Psychology psych week 10 assignment

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Submit: Annotated Bibliography

This week culminates in your submission of an annotated bibliography that should consist of an introduction, followed by two quantitative article annotations, two qualitative article annotations, and two mixed methods article annotations for a total of six annotations, followed by a conclusion.

An annotated bibliography is a document containing selected sources accompanied by a respective annotation. Each annotation consists of a summary, analysis, and application for the purpose of conveying the relevance and value of the selected source. As such, annotations demonstrate a writer’s critical thinking about and authority on the topic represented in the sources.

In preparation for your own future research, an annotated bibliography provides a background for understanding a portion of the existing literature on a particular topic. It is also a useful precursor for gathering sources in preparation for writing a subsequent literature review.

Please review the assignment instructions below and click on the underlined words for information about how to craft each component of an annotation.

Please use the document “Annotated Bibliography Template with Example” for additional guidance. 

It is recommended that you use the grading rubric as a self-evaluation tool before submitting your assignment. 


Be sure to review the Learning Resources before completing this activity.
Click the weekly resources link to access the resources. 




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-purposive 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

� 2007 Sage Publications


hosted at

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.


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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


MM sampling strategies involve the selection of units1 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 Sampling Strategies

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

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• Random sampling—occurs when each sampling unit in a clearly defined population has an

equal chance 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.,

achievement 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


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.

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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 comparability—these techniques are used when

the researcher wants to (a) select a purposive sample that represents a broader 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 different types of cases on a dimension of


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There are six types of purposive sampling procedures that are based on achieving repre-

sentativeness or comparability: typical case sampling, extreme or deviant case sampling,

intensity sampling, maximum variation sampling, homogeneous sampling, and reputa-

tional sampling. Although some of these purposive sampling techniques are aimed at gen-

erating representative cases, most are aimed at producing contrasting cases. Comparisons

or contrasts are at the very core of QUAL data analysis strategies (e.g., Glaser & Strauss,

1967; Mason, 2002; Spradley, 1979, 1980), including the contrast principle and the con-

stant comparative technique.

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. It involves selecting those cases

that are the most outstanding successes or failures related to some topic of interest. Such

extreme successes or failures are expected to yield especially valuable information about

the topic of interest.

Extreme or deviant cases provide interesting contrasts with other cases, thereby allow-

ing for comparability across those cases. 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

A. Sampling to Achieve Representativeness or Comparability

1. Typical Case Sampling
2. Extreme or Deviant Case Sampling (also known as Outlier Sampling)
3. Intensity Sampling
4. Maximum Variation Sampling
5. Homogeneous Sampling
6. Reputational Case Sampling

B. Sampling Special or Unique Cases

7. Revelatory Case Sampling
8. Critical Case Sampling
9. Sampling Politically Important Cases
10. Complete Collection (also known as Criterion Sampling)

C. Sequential Sampling

11. Theoretical sampling (also known as Theory-Based Sampling)
12. Confirming and Disconfirming Cases
13. Opportunistic Sampling (also known as Emergent Sampling)
14. Snowball Sampling (also known as Chain Sampling)

D. Sampling Using Combinations of Purposive Techniques

Figure 2
A Typology of Purposive Sampling Strategies

Source: These techniques were taken from several sources, such as Kuzel (1992), LeCompte and Preissle

(1993), Miles and Huberman (1994), and Patton (2002).

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formal or informal lists of units or cases from which the sample is drawn, and they are dis-

cussed 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. There are four types of purposive sampling techniques that feature spe-

cial or unique cases: revelatory case sampling, critical case sampling, sampling politically

important cases, and complete collection.

An example of this broad category is revelatory case sampling, which involves identify-

ing 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.

There are several examples of revelatory cases spread throughout the social and beha-

vioral sciences. For example, Ward’s (1986) Them Children: A Study in Language Learn-

ing 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. Ward described how the Rosepoint

community provided a ‘‘total environment’’ for the families she studied (especially for the

children) that is quite different from the mainstream United States.

Sequential Sampling

These techniques all involve the principle of gradual selection, which was defined ear-

lier in this article. There are four types of purposive sampling techniques that involve

sequential sampling:

• theoretical sampling,

• confirming and disconfirming cases,

• opportunistic sampling (also known as emergent sampling), and

• snowball sampling (also known as chain sampling).

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. The investigator samples people, institutions,

documents, or wherever the theory leads the investigation.

‘‘Awareness of dying’’ research provides an excellent example of theoretical sampling

utilized by the originators of grounded theory (Glaser & Strauss, 1967). Glaser and

Strauss’s research took them to a variety of sites relevant to their emerging theory regard-

ing different types of awareness of dying. Each site provided unique information that pre-

vious sites had not. These sites included premature baby services, neurological services

with comatose patients, intensive care units, cancer wards, and emergency services. Glaser

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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.

