A simple method to assess and report thematic saturation in qualitative research

Data saturation is the most commonly employed concept for estimating sample sizes in qualitative research. Over the past 20 years, scholars using both empirical research and mathematical/statistical models have made significant contributions to the question: How many qualitative interviews are enoug...

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Published in:	PloS one Vol. 15; no. 5; p. e0232076
Main Authors:	Guest, Greg, Namey, Emily, Chen, Mario
Format:	Journal Article
Language:	English
Published:	United States Public Library of Science 05.05.2020 Public Library of Science (PLoS)
Subjects:	Adequacy Analysis Data collection Datasets Design Empirical analysis Grounded theory Identification methods Interviews Mathematical models Methods Physical Sciences Population Qualitative analysis Qualitative research Research and Analysis Methods Research design Researchers Saturation Size estimation Social Sciences Statistical analysis Statistical models Studies Theory California North Carolina United States > US
ISSN:	1932-6203, 1932-6203
Online Access:	Get full text
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Abstract	Data saturation is the most commonly employed concept for estimating sample sizes in qualitative research. Over the past 20 years, scholars using both empirical research and mathematical/statistical models have made significant contributions to the question: How many qualitative interviews are enough? This body of work has advanced the evidence base for sample size estimation in qualitative inquiry during the design phase of a study, prior to data collection, but it does not provide qualitative researchers with a simple and reliable way to determine the adequacy of sample sizes during and/or after data collection. Using the principle of saturation as a foundation, we describe and validate a simple-to-apply method for assessing and reporting on saturation in the context of inductive thematic analyses. Following a review of the empirical research on data saturation and sample size estimation in qualitative research, we propose an alternative way to evaluate saturation that overcomes the shortcomings and challenges associated with existing methods identified in our review. Our approach includes three primary elements in its calculation and assessment: Base Size, Run Length, and New Information Threshold. We additionally propose a more flexible approach to reporting saturation. To validate our method, we use a bootstrapping technique on three existing thematically coded qualitative datasets generated from in-depth interviews. Results from this analysis indicate the method we propose to assess and report on saturation is feasible and congruent with findings from earlier studies.
AbstractList	Data saturation is the most commonly employed concept for estimating sample sizes in qualitative research. Over the past 20 years, scholars using both empirical research and mathematical/statistical models have made significant contributions to the question: How many qualitative interviews are enough? This body of work has advanced the evidence base for sample size estimation in qualitative inquiry during the design phase of a study, prior to data collection, but it does not provide qualitative researchers with a simple and reliable way to determine the adequacy of sample sizes during and/or after data collection. Using the principle of saturation as a foundation, we describe and validate a simple-to-apply method for assessing and reporting on saturation in the context of inductive thematic analyses. Following a review of the empirical research on data saturation and sample size estimation in qualitative research, we propose an alternative way to evaluate saturation that overcomes the shortcomings and challenges associated with existing methods identified in our review. Our approach includes three primary elements in its calculation and assessment: Base Size, Run Length, and New Information Threshold. We additionally propose a more flexible approach to reporting saturation. To validate our method, we use a bootstrapping technique on three existing thematically coded qualitative datasets generated from in-depth interviews. Results from this analysis indicate the method we propose to assess and report on saturation is feasible and congruent with findings from earlier studies. Data saturation is the most commonly employed concept for estimating sample sizes in qualitative research. Over the past 20 years, scholars using both empirical research and mathematical/statistical models have made significant contributions to the question: How many qualitative interviews are enough? This body of work has advanced the evidence base for sample size estimation in qualitative inquiry during the design phase of a study, prior to data collection, but it does not provide qualitative researchers with a simple and reliable way to determine the adequacy of sample sizes during and/or after data collection. Using the principle of saturation as a foundation, we describe and validate a simple-to-apply method for assessing and reporting on saturation in the context of inductive thematic analyses. Following a review of the empirical research on data saturation and sample size estimation in qualitative research, we propose an alternative way to evaluate saturation that overcomes the shortcomings and challenges associated with existing methods identified in our review. Our approach includes three primary elements in its calculation and assessment: Base Size, Run Length, and New Information Threshold. We additionally propose a more flexible approach to reporting saturation. To validate our method, we use a bootstrapping technique on three existing thematically coded qualitative datasets generated from in-depth interviews. Results from this analysis indicate the method we propose to assess and report on saturation is feasible and congruent with findings from earlier studies.Data saturation is the most commonly employed concept for estimating sample sizes in qualitative research. Over the past 20 years, scholars using both empirical research and mathematical/statistical models have made significant contributions to the question: How many qualitative interviews are enough? This body of work has advanced the evidence base for sample size estimation in qualitative inquiry during the design phase of a study, prior to data collection, but it does not provide qualitative researchers with a simple and reliable way to determine the adequacy of sample sizes during and/or after data collection. Using the principle of saturation as a foundation, we describe and validate a simple-to-apply method for assessing and reporting on saturation in the context of inductive thematic analyses. Following a review of the empirical research on data saturation and sample size estimation in qualitative research, we propose an alternative way to evaluate saturation that overcomes the shortcomings and challenges associated with existing methods identified in our review. Our approach includes three primary elements in its calculation and assessment: Base Size, Run Length, and New Information Threshold. We additionally propose a more flexible approach to reporting saturation. To validate our method, we use a bootstrapping technique on three existing thematically coded qualitative datasets generated from in-depth interviews. Results from this analysis indicate the method we propose to assess and report on saturation is feasible and congruent with findings from earlier studies.
Audience	Academic
Author	Namey, Emily Guest, Greg Chen, Mario
AuthorAffiliation	2 Global Health, Population, and Nutrition, FHI 360, Durham, North Carolina, United States of America University of Birmingham, UNITED KINGDOM 1 Q42 Research, Research Triangle Park, North Carolina, United States of America
AuthorAffiliation_xml	– name: 2 Global Health, Population, and Nutrition, FHI 360, Durham, North Carolina, United States of America – name: 1 Q42 Research, Research Triangle Park, North Carolina, United States of America – name: University of Birmingham, UNITED KINGDOM
Author_xml	– sequence: 1 givenname: Greg surname: Guest fullname: Guest, Greg – sequence: 2 givenname: Emily orcidid: 0000-0003-3200-1633 surname: Namey fullname: Namey, Emily – sequence: 3 givenname: Mario surname: Chen fullname: Chen, Mario
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/32369511$$D View this record in MEDLINE/PubMed
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Copyright	COPYRIGHT 2020 Public Library of Science 2020 Guest et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2020 Guest et al 2020 Guest et al
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SubjectTerms	Adequacy Analysis Data collection Datasets Design Empirical analysis Grounded theory Identification methods Interviews Mathematical models Methods Physical Sciences Population Qualitative analysis Qualitative research Research and Analysis Methods Research design Researchers Saturation Size estimation Social Sciences Statistical analysis Statistical models Studies Theory
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Title	A simple method to assess and report thematic saturation in qualitative research
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