Final 6078-A1 - Scientific Merit Evaluation Paper
Purposeful Sampling in Qualitative Research Synthesis Harsh Suri THE UNIVERSITY OF MELBOURNE
ABSTRACT
Informed decisions about sampling are critical to improving the quality of research synthesis. Even though several qualitative research synthesists have recommended purposeful sampling for synthes- izing qualitative research, the published literature holds sparse discussion on how different strategies for purposeful sampling may be applied to a research synthesis. In primary research, Patton is fre- quently cited as an authority on the topic of purposeful sampling. In Patton’s original texts that are referred to in this article, Patton does not make any suggestion of using purposeful sampling for re- search synthesis. This article makes a unique contribution to the literature by examining the adapt- ability of each of Patton’s 16 purposeful sampling strategies to the process of qualitative research synthesis. It illuminates how different purposeful sampling strategies might be particularly suited to constructing multi-perspectival, emancipatory, participatory and deconstructive interpretations of published research.
Keywords: Purposeful sampling, qualitative research synthesis, meta-synthesis, meta-study, qualit- ative meta-analysis.
Research synthesis is a special type of research review that is not only descriptive, informative
and evaluative, but also connective (Mays, Pope, and Popay, 2005). ‘Synthesis refers to
making a whole into something more than the parts alone imply’ (Noblit & Hare, 1988,
p. 28). The purpose of research synthesis is to produce new knowledge by making explicit
connections and tensions between individual study reports that were not visible before. It
involves purposeful selection, review, analysis and synthesis of primary research reports on
a similar topic. In a rigorous synthesis, readers are provided with sufficient information
about the synthesis process so that they can make informed decisions about the extent to
which the synthesized findings may be adapted to their own contexts.
With the growth of research activity in recent years, each topic tends to be examined by
different researchers in diverse contexts, employing a wide range of methods, invariably
resulting in disparate findings on the same topic. Making useable sense of such complex
bodies of research can be an overwhelming experience for most stakeholders. These stake-
holders include policymakers, administrators, educators, health professionals, funding
agencies, researchers, students, patients, various advocacy groups and the wider community.
Research syntheses can play an important role in disseminating research knowledge and in
shaping further research, practice and public perception. Hence, issues of ethical represent-
ations (Suri, 2008) and methodological rigour in research syntheses are as crucial as they are
in primary research (Petticrew & Roberts, 2006).
Much of the growing body of literature on research synthesis methods has been dominated
by quantitative researchers. In the last two decades, however, an increasing number of re-
searchers from education and healthcare have recognised the importance of synthesizing
qualitative research. These scholars acknowledge that the emphasis of qualitative research
on particularities and complexities of individual contexts is at odds with any synthetic effort.
Yet they assert that some form of synthesis is essential to enhance the practical value of
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qualitative research in policymaking and informing practice at a broader level (Suri & Clarke,
2009). Stressing that any effort of synthesizing qualitative research should be essentially in-
terpretive rather than aggregative, they have proposed interpretive methods of research syn-
thesis under various names, such as meta-ethnography (Noblit & Hare, 1988), cross-case
analysis (Miles & Huberman, 1994), meta-analysis of qualitative research (Jensen & Allen,
1994), qualitative meta-synthesis (Sandelowski, Docherty, and Emden, 1997; Zimmer,
2006), qualitative systematic review (Booth, 2001), meta-study (Paterson, Thorne, Canam,
and Jillings, 2001), and qualitative research synthesis (Major & Savin-Baden, 2010).
Several other methodologists argue that a comprehensive synthesis of research should
include both quantitative and qualitative studies. They reason that quantitative methods are
inappropriate to synthesize methodologically diverse research as quantitative research syn-
thesis methods assume a certain degree of methodological and contextual homogeneity across
studies, which is impractical. They assert that only qualitative synthesis methods are suitable
for synthesizing methodologically diverse quantitative and qualitative research (Suri &
Clarke, 2009). Examples of qualitative methods proposed for synthesizing methodologically
diverse research include qualitative meta-analysis (Kasworm, 1990), exploratory case-study
oriented review (Ogawa & Malen, 1991), interpretivist-oriented review (Eisenhart, 1998),
meta-synthesis (Bair, 1999) and realist synthesis (Pawson, 2006).