For example, Poorman (2002) presented an example of multiple purposive sampling

techniques from the literature related to the abuse and oppression of women. In this study,

Poorman used four different types of purposive sampling techniques (theory based, maxi-

mum variation, snowball, and homogeneous) in combination with one another in selecting

the participants for a series of four focus groups.

General Considerations Concerning
Mixed Methods Sampling

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 investiga-

tion, and they both are concerned with issues of generalizability to an external context or

population (i.e., transferability or external validity).

On the other hand, the remainder of Table 1 presents a series of dichotomous differ-

ences between the characteristics of purposive and probability sampling. For example, a

purposive sample is typically designed to pick a small number of cases that will yield the

most information about a particular phenomenon, whereas a probability sample is planned

to select a large number of cases that are collectively representative of the population of

interest. There is a classic methodological trade-off involved in the sample size difference

between the two techniques: Purposive sampling leads to greater depth of information

from a smaller number of carefully selected cases, whereas probability sampling leads to

greater breadth of information from a larger number of units selected to be representative

of the population (e.g., Patton, 2002).

Another basic difference between the two types of sampling concerns the use of sam-

pling frames, which were defined earlier in this article. As Miles and Huberman (1994)

noted, ‘‘Just thinking in sampling-frame terms is good for your study’s health’’ (p. 33).

Probability sampling frames are usually formally laid out and represent a distribution with

a large number of observations. Purposive sampling frames, on the other hand, are typi-

cally informal ones based on the expert judgment of the researcher or some available

resource identified by the researcher. In purposive sampling, a sampling frame is ‘‘a

resource from which you can select your smaller sample’’ (Mason, 2002, p. 140). (See

Table 1 for more differences between probability and purposive sampling.)

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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. The

information from Table 2 could be inserted into Table 1 between the columns describing

purposive and probability sampling, but we have chosen to present it separately here so

that we can focus on the particular characteristics of MM sampling.

Table 1
Comparisons Between Purposive and Probability Sampling Techniques

Dimension of Contrast Purposive Sampling Probability Sampling

Other names Purposeful sampling

Nonprobability sampling

Qualitative sampling

Scientific sampling

Random sampling

Quantitative sampling

Overall purpose of sampling Designed to generate a sample

that will address research


Designed to generate a sample that

will address research questions

Issue of generalizability Sometimes seeks a form of

generalizability (transferability)

Seeks a form of generalizability

(external validity)

Rationale for selecting


To address specific purposes

related to research questions

The researcher selects cases she

or he can learn the most from


The researcher selects cases that

are collectively representative

of the population

Sample size Typically small (usually 30 cases

or less)

Large enough to establish

representativeness (usually

at least 50 units)

Depth/breadth of information

per case/unit

Focus on depth of information

generated by the cases

Focus on breadth of information

generated by the sampling units

When the sample is selected Before the study begins,

during the study, or both

Before the study begins

How selection is made Utilizes expert judgment Often based on application of

mathematical formulas

Sampling frame Informal sampling frame

somewhat larger than sample

Formal sampling frame typically

much larger than sample

Form of data generated Focus on narrative data

Numeric data can also

be generated

Focus on numeric data

Narrative data can also

be generated

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MM sampling strategies may employ all the probability and purposive techniques dis-

cussed earlier in this article. Indeed, the researcher’s ability to creatively combine these

techniques in answering a study’s questions is one of the defining characteristics of MM


The strand of a research design is an important construct that we use when describing

MM sampling procedures. 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. These strands are typically

either QUAN or QUAL, although transformation from one type to another can occur dur-

ing the course of a study. A QUAL strand of a research study is a strand that is QUAL in

all three stages, whereas a QUAN strand of a research study is a strand that is QUAN in

all three stages.

The MM researcher sometimes chooses procedures that focus on generating representa-

tive samples, especially when addressing a QUAN strand of a study. On the other hand,

when addressing a QUAL strand of a study, the MM researcher typically utilizes sampling

techniques that yield information rich cases. Combining the two orientations allows the

MM researcher to generate complementary databases that include information that has

both depth and breadth regarding the phenomenon under study.

There are typically multiple samples in an MM study, and these samples may vary in

size (dependent on the research strand and question) from a small number of cases to a

large number of units. Using an educational example, one might purposively select four

schools for a study, then give surveys to all 100 teachers in those schools, then conduct

six focus groups of students, followed by interviewing 60 randomly selected students.

Both numeric and narrative data are typically generated from MM samples, but occa-

sionally MM sampling strategies may yield only narrative or only numeric data. Hence, it



Figure 3
Purposive-Mixed-Probability Sampling Continuum

Source: Teddlie (2005).

