Cuyahoga Community Purposeful Sampling and Saturation Article Critique The answer lies in how clearly you articulate the criteria for selecting data source

Cuyahoga Community Purposeful Sampling and Saturation Article Critique The answer lies in how clearly you articulate the criteria for selecting data sources; (b) your ability to purposefully select cases; and (c) the extent to which those cases are “information-rich… for in-depth study” (Patton, 2015, p. 264) with respect to the purpose of the study.

As you prepare for this week’s Discussion, consider turning your attention to the variety of purposeful sampling strategies you may consider in developing your research plan. Also consider that qualitative researchers seek a threshold or cut-off point for when to stop collecting data. There is no magic number (although there are guidelines). Rather, saturation occurs as an interface between the researcher and the data and (b) between data collection and data analysis to determine when enough is enough.

For this Discussion, you will critique a sampling strategy used in a research article.

To prepare for this Discussion:

Review the Guest, Bunce, and Johnson article; the Yob and Brewer article; and the Learning Resources related to sampling and saturation for this week.
BY DAY 3

Prepare a critique of the sampling strategy used by Yob and Brewer (n.d.). Include the following your critique:

The purpose of the study
Research questions
Site selection
The type of purposeful sampling strategy the researchers applied. (Note: Use Table 4.3 in the Ravitch & Carl text or from Patton’s Chapter 5 to identify and describe the strategy that you think best fits what they described.)
An alternative sampling strategy that the researchers could have considered. Explain your choice in terms of how the strategy is consistent with their research purpose and criteria for selecting cases.
Provide a data saturation definition and evaluate the work of the researchers in this article regarding their efforts to achieve data saturation. Note what the researchers could have done differently to convince you that the relevant and important themes emerged.

