Mixed Methods Sampling A Typology With Examples

TL;DR: In this paper, the authors present a discussion of mixed methods sampling techniques, which combines well-established qualitative and quantitative techniques in creative ways to answer research questions posed by MM research designs.

Abstract: This article presents a discussion of mixed methods (MM) sampling techniques. MM sampling involves combining well-established qualitative and quantitative techniques in creative ways to answer research questions posed by MM research designs. Several issues germane to MM sampling are presented including the differences between probability and purposive sampling and the probability-mixed-purposive sampling continuum. Four MM sampling prototypes are introduced: basic MM sampling strategies, sequential MM sampling, concurrent MM sampling, and multilevel MM sampling. Examples of each of these techniques are given as illustrations of how researchers actually generate MM samples. Finally, eight guidelines for MM sampling are presented.

Summary

A Typology With Examples

• Charles Teddlie Fen Yu Louisiana State University, Baton Rouge.
• This article presents a discussion of mixed methods (MM) sampling techniques.
• MM sampling involves combining well-established qualitative and quantitative techniques in creative ways to answer research questions posed by MM research designs.
• Several issues germane to MM sampling are presented including the differences between probability and purposive sampling and the probability-mixed-purposive sampling continuum.
• Examples of each of these techniques are given as illustrations of how researchers actually generate MM samples.

Taxonomy of Sampling Strategies in the Social and Behavioral Sciences

• Purposive), there are actually four broad categories as illustrated in Figure 1.
• Probability samples aim to achieve representativeness, which is the degree to which the sample accurately represents the entire population.
• This fourth general sampling category has been discussed infrequently in the research literature (e.g., Collins, Onwuegbuzie, & Jiao, 2006; Kemper, Stringfield, & Teddlie, 2003), although numerous examples of it exist throughout the behavioral and social sciences.
• The article is divided into four major sections: a description of probability sampling techniques, a discussion of purposive sampling techniques, general considerations concerning MM sampling, and guidelines for MM sampling.

Random Sampling

• Random sampling is perhaps the most well known of all sampling strategies.
• A simple random sample is one is which each unit (e.g., persons, cases) in the accessible population has an equal chance of being included in the sample, and the probability of a unit being selected is not affected by the selection of other units from the accessible population (i.e., the selections are made independently).
• Simple random sample selection may be accomplished in several ways including drawing names or numbers out of a box or using a computer program to generate a sample using random numbers that start with a ‘‘seeded’’ number based on the program’s start time.

Stratified Sampling

• The situation becomes more complicated when the researcher wants various subgroups in the sample to also be representative.
• In such cases, the researcher uses stratified random sampling,3 which combines stratified sampling with random sampling.
• Assume that a researcher wanted a stratified random sample of males and females in a college freshman class.
• The researcher would first separate the entire population of the college class into two groups (or strata): one all male and one all female.
• The researcher would then independently select a random sample from each stratum (one random sample of males, one random sample of females).

Cluster Sampling

• The third type of probability sampling, cluster sampling, occurs when the researcher wants to generate a more efficient probability sample in terms of monetary and/or time resources.
• Instead of sampling individual units, which might be geographically spread over great distances, the researcher samples groups that occur naturally in the population, such as neighborhoods or schools or hospitals.
• At SAGE Publications on October 27, 2008 http://mmr.sagepub.comDownloaded from.

Sampling Using Multiple Probability Techniques

• Researchers often use the three basic probability sampling techniques in conjunction with one another to generate more complex samples.
• Multiple cluster sampling is a technique that involves (a) a first stage of sampling in which the clusters are randomly selected and (b) a second stage of sampling in which the units of interest are sampled within the clusters.
• As noted above, purposive sampling techniques involve selecting certain units or cases ‘‘based on a specific purpose rather than randomly’’ (Tashakkori & Teddlie, 2003a, p. 713).
• Sampling special or unique cases—employed when the individual case itself, or a specific group of cases, is a major focus of the investigation (rather than an issue).
• Sequential sampling—uses the gradual selection principle of sampling when (a) the goal of the research project is the generation of theory (or broadly defined themes) or (b) the sample evolves of its own accord as data are being collected.