Note: Zone A consists of totally qualitative (QUAL) research with purposive sampling, whereas Zone E consists

of totally quantitative (QUAN) research with probability sampling. Zone B represents primarily QUAL

research, with some QUAN components. Zone D represents primarily QUAN research, with some QUAL com-

ponents. Zone C represents totally integrated mixed methods (MM) research and sampling. The arrow repre-

sents the purposive-mixed-probability sampling continuum. Movement toward the middle of the continuum

indicates a greater integration of research methods and sampling. Movement away from the center (and toward

either end) indicates that research methods and sampling (QUAN and QUAL) are more separated or distinct.

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is important to present a brief discussion of the relationship between sampling techniques

and the generation of different types of data.

Table 3 presents a theoretical matrix that crosses type of sampling technique (probabil-

ity, purposive, mixed) by type of data generated by the study (QUAN only, QUAL only,

mixed).5 This 3× 3 matrix illustrates that certain types of sampling techniques are theore-

tically 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 diagonal cells (1, 5, and 9) represent the most frequently

occurring combination of sampling techniques and types of data generated. Despite these

general tendencies, there are other situations where sampling techniques occasionally

(Cells 3, 6, 7, and 8) or rarely (Cells 2 and 4) are associated with studies that generate dif-

ferent types of data.

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 we call

Table 2
Characteristics of Mixed Methods Sampling Strategies

Dimension of Contrast Mixed Methods Sampling

Overall purpose of sampling Designed to generate a sample that will address research questions.

Issue of generalizability For some strands of a research design, there is a focus on external

validity issues. For other strands, the focus is on transferability issues.

Number of techniques All those employed by both probability and purposive sampling.

Rationale for selecting


For some strands of a research design, there is a focus on

representativeness. For other strands, the focus is on seeking out

information rich cases.

Sample size There are multiple samples in the study. Samples vary in size

dependent on the research strand and question from a small number

of cases to a large number of units of analysis.

Depth/breadth of information

per case/unit

Focus on both depth and breadth of information across the research


When the sample is selected Most sampling decisions are made before the study starts, but

QUAL-oriented questions may lead to the emergence

of other samples during the study.

How selection is made There is a focus on expert judgment across the sampling decisions,

especially because they interrelate with one another. Some

QUAN-oriented strands may require application of

mathematical sampling formulae.

Sampling frame Both formal and informal frames are used.

Form of data generated Both numeric and narrative data are typically generated.

Occasionally, mixed methods sampling strategies may yield

only narrative or only numeric data.

Note: QUALqualitative; QUANquantitative.

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the representativeness/saturation trade-off. This trade-off means that the more emphasis

that is placed on the representativeness of the QUAN sample, the less emphasis there is

that can be placed on the saturation of the QUAL sample, and vice versa.

As noted earlier in this article, the aim of sampling in QUAN research is to achieve

representativeness. That is, the researcher wants the sample to reflect the characteristics of

the population of interest, and typically this requires a sample of a certain size relative to

the population (e.g., Wunsch, 1986).

An important sample size issue in QUAL research involves saturation of information

(e.g., Glaser & Strauss, 1967; Strauss & Corbin, 1998).6 For example, in focus group stu-

dies the new information gained from conducting another session typically decreases as

more sessions are held. Krueger and Casey (2000) expressed this guideline as follows:

The rule of thumb is, plan three or four focus groups with any one type of participant. Once

you have conducted these, determine if you have reached saturation. Saturation is a term

used to describe the point when you have heard the range of ideas and aren’t getting new

information. If you were still getting new information after three or four groups, you would

conduct more groups. (p. 26)

Figure 4 presents an illustration of this representativeness/saturation trade-off. In this

example, a student conducting her dissertation research with limited resources had to com-

promise between the requirements of (a) the representatives of her survey sample and (b)

the saturation of information gained from her interview study.

Types of Mixed Methods Sampling Strategies

We now turn our attention to descriptions of different types of MM sampling strategies

with examples. We have defined MM sampling as involving the selection of units of ana-

lysis for a MM study through both probability and purposive sampling strategies. There is

not a large literature on MM sampling strategies per se at this time, so we reviewed the

scant literature devoted to the topic (e.g., Collins et al., 2006;7 Kemper et al., 2003) and

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

Type of Sampling


Generation of

Quantitative Data Only

Generation of

Qualitative Data Only

Generation of Both

Qualitative and

Quantitative Data

Probability sampling


Happens often

(Cell 1)

Happens rarely

(Cell 2)

Happens occasionally

(Cell 3)

Purposive sampling


Happens rarely

(Cell 4)

Happens often

(Cell 5)

Happens occasionally

(Cell 6)

Mixed methods

sampling strategies

Happens occasionally

(Cell 7)

Happens occasionally

(Cell 8)

Happens often

(Cell 9)

Source: Kemper, Stringfield, and Teddlie (2003, p. 285).

