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Are We There Yet? Data Saturation in Qualitative Research

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Abstract

Failure to reach data saturation has an impact on the quality of the research conducted and hampers content validity. The aim of a study should include what determines when data saturation is achieved, for a small study will reach saturation more rapidly than a larger study. Data saturation is reached when there is enough information to replicate the study when the ability to obtain additional new information has been attained, and when further coding is no longer feasible. The following article critiques two qualitative studies for data saturation: Wolcott (2004) and Landau and Drori (2008). Failure to reach data saturation has a negative impact on the validity on one’s research. The intended audience is novice student researchers. © 2015: Patricia I. Fusch, Lawrence R. Ness, and Nova Southeastern University.
The Qualitative Report 2015 Volume 20, Number 9, How To Article 1, 1408-1416
http://www.nova.edu/ssss/QR/QR20/9/fusch1.pdf
Are We There Yet? Data Saturation in Qualitative Research
Patricia I. Fusch and Lawrence R. Ness
Walden University, Minneapolis, Minnesota, USA
Failure to reach data saturation has an impact on the quality of the research
conducted and hampers content validity. The aim of a study should include
what determines when data saturation is achieved, for a small study will reach
saturation more rapidly than a larger study. Data saturation is reached when
there is enough information to replicate the study when the ability to obtain
additional new information has been attained, and when further coding is no
longer feasible. The following article critiques two qualitative studies for data
saturation: Wolcott (2004) and Landau and Drori (2008). Failure to reach
data saturation has a negative impact on the validity on one’s research. The
intended audience is novice student researchers. Keywords: Data Saturation,
Triangulation, Interviews, Personal Lens, Bias.
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
1409 The Qualitative Report 2015
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). The easiest way to differentiate between rich and thick data is
to think of rich as quality and thick as quantity. Thick data is a lot of data; rich data is many-
layered, intricate, detailed, nuanced, and more. One can have a lot of thick data that is not
rich; conversely, one can have rich data but not a lot of it. The trick, if you will, is to have
both. One cannot assume data saturation has been reached just because one has exhausted
the resources. Again, data saturation is not about the numbers per se, but about the depth of
the data (Burmeister & Aitken, 2012). For example, one should choose the sample size that
has the best opportunity for the researcher to reach data saturation. A large sample size does
not guarantee one will reach data saturation, nor does a small sample sizerather, it is what
constitutes the sample size (Burmeister & Aitken, 2012). What some do not recognize is that
no new themes go hand-in-hand with no new data and no new coding (O’Reilly & Parker,
2012). If one has reached the point of no new data, one has also most likely reached the point
of no new themes; therefore, one has reached data saturation. Morse, Lowery, and Steury
(2014) made the point that the concept of data saturation has many meaning to many
researchers; moreover, it is inconsistently assessed and reported. What is interesting about
their study results is that the authors noted that in their review of 560 dissertations that
sample size was rarely if ever chosen for data saturation reasons. Instead, the sample size was
chosen for other reasons (Morse et al., 2014).
Data Collection Methods to Reach Saturation
During the study, a novice researcher can conduct the research in a manner to attain
data saturation (Francis et al., 2010; Gerring, 2011; Gibbert & Ruigrok, 2010; Onwuegbuzie,
Leech, & Collins, 2010) by collecting rich (quality) and thick (quantity) data (Dibley, 2011),
although an appropriate study design should also be considered. One could choose a data
collection methodology that has been used before (Porte, 2013) that demonstrated data
saturation had been reached; moreover, one would correctly document the process as
evidence (Kerr et al., 2010).
Interviews are one method by which one’s study results reach data saturation. Bernard
(2012) stated that the number of interviews needed for a qualitative study to reach data
saturation was a number he could not quantify, but that the researcher takes what he can get.