There is yet another group of methodologists, for example Lather (1999) and Schwandt
(1998), who make a case for post-structural reviews of research which they insist are not
research syntheses per se as they focus on identifying the cracks, or the gaps, in a field rather
than producing a meta-narrative. Unlike Lather (1999) and Schwandt (1998), I have delib-
erately used the term ‘research synthesis’ as a blanket term, which includes critically oriented
post-structural reviews, to reclaim its usage for an inclusive context rather than being limited
to only those syntheses that produce meta-narratives. I have retained the term research syn-
thesis ‘to both circulate and break with the signs that code it’ (Lather, 1993, p. 674) by
rupturing the exclusive notion of research synthesis as an objective and reductionist aggreg-
ation of research findings.
I have used the term qualitative research synthesis as an umbrella term for all qualitative
methods of synthesizing research which are informed by interpretive, critical, emancipatory
and/or postmodern sensibilities. Primary research included in a qualitative research synthesis
may be qualitative and/or quantitative, depending on the purpose of the synthesis.
METHODOLOGICAL UNDERPINNINGS
This article is based on a larger project in which a methodologically inclusive research syn-
thesis (MIRS) framework was conceptualised (Suri, 2007) by distilling and synthesizing di-
verse ideas, theories, and strategies from the extensive bodies of literature on research syn-
thesis methods and primary research methods. The MIRS framework was developed to address
the following overarching question: 'Given that contemporary educational research is marked
by diversity, complexity, and richness of purposes, methods, and perspectives, how can such
variety and complexity be accommodated and reflected at the level of synthesizing educational
research?' In developing the MIRS framework, a combination of purposeful sampling
strategies were employed, some of which are described later in this article to illustrate the
applicability of various purposeful sampling strategies.
The goal of this article is to contest the notion that exhaustive sampling is the only legit-
imate form of sampling for research synthesis. The question that is addressed here is not
64 | Qualitative Research Journal, vol. 11, no. 2, 2011
‘what sampling strategies are typically employed by qualitative research synthesists?' Rather,
by drawing upon hypothetical examples, the question that is being addressed is ‘how might
different purposeful sampling strategies be adapted to expand possibilities within research
syntheses?’ It is hoped that the readers will use this discussion as a departure point to syn-
thesize research for a wide range of purposes, many of which are typically not attempted by
contemporary research synthesists.
I begin this article by building a case for purposeful sampling in research synthesis. Then
I draw on the concepts of data saturation and data sufficiency for guiding decisions related
to enacting closure when searching for relevant evidence in research synthesis. I conclude
this article by outlining key questions which must be considered in making strategic decisions
in relation to sampling in research synthesis. In the literature on primary research methods,
Patton (1990, 2002) has provided a comprehensive discussion of purposeful sampling and
is frequently cited as an authority on purposeful sampling:
The logic and power of purposeful sampling lie in selecting information-rich cases for study
in depth. Information-rich cases are those from which one can learn a great deal about issues
of central importance to the purpose of the inquiry, thus the term purposeful sampling.
Studying information-rich cases yields insights and in-depth understanding rather than em-
pirical generalizations (Patton, 2002, p. 230, emphasis in original).
Patton has suggested 16 strategies for purposeful sampling in qualitative research, each
of which is intended to serve a different purpose. In Patton’s original texts that I refer to,
Patton does not make any suggestion of using purposeful sampling in research syntheses.
Patton’s original texts exclusively discuss utilisation of purposeful sampling in primary re-
search. In this article, I explore the potential adaptation of Patton’s concept of purposeful
sampling to the process of a research synthesis by using three techniques. First, the terms
that I use here are the verbatim terms that Patton (2002) has used to distinguish between
the 16 strategies under the broad umbrella of purposeful sampling. Second, I seamlessly in-
tegrate Patton’s ideas in my discussion of purposeful sampling in research syntheses. In many
instances, I have adapted Patton’s quotes to the process of a research synthesis beyond their
originally intended context of primary research. Third, I illustrate with examples how each
of Patton’s 16 strategies may be utilised in syntheses with varied purposes.
BUILDING A CASE FOR PURPOSEFUL SAMPLING IN RESEARCH SYNTHESIS
Research syntheses on the same topic conducted for different purposes can have different
sampling strategies, each being equally legitimate but tailored to serve the different purposes.
