Discussion Response
Sampling and Collecting Quantitative and Qualitative Data
Despite the fact that mixed methods studies have now become popularized, little has been written on the topic of sampling. Random sampling schemes are presented as belonging to the quantitative paradigm, and non-random sampling schemes belong to the qualitative paradigm, but researchers believe both samplings can be used in quantitative and qualitative studies (Onwuegbuzie & Collins, 2007). The choice of sampling class, according to Onwuegbuzie & Collins (2007), should be based on the type of generalization of interest (statistical vs. analytic).
If the goal is not to generalize to a population but to obtain insights into a phenomenon, individuals, or events, then the researcher purposefully selects for this phase that maximizes understanding of the underlying phenomenon (Onwuegbuzie & Collins, 2007). Purposive sampling techniques (also known as nonprobability sampling or qualitative sampling), involve selecting certain units or cases based on a specific purpose rather than randomly (Teddie & Yu, 2007). Traditional purposive sampling techniques are: sampling to achieve representativeness; sampling special or unique cases; sequential sampling; sampling using multiple purposive techniques (Teddie & Yu, 2007).
The choice of sample size is important because it determines the extent to which the researcher can make statistical and/or analytical generalizations. The size of the sample should be informed by the research objective, research question, and research design. Sample sizes in qualitative research should not be too small as to make it difficult to achieve data or theoretical saturation (ie case study) or too large it is difficult to undertake a deep, case-oriented analysis (ie correlational) (Onwuegbuzie & Collins, 2007).
Data collection methods is the process of gathering information from different sources to find answers to specific problems and questions. Although there is not one single best data collection method or technique, each method has its advantages/disadvantages. It’s important to have the right fit for your research to build strategies based on insights instead of opinions. Focus groups include dialogue with a group of selected participants who discuss a particular topic and led by a moderator. These participants answers influence each other during a discussion. However, time, sensitive topics, respondent feelings, can damper data collection with a focus group.
Reliability is the extent to which measurements are repeatable, when different persons perform the measurements on different occasions under different conditions with alternate instruments. An example used in my practice would be using the Transcutaneous bilirubin tool to measure if a newborn needs a serum bilirubin drawn. Validity is concerned with the meaningfulness of research components. What is intended to be measured, is being measured. Researchers can develop strong support for the validity of their measures. An example used in my practice would be our annual evaluations measured on 5 specific domains specific for Penn Medicine (Drost, 2011).
References
Drost, E. A. (2011). Validity and reliability in social science research. Education Research and Perspectives, 38(1), 105-124.
Onwuegbuzie, A. J., & Collins, K. M. (2007). A typology of mixed methods sampling designs in social science research. The Qualitative Report, 12(2), 281-316. Retrieved from http://nsuworks.nova.edu/tqr/vol12/iss2/9
Teddie, C., & Yu, F. (2007). Mixed methods sampling: A typology with examples. Journal of Mixed Methods Research, 1(1), 77-100. doi:10.1177/1558689806292430
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