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then searched for additional examples throughout the social and behavioral sciences. This

literature search was often frustrating due to the lack of details presented by many authors

with regard to sample selection.

There is no widely accepted typology of MM sampling strategies. In generating the

provisional typology for this article, we faced the general issue of nomenclature in MM

research (e.g., Teddlie & Tashakkori, 2003). One of the major decisions that mixed meth-

odologists have to make concerning nomenclature is whether to

• utilize a bilingual nomenclature that employs both the QUAL and the QUAN terms for basic

issues such as research designs, validity and trustworthiness, sampling, and so forth;

• create a new language for mixed methodology that gives a common name for the existing

sets of QUAL and QUAN terms; or

• combine the first two options by presenting new MM terms that are integrated with well-

known QUAL/QUAN terms in the definition of the overall sampling strategy.

Aaron (2005) was interested in studying the leadership characteristics of the directors
of programs in radiologic technology. She had both quantitatively and qualitatively
oriented research questions. The QUAN questions were answered using an online survey
administered to all radiologic program directors. The QUAL questions were answered
using a telephone interview with a small sample of directors whose responses to the
online survey indicated that they differed on two important dimensions [type of program
administered (baccalaureate, associate, certificate) and type of leadership style
(transformational, transactional)], resulting in six cells. Aaron wanted the survey study to
have a representative sample and the interview study to result in “saturated” QUAL data.

Of the 590 program directors that were sent surveys, 284 responded for a 48%
response rate. Extrapolating from the samples and population sizes (Wunsch, 1986), it
appears that Aaron could be confident that her sample reflected the population within plus
or minus 5 %.

There were no clearly established standards for how large the interview sample should
be to generate trustworthy results. Aaron selected 12 program directors to be interviewees
based on her intuitions, plus the expert advice of her dissertation committee. This number
also allowed her to select a stratified purposive sample (see description later in this
chapter) in which program type and leadership style were the strata. She selected two
interviewees for each of the six cells, resulting in 12 program directors and then
(undeterred by superstition) selected a 13th interviewee whom she felt was a particularly
information rich case (extreme or deviant case sampling).

If Aaron had attempted to increase the sample size of her survey data to reflect the
population within plus or minus 1%, she would have had to send out at least one more
round of surveys to all who had not already participated, thereby decreasing the time she
had left to select and interact with the participants in the interview study. On the other
hand, if she had increased the sample size of the interview study to 24, she would have
had to reduce the amount of time and resources that she invested in the survey study. Her
sampling choices appeared to meet the requirements for representativeness of QUAN
sources and saturation of QUAL sources.

Figure 4
Example of the Representativeness/Saturation Rule

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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 fool-

hardy to try to develop a new terminology. On the other hand, the literature indicates that

mixed methodologists have combined probability and purposive sampling techniques in

certain unique prescribed manners to meet the specification of popular MM designs (e.g.,

concurrent, sequential designs). In such cases, it seems reasonable to overlay the probabil-

ity and purposive sampling terms with MM metaterms that encompass the totality of the

sampling techniques used in the research projects.

The following is our provisional typology of MM sampling strategies:

• basic MM sampling strategies,

• sequential MM sampling,

• concurrent MM sampling,

• multilevel MM sampling, and

• sampling using multiple MM sampling strategies.

The ‘‘backgrounds’’ of the techniques presented in our typology are interesting. The

basic MM sampling strategies discussed in the following section (i.e., stratified purposive

sampling, purposive random sampling) are typically discussed as types of purposive sam-

pling techniques (e.g., Patton, 2002), yet by definition they also include a component of

probability sampling (stratified, random). These basic MM techniques may be used to gen-

erate narrative data only in QUAL oriented research (Cell 8 in Table 3) or to generate

MM data (Cell 9 in Table 3).

Sequential and concurrent MM sampling follow from the well-known design types

described by several authors (e.g., Creswell, Plano Clark, Gutmann, & Hanson, 2003;

Johnson & Onwuegbuzie, 2004). Sequential MM sampling involves the selection of units

of analysis for an MM study through the sequential use of probability and purposive sam-

pling strategies (QUAN-QUAL), or vice versa (QUAL-QUAN). Sequential QUAN-

QUAL sampling is the most common technique that we have encountered in our explora-

tion of the MM literature, as described by Kemper et al. (2003):

In sequential mixed models studies, information from the first sample (typically derived from

a probability sampling procedure) is often required to draw the second sample (typically

derived from a purposive sampling procedure). (p. 284)

Detailed examples of concurrent MM sampling are more difficult to find in the existing

literature, at least from our 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. One type of sampling procedure does not set the stage for the other in

concurrent MM sampling studies; instead, both probability and purposive sampling proce-

dures are used at the same time.