Be sure to support your main post and response post with reference to the week’s Learning Resources and other scholarly evidence in APA style. FIELD METHODS
10.1177/1525822X05279903
Guest
et al. / HOW MANY INTERVIEWS ARE ENOUGH?
How Many Interviews Are Enough?
An Experiment with Data Saturation and
Variability
GREG GUEST
ARWEN BUNCE
LAURA JOHNSON
Family Health International
Guidelines for determining nonprobabilistic sample sizes are virtually nonexistent.
Purposive samples are the most commonly used form of nonprobabilistic sampling,
and their size typically relies on the concept of “saturation,” or the point at which no
new information or themes are observed in the data. Although the idea of saturation
is helpful at the conceptual level, it provides little practical guidance for estimating
sample sizes, prior to data collection, necessary for conducting quality research.
Using data from a study involving sixty in-depth interviews with women in two West
African countries, the authors systematically document the degree of data saturation
and variability over the course of thematic analysis. They operationalize saturation
and make evidence-based recommendations regarding nonprobabilistic sample
sizes for interviews. Based on the data set, they found that saturation occurred within
the first twelve interviews, although basic elements for metathemes were present as
early as six interviews. Variability within the data followed similar patterns.
Keywords:
interviewing; saturation; variability; nonprobability sampling; sample size; purposive
While conducting a literature review of guidelines for qualitative research
in the health sciences, we were struck by how often the term theoretical saturation arose. Article after article recommended that purposive sample sizes
be determined by this milestone (e.g., Morse 1995; Sandelowski 1995; Bluff
1997; Byrne 2001; Fossey et al. 2002), and a good number of journals in the
Financial support for this research was provided by the U.S. Agency for International Development through Family Health International, although the views expressed in this article do not
necessarily reflect those of either organization. The authors thank Kerry McLoughlin (Family
Health International), Betty Akumatey (University of Ghana, Legon), and Lawrence Adeokun,
(Association for Reproductive and Family Health, Ibadan, Nigeria). Without their hard work,
this article would not have been possible.
Field Methods, Vol. 18, No. 1, February 2006 59–82
DOI: 10.1177/1525822X05279903
© 2006 Sage Publications
59
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health sciences require that theoretical saturation be a criterion by which to
justify adequate sample sizes in qualitative inquiry. Saturation has, in fact,
become the gold standard by which purposive sample sizes are determined in
health science research.
Equally striking in our review was that the same literature did a poor job of
operationalizing the concept of saturation, providing no description of how
saturation might be determined and no practical guidelines for estimating
sample sizes for purposively sampled interviews. This dearth led us to carry
out another search through the social and behavioral science literature to see
if, in fact, any generalizable recommendations exist regarding nonprobabilistic sample sizes. After reviewing twenty-four research methods books and
seven databases, our suspicions were confirmed; very little headway has
been made in this regard. Morse’s (1995:147) comments succinctly sum up
the situation; she observed that “saturation is the key to excellent qualitative
work,” but at the same time noted that “there are no published guidelines or
tests of adequacy for estimating the sample size required to reach saturation.”
Our experience, however, tells us that it is precisely a general, numerical
guideline that is most needed, particularly in the applied research sector.
Individuals designing research—lay and experts alike—need to know how
many interviews they should budget for and write into their protocol, before
they enter the field. This article is in response to this need, and we hope it provides an evidence-based foundation on which subsequent researchers can
expand. Using data from a study involving sixty in-depth interviews with
women in two West African countries, we systematically document the
degree of data saturation and variability over the course of our analysis and
make evidence-based recommendations regarding nonprobabilistic sample
sizes.
We stress here that we intentionally do not discuss the substantive findings from our research; they will be presented elsewhere. This is a methodological article, and we felt that including a discussion of our study findings
would be more distracting than informative. We do provide some background for the study, but our focus is mainly on the development and structure of our codebook and its evolution across the analysis process.
NONPROBABILISTIC AND PURPOSIVE SAMPLING
Calculating the adequacy of probabilistic sample sizes is generally straightforward and can be estimated mathematically based on preselected parameters and objectives (i.e., x statistical power with y confidence intervals). In
Guest et al. / HOW MANY INTERVIEWS ARE ENOUGH?
61
theory, all research can (and should when possible) use probabilistic sampling methodology, but in practice, it is virtually impossible to do so in the
field (Bernard 1995:94; Trotter and Schensul 1998:703). This is especially
true for hard-to-reach, stigmatized, or hidden populations.
Research that is field oriented in nature and not concerned with statistical
generalizability often uses nonprobabilistic samples. The most commonly
used samples, particularly in applied research, are purposive (Miles and
Huberman 1994:27). Purposive samples can be of different varieties—
Patton (2002), for example, outlined sixteen types of purposive samples—
but the common element is that participants are selected according to predetermined criteria relevant to a particular research objective. The majority of
articles and books we reviewed recommended that the size of purposive samples be established inductively and sampling continue until “theoretical saturation” (often vaguely defined) occurs. The problem with this approach,
however, is that guidelines for research proposals and protocols often require
stating up front the number of participants to be involved in a study (Cheek
2000). Waiting to reach saturation in the field is generally not an option.
Applied researchers are often stuck with carrying out the number of interviews they prescribe in a proposal, for better or worse.1 A general yardstick is
needed, therefore, to estimate the point at which saturation is likely to occur.
