NR 505 Qualitative Design and Sampling DQ

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NR 505 Qualitative Design and Sampling DQ

NR 505 Qualitative Design and Sampling DQ

 

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With the focus on qualitative design and sampling, this is a great opportunity to compare and contrast quantitative and qualitative research approaches. For each of the following areas, apply information that considers one advantage and one disadvantage regarding

  • control over study conditions with the quantitative research approach;
  • control over study conditions with the qualitative research approach;
  • extending or generalizing results from a sample to a larger group or population with the quantitative research approach; and
  • extending or generalizing results from a sample to a larger group or population with the qualitative research approach.

Be sure to include scholarly references to support your information.Sampling Designs in Qualitative Research: Making the
Sampling Process More Public
Anthony J. Onwuegbuzie
Sam Houston State University, Huntsville, Texas
Nancy L. Leech
University of Colorado at Denver, Denver, Colorado
The purpose of this paper is to provide a typology of sampling designs for
qualitative researchers. We introduce the following sampling strategies:
(a) parallel sampling designs, which represent a body of sampling
strategies that facilitate credible comparisons of two or more different
subgroups that are extracted from the same levels of study; (b) nested
sampling designs, which are sampling strategies that facilitate credible
comparisons of two or more members of the same subgroup, wherein one
or more members of the subgroup represent a sub-sample of the full
sample; and (c) multilevel sampling designs, which represent sampling
strategies that facilitate credible comparisons of two or more subgroups
that are extracted from different levels of study. Key Words: Qualitative
Research, Sampling Designs, Random Sampling, Purposive Sampling, and
Sample Size
Setting the Scene
According to Denzin and Lincoln (2005), qualitative researchers must confront
three crises; representation, legitimation, and praxis. The crisis of representation refers to
the difficulty for qualitative researchers in adequately capturing lived experiences. As
noted by Denzin and Lincoln,
Such experience, it is argued, is created in the social text written by the
researcher. This is the representational crisis. It confronts the inescapable
problem of representation, but does so within a framework that makes the
direct link between experience and text problematic. (p. 19)
Further, according to Denzin and Lincoln (2005), the crisis of representation asks
whether qualitative researchers can use text to represent authentically the experience of
the “Other” (p. 21). The crisis of legitimation refers to “a serious rethinking of such terms
as validity, generalizability, and reliability, terms already retheorized in postpositivist…,
constructivist-naturalistic…, feminist…, interpretive…, poststructural…, and
critical…discourses” (Denzin & Lincoln, p. 19) [italics in original]. Finally, the crisis of
praxis leads qualitative researchers to ask, “how are qualitative studies to be evaluated in
the contemporary, poststructural moment?” (Denzin & Lincoln, pp. 19-20).
239 The Qualitative Report June 2007
The crises of representation, legitimation, and praxis threaten qualitative
researchers’ ability to extract meaning from their data. As noted by Onwuegbuzie and
Leech (2004a),
In particular, lack of representation means that the evaluator has not
adequately captured the data. Lack of legitimation means that the extent to
which the data have been captured has not been adequately assessed, or
that any such assessment has not provided support for legitimation. Thus,
the significance of findings in qualitative research is affected by these
crises. (p. 778)
In an attempt to address these crises and to prevent “the naturalistic approach…
[from being] tarred with the brush of ‘sloppy research’” (Guba, 1981, p. 90), in recent
years, there has been increased focus on rigor in qualitative research, where rigor is
defined as the goal of making “data and explanatory schemes as public and replicable as
possible” (Denzin, 1978, p. 7). More specifically, recent attempts have been made to
make the research process more public (cf. Anfara, Brown, & Mangione, 2002). In
particular, qualitative methodologists have provided frameworks for making qualitative
data analyses more explicit (Anfara et al.; Constas, 1992), so that qualitative studies
promote “openness on the grounds of refutability and freedom from bias” (Anfara et al.,
p. 28).
In contrast, scant discussion has taken place vis-à-vis sampling in qualitative
research. Indeed, using the keywords “qualitative research” and “sampling,” as well as
“qualitative research” and “sample size,” a review of the most prominent academic
literature databases (e.g., ERIC, PsycINFO) yielded only seven published journal articles
(i.e., Crowley, 1994; Curtis, Gesler, Smith, & Washburn, 2000; Jones, 2002; Merriam,
1995; Onwuegbuzie & Leech, 2004b, 2005b; Sandelowski, 1995) that discussed the issue
of sampling and/or sample size in qualitative research. Additionally, Onwuegbuzie and
Leech (2005a), Collins, Onwuegbuzie, and Jiao (2006, 2007), and Teddlie and Yu (2007)
have added to the body of literature in this area. All of these articles have focused on the
issue of sample size and/or sampling schemes. Although these concepts are extremely
important in interpretivist research, none of these articles provide a superordinate concept
of sampling designs. For the purposes of the present essay, we distinguish between
sampling schemes and sampling designs. We define sampling schemes as specific
techniques that are utilized to select units (e.g., people, groups, subgroups, situations,
events). In contrast, as do Onwuegbuzie and Collins (2007), we define sampling designs
as representing the framework within which the sampling occurs, comprising the number
and types of sampling schemes and the sample size.
With this in mind, the purpose of this paper is to provide a framework for
developing sampling designs in qualitative research. In particular, we provide a typology
of sampling designs for qualitative researchers. Using this typology, we introduce the
following sampling strategies of inquiry: (a) parallel sampling designs, which represent a
body of sampling strategies that facilitate credible comparisons of two or more different
subgroups (e.g., girls vs. boys) that are extracted from the same levels of study (e.g.,
third-grade students); (b) nested sampling designs, which are sampling strategies that
facilitate credible comparisons of two or more members of the same subgroup, wherein
Anthony J. Onwuegbuzie and Nancy L. Leech 240

