Improving generalisability in qualitative research involves focusing on the ability to apply findings to similar contexts or develop broader theoretical insights, rather than aiming for statistical representativeness of a larger population. Unlike quantitative research, which often relies on methods like random sampling to achieve external validity, qualitative approaches employ different strategies to enhance the applicability and relevance of their findings.
Understanding Generalisability: Qualitative vs. Quantitative
It's crucial to distinguish between the types of generalisability sought in different research paradigms:
- Quantitative Generalisability (External Validity): This aims to make statistical inferences from a sample to a defined population. It typically relies on probability sampling methods, such as random sampling. As noted, "Use random sampling. If the sample is truly random (i.e., everyone in the population is equally likely to be chosen for the sample), then you can avoid sampling bias and ensure that the sample will be representative of the population." This approach is designed for predicting or describing population characteristics based on sample data.
- Qualitative Generalisability (Transferability, Analytical Generalisation): Qualitative research does not typically seek to generalise findings statistically to a population based on a randomly selected sample. Instead, it often aims for:
- Transferability: Allowing readers to judge whether the findings are applicable to their own contexts or similar situations.
- Analytical Generalisation: Using findings to develop or refine theories that can be applied to other cases or settings.
Given these distinct goals, the methods for enhancing generalisability in qualitative research differ significantly from the quantitative approach described in the reference.
Strategies for Improving Qualitative Generalisability (Transferability & Analytical Generalisation)
Qualitative researchers employ various techniques to enhance the potential for their findings to be meaningful and applicable beyond the immediate study participants or setting. These strategies focus on providing rich detail and theoretical depth:
- Provide Rich, Thick Description: This is perhaps the most important strategy for transferability. Researchers offer detailed accounts of the research setting, participants, interactions, and the context surrounding the data. This allows readers to thoroughly understand the research environment and judge how similar it is to their own context, enabling them to assess the potential transferability of the findings.
- Utilize Purposeful Sampling: Unlike random sampling used for population representativeness, qualitative research typically uses non-probability sampling methods like purposeful sampling (using a hypothetical link for illustrative purposes). This involves selecting information-rich cases that are particularly relevant to the research question. Strategies include:
- Maximum Variation Sampling: Capturing a wide range of perspectives.
- Critical Case Sampling: Studying a case that is crucial for testing a theory.
- Snowball Sampling: Identifying cases through participants.
While not aiming for statistical representativeness, purposeful sampling ensures that the selected cases provide deep insights relevant to the phenomenon under study, contributing to analytical generalisation.
- Employ Theoretical Sampling: In grounded theory or similar approaches, sampling decisions are made iteratively based on the emerging data and theory. This helps refine theoretical categories and relationships, leading to a more robust and potentially generalisable theory.
- Establish Audit Trails: Maintaining detailed records of methodological decisions, data collection, analysis, and interpretation processes enhances the trustworthiness (credibility, dependability, confirmability) of the study. This transparency allows others to evaluate the rigor of the research, which indirectly supports the assessment of transferability.
- Conduct Member Checking and Peer Debriefing: While primarily techniques for ensuring credibility, discussing findings with participants (member checking) or peers (peer debriefing) helps validate interpretations and can refine the understanding of the phenomenon, making the findings more robust and potentially transferable.
- Compare and Contrast Findings: Relating the study's findings to existing literature, theories, or other cases strengthens analytical generalisation. Identifying similarities and differences helps to understand the boundaries and conditions under which the findings might hold true.
In summary, while quantitative research aims for statistical generalisability to a population often via methods like random sampling to ensure representativeness, qualitative research enhances its form of generalisability (transferability and analytical generalisation) through detailed description, purposeful sampling, and rigorous analytical processes that allow readers and researchers to assess the applicability of findings to other relevant contexts or theoretical frameworks.