Although numerous works we reviewed explain how to select participants
(e.g., Johnson 1990; Trotter 1991) or provide readers with factors to consider
when determining nonprobabilistic sample sizes (Miles and Huberman
1994; Bernard 1995; Morse 1995; Rubin and Rubin 1995; Flick 1998;
LeCompte and Schensul 1999; Schensul, Schensul, and LeCompte 1999;
Patton 2002), we found only seven sources that provided guidelines for
actual sample sizes. Bernard (2000:178) observed that most ethnographic
studies are based on thirty-sixty interviews, while Bertaux (1981) argued that
fifteen is the smallest acceptable sample size in qualitative research. Morse
(1994:225) outlined more detailed guidelines. She recommended at least six
participants for phenomenological studies; approximately thirty-fifty participants for ethnographies, grounded theory studies, and ethnoscience studies;
and one hundred to two hundred units of the item being studied in qualitative
ethology. Creswell’s (1998) ranges are a little different. He recommended
between five and twenty-five interviews for a phenomenological study and
twenty-thirty for a grounded theory study. Kuzel (1992:41) tied his recommendations to sample heterogeneity and research objectives, recommending
six to eight interviews for a homogeneous sample and twelve to twenty data
sources “when looking for disconfirming evidence or trying to achieve maximum variation.” None of these works present evidence for their recommen-
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dations. The remaining two references—Romney, Batchelder, and Weller
(1986) and Graves (2002)—do provide rationale for their recommendations
for quantitative data and are discussed later in the article.
STUDY BACKGROUND
The original study for which our data were collected examined perceptions of social desirability bias (SDB) and accuracy of self-reported behavior
in the context of reproductive health research. Self-reports are the most commonly used measure of sexual behavior in the health sciences, and yet concern has been raised about the accuracy of these measures (Brody 1995;
Zenilman et al. 1995; Weinhardt et al. 1998; Schwarz 1999; Weir et al. 1999;
Crosby et al. 2002). One of the key factors identified as affecting report accuracy is a participant’s concern with providing socially desirable answers
(Paulhus 1991; Geary et al. 2003). Our study was therefore designed to
inform HIV research and intervention programs that rely on self-reported
measures of sexual behavior.
Using semistructured, open-ended interviews, we examined how women
talk about sex and their perceptions of self-report accuracy in two West African countries—Nigeria and Ghana. We solicited suggestions for reducing
SDB and improving self-report accuracy within various contexts. In addition, we asked participants to provide feedback regarding methods currently
used to mitigate SDB within the context of HIV research and prevention,
such as audio-computer-assisted self-interviews (ACASI) and manipulating
various aspects of the interview.
METHODS
Sampling and Study Population
A nonprobabilistic, purposive sampling approach was used. We wanted
to interview participants at high risk for acquisition of HIV and who would
be appropriate candidates for HIV prevention programs in the two study
sites. We therefore interviewed women who met at least three basic criteria:
(1) were eighteen years of age or older, (2) had vaginal sex with more than one
male partner in the past three months, and (3) had vaginal sex three or more
times in an average week. Women at the highest risk for HIV in Nigeria and
Ghana tend to be engaged in some form of sex work (although not all selfidentify as sex workers), so fieldworkers recruited sex workers for our study.
Guest et al. / HOW MANY INTERVIEWS ARE ENOUGH?
63
TABLE 1
Sample Characteristics
Ghana (n = 30)
Age
Mean
Range
Years of education
Mean
Range
Number of ethnic groups
Marital status
Single
Married
Divorced/separated
Widowed
Previous research experience
Nigeria (n = 30)
Combined (N = 60)
26.3
20–35
32.0
1–53
29.1
19–53
6.8
0–12
12
10.3
0–17
3
8.5
0–17
15
20 (66.7%)
0
10 (33.3%)
0
13 (43.3%)
13 (43.3%)
1 (3.3%)
14 (46.7%)
2 (6.7%)
6 (20%)
33 (55%)
1 (1.7%)
24 (40%)
2 (3.3%)
19 (31.7%)
In Nigeria, thirty high-risk women were recruited from three sites in the
city of Ibadan, which correspond to different socioeconomic environments:
brothels, a college campus, and known pick-up points for sex workers. The
sampling process was similar in Ghana. Thirty high-risk women were
recruited from greater Accra. Three high-risk sites were identified for
recruitment and included a red light area, a hotel, and a hostel. Table 1 presents the sample characteristics for the two sites.
Data Collection and Analysis
The interview guide consisted of six structured demographically oriented
questions, sixteen open-ended main questions, and fourteen open-ended
subquestions. Subquestions were asked only if a participant’s response to the
initial question did not cover certain topics of interest. All respondents were
asked identical questions in the same sequence, but interviewers probed
inductively on key responses. The guide was divided into the following six
domains of inquiry:
•
•
•
•
•
•
perceptions of sexually oriented research,
discussion of sex and condoms within the community (i.e., among peers),
discussion of sex and condoms within the research context,
interviewer characteristics,
remote interviewing techniques (ACASI, phone interviews), and
manipulating the environment of an interview.
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FIELD METHODS
Data were collected between September 15 and December 12, 2003.
Interviews were conducted in English, Twi, and Ga in Ghana and in English,
Pidgin English, and Yoruba in Nigeria. All interviews were tape recorded,
and verbatim responses to each question were translated and transcribed by
local researchers, using a standardized transcription protocol (McLellan,
MacQueen, and Niedig 2003). Transcripts were reviewed by the principal
investigator at each site for translation accuracy and revised when necessary.
Thematic analysis was performed on the translated transcripts using Analysis Software for Word-based Records (AnSWR; Centers for Disease Control
and Prevention 2004).
A codebook was developed by two data analysts, using a standard iterative process (MacQueen et al. 1998). In this process, each code definition has
five parts: (1) a “brief definition” to jog the analyst’s memory; (2) a “full definition” that more fully explains the code; (3) a “when to use” section that
gives specific instances, usually based on the data, in which the code should
