevant characteristics. |
When the study aims to describe similarities and differences across contexts. |
| Homogeneous sampling |
Recruit participants who share important characteristics or circumstances. |
When a focused description of a relatively specific group is needed. |
| Typical case sampling |
Select cases considered reasonably illustrative of an ordinary situation within the defined context. |
When the purpose is to describe routine experiences or practices rather than unusual cases. |
| Snowball or chain-referral sampling |
Ask participants or other contacts to identify additional potentially eligible individuals. |
When relevant participants are difficult to identify through ordinary recruitment channels. |
| Convenience sampling |
Recruit individuals primarily because they are accessible and willing. |
When access is constrained, although the limitations and possible selection bias require explicit consideration. |
These strategies can overlap. A researcher might use criterion sampling to establish eligibility and maximum variation sampling to seek differences among eligible participants.
For example, all participants might need experience using AI-assisted assessment tools, while recruitment deliberately includes teachers from different subject areas and levels of institutional support.
Such a combination is defensible when each selection decision contributes to answering the research question.
How Should Inclusion and Exclusion Criteria Be Defined?
Inclusion criteria identify the conditions participants must satisfy to be eligible. Exclusion criteria identify circumstances that make participation inappropriate for the particular study.
Good criteria should follow from the research question and the kind of evidence required.
For a study of teachers' experiences preparing assessments with generative AI, inclusion criteria might require participants to have direct experience using such tools for a defined assessment-related activity.
The researcher may also specify a relevant educational setting or period of experience if those boundaries are necessary for the inquiry.
Exclusion criteria should not be introduced merely to make the sample easier to manage. Excluding participants with less experience, for example, could eliminate precisely the difficulties that the study needs to understand.
Eligibility criteria should be sufficiently clear that another researcher could understand why a particular participant qualified. Yet they should not be so restrictive that the resulting sample captures only a narrow and unacknowledged portion of the phenomenon.
Should the Sample Be Homogeneous or Diverse?
Neither approach is universally preferable.
A relatively homogeneous sample may be appropriate when the research question concerns a specific experience within a clearly defined group. It can help the researcher develop a focused description without introducing contextual differences that are outside the study's purpose.
Maximum variation sampling may be preferable when the researcher expects experiences to differ across institutional settings, professional roles, access to resources, or other relevant characteristics.
Focused or homogeneous sampling
Prioritizes a detailed account of a relatively specific group sharing characteristics important to the research question.
Maximum variation sampling
Seeks meaningful differences across selected characteristics to examine the range and contextual variation of experiences.
Variation must be purposeful. Including participants from many unrelated categories does not automatically produce a stronger descriptive study.
For example, recruiting teachers from several educational levels may increase diversity, but it could also introduce substantial differences in assessment practices that the study is not designed to examine.
Researchers should identify which characteristics are likely to matter and explain how those characteristics influence recruitment.
How Many Participants Does a Qualitative Descriptive Study Need?
There is no universal sample-size requirement for qualitative descriptive research.
The appropriate number depends on the specificity of the research question, the characteristics of the participant group, the expected variation, the quality of the evidence, and the analytical strategy.
Malterud, Siersma, and Guassora (2016) proposed the concept of information power for qualitative interview studies. Their argument is that sample adequacy depends partly on how much relevant information the sample contributes to the research purpose.
A focused study involving participants with highly specific experience may require fewer interviews than a broad investigation across substantially different settings. However, information power does not provide a fixed numerical formula.
Researchers should distinguish between an initial recruitment target and a final justification of sample adequacy.
An initial target may be needed for ethics applications, resource planning, or recruitment logistics. The final report should explain whether the collected material was sufficient to support the intended descriptive account and what limitations remain.
Published sample-size ranges from other qualitative studies can provide context, but they should not be treated as universal methodological rules.
Should Recruitment Continue Until Saturation?
Saturation is frequently invoked in qualitative research, but the term has different meanings across methodologies and analytical approaches.
In some studies, researchers use it to describe a point at which additional data no longer generate new codes or categories. In others, saturation involves more demanding expectations concerning conceptual depth or theoretical development.
Hennink, Kaiser, and Marconi (2017) distinguish between code saturation and meaning saturation, showing why identifying a range of issues is not equivalent to developing a sufficiently nuanced understanding of them.
For qualitative description, researchers should consider whether the available material adequately captures the relevant experiences, distinctions, and contextual variation.
Claiming saturation simply because several consecutive interviews produced no new codes may be insufficient, especially if important participant groups remain underrepresented or existing categories lack detail.
Researchers should explain the stopping rationale they actually used. If they claim saturation, they should define what was assessed and how the judgment was reached.
How Can Recruitment Bias Affect the Findings?
Recruitment procedures influence who participates and whose experiences remain absent.
For example, teachers who are enthusiastic about educational technology may be more likely to volunteer for a study about AI-assisted assessment. Their accounts could differ from those of teachers who are skeptical, have discontinued using the technology, or have encountered serious implementation difficulties.
Recruitment through school administrators may introduce additional concerns. Potential participants could perceive participation as institutionally expected, even when researchers intend it to be voluntary.
Researchers should therefore consider how recruitment channels, gatekeepers, eligibility criteria, and willingness to participate may shape the sample.
Possible responses include using more than one recruitment channel, deliberately seeking relevant underrepresented perspectives, and clearly communicating that participation is voluntary.
These measures cannot eliminate every source of selection bias, but they can make recruitment more defensible and its limitations more transparent.
What Ethical Issues Matter During Participant Selection?
Sampling decisions have ethical consequences, particularly when participants are recruited through institutions where professional or educational relationships involve unequal power.
A researcher studying teachers within their own institution, for example, should consider whether colleagues or subordinates might feel pressured to participate.
Eligibility screening should collect only information necessary to determine participation. Recruitment procedures should protect privacy, explain the voluntary nature of participation, and avoid promising confidentiality that the research design cannot reasonably provide.
In small professional communities, combining participant characteristics in the findings may make individuals identifiable even when names are removed.
Researchers should consider these risks when planning sampling, collecting demographic information, and reporting contextual details.
Ethics approval and consent requirements vary by institution and jurisdiction. Relevant requirements should be verified before recruitment begins.
How Should Participant Selection Be Reported?
A rigorous methodology should explain how participants were identified, why they were appropriate, and what the sampling strategy allows the study to claim.
Useful reporting information includes the eligibility criteria, recruitment setting, sampling approach, recruitment procedures, final sample characteristics, and rationale for sample adequacy.
Researchers should also explain important deviations from the original plan, such as difficulties recruiting a particular group or changes made to improve contextual coverage.
The Standards for Reporting Qualitative Research (SRQR) and the Consolidated Criteria for Reporting Qualitative Research (COREQ) provide relevant reporting considerations, although neither establishes a universal sample-size rule.
Clear reporting contributes to the rigor of qualitative descriptive research by allowing readers to evaluate whether the participants and evidence were appropriate to the stated purpose.
