How to select the right sampling strategy?
Overview of sampling strategies in qualitative research
For more best practices see our method overview
Introduction to sampling in qualitative research
Selecting the best sampling method for qualitative research is a challenge, which we would like to help you master. Sampling in qualitative research is often a purposeful and strategic process designed to maximize depth, rather than breadth. Unlike quantitative research, which relies on random probability sampling for generalizability, qualitative research focuses on selecting information-rich cases that provide detailed insights into a phenomenon (Patton, 2015).
The choice of sampling strategy depends on the research objectives, theoretical framework, and methodological approach. Some strategies aim to capture diversity, while others focus on theoretical saturation or accessibility. Understanding these different strategies helps researchers strengthen credibility, transferability, and rigor in their studies (Lincoln & Guba, 1985).
The choice of sampling strategy depends on the research objectives, theoretical framework, and methodological approach. Some strategies aim to capture diversity, while others focus on theoretical saturation or accessibility. Understanding these different strategies helps researchers strengthen credibility, transferability, and rigor in their studies (Lincoln & Guba, 1985).
Key qualitative sampling strategies
In qualitative research, choosing the right sampling strategy shapes the depth and direction of a study. Different approaches offer unique advantages depending on the research goals—some prioritize flexibility and emerging insights, while others focus on capturing typical experiences or diverse perspectives. For example, theoretical and opportunistic sampling allow researchers to adapt their approach as new findings emerge, while criterion and typical case sampling ensure consistency in participant selection. Other strategies, such as extreme case or polar sampling, highlight contrasts that can challenge assumptions and refine theories. Understanding these methods helps researchers design studies that generate meaningful and well-rounded insights.
In the following we give a structured overview of sampling strategies and discuss when a certain strategy is most appropriate, what its strengths are as well as its limitations.
In the following we give a structured overview of sampling strategies and discuss when a certain strategy is most appropriate, what its strengths are as well as its limitations.
Theoretical Sampling
Theoretical sampling is a key strategy in Grounded Theory research (Glaser & Strauss, 1967). Unlike pre-planned sampling approaches, theoretical sampling is an iterative process, where data collection and analysis occur simultaneously, guiding the researcher in selecting new cases based on emerging findings. This ensures that categories are fully developed before stopping data collection. The stopping criterion for this process is called Theoretical Saturation.- When to use: In Grounded Theory studies, or when refining emerging concepts.
- Strengths: Supports theory development and conceptual refinement.
- Limitations: Requires flexibility, as sampling decisions evolve with data analysis.
Typical Case Sampling
Typical case sampling selects participants or cases that represent the norm within a particular context (Yin, 2018). This method is useful when the goal is descriptive rather than exploratory.- When to use: When studying everyday practices in a specific setting.
- Strengths: Provides a realistic understanding of the phenomenon.
- Limitations: May overlook marginalized or extreme perspectives.
Extreme or Deviant Case Sampling
This sampling method focuses on exceptional or unusual cases to gain insights into outliers (Flyvbjerg, 2006). For instance, a researcher studying organizational performance might select both the highest-performing and lowest-performing teams to analyze factors contributing to success or failure.- When to use: When outliers provide unique insights into a phenomenon.
- Strengths: Helps challenge assumptions and develop new theories.
- Limitations: Findings may not apply to average cases.
Polar Sampling
Polar sampling selects cases that sit at extreme ends of a spectrum, allowing for comparative analysis. This is commonly used in organizational or behavioral research.- When to use: When studying contrasting viewpoints (e.g., most satisfied vs. least satisfied employees).
- Strengths: Highlights key differences that drive a phenomenon.
- Limitations: Findings may not represent middle-ground perspectives.
Snowball Sampling
Snowball sampling is used when participants are difficult to locate or belong to hidden populations (Biernacki & Waldorf, 1981). In this method, existing participants recruit new participants, creating a network-based sample. It is commonly used in studies involving marginalized groups.- When to use: When the population is hard to access (e.g., undocumented workers, refugees).
- Strengths: Expands access to otherwise unreachable participants.
- Limitations: Can introduce selection bias, as participants recruit people they know.
Criterion Sampling
Criterion sampling selects participants who meet specific, predefined criteria relevant to the research question (Moustakas, 1994). It is commonly used in phenomenological research, where all participants must have experienced a particular phenomenon.- When to use: In studies where consistent participant characteristics are necessary.
- Strengths: Ensures homogeneity across cases, making comparisons easier.
- Limitations: May exclude valuable perspectives if criteria are too narrow.
Maximum Variation Sampling
This sampling strategy is used to capture a wide range of perspectives within a phenomenon (Patton, 2015). Researchers select participants with diverse characteristics, backgrounds, or experiences to explore common patterns and variations.- When to use: When aiming for transferability and comprehensive understanding.
- Strengths: Enhances comparability by showing similarities and differences.
- Limitations: Complex data synthesis due to high variability in responses.
Opportunistic (Emergent) Sampling
This sampling method allows researchers to adjust sampling based on unexpected opportunities during fieldwork. It is widely used in ethnographic research, where researchers may identify new participants organically.- When to use: When conducting fieldwork with evolving research questions.
- Strengths: Provides flexibility in dynamic research settings.
- Limitations: Risks inconsistency if not carefully documented. Limited transferability.
Considerations with qualitative sampling
Unlike quantitative sampling, which prioritizes representativeness to a population, qualitative sampling strategies often emphasize relevance, ensuring that each participant contribute meaningfully to the research question, adding depth to the explanation of the explored phenomenon. A major strength is the ability to capture detailed narratives, which help explain meanings, processes, and lived experiences (Denzin & Lincoln, 2018).
However, qualitative sampling also presents challenges. Because it relies on researcher judgment, there is a risk of selection bias. Additionally, small sample sizes can limit generalizability. Nevertheless, qualitative research prioritizes transferability over statistical generalizability, meaning findings can still be applied to similar contexts.
However, qualitative sampling also presents challenges. Because it relies on researcher judgment, there is a risk of selection bias. Additionally, small sample sizes can limit generalizability. Nevertheless, qualitative research prioritizes transferability over statistical generalizability, meaning findings can still be applied to similar contexts.
Conclusion
Choosing the best sampling strategy is essential for ensuring the depth, credibility, and rigor of qualitative research. While theoretical sampling supports emerging theory development, maximum variation sampling captures diverse experiences. Snowball sampling allows access to hidden populations, whereas polar sampling provides insights into contrasting perspectives. Each method has unique strengths and limitations, and you should select the approach that best aligns with your research question and methodological framework.
By systematically documenting sampling decisions, researchers using QDAcity can enhance transparency and consistency, ensuring their studies are both methodologically sound and analytically robust.
By systematically documenting sampling decisions, researchers using QDAcity can enhance transparency and consistency, ensuring their studies are both methodologically sound and analytically robust.
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