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Saturation in qualitative research: exploring its conceptualization and operationalization

Overview of attention for article published in Quality & Quantity, September 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#4 of 317)
  • High Attention Score compared to outputs of the same age (95th percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

Mentioned by

news
1 news outlet
policy
1 policy source
twitter
47 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
309 Dimensions

Readers on

mendeley
1038 Mendeley
Title
Saturation in qualitative research: exploring its conceptualization and operationalization
Published in
Quality & Quantity, September 2017
DOI 10.1007/s11135-017-0574-8
Pubmed ID
Authors

Benjamin Saunders, Julius Sim, Tom Kingstone, Shula Baker, Jackie Waterfield, Bernadette Bartlam, Heather Burroughs, Clare Jinks

Abstract

Saturation has attained widespread acceptance as a methodological principle in qualitative research. It is commonly taken to indicate that, on the basis of the data that have been collected or analysed hitherto, further data collection and/or analysis are unnecessary. However, there appears to be uncertainty as to how saturation should be conceptualized, and inconsistencies in its use. In this paper, we look to clarify the nature, purposes and uses of saturation, and in doing so add to theoretical debate on the role of saturation across different methodologies. We identify four distinct approaches to saturation, which differ in terms of the extent to which an inductive or a deductive logic is adopted, and the relative emphasis on data collection, data analysis, and theorizing. We explore the purposes saturation might serve in relation to these different approaches, and the implications for how and when saturation will be sought. In examining these issues, we highlight the uncertain logic underlying saturation-as essentially a predictive statement about the unobserved based on the observed, a judgement that, we argue, results in equivocation, and may in part explain the confusion surrounding its use. We conclude that saturation should be operationalized in a way that is consistent with the research question(s), and the theoretical position and analytic framework adopted, but also that there should be some limit to its scope, so as not to risk saturation losing its coherence and potency if its conceptualization and uses are stretched too widely.

Twitter Demographics

The data shown below were collected from the profiles of 47 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 1,038 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1038 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 271 26%
Student > Master 220 21%
Unspecified 124 12%
Student > Bachelor 100 10%
Researcher 95 9%
Other 228 22%
Readers by discipline Count As %
Social Sciences 185 18%
Unspecified 183 18%
Nursing and Health Professions 141 14%
Medicine and Dentistry 140 13%
Business, Management and Accounting 103 10%
Other 286 28%

Attention Score in Context

This research output has an Altmetric Attention Score of 50. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 July 2019.
All research outputs
#339,008
of 13,352,827 outputs
Outputs from Quality & Quantity
#4
of 317 outputs
Outputs of similar age
#15,510
of 312,266 outputs
Outputs of similar age from Quality & Quantity
#1
of 7 outputs
Altmetric has tracked 13,352,827 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 317 research outputs from this source. They receive a mean Attention Score of 3.5. This one has done particularly well, scoring higher than 98% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 312,266 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them