Moreover, interview questions should be structured to facilitate asking multiple participants
the same questions, otherwise one would not be able to achieve data saturation as it would be
Patricia I. Fusch and Lawrence R. Ness 1410
a constantly moving target (Guest et al., 2006). To further enhance data saturation, Bernard
(2012) recommended including the interviewing of people that one would not normally
consider. He cautioned against the shaman effect, in that someone with specialized
information on a topic can overshadow the data, whether intentionally or inadvertently
(Bernard, 2012). Finally, care should be taken when confronting gatekeepers at the research
site who may restrict access to key informants (Holloway, Brown, & Shipway, 2010) which
would hamper complete data collection and data saturation.
Another example of data collection methods would be a focus group session. A focus
group interview is a flexible, unstructured dialogue between the members of a group and an
experienced facilitator/moderator that meets in a convenient location (Brockman et al., 2010;
Jayawardana & O’Donnell, 2009; Packer-Muti, 2010). The focus group interview is a way to
elicit multiple perspectives on a given topic but may not be as effective for sensitive areas
(Nepomuceno & Porto, 2010). This method drives research through openness, which is about
receiving multiple perspectives about the meaning of truth in situations where the observer
cannot be separated from the phenomenon (Natasia & Rakow, 2010). This concept is found
in interpretive theory wherein the researcher operates thorough a belief in the multiplicity of
peoples, cultures, and means of knowing and understanding (Natasia & Rakow, 2010).
For focus groups it is recommended that the size of the group include between six and
12 participants, so that the group is small enough for all members to talk and share their
thoughts, and yet large enough to create a diverse group (Lasch et al., 2010; Onwuegbuzie et
al., 2010). Focus groups have limitations pertaining to a propensity for groupthink in that
members pressure others to conform to group consensus (Dimitroff, Schmidt, & Bond, 2005).
Furthermore, a focus group session that elicits useful information can be dependent on the
skills of the facilitator as well as the failure to monitor subgroups with the focus group
(Onwuegbuzie et al., 2010). Therefore, a focus group is one way to elicit a number of
perspectives on a given topic to reach data saturation if one had a large pool of potential
participants to draw from. This would be appropriate if one were already conducting
individual interviews with a small number of participants and one would like to get a group
perspective about the phenomenon. For example, after interviewing five senior executive
level leaders individually, one could interview 5-8 more senior executive level leaders as a
group. To be sure, there are individual perspectives that should be explored as well as a
group perspective that could also be relevant. It is a good strategy to use to gather a great deal
of data in a short amount of time.
Other methods to ensure that data saturation has been achieved include having the
researcher construct a saturation grid, wherein major topics are listed on the vertical and
interviews to be conducted are listed on the horizontal (Brod, Tesler, & Christiansen, 2009).
Further recommendations include the possibility of having a second party conduct coding of
transcripts to ensure data saturation has been reached (Brod et al., 2009). Additionally, the
researcher should avoid including a one-time phenomenon that elicits the dominant mood of
one participant (Onwuegbuzie, Leech, Slate, Stark, & Sharma, 2012) that could hamper the
validity and transferability of the study results. At the end of the study, if new information is
obtained in the final analysis, then further interviews should be conducted as needed until
saturation is reached (Brod et al., 2009; Rubin & Rubin, 2012).
The Researcher’s Personal Lens and Data Saturation
The role of the researcher is an important part of a study. One of the challenges in
addressing data saturation is about the use of a personal lens primarily because novice
researchers (such as students) assume that they have no bias in their data collection and may
not recognize when the data is indeed saturated. However, it is important to remember that a
1411 The Qualitative Report 2015
participant’s as well as the researcher’s bias/worldview is present in all social research, both
intentionally and unintentionally (Fields & Kafai, 2009). To address the concept of a
personal lens, in qualitative research, the researcher is the data collection instrument and
cannot separate themselves from the research (Jackson, 1990) which brings up special
concerns. To be clear here, the researcher operates between multiple worlds while engaging
in research, which includes the cultural world of the study participants as well as the world of
one’s own perspective (Denzin, 2009). Hence, it becomes imperative that the interpretation of
the phenomena represent that of participants and not of the researcher (Holloway et al., 2010) in
order for the data to be saturated. Hearing and understanding the perspective of others may be
one of the most difficult dilemmas that face the researcher. The better a researcher is able to
recognize his/her personal view of the world and to discern the presence of a personal lens,
the better one is able to hear and interpret the behavior and reflections of others (Dibley,
2011; Fields & Kafai, 2009) and represent them in the data that is collected. How one
addresses and mitigates a personal lens/worldview during data collection and analysis is a
key component for the study. It is important that a novice researcher recognizes their own
personal role in the study and mitigates any concerns during data collection (Chenail, 2011).