Synthesists must carefully identify sampling strategies that are conceptually aligned with the
synthesis purpose, that credibly and sufficiently address the synthesis purpose, and that are
feasible, ethical and efficient (Kemper, Stringfield, and Teddlie, 2003). Synthesists must
also delineate the caveats associated with their sampling strategies and speculate on how
these caveats may impact upon the synthesis findings. In doing so, it is worth reflecting on
the politics of publishing: that is, who/what is more likely to get published. This involves
being aware of various publication biases, that is, outcome bias, confirmatory bias, funding
bias and methodological bias (for a methodologically inclusive discussion of these biases, see
Suri, 2008). Synthesists must be reflexive about how these potential biases might impact
Harsh Suri,'Purposeful Sampling in Qualitative Research Synthesis' | 65
upon their synthesized product as well as how their synthesis might reify/contest some of
these biases.
A rigorous research synthesis makes much more demand on time and resources when
compared with ad hoc reviews (Elmore, 1991; Ogawa & Malen, 1991; Stock, Benito, and
Lasa, 1996). Just as the perfect primary research study has never been conducted, neither
has the perfect synthesis. The issue that confronts a synthesist is often ‘how to maximise the
quality of the synthesis within the available resources’, rather than ‘how to do the most rig-
orous synthesis’. Several primary researchers discuss various pragmatic constraints which
must be kept in mind when identifying a suitable purpose for study (e.g. Blaxter, Hughes,
and Tight, 2001). Research synthesists are also bound by the pragmatic constraints of time,
resources and access to information and expertise (for a detailed discussion of these pragmatic
constraints, see Suri, 2007). Often decision-makers and stakeholders want relevant, under-
standable, and accurate information which they can use soon. In many practical situations,
synthesists may find that a highly rigorous approach is overly formalistic, too time consuming,
and unnecessarily expensive within the available resources and deadlines (Patton, 1991, pp.
287-289).
Many research syntheses which attract large funding, such as the systematic reviews of
the EPPI-centre, cost about ₤75,000 (Oakley, 2003, p. 28). Often these reviews tend to
address the questions of politicians or other decision-making bodies who can provide adequate
funds. Systematic reviewers typically aim for extensive sampling and assume sufficient access
to financial resources, information and expertise. Unless synthesists strategically design
syntheses within various pragmatic constraints, they may inadvertently contribute to the si-
lencing of concerns of certain groups of stakeholders who cannot commission syntheses that
require large scale funding.
Many qualitative research synthesists also question the viability of holistically synthesizing
a large number of qualitative reports in a way that adequately attends to the intersections
between the findings, contexts, epistemologies, ontologies and methodologies of individual
studies. These qualitative synthesists recommend that an in-depth synthesis of purposefully
selected studies is more desirable than a superficial synthesis of a large number of studies
(e.g. Bondas & Hall, 2007; Booth, 2001; Lloyd Jones, 2004; Noblit & Hare, 1988; Pawson,
Greenhalgh, Harvey, and Walshe, 2005). Even though several qualitative research synthesists
have recommended purposeful sampling in research syntheses, the published literature holds
sparse discussion on how different strategies for purposeful sampling might be applied to
research synthesis. In the next section, I attempt to partially fill this gap in the literature by
discussing how different purposeful sampling strategies may be adapted to synthesize research
for facilitating understanding, participation, emancipation and/or deconstruction.
EXAMINING THE ADAPTABILITY OF PATTON’S PURPOSEFUL SAMPLING STRATEGIES TO QUALITATIVE RESEARCH SYNTHESES
Purposeful sampling requires access to key informants in the field who can help in identifying
information-rich cases. Qualitative research synthesists can draw upon the ‘invisible college’
technique frequently utilised by quantitative researchers. A traditional invisible college involves
a group of central figures investigating a particular field along with the numerous researchers
who are in touch with any of these key researchers. Research retrieved through this channel
is likely to be biased towards the beliefs prevalent among these key researchers (Rosenthal,
1994). Electronic invisible colleges include listservs or newsgroups, some of which might
66 | Qualitative Research Journal, vol. 11, no. 2, 2011
focus on research while others might focus on contemporary practices (Cooper, 1998).
Listservs with a research focus can be useful in identifying primary research studies or previous
syntheses. Practice-focused listservs can be useful in identifying reports that are particularly
valued among practitioners or in identifying the synthesis questions that might be of partic-
ular interest to practitioners. Browsing through listservs can also help identify researchers
and practitioners who have expertise in the substantive domain of interest. The synthesist
can later contact these experts directly to request references to the specific studies on which
their claims and opinions are based.
