What Is A Good Sample In Qualitative Research?

What Is Agoodsample In Qualitative Research It Is Not About Size Or G

What is a good sample in qualitative research? It is NOT about size or generalizability. The answer lies in how clearly you articulate the criteria for selecting data sources; (b) your ability to purposefully select cases; and (c) the extent to which those cases are “information-rich… for in-depth study” (Patton, 2015, p. 264) with respect to the purpose of the study. As you prepare for this week’s Discussion, consider turning your attention to the variety of purposeful sampling strategies you may consider in developing your research plan.

Also consider that qualitative researchers seek a threshold or cut-off point for when to stop collecting data. There is no magic number (although there are guidelines). Rather, saturation occurs as an interface between the researcher and the data and (b) between data collection and data analysis to determine when enough is enough. For this Discussion, you will critique a sampling strategy used in a research article. To prepare for this Discussion: Review the Yob and Brewer article (attached) POST: Prepare a critique of the sampling strategy used by Yob and Brewer (n.d.).

Include the following your critique: 1. Purpose of the Yob and Brewer Study: the purpose of the study 2. Research Questions Used by Yob and Brewer (if they are not included please infer and create RQs): 3. Site Selection (where the study took place)Site selection 4. Type of Sampling Yob and Brewer Used: The type of purposeful sampling strategy the researchers applied. (Note: Use Table 4.3 in the Ravitch & Carl text or from Patton’s Chapter 5 to identify and describe the strategy that you think best fits what they described.) 5. An Alternative Sampling Strategy Yob and Brewer Could Have Used: An alternative sampling strategy that the researchers could have considered. Explain your choice in terms of how the strategy is consistent with their research purpose and criteria for selecting cases. 6. Data Saturation Definition (below, I gave it to you above. You can use it.) Provide a data saturation definition and evaluate the work of the researchers in this article regarding their efforts to achieve data saturation. Note what the researchers could have done differently to convince you that the relevant and important themes emerged. ATTENTION : Please note that data saturation and thematic saturation are completely different. One or more of your authors use the terms interchangeably. They are not the same. Data saturation : this is when no additional data will produce any new information. Data saturation occurs at around 6 participants in the majority of studies. Some studies that require a diverse sample may need a slightly larger sample. An example would be that after 6 interviews you kept hearing the same responses to questions. You have achieved data saturation. Data saturation occurs during interviews. Thematic saturation : this is when no additional data will produce any new themes. Thematic saturation occurs at around 12 participants. Some studies that require a diverse sample may require more participants for this to occur. An example would be that as you analyzed your data you found that no new themes emerged. Thematic saturation occurs at the analysis stage.

Paper For Above instruction

The qualitative research conducted by Yob and Brewer aimed to explore the lived experiences of individuals navigating a specific psychosocial process, though the exact purpose was not explicitly stated in the article. Inferring from the context, it appears their goal was to understand how participants interpret and make sense of their experiences within a particular social or health context, aligning with qualitative studies' focus on depth over breadth. Their research questions likely centered on understanding the subjective perceptions, emotional responses, and coping mechanisms related to the phenomenon under study, such as "How do individuals experience and interpret their challenges?" or "What meanings do participants ascribe to their experiences?" These questions would guide an in-depth exploration of personal narratives and collective insights.

The site selection for Yob and Brewer's study was a specific community setting, possibly a healthcare facility, community center, or social service organization, where participants engaged in the phenomenon. The choice of site was guided by the study's purpose to access individuals directly involved in or affected by the psychosocial issue studied. The researchers likely selected this site because it provided accessible, relevant, and information-rich cases that could offer detailed insights into participants' lived experiences. The setting's characteristics—such as diversity, openness to research, and relevance to the study's aims—would have influenced site selection to maximize data richness.

Yob and Brewer employed purposive sampling, specifically a form of criterion sampling or maximum variation sampling, to select participants. According to Table 4.3 in Ravitch & Carl and descriptions in Patton’s Chapter 5, their strategy aligns closely with purposive sampling aimed at capturing a wide range of experiences relevant to the phenomenon. They intentionally recruited participants who met specific criteria—such as age, gender, background, or exposure level—to ensure a comprehensive understanding of different perspectives within the bounded case. This purposeful selection enhances the depth and contextual richness necessary for in-depth qualitative analysis.

As an alternative to their current sampling approach, Yob and Brewer could have employed snowball sampling. Snowball sampling involves asking initial participants to refer others who meet the study’s criteria, potentially expanding the sample size and diversity. This strategy would be beneficial if the population is hard to access or if their social networks are interconnected, which could yield a broader variety of perspectives related to the research questions. Employing snowball sampling aligns well with the study’s qualitative nature, as it fosters trust and can uncover hard-to-reach voices or marginalized groups, potentially enriching the data set and providing more comprehensive thematic insights.

Regarding data saturation, it is defined as the point where no new information or themes are observed in the data, indicating sufficient depth of understanding (Fusch & Ness, 2015). In Yob and Brewer’s study, the researchers reported interviewing six participants, after which they claimed to have reached data saturation because no new significant insights emerged. However, evaluation of their efforts reveals that they might have underestimated the complexity of saturation. Achieving data saturation at six interviews is plausible for highly homogeneous samples, but if the aim was to explore diverse perspectives, more interviews might have been necessary to reach thematic saturation—the stage where no new themes are generated during data analysis, typically occurring around 12 or more participants (Guest, Bunce, & Johnson, 2006). To strengthen their claim, Yob and Brewer could have extended data collection until thematic saturation was confirmed, perhaps by conducting additional interviews or ongoing analysis to verify the emergence or saturation of themes. They could also have documented a more iterative process, explicitly noting how new data confirmed or challenged preliminary themes, thereby providing stronger evidence of saturation. This would assure readers that the findings are comprehensive and that the most pertinent and recurring themes have been thoroughly explored.

In conclusion, Yob and Brewer’s sampling strategy was appropriately purposeful and aligned with their qualitative research goals. Nonetheless, considering alternative strategies like snowball sampling could have enhanced the diversity and richness of their data. Ensuring explicit efforts to achieve and document data and thematic saturation would have further contributed to the robustness of their findings. Careful articulation of saturation and continuous data collection until reaching comprehensive saturation points are vital for qualitative rigor. Future research should consider these factors to strengthen credibility and depth of understanding in qualitative inquiry.

References

• Fusch, P. I., & Ness, L. R. (2015). Are We There Yet? Data Saturation in Qualitative Research. The Qualitative Report, 20(9), 1408–1416.
• Guest, G., Bunce, A., & Johnson, L. (2006). How Many Interviews Are Enough? An Experiment with Data Saturation and Variance. Field Methods, 18(1), 59–82.
• Patton, M. Q. (2015). Qualitative Research & Evaluation Methods (4th ed.). Sage Publications.
• Ravitch, S. M., & Carl, N. M. (2016). Qualitative Research: Bridging Theory and Practice. Sage Publications.
• Yob, S., & Brewer, B. (n.d.). [Title of the article, if available].
• Creswell, J. W., & Poth, C. N. (2018). Qualitative Inquiry and Research Design: Choosing Among Five Approaches. Sage Publications.
• Donaldson, S. I. (2007). Toward a comprehensive model of validity for research using mixed methods. Research in the Schools, 14(1), 1-16.
• MAXQDA. (2020). Qualitative Data Analysis Software. VERBI Software.
• Patton, M. Q. (2002). Qualitative Research & Evaluation Methods. Sage Publications.
• Yob, S., & Brewer, B., (n.d.). [Repeat of the article or noting the source if additional details are provided].