Part of the discussion should address how this is demonstrated through understanding when
the data is saturated by mitigating the use of one’s personal lens during the data collection
process of the study (Dibley, 2011). Hence, a researcher's cultural and experiential background
will contain biases, values, and ideologies (Chenail, 2011) that can affect when the data is
indeed saturated (Bernard, 2012).
The Relationship Between Data Triangulation and Data Saturation
To reiterate, data saturation can be attained in a number of methods; however, a
researcher should keep in mind the importance of data triangulation (Denzin, 2009, 2012).
To be sure, the application of triangulation (multiple sources of data) will go a long way
towards enhancing the reliability of results (Stavros & Westberg, 2009) and the attainment of
data saturation. Denzin (2009) noted that triangulation involves the employment of multiple
external methods to collect data as well as the analysis of the data. To enhance objectivity,
truth, and validity, Denzin (2009) categorized four types of triangulation for social research.
Denzin (2009) suggested data triangulation for correlating people, time, and space;
investigator triangulation for correlating the findings from multiple researchers in a study;
theory triangulation for using and correlating multiple theoretical strategies; and
methodological triangulation for correlating data from multiple data collection methods.
Multiple external analysis methods concerning the same events and the validity of the process
may be enhanced by multiple sources of data (Fusch, 2008, 2013; Holloway et al., 2010).
There is a direct link between data triangulation and data saturation; the one (data
triangulation) ensures the other (data saturation). In other words, data triangulation is a
method to get to data saturation. Denzin (2009) argued that no single method, theory, or
observer can capture all that is relevant or important. Denzin (2006), however, did state that
triangulation is the method in which the researcher “must learn to employ multiple external
methods in the analysis of the same empirical events" (p. 13). Moreover, triangulation is the
way in which one explores different levels and perspectives of the same phenomenon. It is
one method by which the validity of the study results are ensured. Novice researchers in
particular should keep in mind that the triangulation of data can result in sometimes
contradictory and inconsistent results; however, it is up to the researcher to make sense of
them for the reader and to demonstrate the richness of the information gleaned from the data
(O’Reilly & Parker, 2012). Saturation is important in any study, whether quantitative,
qualitative, or mixed methods. Methodological triangulation goes a long ways towards
Patricia I. Fusch and Lawrence R. Ness 1412
ensuring this (Bekhet & Zauszniewski, 2012) through multiple data sources. Methodological
triangulation ensures that that data is rich in depth. Denzin (2012) made the point that it is
somewhat like looking through a crystal to perceive all the facets/viewpoints of the data.
Moreover, he posited that triangulation should be reframed as crystal refraction (many points
of light) to extrapolate the meaning inherent in the data. This is especially important in
ethnographic research where one is expected to have multiple data collection techniques to
find the meaning that participants use to frame their world (Forsey, 2010). One does not
necessarily triangulate; one crystallizes thorough recognizing that there are many sides from
which to approach a concept (Richardson & Adams St. Pierre, 2008), although this
distinction may be merely the same concept with a different label.
