Data Saturation and Purposeful Sampling Strategy Part 2

I was once told that a good sample is not good in terms of size or generalizability. A good sample requires an understanding of purposeful sample strategies. Ineffectiveness in data saturation is the failure to reach saturation point that can hampers content and research validity (Fusch & Ness, 2015). Additionally, data saturation is reached when there is enough information to reproduce similar study; and when the capability to obtain extra new information has been reached, or when further coding is no longer practicable. There is no one-size-fits-all method to reach data saturation (Sandelowski, 1995). The article of Patton (2015) described the important of effective purposeful sampling strategy, a key to outlining data analysis and findings. Upon reviewing the types and forms of purposeful sampling strategy used by Yob & Brewer (n.d.) article, I believed the most closely preferred strategy to use is the comparison-focused sampling is helpful when selecting cases to compare and contrast and to learn about factors that explain case similarities or differences ( Patton, 2015).

References

Fusch, P. I., & Ness, L. R. (2015). Are we there yet? data saturation in qualitative research. The Qualitative Report, 20(9), 1408-1416. Retrieved from http://search.proquest.com.ezp.waldenulibrary.org/docview/1721368991?accountid=14872

Sandelowski, M. (1995). Sample size in qualitative research. Research in Nursing and Health. 18, 179-183.

Yob, I., & Brewer, P. (n.d.). Working toward the common good:  An online university’s perspective on social change [Course material].  Retrieved fromhttps://class.waldenu.edu