Sampling to Achieve Representativeness or Comparability

• At SAGE Publications on October 27, 2008 http://mmr.sagepub.comDownloaded from.
• Most are aimed at producing contrasting cases.
• It involves selecting those cases that are the most outstanding successes or failures related to some topic of interest.
• These comparisons require that the investigator first determine a dimension of interest, then visualize a distribution of cases or individuals or some other sampling unit on that dimension (which is the QUAL researcher’s informal sampling frame), and then locate extreme cases in that distribution.
• (Sampling frames are at SAGE Publications on October 27, 2008 http://mmr.sagepub.comDownloaded from formal or informal lists of units or cases from which the sample is drawn, and they are discussed in more detail later in this article.).

Sampling Special or Unique Cases

• These sampling techniques include special or unique cases, which have long been a focus of QUAL research, especially in anthropology and sociology.
• Stake (1995) described an intrinsic case study as one in which the case itself is of primary importance, rather than some overall issue.
• An example of this broad category is revelatory case sampling, which involves identifying and gaining entr ee to a single case representing a phenomenon that had previously been ‘‘inaccessible to scientific investigation’’ (Yin, 2003, p. 42).
• Such cases are rare and difficult to study, yet yield very valuable information about heretofore unstudied phenomena.
• A Study in Language Learning derives its revelatory nature from its depiction of a unique environment, the ‘‘Rosepoint’’ community, which was a former sugar plantation that is now a poor, rural African American community near New Orleans, also known as Ward’s (1986) Them Children.

Sequential Sampling

• These techniques all involve the principle of gradual selection, which was defined earlier in this article.
• An example from this broad category is theoretical sampling, in which the researcher examines particular instances of the phenomenon of interest so that she or he can define and elaborate on its various manifestations.
• ‘‘Awareness of dying’’ research provides an excellent example of theoretical sampling utilized by the originators of grounded theory (Glaser & Strauss, 1967).
• Each site provided unique information that previous sites had not.
• Glaser at SAGE Publications on October 27, 2008 http://mmr.sagepub.comDownloaded from and Strauss followed the dictates of gradual selection to that site or case that would yield the most valuable information for the further refinement of the theory.

Sampling Using Multiple Purposive Techniques

• Sampling using combinations of purposive techniques involves using two or more of those sampling strategies when selecting units or cases for a research study.
• Many QUAL studies reported in the literature utilize more than one purposive sampling technique due to the complexities of the issues being examined.
• Poorman (2002) presented an example of multiple purposive sampling techniques from the literature related to the abuse and oppression of women.
• Poorman used four different types of purposive sampling techniques (theory based, maximum variation, snowball, and homogeneous) in combination with one another in selecting the participants for a series of four focus groups.

Differences Between Probability and Purposive Sampling

• Table 1 presents comparisons between probability and purposive sampling strategies.
• There are a couple of similarities between purposive and probability sampling:.
• They both are designed to provide a sample that will answer the research questions under investigation, and they both are concerned with issues of generalizability to an external context or population (i.e., transferability or external validity).
• Another basic difference between the two types of sampling concerns the use of sampling frames, which were defined earlier in this article.

The Purposive-Mixed-Probability Sampling Continuum

• The dichotomy between probability and purposive becomes a continuum when MM sampling is added as a third type of sampling strategy technique.
• Many of the dichotomies presented in Table 1 are better understood as continua with purposive sampling techniques on one end, MM sampling strategies in the middle, and probability sampling techniques on the other end.
• The ‘‘Purposive-Mixed-Probability Sampling Continuum’’ in Figure 3 illustrates this continuum.

Characteristics of Mixed Methods Sampling Strategies

• Table 2 presents the characteristics of MM sampling strategies, which are combinations of (or intermediate points between) the probability and purposive sampling positions.
• This term was defined in Tashakkori and Teddlie (2003b) as a phase of a study that includes three stages: the conceptualization stage, the experiential stage (methodological/analytical), and the inferential stage.
• The MM researcher sometimes chooses procedures that focus on generating representative samples, especially when addressing a QUAN strand of a study.
• Zone B represents primarily QUAL research, with some QUAN components.
• This 3× 3 matrix illustrates that certain types of sampling techniques are theoretically more frequently associated with certain types of data: probability samples with QUAN data (Cell 1), purposive samples with QUAL data (Cell 5), and mixed samples with mixed data (Cell 9).

The Representativeness/Saturation Trade-Off

• Researchers often have to make sampling decisions based on available resources (e.g., time, money).
• Researchers conducting MM research sometimes make a compromise between the requirements of the QUAN and QUAL samples in their study, which the authors call at SAGE Publications on October 27, 2008 http://mmr.sagepub.comDownloaded from the representativeness/saturation trade-off.
• Krueger and Casey (2000) expressed this guideline as follows:.
• Once you have conducted these, determine if you have reached saturation.
• If you were still getting new information after three or four groups, you would conduct more groups.