Two Examples
Rich and thick data results may not represent data saturation, particularly when it
comes to a type of study known as an auto-ethnography (Wolcott, 2004). Auto-ethnography
was coined by David Hayano (1979) to describe a study where the researcher was an insider
member of the group being studied; in his case it was a group of people he was acquainted
with who gathered to play cards (Wolcott, 2004). This is in contrast to the traditional role
played by anthropologists where they are on the outskirts of a group, as “a peripheral
participant” (Wolcott, 2004, p. 98). Renowned anthropologist H. F. Wolcott wrote about the
confusion between the terms auto-ethnography and ethnographic autobiography (Wolcott,
2004). Wolcott used his seminal study of a sneaky kid, a seminal work in auto-ethnographic
studies, to illustrate how the term auto-ethnography morphed from a meaning about the
researcher as a part of a studied group to a term illustrating a personal history as biography
(Wolcott, 2004). The term auto-ethnography in the classic sense came to describe the
“narratives of the self” (Wolcott, 2004, p. 99), as opposed to more contemporary definitions
such as evocative autoethnography which offers one an opportunity to reflect on personal
experience or analytic autoethnography which uses personal data to address a broader social
phenomenon (Anderson, 2006). Therefore, as Wolcott stated, an ethnographic autobiography
is “a life story told to an anthropologist” (Wolcott, 2004, p. 93). One can see the apparent
data saturation issues present in this type of study, regardless of the detail, as the data is
limited to self-reported data presented by the subject. In particular, upon review of Wolcott’s
study of the sneaky kid, one notes the absence of collaborating data about the life history of
the subject, including court records or data provided by third parties associated with the
subject. While the authors of this article harbor great respect for Wolcott and his seminal
work in ethnography, they are also somewhat uncomfortable with this type of research due to
the lack of methodological triangulation.
In contrast to Wolcott’s study of the sneaky kid, Landau and Drori’s (2008)
qualitative study included data triangulation as evidenced by multiple sources of data and
analysis. Their research centered on an R & D laboratory in Israel that had recently
experienced a change in direction from science-based research to profit-making production
(Landau & Drori, 2008). The researchers conducted a three-year ethnographic field study
using participant observation, induction, interpretation, close proximity and unmediated
relationships (Landau & Drori, 2008). They conducted their work between 1996 and 1999
and based it on an inductive grounded theory case study analysis that used both specific and
general questions asked of participants to determine viewpoints, and included a cross section
of the organization’s employees including scientists and managers (Landau & Drori, 2008).
They found that confrontational sense-making resulted from the conflict between scientists
and mangers’ efforts to construct a new organization culture from the old of pure science to
the new of profitability (Landau & Drori, 2008). The viewpoints were perceived as mutually
1413 The Qualitative Report 2015
exclusive at the beginning of the process, until management allowed “both to save face by
promoting sense-making accounts sufficiently blurred to enable each side to admit its own
cultural rationale” (Landau & Drori, 2008, p. 713) for the lab’s existence. Mixed sense-
making tolerates the side-by-side existence of both past and present into a cultural pool that
allows an organization to move forward when choosing strategies to address change (Landau
& Drori, 2008).
Are We There Yet?
In C.S. Forester’s book Beat to Quarters, the author describes the leadership abilities
of his hero, as …“like a calculating machine, judging wind and sea, time and distance…” (p.
160), as an illustration of how Horatio Hornblower was able to so effectively wage his
English sea war against the Napoleonic juggernaut in the early 1800s. So, too, must
qualitative researchers account for multiple sources of data and perspectives to insure that
their study results demonstrate validity through data saturation, so that they too may hear of
their research…“I am both astonished and pleased at the work you have accomplished” (p.
167). It can be said that failure to reach data saturation has a negative impact on the validity
on one’s study results (Kerr et al., 2010; Roe & Just, 2009); however, there is no one-size-
fits-all method to reach data saturation; moreover, more is not necessarily better than less and
vice versa. There are, rather, data collection methods that are more likely to reach data
saturation than others, although these methods are highly dependent on the study design. To
be sure, the concept of data saturation may be easy to understand; the execution is another
matter entirely (Guest et al., 2006). 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). Rich and thick data
descriptions obtained through relevant data collection methods can go a long ways towards
assisting with this process when coupled with an appropriate research study design that has
the best opportunity to answer the research question.
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Author Note
Dr. Patricia Fusch is contributing faculty at Walden University. Her research focuses
on leadership, manufacturing, women in business; ethnographic design, case study design,
change management initiatives, focus group facilitation, and organizational development. Dr.
Fusch has experience as a performance improvement consultant in the public and private
sector. Her publications can be found in The Qualitative Report and in The International
Journal of Applied Management and Technology. She can be reached at
patricia.fusch@waldenu.edu.