Extreme or Deviant Case Sampling The extreme or deviant case sampling in a research synthesis would involve selecting ‘illu-
minative cases’ (Patton, 2002, p. 232) that exemplify contexts where an innovation was
perceived notably as a success or a failure. The main weakness of extreme case sampling is
its lack of generalisability through representativeness. This weakness is of less concern for
synthesists who focus on how things should be or could be rather than how things are. This
strategy would be particularly suitable for ‘realist syntheses’, proposed by Pawson (2006),
which investigate how a program is likely to work under particular circumstances by examin-
ing successful as well as unsuccessful implementations of the program.
Intensity Sampling Intensity sampling in a research synthesis would involve selecting studies that are ‘excellent
or rich examples of the phenomenon of interest, but not highly unusual cases… cases that
manifest sufficient intensity to illuminate the nature of success or failure, but not at the ex-
treme’ (Patton, 2002, p. 234). To develop a comprehensive understanding of many educa-
tional changes, it is crucial to examine cases where these changes have been embedded
thoroughly in the system over a sufficient period of time. However, such intense manifestation
of an innovation can be cost-intensive and/or associated with high risk-factors. As a result,
the innovation might be implemented with sufficient intensity in only a few studies. Many
other studies might examine the implementation of the innovation over short durations of
time with minimal interventions. As an example, a large number of studies have been con-
ducted to investigate how students learn in collaborative environments. Given the individu-
alistic nature of most high stake testing, most studies use collaborative learning strategies as
an add-on to regular teaching and learning activities. Many students in these studies might
engage with collaborative tasks superficially. In a small number of studies, the learning
activities as well as assessment tasks have been revised intensely to reward collaboration. An
in-depth synthesis of the latter type of studies would be particularly useful in illuminating
a range of opportunities, challenges, advantages and disadvantages associated with curricula
driven by an ethos of collaborative learning.
Maximum Variation (Heterogeneity) Sampling A maximum variation sample is constructed by identifying key dimensions of variations and
then finding cases that vary from each other as much as possible. This sampling yields: ‘(1)
high-quality, detailed descriptions of each case, which are useful for documenting uniqueness,
and (2) important shared patterns that cut across cases and derive their significance from
having emerged out of heterogeneity’ (Patton, 2002, p. 235). Employing maximum variation
sampling, research synthesists can identify essential features and variable features of a phe-
nomenon as experienced by diverse stakeholders among varied contexts to facilitate informed
Harsh Suri,'Purposeful Sampling in Qualitative Research Synthesis' | 67
global decision-making. Presuming that different study designs illuminate different aspects
of a phenomenon, maximum variation sampling can be utilised to construct an holistic
understanding of the phenomenon by synthesizing studies that differ in their study designs
on several dimensions. In conceptualising the MIRS framework, I used a combination of
sampling strategies, one of which is maximum variation sampling. I deliberately drew ideas
from methods of primary research and research synthesis that were markedly different from
each other on many dimensions.
Homogenous Sample ‘In direct contrast to maximum variation sampling is the strategy of picking a small, homo-
genous sample, the purpose of which is to describe some particular subgroup in depth’
(Patton, 2002, p. 235). Research synthesists are frequently criticised for ‘mixing apples and
oranges’. Research synthesists can overcome this problem to some extent by selecting studies
that are relatively homogenous in their study designs and conceptual scope. Homogenous
samples can facilitate meaningful comparisons across studies. Underscoring the epistemolo-
gical incommensurability of different qualitative methods, some qualitative research synthesists
recommend a certain level of methodological homogeneity among primary research studies
which are included in a qualitative research synthesis (e.g. Eastabrooks, Field, and Morse,
1994; Paterson et al., 2001). Homogenous samples are particularly suitable for participatory
syntheses in which the synthesist co-synthesizes research with practitioners about a phenomen-
on that has direct implications for their practice (for a detailed discussion of participatory
synthesis, see Suri, 2007). For instance, a group of secondary math teachers intending to
introduce collaborative learning activities into their classroom might benefit more from co-
synthesizing collaborative learning research in secondary math rather than collaborative
learning research across all grade-levels and different disciplines.