be applied; (4) a “when not to use” section that gives instances in which the
code might be considered but should not be applied (often because another
code would be more appropriate); and (5) an “example” section of quotes
pulled from the data that are good examples of the code.
In our analysis, the lead analyst created an initial content-based coding
scheme for each set of six interviews. Intercoder agreement was assessed for
every third interview using combined segment-based Kappa scores run on
two double-coded transcripts (Carey, Morgan, and Oxtoby 1996). Coding
discrepancies (individual codes receiving Kappa scores of 0.5 or less) were
discussed and resolved by the analysis team, the codebook revised accordingly, and recoding performed when necessary to ensure consistent application of codes. The resulting overall Kappa score, by individual question, was
0.82. To identify key themes, we ran frequency reports in AnSWR.
THE EXPERIMENT
The Methods section refers to the procedures we used in our substantive
analysis, yet these procedures did not provide us with the data we needed to
determine thematic development and evolution over time and eventually the
point at which saturation occurred in our data. We had to develop additional
procedures and methods to operationalize and document data saturation.
Saturation can be of various types, with the most commonly written about
form being “theoretical saturation.” Glaser and Strauss (1967:65) first
defined this milestone as the point at which “no additional data are being
found whereby the (researcher) can develop properties of the category. As he
Guest et al. / HOW MANY INTERVIEWS ARE ENOUGH?
65
sees similar instances over and over again, the researcher becomes empirically confident that a category is saturated . . . when one category is saturated,
nothing remains but to go on to new groups for data on other categories, and
attempt to saturate these categories also.”
For serious practitioners of the grounded theory approach, the term theoretical saturation refers specifically to the development of theory. Theoretical saturation occurs when all of the main variations of the phenomenon have
been identified and incorporated into the emerging theory. In this approach,
the researcher deliberately searches for extreme variations of each concept in
the theory to exhaustion.
Although theoretical saturation is the most commonly used term in published works, frequency of use within multiple bodies of literature has
resulted in its meaning becoming diffuse and vague. To avoid propagating
this transgression, we rely on a more general notion of data saturation and
operationalize the concept as the point in data collection and analysis when
new information produces little or no change to the codebook. We wanted to
find out how many interviews were needed to get a reliable sense of thematic
exhaustion and variability within our data set. Did six interviews, for example, render as much useful information as twelve, eighteen, twenty-four, or
thirty interviews? Did any new themes, for example, emerge from the data
gathered between interview thirteen and interview thirty? Did adding thirty
more interviews from another country make any difference?
To answer these questions, we documented the progression of theme
identification—that is, the codebook structure—after each set of six interviews, for a total of ten analysis rounds.2 We monitored the code network and
noted any newly created codes and changes to existing code definitions. We
also documented frequency of code application after each set of six interviews was added. The reasoning behind this latter measure was to see if the
relative prevalence of thematic expression across participants changed over
time. It could be the case, to take a hypothetical example, that one code in the
first round of analysis was applied to all six of the transcripts from one site,
implying an initial high prevalence across participants. It could also be true
that the same code was never applied in the remaining twenty-four transcripts and that another code emerged for the first time in the seventh transcript and was applied to the rest of the transcripts for a frequency of twentyfour. We needed to assess this variability.
We created a cumulative audit trail, updating our records after analysis of
each set of six transcripts. So, in our first analysis, we analyzed the first six
transcripts, then added six more in our second analysis for an n of twelve, and
so on. We started with the data from Ghana and kept adding six transcripts
until we had completed all thirty interviews from this site. Six transcripts
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FIELD METHODS
from Nigeria were then added to the analysis for an n of 36, and the process
was repeated until all sixty of the interviews from both sites had been analyzed and the code definitions finalized. In all, we completed ten successive
and cumulative rounds of analysis on sets of six interviews. Internal codebook structure (conceptually relating codes to one another) was not manipulated until all of the base codes had been identified and all sixty transcripts
coded.
Below, we provide a summary of these data. Specifically, we present data
illustrating the point in analysis when codes were created or definitions
changed. We also examine frequency of code application across participants
and estimate the point at which the distribution of code frequency stabilized.
For all of our analyses, the unit of analysis is the individual participant (i.e.,
transcript) and data items the individual codes (i.e., expressions of themes).
Code Development
After analyzing all thirty interviews from Ghana, the codebook contained
a total of 109 content-driven codes, all of which had been applied to at least
one transcript. Of these codes, 80 (73%) were identified within the first six
transcripts. An additional 20 codes were identified in the next six transcripts,
for a cumulative total of 100, or 92% of all codes applied to the Ghana data.
As one would expect, the remaining 9 codes were identified with progressively less frequency (see Figure 1, the five columns on the left, interviews 1–
30). Clearly, the full range of thematic discovery occurred almost completely
within the first twelve interviews—at least based on the codebook we developed (more on this later).
Surprisingly, not much happened to the number of codes once we started
adding data from the other country. Only five new codes (out of a total of
114) had to be created to accommodate the Nigerian data (see Figure 1, the
five columns on the right, interviews 31–60), one of which was new in substance. Fo…
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