Multilevel MM sampling is a general sampling strategy in which probability and purpo-

sive sampling techniques are used at different levels of the study (Tashakkori & Teddlie,

2003a, p. 712).8 This sampling strategy is common in contexts or settings in which differ-

ent units of analysis are ‘‘nested’’ within one another, such as schools, hospitals, and var-

ious types of bureaucracies.

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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 characteris-

tic of purposive sampling. In this technique, the researcher first divides the group of inter-

est into strata (e.g., above average, average, below average students) and then selects a

small number of cases to study intensively within each strata based on purposive sampling

techniques. This allows the researcher to discover and describe in detail characteristics

that are similar or different across the strata or subgroups. Patton (2002) described this

technique as selecting ‘‘samples within samples.’’

An example of stratified purposive sampling comes from Kemper and Teddlie (2000),

who in one phase of a multiphase study generated six strata based on two dimensions (three

levels of community type crossed by two levels of implementation of innovation). Their

final sample had only six schools in it (one purposively selected school per stratum): one

‘‘typical’’ urban, one ‘‘typical’’ suburban, one ‘‘typical’’ rural, one ‘‘better’’ urban, one ‘‘bet-

ter’’ suburban, and one ‘‘better’’ rural. This sampling scheme allowed the researchers to dis-

cuss the differences between ‘‘typical’’ and ‘‘better’’ schools at program implementation

across a variety of community types. What differentiated a pair of schools in one strata or

context (e.g., urban) could be quite different from what differentiated a pair of schools in

another (e.g., rural).

Purposive random sampling involves taking a random sample of a small number of

units from a much larger target population (Kemper et al., 2003). Kalafat and Illback

(1999) presented an example of purposive random sampling in their evaluation of a large

statewide program that used a school-based family support system to enhance the educa-

tional experiences of at-risk students. There were almost 600 statewide sites in this pro-

gram, and a statistically valid sample would have required in-depth descriptions of more

than 200 cases (Wunsch, 1986), which was well beyond the resources allocated to the eva-

luation. In an early stage of the study before the intervention began, the researchers uti-

lized a purposive random sampling approach to select 12 cases from the overall target

population. The researchers then closely followed these cases throughout the life of the

project. This purposive random sample of a small number of cases from a much larger tar-

get 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. Typically, the methodology and results

from the first strand inform the methodology employed in the second strand.9 In our exam-

ination of the literature, we found more examples of QUAN-QUAL studies in which the

methodology and/or results from the QUAN strand influenced the methodology subse-

quently employed in the QUAL strand. In many of these cases, the final sample used in

the QUAN strand was then used as the sampling frame for the subsequent QUAL strand.

In these studies, the QUAL strand used a subsample of the QUAN sample.

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One example of QUAN-QUAL mixed methods sampling comes from the work of Han-

cock, Calnan, and Manley (1999) in a study of perceptions and experiences of residents

concerning dental service in the United Kingdom. In the QUAN portion of the study, the

researchers conducted a postal survey that involved both cluster and random sampling: (a)

The researchers selected 13 wards out of 365 in a county in southern England using cluster

sampling, and (b) they randomly selected 1 out of every 28 residents in those wards result-

ing in an accessible population of 2,747 individuals, from which they received 1,506

responses (55%). The researchers could be confident that their sample reflected the acces-

sible population within plus or minus 5% (Wunsch, 1986).

The questionnaires included five items measuring satisfaction with dental care (DentSat

scores). The researchers next selected their sample for the QUAL strand of the study using

intensity and homogeneous sampling: (a) 20 individuals were selected who had high Dent-

Sat scores through intensity sampling, (b) 20 individuals were selected who had low Dent-

Sat scores through intensity sampling, and (c) 10 individuals were selected who had not

received dental care in the past 5 years, but also who did not have full dentures, using

homogeneous sampling. In this study, the information generated through the QUAN strand

was necessary to select participants with particular characteristics for the QUAL strand.

An example of a QUAL-QUAN sampling procedure comes from the work of Nieto,

Mendez, and Carrasquilla (1999) in a study of malaria control in Colombia. The study

was conducted in the area of Colombia where the incidence of the disease is the highest.

In the QUAL strand of the study, the research team asked leaders from five urban districts

to select individuals for participation in focus groups. The focus groups were formed using

the following criteria: (a) The participants should belong to one of the local community

organizations; (b) they should represent different geographical and age groups; (c) they

should recognize the community’s leadership and be fully committed to the community;

and (d) the groups should be as homogeneous as possible with regard to educational level

and socioeconomic and cultural status, which involved face-to-face interviews.