Dr. Lawrence R. Ness is Adjunct Faculty of IT Management, Business
Administration, and Doctoral Research. His research focuses on information technology
management strategies towards increased effectiveness and business alignment. Dr. Ness has
extensive corporate experience in the area of information technology management and has
successfully chaired over 70 doctoral dissertation graduates. Dr. Ness is Founder of
Dissertation101 Mentoring Services, LLC and can be reached at
drness@dissertation101.com.
Copyright 2015: Patricia I. Fusch, Lawrence R. Ness, and Nova Southeastern
University.
Article Citation
Fusch, P. I., & Ness, L. R. (2015). Are we there yet? Data saturation in qualitative research.
The Qualitative Report, 20(9), 1408-1416. Retrieved from
http://www.nova.edu/ssss/QR/QR20/9/fusch1.pdf
... Sample size was guided by data saturation, meaning recruitment stopped once interviewing more participants did not provide additional insights regarding the study's main objectives. In other words, recruitment ended once participants' stories, which included different details, all began to allude to the same themes [30,31]. ...
... Data saturation was reached at about 15 participants in each group, but five more participants per group were recruited. A total of forty participants (20 [50%] 30 [75%] with at least a high school diploma) completed interviews and short questionnaires. Eighteen participants in the home dialysis group were undergoing PD, and the other two were undergoing home hemodialysis. ...
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Background Patients with end-stage kidney disease (ESKD) may choose to undergo dialysis in-center or at home, but uptake of home dialysis in the US has been minimal despite its benefits over in-center dialysis. Factors that may have led patients to select home dialysis over in-center dialysis are poorly understood in the literature, and interventions to improve selection of home dialysis have focused on patient knowledge and shared decision-making processes between patients and providers. The purpose of this study was to explore micro- and macro-level factors surrounding dialysis modality decision-making among patients undergoing in-center and home dialysis, and explore what leads patients to select home dialysis over in-center dialysis. Methods Semi-structured qualitative interviews were conducted in a dialysis clinic at a large Midwestern research hospital, from September 2019 to December 2020. Participants were 18 years or older, undergoing dialysis for ESKD, and had the cognitive ability to provide consent. Surveys assessing demographic and clinical information were administered to participants following their interviews. Results Forty patients completed interviews and surveys (20 [50%] in-center dialysis, 17 [43%] female, mean [SD] age, 59 [15.99] years). Qualitative findings suggested that healthcare access and engagement before entering nephrology care, after entering nephrology care, and following dialysis initiation influenced patients’ awareness regarding their kidney disease status, progression toward ESKD, and dialysis options. Potential modifiers of these outcomes include race, ethnicity, and language barriers. Most participants adopted a passive-approach during decision-making. Finally, fatigue, concerns regarding one’s dialyzing schedule, and problems with fistula/catheter access sites contributed to overall satisfaction with one’s dialysis modality. Conclusions Findings point to broader factors affecting dialysis selection, including healthcare access and racial/ethnic inequities. Providing dialysis information before entering nephrology and after dialysis initiation may improve patient agency in decision-making. Additional resources should be prioritized for patients of underrepresented backgrounds. Dialysis decision-making may be appropriately modeled under the social-ecological framework to inform future interventions.
... This sample size was chosen with a goal of reaching saturation. [78][79][80] The goal will be to conduct 4-6 focus groups with 8-12 participants in each. Groups will be heterogeneous, with efforts to include an equal distribution of providers and older adults/ family caregivers in each. ...