Types of Mixed Methods Sampling Strategies

• The authors have defined MM sampling as involving the selection of units of analysis for a MM study through both probability and purposive sampling strategies.
• At SAGE Publications on October 27, 2008 http://mmr.sagepub.comDownloaded from Sampling in the social and behavioral sciences has so many well-defined and specified QUAL and QUAN techniques, with commonly understood names, that it would be foolhardy to try to develop a new terminology.
• The ‘‘backgrounds’’ of the techniques presented in their typology are interesting.
• (p. 284) Detailed examples of concurrent MM sampling are more difficult to find in the existing literature, at least from their review of it.
• Concurrent MM sampling involves the selection of units of analysis for an MM study through the simultaneous use of both probability and purposive sampling.

Basic Mixed Methods Sampling Strategies

• One well-known basic MM sampling strategy is stratified purposive sampling (quota sampling).
• The stratified nature of this sampling procedure is characteristic of probability sampling, whereas the small number of cases typically generated through it is characteristic of purposive sampling.
• This allows the researcher to discover and describe in detail characteristics that are similar or different across the strata or subgroups.
• This sampling scheme allowed the researchers to discuss the differences between ‘‘typical’’ and ‘‘better’’ schools at program implementation across a variety of community types.
• This purposive random sample of a small number of cases from a much larger target population added credibility to the evaluation by generating QUAL, process-oriented results to complement the large-scale QUAN-oriented research that also took place.

Sequential Mixed Methods Sampling

• There are examples of QUAN-QUAL and QUAL-QUAN MM sampling procedures throughout the social and behavioral sciences.
• The information generated through the QUAN strand was necessary to select participants with particular characteristics for the QUAL strand.

Concurrent Mixed Methods Sampling

• The authors analyzed numerous MM articles while writing this article, but the lack of details regarding sampling in many of them precluded their inclusion in this article.
• Concurrent MM sampling utilizing a single sample generated through the joint use of prob- ability and purposive techniques to generate data for both the QUAN and QUAL strands of a MM study.
• This purposive sampling process resulted in four types of schools: urban–high achievement, urban–low achievement, rural–high achievement, and rural–low achievement.
• There were a relatively large number of Caucasian deaf students on the campus, and a randomly selected number of them were sent surveys through regular mail and e-mail.

Multilevel Mixed Methods Sampling

• Multilevel MM sampling strategies are very common in research examining organizations in which different units of analysis are ‘‘nested within one another.’’.
• Multilevel MM sampling from K-12 educational settings often involve the following five levels: state school systems, school districts, schools, teachers or classrooms, and students.
• The resultant overall sampling strategy quite often requires multiple sampling techniques, each of which is employed to address one of more of the research questions.
• Many educational research studies focus on the school and teacher levels because those are the levels that most directly impact students’ learning (e.g., Reynolds & Teddlie, 2000; at SAGE Publications on October 27, 2008 http://mmr.sagepub.comDownloaded from Rosenshine & Stevens, 1986).
• Altogether, this example involves eight sampling techniques at five levels.

A Final Note on Mixed Methods Sampling Strategies

• This section of the article has presented a provisional typology of MM sampling strategies, based on their review of studies using MM sampling throughout the social and behavioral sciences.
• Concurrent and sequential MM sampling procedures are based on design types, and those design types are based on strands (QUAL and QUAN).
• The major QUAL data used to answer this question were classroom- and school-level observations and interviews with students, teachers, and principals.
• These are general guidelines that researchers should consider when putting together a sampling procedure for a MM study.

4. The sampling strategy should allow the researchers to draw clear inferences from both the

• This guideline refers to the researchers’ ability to ‘‘get it right’’ with regard to explaining what happened in their study or what they learned from their study.
• From the QUAL design perspective, this guideline refers to the credibility of the inferences.
• The sampling strategy must be ethical.

7. The sampling strategy should allow the research team to transfer or generalize the conclusions of their study to other individuals, groups, contexts, and so forth if that is a purpose

• This guideline refers to the external validity and transferability issues that were discussed throughout this article.
• Thus, when purposive sampling decisions are made, the researchers should know the characteristics of the study sample (sending context) and the characteristics of other contexts to which they want to transfer their study results (receiving contexts).
• Common sense dictates that the diagonal cells (1, 5, and 9) in Table 3 represent the most frequently occurring combinations of sampling techniques and types of data generated.
• Collins, Onwuegbuzie, and Jiao (2006) presented their own typology of mixed methods sampling designs.
• Concurrent MM sampling requires at least two strands and at SAGE Publications on October 27, 2008 http://mmr.sagepub.comDownloaded from typically focuses on just one level or unit of analysis.

