Data Saturation
A major strategy of improving data saturation is making sure the recruited units for sampling have achieved saturation point (Patton, 2015). Likewise, this process means reaching a point in which all the relevant data of the research analysis have been obtained. For example, the recruited sample of patients with Alzheimer dementia have reached a basis for concluding or finalizing the research explanation that could lead to consequent data-wide.
Additionally, it is important to note the limitations that determine enough sample size. In some cases, researchers’ judgment and experiences, including appraising the quality of the composed data; type of research and the analytical approach may determine the limitations of sampling size (Sandelowski, 1995). A comparative study was used to describe how effective sampling strategies could be used for truck destination choice model (Park, Park, Kim, Kim & Park, 2013). Effective sampling strategies tell a lot about the quality of research study (Park et al., 2013).
References
Park, H., Park, D., Kim, C., Kim, H., & Park, M. (2013). A comparative study on sampling strategies for truck destination choice model: case of Seoul Metropolitan Area. Canadian Journal Of Civil Engineering, 40(1), 19-26. doi:10.1139/cjce-2012-0433
Patton, M. Q. (2015). Chapter 5, Module 30: Purposeful sampling and case selection: Overview of strategies and options. In Qualitative research and evaluation methods (4th ed., pp. 264-315). Thousand Oaks, CA: Sage Publications.
Sandelowski, M. (1995). “Sample size in qualitative research.” Research in Nursing and Health. 18, 179-183.