one or more members of the subgroup represent a sub-sample (e.g., key informants) of
the full sample; and (c) multilevel sampling designs, which represent sampling strategies
that facilitate credible comparisons of two or more subgroups that are extracted from
different levels of study (e.g., students vs. teachers). We show how such designs, because
they facilitate comparisons, are consistent with Turner’s (1980) notion that all
explanation is essentially comparative and takes the form of translation of metaphors
(i.e., literal translation or idiomatic translation; Barnwell, 1980). Also, we link sampling
designs to various qualitative data analysis techniques (e.g., within-case analyses, crosscase analyses). We contend that our sampling framework arises from a desire to construct
more adequate interpretive explanations, as well as to follow the lead of Constas (1992),
who surmised that “since we are committed to opening the private lives of participants to
the public, it is ironic that our methods of data collection and analysis often remain
private and unavailable for public inspection” (p. 254).
Sampling Schemes
In quantitative research, generally, only one type of statistical generalization is
pertinent, namely generalizing findings from the sample to the underlying population. In
contrast, in interpreting their data, qualitative researchers typically tend to make one of
the following types of generalizations: (a) statistical generalizations, (b) analytic
generalizations, and (c) case-to-case transfer (Curtis et al., 2000; Firestone, 1993;
Kennedy, 1979; Miles & Huberman, 1994). As illustrated in Figure 1, in qualitative
research, the authors believe that there are two types of statistical generalizations;
external statistical generalizations and internal statistical generalizations. External
statistical generalization, which is identical to the traditional notion of statistical
generalization in quantitative research, involves making generalizations or inferences on
data extracted from a representative statistical sample to the population from which the
sample was drawn. In contrast, internal statistical generalization involves making
generalizations or inferences on data extracted from one or more representative or elite
participants to the sample from which the participant(s) was drawn. Analytic
generalizations are “applied to wider theory on the basis of how selected cases ‘fit’ with
general constructs” (Curtis et al., p. 1002). Finally, case-to-case transfer involves making
generalizations from one case to another (similar) case (Firestone; Kennedy).
Qualitative researchers typically do not make external statistical generalizations
because their goal usually is not to make inferences about the underlying population, but
to attempt to obtain insights into particular educational, social, and familial processes and
practices that exist within a specific location and context (Connolly, 1998). Moreover,
interpretivists study phenomena in their natural settings and strive to make sense of, or to
interpret, phenomena with respect to the meanings people bring (Denzin & Lincoln,
2005). However, the other three types of generalizations (i.e., internal statistical
generalizations, analytic generalizations, and case-to-case transfers) are very common in
qualitative research, with analytic generalizations being the most popular. More
specifically, qualitative researchers “generalize words and observations… to the
population of words/observations (i.e., the “truth space”) representing the underlying
context” (Onwuegbuzie, 2003, p. 400). As noted by Williamson Shafer and Serlin (2005),
241 The Qualitative Report June 2007
The observations in any qualitative study are necessarily a subset of all
other things that might have been observed using a particular set of tools
and techniques in a particular setting. From this subset of all possible
observations, a further subset is extracted to form the basis of qualitative
inferences, since no qualitative analysis accounts for all of the
observational data in equal measure. (p. 20)
Figure 1. Types of generalization in qualitative research.

Type of Generalization
Case-to-Case
Transfer
Statistical
Generalization
Analytical
Generalization
External
Statistical
Generalization
Internal
Statistical
Generalization
Therefore, sampling is an essential step in the qualitative research process. As such,
choice of sampling scheme is an important consideration that all qualitative researchers
should make. Encouragingly, qualitative researchers have many sampling schemes from
which to choose. Indeed, extending the work of Patton (1990) and Miles and Huberman
(1994), Onwuegbuzie and Leech (2004b) identified 24 sampling schemes that are
available to researchers including qualitative, quantitative, and mixed methods
researchers. All of these sampling schemes can be classified as representing either
random sampling (i.e., probabilistic sampling) schemes or non-random sampling (i.e.,
Anthony J. Onwuegbuzie and Nancy L. Leech 242

