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      Open-ended interview questions and saturation

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          Abstract

          Sample size determination for open-ended questions or qualitative interviews relies primarily on custom and finding the point where little new information is obtained (thematic saturation). Here, we propose and test a refined definition of saturation as obtaining the most salient items in a set of qualitative interviews (where items can be material things or concepts, depending on the topic of study) rather than attempting to obtain all the items. Salient items have higher prevalence and are more culturally important. To do this, we explore saturation, salience, sample size, and domain size in 28 sets of interviews in which respondents were asked to list all the things they could think of in one of 18 topical domains. The domains—like kinds of fruits (highly bounded) and things that mothers do (unbounded)—varied greatly in size. The datasets comprise 20–99 interviews each (1,147 total interviews). When saturation was defined as the point where less than one new item per person would be expected, the median sample size for reaching saturation was 75 (range = 15–194). Thematic saturation was, as expected, related to domain size. It was also related to the amount of information contributed by each respondent but, unexpectedly, was reached more quickly when respondents contributed less information. In contrast, a greater amount of information per person increased the retrieval of salient items. Even small samples ( n = 10) produced 95% of the most salient ideas with exhaustive listing, but only 53% of those items were captured with limited responses per person (three). For most domains, item salience appeared to be a more useful concept for thinking about sample size adequacy than finding the point of thematic saturation. Thus, we advance the concept of saturation in salience and emphasize probing to increase the amount of information collected per respondent to increase sample efficiency.

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          Most cited references23

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          Supporting thinking on sample sizes for thematic analyses: a quantitative tool

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            (I Can’t Get No) Saturation: A simulation and guidelines for sample sizes in qualitative research

            I explore the sample size in qualitative research that is required to reach theoretical saturation. I conceptualize a population as consisting of sub-populations that contain different types of information sources that hold a number of codes. Theoretical saturation is reached after all the codes in the population have been observed once in the sample. I delineate three different scenarios to sample information sources: “random chance,” which is based on probability sampling, “minimal information,” which yields at least one new code per sampling step, and “maximum information,” which yields the largest number of new codes per sampling step. Next, I use simulations to assess the minimum sample size for each scenario for systematically varying hypothetical populations. I show that theoretical saturation is more dependent on the mean probability of observing codes than on the number of codes in a population. Moreover, the minimal and maximal information scenarios are significantly more efficient than random chance, but yield fewer repetitions per code to validate the findings. I formulate guidelines for purposive sampling and recommend that researchers follow a minimum information scenario.
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              Quantifying Thematic Saturation in Qualitative Data Analysis

                Author and article information

                Contributors
                Role: ConceptualizationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – original draft
                Role: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: ConceptualizationRole: MethodologyRole: Writing – review & editing
                Role: MethodologyRole: Writing – review & editing
                Role: MethodologyRole: ResourcesRole: Writing – review & editing
                Role: MethodologyRole: ResourcesRole: Writing – review & editing
                Role: ConceptualizationRole: MethodologyRole: ResourcesRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                20 June 2018
                2018
                : 13
                : 6
                : e0198606
                Affiliations
                [1 ] Department of Preventive Medicine & Community Health, University of Texas Medical Branch, Galveston, Texas, United States of America
                [2 ] Institute for Social Research, Arizona State University, Tempe, Arizona/University of Florida, Gainesville, Florida, United States of America
                [3 ] Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America
                [4 ] Department of Management, University of Kentucky, Lexington, Kentucky, United States of America
                [5 ] Department of Anthropology, University of Florida, Gainesville, Florida, United States of America
                University of Birmingham, UNITED KINGDOM
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                ‡ These authors also contributed equally to this work.

                Author information
                http://orcid.org/0000-0002-0695-736X
                Article
                PONE-D-18-05267
                10.1371/journal.pone.0198606
                6010234
                29924873
                107bb768-78e5-4802-aa0c-a0c74da00703
                © 2018 Weller et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 16 February 2018
                : 22 May 2018
                Page count
                Figures: 2, Tables: 4, Pages: 18
                Funding
                This project was partially supported by the Agency for Healthcare Research and Quality (R24HS022134). Funding for the original data sets was from the National Science Foundation (#BCS-0244104) for Gravlee et al. (2013), from the National Institute on Drug Abuse (R29DA10640) for Brewer et al. (2002), and from the Air Force Office of Scientific Research for Brewer (1995). Content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.
                Categories
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                Plant Science
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                Research and Analysis Methods
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                Qualitative Studies
                Social Sciences
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                Mathematics
                Statistics (Mathematics)
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                Information Retrieval
                Biology and Life Sciences
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                All relevant data are available as an Excel file in the Supporting Information files.

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