The five focus groups met for three sessions each and discussed a wide range of issues

related to health problems in general and malaria in particular. The groups ranged in size

from 15 to 18 members, and subgroups were formed during the sessions to encourage

greater participation in the process. The focus group results were then used by the research

team to design the QUAN survey, which was subsequently given to a large sample of

households. The research team used stratified random sampling, with three geographical

zones constituting the strata. The total sample for the QUAN strand was 1,380 households,

each of which was visited by a member of the researcher team.

The QUAL and QUAN data gathered through the overall MM sampling strategy was very

comparable in terms of the participants’ knowledge of symptoms, perceptions of the causes

of malaria transmission, and prevention practices. The QUAN strand of this study could not

have been conducted without the information initially gleaned from the QUAL strand.

Concurrent Mixed Methods Sampling

We 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. In particular,

very few articles that we analyzed included a concurrent MM design with an explicit

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discussion of both the purposive and probability sampling techniques that were used to gen-

erate it. Concurrent MM designs allow researchers to triangulate the results from the sepa-

rate QUAN and QUAL components of their research, thereby allowing them to ‘‘confirm,

cross-validate, or corroborate findings within a singe study’’ (Creswell et al., 2003, p. 229).

Nevertheless, we were successful in locating a few articles that enhanced our under-

standing of how researchers actually combine probability and purposive sampling in their

concurrent MM studies. We have delineated two basic overall concurrent MM sampling

procedures, but we are certain that there are others. These two basic procedures are as


1. Concurrent MM sampling in which probability sampling techniques are used to generate

data for the QUAN strand and purposive sampling techniques are used to generate data for

the QUAL strand. These sampling procedures occur independently.

2. 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 occurs, for example, when a sample of participants, selected through the

joint application of probability and purposive techniques, responds to a MM survey that

contains both closed-ended and open-ended questions.

Lasserre-Cortez (2006) presented a study that is an example of the first type of concur-

rent MM sampling procedure in which a probability sample addresses the QUAN strand

and a purposive sample addresses the QUAL strand independently. The goals of the Las-

serre-Cortez study were twofold:

• She wanted to test some QUAN research hypotheses regarding the differences in the charac-

teristic of teachers and schools participating in professional action research collaboratives

(PARCs) as opposed to matched control schools, and

• she wanted to answer QUAL research questions about the manner in which school climate

affects teacher effectiveness in PARC schools.

Lasserre-Cortez (2006) drew two different samples, a probability sample to answer the

QUAN research hypotheses and a purposive sample to answer the QUAL research ques-

tions. The probability sample involved a multiple cluster sample of schools participating

in PARC programs and a set of control schools, which were matched to the PARC schools

with regard to socioeconomic status of students and community type. A total of 165

schools (approximately half being PARC schools and half being control schools) were

selected, and three teachers were then randomly selected within each school to complete

school climate surveys.

The purposive sample involved 8 schools (4 PARC schools matched with 4 control

schools) from the larger, 165-school sample. These 8 schools were chosen using maxi-

mum variation sampling, a purposive technique ‘‘that documents diverse variations and

identifies common patterns’’ (Miles & Huberman, 1994, p. 28). The two selection vari-

ables were schoolwide achievement on a state test and community type (urban, rural).

This purposive sampling process resulted in four types of schools: urban–high achieve-

ment, urban–low achievement, rural–high achievement, and rural–low achievement.

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Lasserre-Cortez (2006) used two very different sampling procedures (one probability,

one purposive) to separately answer her QUAN hypotheses and QUAL questions. The

only point of commonality between the two samples was that the purposively drawn sam-

ple was a subset of the probability drawn sample. The data were collected concurrently

and triangulated in the final phases of the data analysis.

Parasnis, Samar, and Fischer (2005) presented a study that is an example of the second

type of concurrent MM sampling procedure: the single sample servicing the requirements

of both the QUAL and QUAN strands. Their study was conducted on a college campus

where there were a relatively large number of deaf students (around 1,200). Selected stu-

dents were sent surveys that included both closed-ended and open-ended items; therefore,

data for the QUAN and QUAL strands were gathered simultaneously. The analysis of data

from each strand informed the analysis of the other.

The MM sampling procedure included both purposive and probability sampling techni-

ques. First, all the individuals in the overall sample were deaf college students, which is an

example of homogeneous sampling. The research team had separate sampling procedures

for selecting racial/ethnic minority deaf students and for selecting Caucasian deaf students.

There were a relatively large number of Caucasian deaf students on the campus, and a ran-

domly selected number of them were sent surveys through regular mail and e-mail. 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. In this

technique, all members of a population of interest are selected who meet some special criter-

ion, in this case being a deaf racial/ethnic minority student on a certain college campus.