Figures

Figure 2 A Typology of Purposive Sampling Strategies

Table 2 Characteristics of Mixed Methods Sampling Strategies

Figure 3 Purposive-Mixed-Probability Sampling Continuum

Figure 1 Taxonomy of Sampling Techniques for the Social and Behavioral Sciences

Figure 4 Example of the Representativeness/Saturation Rule

Figure 5 Illustration of Multilevel Mixed Methods Sampling in K-12 Educational Settings

Table 3 Theoretical Matrix Crossing Type of Sampling Technique by Type of Data Generated

Table 1 Comparisons Between Purposive and Probability Sampling Techniques

Figure 6 An Example of ‘‘Nested’’ Mixed Methods Sampling Strategies: The Louisiana School Effectiveness Study, Phases III-V

Full text

http://mmr.sagepub.com
Journal of Mixed Methods Research
DOI: 10.1177/2345678906292430
2007; 1; 77 Journal of Mixed Methods Research
Charles Teddlie and Fen Yu
Mixed Methods Sampling: A Typology With Examples
http://mmr.sagepub.com/cgi/content/abstract/1/1/77
The online version of this article can be found at:
Published by:
http://www.sagepublications.com
can be found at:Journal of Mixed Methods Research Additional services and information for
http://mmr.sagepub.com/cgi/alerts Email Alerts:
http://mmr.sagepub.com/subscriptions Subscriptions:
http://www.sagepub.com/journalsReprints.navReprints:
http://www.sagepub.com/journalsPermissions.navPermissions:
http://mmr.sagepub.com/cgi/content/refs/1/1/77 Citations
at SAGE Publications on October 27, 2008 http://mmr.sagepub.comDownloaded from

Mixed Methods Sampling
A Typology With Examples
Charles Teddlie
Fen Yu
Louisiana State University, Baton Rouge
This article presents a discussion of mixed methods (MM) sampling techniques. MM sam-
pling involves combining well-established qualitative and quantitative techniques in creative
ways to answer research questions posed by MM research designs. Several issues germane to
MM sampling are presented including the differences between probability and purposive
sampling and the probability-mixed-purpos ive sampling continuum. Four MM sampling pro-
totypes are introduced: basic MM sampling strategies, sequential MM sampling, concurrent
MM sampling, and multilevel MM sampling. Examples of each of these techniques are given
as illustrations of how researchers actually generate MM samples. Finally, eight guidelines
for MM sampling are presented.
Keywords: mixed methods sampling; mixed methods research; multilevel mixed methods
sampling; representativeness/saturation trade-off
Taxonomy of Sampling Strategies in
the Social and Behavioral Sciences
Although sampling procedures in the social and behavioral sciences are often divided into
two groups (probability, purposive), there are actually four broad categories as illustrated in
Figure 1. Probability, purposive, and convenience sampling are discussed briefly in the fol-
lowing sections to provide a background for mixed methods (MM) sampling strategies.
Probability sampling techniques are primarily used in quantitatively oriented studies
and involve ‘‘selecting a relatively large number of units from a population, or from speci-
fic subgroups (strata) of a population, in a random manner where the probability of inclu-
sion for every member of the population is determinable’’ (Tashakkori & Teddlie, 2003a,
p. 713). Probability samples aim to achieve representativeness, which is the degree to
which the sample accurately represents the entire population.
Purposive sampling techniques are primarily used in qualitative (QUAL) studies and
may be defined as selecting units (e.g., individuals, groups of individuals, institutions)
based on specific purposes associated with answering a research study’s questions. Max-
well (1997) further defined purposive sampling as a type of sampling in which, ‘‘particular
settings, persons, or events are deliberately selected for the important information they
can provide that cannot be gotten as well from other choices’’ (p. 87).
Journal of Mixed
Methods Research
Volume 1 Number 1
January 2007 77-100
Ó 2007 Sage Publications
10.1177/2345678906292430
http://jmmr.sagepub.com
hosted at
http://online.sagepub.com
Authors’ Note: This article is partially based on a paper presented at the 2006 annual meeting of the Ameri-
can Educational Research Association, San Francisco.
77
at SAGE Publications on October 27, 2008 http://mmr.sagepub.comDownloaded from