Typical Case Sampling The purpose of typical case sampling ‘is to describe and illustrate what is typical to those
unfamiliar with the setting’. Typical cases are selected ‘with the cooperation of key informants’
or using ‘statistical data… to identify “average-like” cases’. When employing typical case
sampling, it is crucial ‘to attempt to get broad consensus about which cases are typical–and
what criteria are being used to define typicality’ (Patton, 2002, p. 236). Research synthesists
can select typical primary research studies employed in the field with the cooperation of key
researchers in the field to describe typical methodologies and study designs employed to
examine the phenomenon. This would be particularly useful for studying how common
themes recurring in the published literature might be related to the relative strengths and
weaknesses of the typical methodologies or theories underpinning the typical studies.
Critical Case Sampling Critical case sampling can facilitate ‘logical generalizations’ with the reasoning ‘that “if it
happens there, it will happen anywhere,” or, vice versa, “if it doesn’t happen there, it won’t
happen anywhere”’ (Patton, 2002, p. 236). Critical case sampling in a research synthesis
might be employed to assist stakeholders in making informed decisions about the viability
of an educational program. For example, consider an innovation that produces desirable
outcomes, but is being rejected by many practitioners as they believe that its implementation
requires substantial resources. A synthesis of primary research studies which describe in detail
successful implementation of the innovation with minimal resources might be useful to al-
68 | Qualitative Research Journal, vol. 11, no. 2, 2011
leviate the practitioners’ resistance towards the innovation. Alternatively, consider an innov-
ation which requires substantial financial resources. However, the proponents of the innov-
ation assert that the innovation is cost-effective provided sufficient resources are invested in
its implementation. In such an area, a research synthesist can selectively synthesize cases re-
ported in primary research studies that were sufficiently endowed with resources to logically
verify, or challenge, the claims made by those advocating the innovation.
Snowball or Chain Sampling Snowball sampling involves seeking information from key informants about details of other
‘information-rich cases’ in the field. ‘The chain of recommended informants would typically
diverge initially as many possible sources are recommended, then converge as a few key
names get mentioned over and over’ (Patton, 2002, p. 237). Snowball sampling can be
utilised by seeking information from various listservs to identify primary research reports
that are frequently referred to by various stakeholders interested in the phenomenon. Even
though snowball sampling can introduce an ‘expert bias (e.g. preferences for large samples
or frequently cited studies)’ (Light & Pillemer, 1984, p. 40), it is particularly useful for
capitalising on expert wisdom, identifying studies that are highly valued by different stake-
holders and identifying studies outside the academic mainstream. Another way in which
snowball sampling may be utilised in a research synthesis is based on the assumption that
the most cited primary research reports are the most information-rich cases. The synthesist
might identify the most cited primary research reports by ‘footnote chasing’, which involves
searching the citation indices, or by browsing through the bibliographies of selected reports
on the relevant topic: previous research syntheses, primary research reports, policy documents,
papers written by practitioners and papers written for practitioners. Unlike footnote chasing
for exhaustive sampling, footnote chasing for snowball sampling would involve locating the
most cited papers. However, this approach can reinforce confirmatory bias where studies
that agree with the prevalent wisdom are more likely to be published and cited while studies
that contest the conventional wisdom are less likely to be published or cited (Suri, 2008).
To overcome this problem, Booth (2001) suggests that we regard a key article as one which
references a broad selection of key articles to demonstrate that the study is informed by a
wide range of perspectives. However, Booth’s suggestion can also be problematic as it may
exclude methodologically rigorous and relevant studies which focus deeply, rather than
broadly, on a certain aspect of the phenomenon studied.
Criterion Sampling Criterion sampling involves reviewing and studying ‘all cases that meet some predetermined
criterion of importance’ (Patton, 2002, p. 238). This approach is frequently employed by
research synthesists to construct a comprehensive understanding of all the studies that meet
certain pre-determined criteria. Most research synthesists employ criterion sampling by
stating explicit inclusion/exclusion criteria which includes specifications for methodological
rigour. It is crucial to reflect critically and realistically on the criteria being used, especially
the criteria for methodological rigour. Very strict criteria for methodological rigour can
result in inclusion of such a small number of studies that the transferability of synthesis
findings becomes questionable. At the same time, including methodologically weak studies
can also result in the synthesis findings being based on questionable evidence. Rather than
looking for methodologically perfect studies, the synthesist must reflect on how the method-
ological specifications of the study might have influenced its report.