Altogether, the research team distributed 500 surveys and received a total of 189

responses, 32 of which were eliminated because they were foreign students. Of the

remaining 157 respondents, 81 were from racial/ethnic minority groups (African Ameri-

cans, Asians, Hispanics), and 76 were Caucasians. The combination of purposive (com-

plete collection) and probability (random) sampling techniques in this concurrent MM

study yielded a sample that allowed interesting comparisons between the two racial sub-

groups on a variety of issues, such as their perception of the social psychological climate

on campus and the availability of role models.

Multilevel Mixed Methods Sampling

Multilevel MM sampling strategies are very common in research examining organiza-

tions in which different units of analysis are ‘‘nested within one another.’’ In studies of

these nested organizations, researchers are often interested in answering questions related

to two or more levels or units of analysis.

Multilevel MM sampling from K-12 educational settings often involve the following

five levels: state school systems, school districts, schools, teachers or classrooms, and stu-

dents. Figure 5 presents an illustration of the structure of the sampling decisions required

in studies conducted in K-12 settings. 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;

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Sampling state school systems

• Purposive or convenience sampling

• Sampling scheme depends on practical issues

Sampling school districts

• Often involves probability sampling of districts, which are clusters of

• Also involves stratified or stratified purposive selection of specific districts

Sampling schools within districts

• Purposive sampling of schools often includes deviant/extreme, intensity,
or typical case sampling

Sampling teachers or classrooms within schools

• Probability sampling of teachers or classrooms often involves random
sampling or stratified random sampling, or

• Purposive sampling, such as intensity, or typical case sampling

Sampling students within classrooms

• May involve probability sampling of students such as random
sampling, or

• Purposive sampling such as typical case or complete collection
(criterion) sampling

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

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Rosenshine & Stevens, 1986). Figure 6 contains an example of a school/teacher effective-

ness study that involved a multilevel MM sampling strategy, with purposive sampling at

the school level and probability sampling at the classroom level. 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 strate-

gies, based on our review of studies using MM sampling throughout the social and beha-

vioral sciences. This typology is, in fact, a simplified version of the range of MM

sampling strategies that actually exist.

For instance, concurrent and sequential MM sampling procedures are based on design

types, and those design types are based on strands (QUAL and QUAN). These strands as

described by Tashakkori and Teddlie (2003b) did not take into consideration multiple units

Teddlie and Stringfield (1993) described the following five levels of sampling in two
phases of the Louisiana School Effectiveness Study:

1. Twelve school systems were selected based on maximum variation sampling so
that a wide range of district conditions were included. An additional school district was
included because of pressures to include it from a stakeholder group, thereby
introducing sampling politically important cases. A district is a cluster of schools, and
cluster sampling is a probability technique.

2. Pairs of school were selected within districts. Each pair of schools included one
school that was more effective and one that was less effective, based on their students’
scores on standardized tests. Intensity sampling was used in selecting these pairs of
more effective or less effective schools, such that the schools were above average or
below average, but not extremely so. The schools in each pair were matched on other
important dimensions. Among the potential pairs of schools, three pairs were selected to
be from rural areas, three from suburban areas, and three from urban areas. This is an
example of stratified purposive sampling.

3. The third grade at each school was selected for closer examination. The selection
procedure for grade level was homogeneous sampling, used to reduce variation across
schools and to simplify data analyses. Other grade levels were also used to gather the
classroom observation data, but the student and parental level data were gathered at
the third grade level.

4. Classrooms for observation were selected using stratified random sampling such
that all grades were selected and classes were randomly selected within grade level.

5. Student test and attitudinal data and parent attitudinal data were collected at the
third grade only, and involved criterion or complete collection of information on all third
graders and their parents. Of course there was some missing data, but this was kept to
a minimum by administering the student tests and questionnaires during regularly
scheduled class periods.

Figure 6
An Example of ‘‘Nested’’ Mixed Methods Sampling Strategies:

The Louisiana School Effectiveness Study, Phases III-V

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of analysis because that would have further complicated the already complex design typol-

ogy that they presented. The Methods-Strands Matrix presented by Tashakori and Teddlie

(2003b) implicitly limited each QUAN or QUAL research strand to one level of analysis.

Multilevel MM sampling, on the other hand, is based on multiple levels of analysis, not

strands, and explicitly indicates that there is more than one unit of analysis per strand. A

logical question arises: How are multilevel MM sampling designs combined with concur-

rent and sequential MM designs? What happens when researchers combine sequential

MM sampling with multilevel MM sampling or combine concurrent MM sampling with

multilevel MM sampling? This type of complex sampling involves combinations of multi-

ple strands of a research study with multiple levels of sampling within strands.