Convenience sampling involves drawing samples that are both easily accessible and
willing to participate in a study. Two types of convenience samples are captive samples
and volunteer samples. We do not discuss convenience samples in any detail in this arti-
cle, which focuses on how probability and purposive samples can be used to generate MM
samples.
MM sampling strategies involve the selection of units
1
or cases for a research study
using both probability sampling (to increase external validity) and purposive sampling
strategies (to increase transferability).
2
This fourth general sampling category has been
discussed infrequently in the research literature (e.g., Collins, Onwuegbuzie, & Jiao,
2006; Kemper, Stringfield, & Teddlie, 2003), although numerous examples of it exist
throughout the behavioral and social sciences.
The article is divided into four major sections: a description of probability sampling
techniques, a discussion of purposive sampling techniques, general considerations con-
cerning MM sampling, and guidelines for MM sampling. The third section on general con-
siderations regarding MM sampling contains examples of various techniques, plus
illustrations of how researchers actually generate MM samples.
Traditional Probability Sampling Techniques
An Introduction to Probability Sampling
There are three basic types of probability sampling, plus a category that involves multi-
ple probability techniques:
I. Probability Sampling
A. Random Sampling
B. Stratified Sampling
C. Cluster Sampling
D. Sampling Using Multiple Probability Techniques
II. Purposive Sampling
A. Sampling to Achieve Representativeness or Comparability
B. Sampling Special or Unique Cases
C. Sequential Sampling
D. Sampling Using Multiple Purposive Techniques
III. Convenience Sampling
A. Captive Sample
B. Volunteer Sample
IV. Mixed Methods Sampling
A. Basic Mixed Methods Sampling
B. Sequential Mixed Methods Sampling
C. Concurrent Mixed Methods Sampling
D. Multilevel Mixed Methods Sampling
E. Combination of Mixed Methods Samplin
g
Strate
g
ies
Figure 1
Taxonomy of Sampling Techniques for the Social and Behavioral Sciences
78 Journal of Mixed Methods Research
at SAGE Publications on October 27, 2008 http://mmr.sagepub.comDownloaded from

• Random sampling—occurs when each sampling unit in a clearly defined population has an
equal chanc e of being included in the sample.
• Stratified sampling—occurs when the researcher divides the population into subgroups (or
strata) such that each unit belongs to a single stratum (e.g., low income, medium income,
high income) and then selects units from those strata.
• Cluster sampling—occurs when the sampling unit is not an individual but a group (cluster) that
occurs naturally in the population such as neighborhoods, hospitals, schools, or classrooms.
• Sampling using multiple probability techniques—involves the use of multiple quantitative
(QUAN) techniques in the same study.
Probability sampling is based on underlying theoretical distributions of observations, or
sampling distributions, the best known of which is the normal curve.
Random Sampling
Random sampling is perhaps the most well known of all sampling strategies. A simple
random sample is one is which each unit (e.g., persons, cases) in the accessible population
has an equal chance of being included in the sample, and the probability of a unit being
selected is not affected by the selection of other units from the accessible population (i.e.,
the selections are made independently). Simple random sample selection may be accom-
plished in several ways including drawing names or numbers out of a box or using a com-
puter program to generate a sample using random numbers that start with a ‘‘seeded’’
number based on the program’s start time.
Stratified Sampling
If a researcher is interested in drawing a random sample, then she or he typically wants
the sample to be representative of the population on some characteristic of interest (e.g.,
achieveme nt scores). The situation becomes more complicated when the researcher wants
various subgroups in the sample to also be representative. In such cases, the researcher
uses stratified random sampling,
3
which combines stratified sampling with random
sampling.
For example, assume that a researcher wanted a stratified random sample of males and
females in a college freshman class. The researcher would first separate the entire popula-
tion of the college class into two groups (or strata): one all male and one all female. The
researcher would then independently select a random sample from each stratum (one ran-
dom sample of males, one random sample of females).
Cluster Sampling
The third type of probability sampling, cluster sampling, occurs when the researcher
wants to generate a more efficient probability sample in terms of monetary and/or time
resources. Instead of sampling individual units, which might be geographically spread
over great distances, the researcher samples groups (clusters) that occur naturally in the
population, such as neighborhoods or schools or hospitals.
Teddlie, Yu / Mixed Methods Sampling 79
at SAGE Publications on October 27, 2008 http://mmr.sagepub.comDownloaded from