Harsh Suri,'Purposeful Sampling in Qualitative Research Synthesis' | 69
Theory-Based Sampling, Operational Construct Sampling, and Theoretical Sampling Theory-based sampling involves selecting cases that represent important theoretical constructs
about the phenomenon of interest. This is similar to operational construct sampling in which
one selects cases that represent ‘real-world examples (i.e. operational examples) of the con-
structs in which one is interested’ (Patton, 2002, pp. 238-239, emphasis in original).
Grounded-theorists define theoretical sampling as the sampling that is based on the concepts
emerging from the data for the purpose of exploring ‘the dimensional range or varied condi-
tions along which the properties of concepts vary’ (Strauss & Corbin, 1998, p. 73). Research
synthesists who employ constant comparative methods or grounded-theory approaches can
fruitfully utilise theoretical sampling to systematically elucidate and refine the ‘variations in,
manifestations of, and meanings of a concept as it is found’ (Patton, 1978, p. 238) in the
selected primary research studies. Many qualitative synthesists recommend theoretical
sampling as a suitable option for research syntheses (Dixon-Woods, Agarwal, Jones, Young,
and Sutton, 2005; Mays et al., 2005). For example, in their meta-study, Paterson and her
colleagues (2001) draw on theory-based sampling or operational construct sampling by setting
out operational definitions of the key constructs about the phenomenon of interest. The
boundaries of these operational definitions are further articulated by explicitly stating inclu-
sion/exclusion criteria in relation to selecting primary research reports for the synthesis.
Confirming and Disconfirming Cases ‘Confirmatory cases are additional examples that fit already emergent patterns; these cases
confirm and elaborate the findings, adding richness, depth, and credibility’ (Patton, 2002,
p. 239). Confirmatory cases may be sought in an openly ideological synthesis when the
synthesist wishes to advocate a particular stance for ethical, moral and/or political reasons.
A synthesist may also seek confirming cases in order to validate the perceptions of a particular
group of marginalised stakeholders. Disconfirming cases ‘are the examples that don’t fit.
They are a source of rival interpretations as well as a way of placing boundaries around
confirmed findings’ (Patton, 2002, p. 239). To shake our complacent acceptance of popular
myths and/or generalisations in a field, synthesists can exclusively seek primary research
studies that disconfirm generalisations proposed in policy documents, previous syntheses or
primary research studies.
Stratified Purposeful Sampling ‘Stratified samples are samples within samples’ where each stratum is ‘fairly homogenous’.
The purpose of stratified purposeful sampling is ‘to capture major variations’ even though
‘a common core… may also emerge in the analysis’ (Patton, 2002, p. 240). Stratified pur-
poseful sampling is useful for examining the variations in the manifestation of a phenomenon
as any key factor associated with the phenomenon is varied. In a research synthesis, this
factor may be contextual, methodological, or conceptual. It is particularly useful to study
different models of implementing a particular teaching and learning strategy, such as distinct
models of cooperative learning that are commonly used by teachers. Often, traditional re-
viewers tacitly draw on stratified purposeful sampling by clustering studies according to a
key dimension of variation and then discussing each cluster in-depth. In developing the
MIRS framework, I employed stratified purposeful sampling to select key publications from
many distinct qualitative research traditions. By seeking input from qualitative researchers
with diverse methodological orientations and reading general qualitative research methods
70 | Qualitative Research Journal, vol. 11, no. 2, 2011
texts, I identified distinct strata of qualitative research methodologies and clusters of key
texts within each stratum.
Opportunistic or Emergent Sampling ‘Opportunistic, emergent sampling takes advantage of whatever unfolds as it unfolds’ by
utilising ‘the option of adding to a sample to take advantage of unforeseen opportunities
after fieldwork has begun’ (Patton, 2002, p. 240, emphasis in original). Opportunistic or
emergent sampling can be useful for synthesizing a research area which is at its exploratory
stage, such as mobile learning, or when the synthesist does not have an emic or insider status
in the relevant field of research. Emergent sampling is also suited to participatory syntheses
where the synthesis purpose evolves in response to the changing needs of the participant co-
synthesists (Suri, 2007). For instance, the purpose of a synthesis in the area of mobile
learning might be guided by the key questions or concerns of a group of professors who are
teaching with mobile technologies. The synthesist might then enter the field and search for
reports to address these questions. When the synthesist feeds this information back to the
professors, their questions might also change. In response to their changing questions, the
synthesist might seek further studies with a different set of criteria. While pursuing these
searches, the synthesist is also likely, serendipitously, to find primary research reports that
will provide useful insights into the phenomenon of mobile learning. Given the exploratory
nature of the process of developing the MIRS framework, I employed opportunistic sampling
at the broadest level.