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Introduction The COVID-19 pandemic exacerbated existing challenges within the Canadian healthcare system and reinforced the need for long-term care (LTC) reform to prioritise building an integrated continuum of services to meet the needs of older adults. Almost all Canadians want to live, age and receive care at home, yet funding for home and community-based care and support services is limited and integration with primary care and specialised geriatric services is sparse. Optimisation of existing home and community care services would equip the healthcare system to proactively meet the needs of older Canadians and enhance capacity within the hospital and residential care sectors to facilitate access and reduce wait times for those whose needs are best served in these settings. The aim of this study is to design a model of long-term ‘life care’ at home (LTlifeC model) to sustainably meet the needs of a greater number of community-dwelling older adults. Methods and analysis An explanatory sequential mixed methods design will be applied across three phases. In the quantitative phase, secondary data analysis will be applied to historical Ontario Home Care data to develop unique groupings of patient needs according to known predictors of residential LTC home admission, and to define unique patient vignettes using dominant care needs. In the qualitative phase, a modified eDelphi process and focus groups will engage community-based clinicians, older adults and family caregivers in the development of needs-based home care packages. The third phase involves triangulation to determine initial model feasibility. Ethics and dissemination This study has received ethics clearance from the University of Waterloo Research Ethics Board (ORE #42182). Results of this study will be disseminated through peer-reviewed publications and local, national and international conferences. Other forms of knowledge mobilisation will include webinars, policy briefs and lay summaries to elicit support for implementation and pilot testing phases.
... Moreover, to increase the validity of study findings through triangulation and the collection of data from all study participants can use both focus groups and interviews (Carter, 2014, p. 257). Lastly, data saturation was achieved for focus groups and interviews once researchers noted information was repetative (Fusch & Ness, 2015). Whittemore et al. (2001) strategies to improve trustworthiness of data are classified into four primary validity criteria (credibility, authenticity, criticality, and integrity). ...
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The capability of US mental health clinicians to confidently use scientific, rigorous information to authenticate their counseling practice is critical to successful mental health provision. In this age of genetics, the provision of substandard, ill-informed mental health professional services through non-evidence-based science has negative clinical implications for patients, families, and the behavioral health field. With exponentially increasing access to genetic testing, the high prevalence of discovering an unknown genetic parentage is a new phenomenon. This study aims to assess in-service and accredited training within NAMI mental health professional disciplines which serve US donor-conceived (DC). DC adults are one specific type of misattributed parentage (MP) population. Misattributed parentage situations include: unknown/undisclosed extramarital affair/tryst, adoption (hidden, orphan, foster care, late discovery adoptees), assisted conception (DC, sperm donation, egg donation, embryo donation, surrogacy, dna clinic mix-up etc.), rape or assault, or another event. The study’s findings included the lack of DC/MP curricula, clinical advocacy benefits, and clinicians’ need to understand genetics. Also, the study found future participatory research must include DC-related stakeholders (DC, donors, social/biological nuclear family, fertility industry, DNA testing companies, etc.). Clinicians called for cross-discipline communities of practice, the need for federal DC reporting, and research on modalities to treat specific DC-trauma/identity issues (eg. excessive numbers of siblings, specific legal/ethical issues, wide denial of known relationships, etc.). Most notably, clinicians agreed on the need to educate themselves via revamped association in-service and accredited academic training using donor-conceived-client-centered approaches and industry-standard behavioral health practices to improve donor-conceived peoples' health and mental well-being.
... We purposively recruited a maximum variation sample of care home staff members, interviewing nurses and care assistants who were diverse in terms of role, age, sex, and ethnicity. We continued until we reached theoretical saturation when any additional interviews did not add new information to the data already collected [26]. ...
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Background Sleep disturbances affect 38% of care home residents living with dementia. They are often treated with medication, but non-pharmacological interventions may be safer and effective yet more difficult to implement. In the SIESTA study (Sleep problems In dEmentia: interviews with care home STAff) we explored care home staffs’ experience of managing sleep disturbances in their residents living with dementia. Methods We conducted one-to-one semi-structured interviews in four UK care homes, and purposively recruited a maximum variation sample of 18 nurses and care assistants, who were each interviewed once. We used a topic guide and audio-recorded the interviews. Two researchers independently analysed themes from transcribed interviews. Results Staff used a range of techniques that often worked in improving or preventing residents’ sleep disturbance. During the daytime, staff encouraged residents to eat well, and be physically active and stimulated to limit daytime sleep. In the evening, staff settled residents into dark, quiet, comfortable bedrooms often after a snack. When residents woke at night, they gave them caffeinated tea or food, considered possible pain and discomfort, and reassured residents they were safe. If residents remained unsettled, staff would engage them in activities. They used telecare to monitor night-time risk. Staff found minimising daytime napping difficult, described insufficient staffing at night to attend to reorient and guide awake residents and said residents frequently did not know it was night-time. Conclusions Some common techniques, such as caffeinated drinks, may be counterproductive. Future non-pharmacological interventions should consider practical difficulties staff face in managing sleep disturbances, including struggling to limit daytime napping, identifying residents’ night-time needs, day-night disorientation, and insufficient night-time staffing.
