Academic journal article The Qualitative Report

Are We There Yet? Data Saturation in Qualitative Research

Academic journal article The Qualitative Report

Are We There Yet? Data Saturation in Qualitative Research

Article excerpt

Failure to reach data saturation has an impact on the quality of the research conducted and hampers content validity (Bowen, 2008; Kerr, Nixon, & Wild, 2010). Students who design a qualitative research study come up against the dilemma of data saturation when interviewing study participants (O'Reilly & Parker, 2012; Walker, 2012). In particular, students must address the question of how many interviews are enough to reach data saturation (Guest, Bunce, & Johnson, 2006). A frequent reference for answering this question is Mason (2010), who presented an extensive discussion of data saturation in qualitative research. However, the paper's references are somewhat dated for doctoral students today, ranging in dates from 1981-2005 and consisting mainly of textbooks. Although the publication date of the article is 2010, this is one of those types of articles that have older data masquerading as newer. The Mason (2010) article was recently updated to reflect a more contemporary date; however, the article did not update the content other than a few more recent citations. That is not to say that the article has no merit; instead, the concepts behind data saturation remain universal and timeless. Mason has a talent for explaining the difficult in terms that most can understand. Moreover, many students use Mason's work as support for their proposals and studies. To be sure, the concept of data saturation is not new and it is a universal one, as well. What is of concern is that Mason supported his assertions with textbooks and dated sources.

When deciding on a study design, the student should aim for one that is explicit regarding how data saturation is reached. Data saturation is reached when there is enough information to replicate the study (O'Reilly & Parker, 2012; Walker, 2012), when the ability to obtain additional new information has been attained (Guest et al., 2006), and when further coding is no longer feasible (Guest et al., 2006).

One Size Does Not Fit All

The field of data saturation is a neglected one. The reason for this is because it is a concept that is hard to define. This is especially problematic because of the many hundreds if not thousands of research designs out there (Marshall & Rossman, 2011). What is data saturation for one is not nearly enough for another. Case in point: ethnography is known for a great deal of data saturation because of the lengthy timelines to complete a study as well as the multitude of data collection methods used. In contrast, meta-analysis can be problematic because the researcher is using already established databases for the information; therefore, the researcher is dependent upon prior researchers reaching data saturation. In the case of a phenomenological study design, the point at which data saturation has been attained is different than if one were using a case study design. To be sure, the use of probing questions and creating a state of epoché in a phenomenological study design will assist the researcher in the quest for data saturation; however, a case study design parameters are more explicit (Amerson, 2011; Bucic, Robinson, & Ramburuth, 2010).

There is no one-size-fits-all method to reach data saturation. This is because study designs are not universal. However, researchers do agree on some general principles and concepts: no new data, no new themes, no new coding, and ability to replicate the study (Guest et al., 2006). When and how one reaches those levels of saturation will vary from study design to study design. The idea of data saturation in studies is helpful; however, it does not provide any pragmatic guidelines for when data saturation has been reached (Guest et al., 2006). Guest et al noted that data saturation may be attained by as little as six interviews depending on the sample size of the population. However, it may be best to think of data in terms of rich and thick (Dibley, 2011) rather than the size of the sample (Burmeister, & Aitken, 2012). …

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