Purposeful Random Sampling ‘For many audiences, random sampling, even of small samples, will substantially increase
the credibility of the results’ (Patton, 2002, pp. 240-241). In theory, research synthesists
can employ exhaustive searches to locate most of the primary research reported on a topic
and then randomly select a few reports from this pool of reports for in-depth discussion.
However, given the resources required for locating all primary research reports on a topic,
it would be cost-ineffective to randomly discard studies from further consideration. Hence,
this sampling has little appeal in practice.
Sampling Politically Important Cases Sampling politically important cases involves ‘selecting (or sometimes avoiding) a politically
sensitive site or unit of analysis’ (Patton, 2002, p. 241). Like most scholarly endeavours in
education, research syntheses are essentially political. A synthesist might consciously select
politically important reports so that the synthesis gains the attention of different stakeholders
and the synthesis findings get used. For instance, in a synthesis of key criticisms of educa-
tional research published in the 1990s, Oancea (2005) illustrated her key observations
through a detailed analysis of three politically important documents that were frequently
cited in the newspapers. Syntheses of hot topics, in which several stakeholders are interested,
are also likely to attract appropriate funding and more impact (Elmore, 1991).
Convenience Sampling Convenience sampling is ‘probably the most common sampling strategy–and the least desir-
able’. It would involve selecting reports that are ‘easy to access and inexpensive to study’.
This form of sampling is ‘neither purposeful nor strategic’ (Patton, 2002, pp. 241, emphasis
in original). As in many primary research studies, convenience sampling is also employed
Harsh Suri,'Purposeful Sampling in Qualitative Research Synthesis' | 71
in many research reviews. Both primary researchers and research synthesists must resort to
convenience sampling as the last option. First, synthesists must reflect on various other
purposeful sampling strategies to identify a strategy that is most suitable for their purpose
and is also viable within the pragmatic constraints associated with the synthesis. When
convenience sampling has been employed in a research synthesis, the nature of its use and
associated caveats must be clearly described.
Combination or Mixed Purposeful Sampling Synthesists often employ a combination of two or more sampling strategies to select evidence
that adequately addresses their purpose. Mixed purposeful sampling can facilitate triangulation
and flexibility in meeting the needs of multiple stakeholders (Patton, 2002). For example,
synthesists may strategically utilise extensive sampling to draw generalisations at a higher
level of abstraction. Then, they may employ typical case sampling to provide readers with
an immediacy of typical studies that contributed towards informing the more abstract gen-
eralisations. When selecting a combination of sampling strategies, synthesists must reflect
on how those strategies complement each other.
SAMPLE SIZE AND ENACTING CLOSURE TO FURTHER SEARCHES
Decisions associated with enacting closure to further searches for evidence must be guided
by the purpose of the synthesis, the overarching logic of sampling, and pragmatic constraints.
There are two main logics associated with these decisions in primary research as well as re-
search synthesis: data saturation and data sufficiency.
Data Saturation Data saturation may be associated with the stage when further collection of evidence provides
little in terms of further themes, insights, perspectives or information in a qualitative research
synthesis. The concept of data saturation is dependent on the nature of the data source as
well as the synthesis question. There is a higher likelihood of reaching data saturation if the
data collection is purposeful. The more precise a question, the quicker it tends to reach data
saturation. Progressive refinement of a synthesis question is likely to bring an earlier stage
of data saturation. With open ended questions, every new report is likely to offer additional
information. A broad question, like ‘what does research tell us about virtual classrooms?’, is
not likely to bring about a sense of closure or data saturation. On the other hand, the syn-
thesist is likely to reach the data saturation stage earlier with a focused question like ‘what
are the key methodologies being employed to examine gender differences in math achievement
on standardised tests among middle school students?’
Data Sufficiency Most research synthesists refrain from rigidly prescribing a minimum or maximum number
of primary research studies to be included in a synthesis. Some methodologists recommend
their methods are suitable for synthesizing even a small number of qualitative research
studies: for example, meta-ethnography for three studies (Noblit & Hare, 1988), aggregated
analysis for four studies (Eastabrooks et al., 1994) and meta-study for twelve studies (Paterson
et al., 2001, p. 38). Many qualitative research synthesists who synthesize methodologically
diverse research tend to conduct extensive searches and include a large number of studies.