... In addition to the selected cases, additional net-maps were conducted with other households, until a saturation point was reached (Fusch & Ness, 2015). Eight net-maps in total were produced for this purpose. ...
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To end hunger and increase food security, substantial investments will be required. In sub-Saharan Africa, smallholder agricultural households have a major role to play. However, finding the right instruments to stimulate agricultural growth at the household level requires an accurate understanding of agrarian households’ behaviour. This thesis aimed to contribute to a better understanding of agrarian households, focusing on the institutional conditions shaping cooperation within polygynous households. Indeed, polygynous households are widespread in African societies, but their specific features are relatively neglected in the agricultural development literature. Moreover, the theory of collective action has, so far, been applied at the community-level, but rarely at the intra-household level. To address this knowledge gap, case studies of two ethnic communities, the Fulani and the Mossi, were conducted. The research explored the institutional arrangements shaping the allocation of resources within polygynous households and examined the structural conditions under which cooperation occurs. The second chapter of this thesis reviews the discourse on agricultural households’ behaviour. Reviewing the empirical evidence, the chapter examines the adequacy of existing economic conceptualisations of agricultural households and their generalisability to West-African settings. Drawing on insights from anthropology and feminist perspectives, the chapter highlights the shortcomings of conventional household models, and the failure to consider gender and intergenerational relations of production. The third chapter analyses the challenges underlying cooperation in agricultural households. The chapter uncovers the contractual arrangements shaping the allocation of resources for food production. Drawing on the natural resource management literature, the chapter examines how households’ member’s characteristics, including their socially accepted roles and responsibilities, shape their incentive structures and determine resource pooling. Chapter 4 examines an essential determinant of collective action: trust. The chapter investigates the correlation between trust and productive and reproductive activities. The chapter makes an innovative methodological contribution to the study of cooperation, applying an experimental trust game to co-wives in polygynous households. The critical review of the economic literature challenges the existing representations of agrarian households in sub-Saharan Africa. The review calls for a redefinition of the units of production and for cautious assessment of conventional economic theories. The review recommends a framework that encompasses the complexities and diversity of behaviour in agrarian households. Integrating theories from feminist and anthropological literature can support this endeavour. Chapter 3 reveals that the contractual arrangements embedded in the rules and norms defining socially-accepted behaviour, influence the patterns of intrahousehold resource mobilisation and the likelihood of cooperation among household members. Agricultural household members were found to pool, exchange or split resources based on their roles and positions within the household arena. Implicit monitoring and sanction systems were identified, which shape the incentive structures of agrarian household member and determine whether cooperation will occur. The final chapter revealed that trust can mediate cooperation between co-wives, depending on the nature of the activity. No correlation was found between trust and co-wives’ likelihood to pool labour on individual plots. However, a strong correlation was identified between trust among co-wives and income pooling for food purchase, highlighting the importance of uncertainty and of existing norms on the outcomes of cooperation in agricultural households. The thesis concludes that collective action in polygynous agrarian households does not occur in a vacuum. Rather, collective action is the outcome of several processes and mechanisms. Policymakers should be aware of these internal arrangements, and their implications for intra-household resource allocation. The success of agricultural policies depends on these considerations.
... According to Guest, Bunce and Johnson (2006) data saturation may be attained in as few as six interviews, depending on the total sample size of the population. However, the most important sign that data saturation has been reached is to recognize when no new data or themes are obtainable (Fusch & Ness, 2015;O'reilly & Parker, 2013). The researcher observed data saturation when no new categories or themes were being developed after about the sixth or seventh interview. ...
Thesis
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