non-probabilistic sampling) schemes. Each of these sampling schemes is presented by
sampling type (i.e., random vs. nonrandom sampling scheme) in Onwuegbuzie and
Collins (2007). Although relatively rare, if the objective of the study is to generalize
qualitative findings from the sample to the population, then the researcher should attempt
to select a sample that is representative. Given a large enough sample, of all sampling
schemes, random sampling offers the best chance for a researcher to obtain a
representative sample. Thus, if external statistical generalization is the goal, which
typically is not the case, then qualitative researchers should consider selecting one of the
five random sampling schemes (i.e., simple random sampling, stratified random
sampling, cluster random sampling, systematic random sampling, and multi-stage random
sampling).
Conversely, if the goal is not to generalize to a population but to obtain insights
into a phenomenon, individuals, or events, as is most often the case in interpretivist
studies, then the qualitative researcher purposefully selects individuals, groups, and
settings for this phase that increases understanding of phenomena. In this situation, the
researcher should select one of the 19 purposive sampling schemes.
Sample Size
Even though qualitative investigations typically involve the use of small samples,
choice of sample size still is an important consideration because it determines the extent
to which the researcher can make each of the four types of generalizations (Onwuegbuzie
& Leech, 2005b). As noted by Sandelowski (1995), “a common misconception about
sampling in qualitative research is that numbers are unimportant in ensuring the adequacy
of a sampling strategy” (p. 179). Nevertheless, some methodologists have provided
guidelines for selecting samples in qualitative studies based on the research design (e.g.,
case study, ethnography, phenomenology, grounded theory) or research method (e.g.,
focus group). These recommendations are presented in Onwuegbuzie and Collins (2007).
In general, sample sizes in qualitative research should not be too large that it is difficult
to extract thick, rich data. At the same time, as noted by Sandelowski, the sample should
not be too small that it is difficult to achieve data saturation (Flick, 1998; Morse, 1995),
theoretical saturation (Strauss & Corbin, 1990), or informational redundancy (Lincoln &
Guba, 1985).
Qualitative Sampling Designs
Most research questions in qualitative studies lead to one of two classes of
analyses; within-case analyses or cross-case analyses. As delineated by Miles and
Huberman (1994), within-case analyses involve analyzing, interpreting, and legitimizing
data that help to explain “phenomena in a bounded context that make up a single ‘case’—
whether that case is an individual in a setting, a small group, or a larger unit such as a
department, organization, or community” (p. 90). In fact, within-case analyses are
appropriate in samples with more than one case, providing that the researcher’s goal is
not to compare the cases. As such, when a within-case analysis represents the method of
choice, the researcher’s sampling design involves selection of both the sample size and
sampling scheme.
243 The Qualitative Report June 2007

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On the other hand, as noted by Yin (2003), selecting multiple cases represents
replication logic. That is, additional participants are chosen for study because they are
expected to yield similar data or different but predictable findings (Schwandt, 2001).
Stake (2000) referred to these designs as collective case studies. According to Stake,
collective case studies involve the
study [of] a number of cases in order to investigate a phenomenon,
population, or general condition….[who] are chosen because it is believed
that understanding them will lead to better understanding, perhaps better
theorizing, about a still larger collection of cases. (p. 437)

Thus, when qualitative research designs involving multiple cases are used, a major goal
of the researcher is to compare and contrast the selected cases. In such instances, a crosscase analysis is a natural choice. A cross-case analysis involves analyzing data across the
cases (Schwandt). Moreover, it represents a thematic analysis across cases (Creswell,
2007).
Because collective case studies typically necessitate researchers to choose their
cases (Stake, 2000), being able to investigate thoroughly and understand the phenomenon
of interest depends heavily on appropriate selection of each case (Patton, 1990; Stake;
Vaughan, 1992; Yin, 2003). In fact, in collective case studies, “nothing is more important
than making a proper selection of cases” (Stake, p. 446). Unfortunately, little or no
guidance is provided in the literature as to how to select cases in collective case studies.
Thus, in what follows, we introduce a typology of sampling designs that qualitative
researchers might find useful when selecting participants in multiple-case studies.1
This
typology centers on the relationship of the selected cases to each other. These
relationships either can be parallel, nested, or multilevel leading to parallel sampling
designs, nested sampling designs, and multilevel sampling designs, respectively. Each of
these classes of qualitative sampling designs is discussed in the following sections.
Parallel Sampling Designs
Parallel sampling designs represent a body of sampling strategies that facilitate
credible comparisons of two or more cases. These designs can involve comparing each
case to all others in the sample (i.e., pairwise sampling designs) or it can involve
comparing subgroups of cases (i.e., subgroup sampling designs). Choice of these
sampling designs stem from the research question(s) and the research design (e.g., case
study, ethnography, phenomenology, grounded theory).
Pairwise sampling designs traditionally have been the most common types of
qualitative sampling designs. These sampling designs are called “pairwise” because all
the selected cases are treated as a set and their “voice” is compared to all other cases one
at a time in order to understand better the underlying phenomenon, assuming that the
collective voices generated by the set of cases lead to data saturation. In situations where
theoretical saturation is reached, analyzing these sets of voices can lead to the generation
of theory.

1 For the purposes of this article, multiple-case studies refer to any studies that result in more than one case
being selected (e.g., collective case study, ethnography, phenomenology, groN,M

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