For example, Wideen and colleagues (1998) included 93 studies and Kasworm (1990) in-
72 | Qualitative Research Journal, vol. 11, no. 2, 2011
cluded 96 documents. Paterson and her colleagues identify the following principles that
ought to guide the sample size of the synthesis: ‘the data should be sufficient to permit
comparisons among selected dimensions and constructs’; ‘the reports should reflect the work
of several distinct and independent investigators’; and ‘the data should be sufficient to answer
the research question’ (Paterson et al., 2001, p. 37). These principles can be usefully applied
to most qualitative research syntheses. A research synthesist, like a primary researcher, is often
confronted with various pragmatic constraints of time and resources as well as access to ex-
pertise and information. The stage of data saturation is not frequently reached in either
primary research or research synthesis projects. The logic of data sufficiency is guided by
the synthesist’s perception of what constitutes sufficient evidence for achieving the synthesis
purpose. The synthesist must repeatedly ensure that the claims made in the synthesis are
sufficiently grounded in the evidence collected for the synthesis.
USING THIS DISCUSSION AS A DEPARTURE POINT
In general, synthesists must leave an ‘interpretive trail’ of the different ways in which studies
have been used or omitted (Pawson et al., 2005, p. 31). In leaving an interpretive trail of
their searches, research synthesists must critically reflect and report on the following questions:
• What sampling logic is amenable to meet the synthesis purpose, within the available re-
sources and pragmatic constraints, efficiently and sufficiently?
• What logic will guide the decision to cease searching for further evidence?
• What are the justifications for these decisions?
• What are the caveats associated with these decisions?
In this article, I have illustrated how different, purposeful sampling strategies may be
suited for research syntheses conducted for diverse purposes. By drawing on a range of hy-
pothetical examples, I have illuminated how various purposeful sampling strategies might
be particularly suitable for syntheses geared to facilitate understanding, participation,
emancipation and deconstruction. I have discussed how research synthesists can draw upon
the concepts of data saturation and data sufficiency to inform their decisions to cease searches
for further studies. In my discussion of strategic sampling in research syntheses, I urge syn-
thesists to carefully identify sampling strategies which address the synthesis purpose efficiently,
credibly, sufficiently and ethically. I make no attempt to prescribe certain sampling strategies
for research synthesis. Rather, the intention here is to expand methodological possibilities
within research syntheses by proposing new ways of thinking about the methodology of
synthesis. I hope the users and producers of research synthesis will use this article as a depar-
ture point to think creatively and critically about purposes and amenable sampling strategies
for a research synthesis.
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ABOUT THE AUTHOR Harsh Suri (PhD) is a Lecturer at the Centre for the Study of Higher Education, The University of Melbourne, Australia. She has developed a methodologically inclusive research synthesis (MIRS) framework for designing and evaluating research syntheses from distinct methodological orienta- tions. She has written several papers on contemporary methods of research synthesis. Two of her early papers were recognised as outstanding presentations: Early Career Researcher Award at the Mathematics Education Research Group of Australasia (MERGA) conference in 1997 and Best Graduate Presentation at the AQR conference in 1999. She is cited in most current publications on research synthesis methods which are inclusive of qualitative research.
Email: hsuri@unimelb.edu.au
Harsh Suri,'Purposeful Sampling in Qualitative Research Synthesis' | 75
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- ADM_141110_20110102_00006.pdf
- Purposeful Sampling in Qualitative Research Synthesis
- Methodological Underpinnings
- Building a Case for Purposeful Sampling in Research Synthesis
- Examining the Adaptability of Patton’s Purposeful Sampling Strategies to Qualitative Research Syntheses
- Extreme or Deviant Case Sampling
- Intensity Sampling
- Maximum Variation (Heterogeneity) Sampling
- Homogenous Sample
- Typical Case Sampling
- Critical Case Sampling
- Snowball or Chain Sampling
- Criterion Sampling
- Theory-Based Sampling, Operational Construct Sampling, and Theoretical Sampling
- Confirming and Disconfirming Cases
- Stratified Purposeful Sampling
- Opportunistic or Emergent Sampling
- Purposeful Random Sampling
- Sampling Politically Important Cases
- Convenience Sampling
- Combination or Mixed Purposeful Sampling
- Sample Size and Enacting Closure to Further Searches
- Data Saturation
- Data Sufficiency
- Using this Discussion as a Departure Point
- References
- About the Author