Sampling Using Multiple Probability Techniques
Researchers often use the three basic probability sampling techniques in conjunction
with one another to generate more complex samples. For example, multiple cluster sam-
pling is a technique that involves (a) a first stage of sampling in which the clusters are ran-
domly selected and (b) a second stage of sampling in which the units of interest are
sampled within the clusters. A common example of this from educational research occurs
when schools (the clusters) are randomly selected and then teachers (the units of interest)
in those schools are randomly sampled.
Traditional Purposive Sampling Techniques
An Introduction to Purposive Sampling
Purposive sampling techniques have also been referred to as nonprobability sampling
or purposeful sampling or ‘‘qualitative sampling.’’ As noted above, purposive sampling
techniques involve selecting certain units or cases ‘‘based on a specific purpose rather than
randomly’’ (Tashakkori & Teddlie, 2003a, p. 713). Several other authors (e.g., Kuzel,
1992; LeCompte & Preissle, 1993; Miles & Huberman, 1994; Patton, 2002) have also pre-
sented typologies of purposive sampling techniques.
As detailed in Figure 2, there are three broad categories of purposive sampling techni-
ques (plus a category involving multiple purposive techniques), each of which encompass
several specific types of strategies:
• Sampling to achieve representativeness or com parability—these techniques are used when
the researcher wants to (a) select a purposive sample that represents a broad er group of cases
as closely as possible or (b) set up comparisons among different types of cases.
• Sampling special or unique cases—employed when the individual case itself, or a specific
group of cases, is a major focus of the investigation (rather than an issue).
• Sequential sampling—uses the gradual selection principle of sampling when (a) the goal of
the research project is the generation of theory (or broadly defined themes) or (b) the sample
evolves of its own accord as data are being collected. Gradual selection may be defined as
the sequential selection of units or cases based on their relevance to the research questions,
not their representativeness (e.g., Flick, 1998).
• Sampling using multiple purposive techniques—involves the use of multiple QUAL techni-
ques in the same study.
Sampling to Achieve Representativeness or Comparability
The first broad category of purposive sampling techniques involves two goals:
• sampling to find instances that are representative or typical of a particular type of case on a
dimension of interest, and
• sampling to achieve comparability across differe nt types of cases on a dimension of
interest.
80 Journal of Mixed Methods Research
at SAGE Publications on October 27, 2008 http://mmr.sagepub.comDownloaded from

Frequently asked questions

Q1. What are the future works in "A typology with examples" ?

8. The researchers should describe their sampling strategy in enough detail so that other investigators can understand what they actually did and perhaps use those strategies ( or variants thereof ) in future studies. The literature related to MM sampling strategies is in its infancy, and more detailed descriptions of those strategies in the literature will help guide other investigators in drawing complex samples. There are three general types of units that can be sampled: cases ( e. g., individuals, institutions ), materials, and other elements in the social situation. 3. Stratified sampling may be both a probability sampling technique and a purposeful sampling technique. 

Q2. What are the contributions mentioned in the paper "A typology with examples" ?

The use of stratified sampling as a purposive technique is discussed later in this article under the topic of basic mixed methods ( MM ) sampling strategies this paper. 

Q3. What are the four types of purposive sampling techniques?

There are four types of purposive sampling techniques that feature special or unique cases: revelatory case sampling, critical case sampling, sampling politically important cases, and complete collection. 

Q4. How many respondents were from racial/ethnic minority groups?

Of the remaining 157 respondents, 81 were from racial/ethnic minority groups (African Americans, Asians, Hispanics), and 76 were Caucasians. 

Q5. Why was the purposive sampling technique used?

Because there were a much smaller number of racial/ethnic minority deaf students, the purposive sampling technique known as complete collection (criterion sampling) was used. 

Q6. What is the definition of a concurrent MM sampling?

Concurrent MM sampling involves the selection of units of analysis for an MM study through the simultaneous use of both probability and purposive sampling. 

Q7. What is the broad category of purposive sampling?

An example of this broad category is revelatory case sampling, which involves identifying and gaining entr ee to a single case representing a phenomenon that had previously been ‘‘inaccessible to scientific investigation’’ (Yin, 2003, p. 42). 

Q8. What is the definition of purposive sampling?

An example of this broad category of purposive sampling is extreme or deviant case sampling, which is also known as ‘‘outlier sampling’’ because it involves selecting cases near the ‘‘ends’’ of the distribution of cases of interest. 

Q9. What type of schools were created by purposive sampling?

This purposive sampling process resulted in four types of schools: urban–high achievement, urban–low achievement, rural–high achievement, and rural–low achievement.