The Louisiana School Effectiveness Study described in Figure 6 actually included two

concurrent strands (one QUAL, one QUAN) along with the following two major research

questions of the study:

• Would the eight matched pairs of more effective and less effective schools remain differen-

tially effective over time, or would some schools increase or decrease in effectiveness status

over time? The major QUAN data used to answer this question were achievement scores

and indices of student socioeconomic status.

• What are the processes whereby schools remained the same or changed over time with

regard to how well they educated their students? The major QUAL data used to answer this

question were classroom- and school-level observations and interviews with students, tea-

chers, and principals.

Both the QUAN and QUAL strands of the Louisiana School Effectiveness Study used

the same multilevel MM sampling strategy presented in Figure 6 (same school systems,

same pairs of schools, same grade level for closer examination, same classrooms for

observations) because the QUAL and QUAN questions were so tightly linked. Other

research situations with more diverse QUAL and QUAN strands will require multilevel

MM strategies that are quite different from one another.

Guidelines for Mixed Methods Sampling

The following section borrows from guidelines presented by other authors (e.g., Curtis,

Gesler, Smith, & Washburn, 2000; Kemper et al., 2003; Miles & Huberman, 1994), plus

consideration of important issues discussed in this article. These are general guidelines that

researchers should consider when putting together a sampling procedure for a MM study.

1. The sampling strategy should stem logically from the research questions and hypotheses

that are being addressed by the study. In most MM studies, this will involve both probabil-

ity and purposive techniques, but there are some cases where either probability sampling

(see Cell 3 in Table 3) or purposive sampling (see Cell 6 in Table 3) alone is appropriate.

The researcher typically asks two basic questions:

a. Will the purposive sampling strategy lead to the collection of data focused on the

QUAL questions under investigation?

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b. Will the probability sampling strategy lead to the collection of data focused on the

QUAN hypotheses or questions under investigation?

2. Researchers should be sure to follow the assumptions of the probability and purposive sam-

pling techniques that they are using. In several of the MM studies that we have analyzed,

the researchers started out with established probability and purposive techniques but vio-

lated the assumptions of one or the other during the course of the study. This is particularly

the case with the probability sampling component because failure to recruit properly or

attrition can lead to a convenience sample.

3. The sampling strategy should generate thorough QUAL and QUAN databases on

the research questions under study. This guideline relates to the representativeness/satura-

tion trade-off discussed earlier in this article.

a Is the overall sampling strategy sufficiently focused to allow researchers to actually

gather the data necessary to answer the research questions?

b. Will the purposive sampling techniques utilized in the study generate ‘‘saturated’’

information on the QUAL research questions?

c. Will the probability sampling techniques utilized in the study generate a representative

sample related to the QUAN research questions?

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

QUAL and QUAN data. 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. Sampling decisions are important here because if you do not have a good sample of

the phenomena of interest, then your inferences related to the research questions will lack

clarity or be inadequate.

a. From the QUAL design perspective, this guideline refers to the credibility of the


b. From the QUAN design perspective, this guideline refers to the internal validity of the


5. The sampling strategy must be ethical. There are very important ethical considerations in

MM research. Specific issues related to sampling include informed consent to participate in

the study, whether participants can actually give informed consent to participate, the poten-

tial benefits and risks to the participants, the need for absolute assurances that any promised

confidentiality can be maintained, and the right to withdraw from the study at any time.

6. The sampling strategy should be feasible and efficient. Kemper et al. (2003) noted that

‘‘sampling issues are inherently practical’’ (p. 273).

a. The feasibility or practicality of a MM sampling strategy involves several issues. Do

the researchers have the time and money to complete the sampling strategy? Do the

researchers actually have access to all of the data sources? Is the selected sampling

strategy congruent with the abilities of the researchers?

b. The efficiency of a MM sampling strategy involves techniques for focusing the finite

energies of the research team on the central research questions.

7. The sampling strategy should allow the research team to transfer or generalize the conclu-

sions of their study to other individuals, groups, contexts, and so forth if that is a purpose

of the MM research. This guideline refers to the external validity and transferability issues

that were discussed throughout this article. It should be noted that not all MM studies are

intended to be transferred or generalized.

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a. From the QUAL design perspective, this guideline indicates that the researchers should

know a lot of information about the characteristics of ‘‘both sending and receiving con-

texts’’ (Lincoln & Guba, 1985, p. 297). 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).

b. From the QUAN design perspective, this guideline indicates that the researchers would

want to increase the representativeness of the study sample as much as possible. Tech-

niques to accomplish this include increasing sample size, using methods to ensure that

that all subjects have an equal probability of participating, and so forth.

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.

Creativity and flexibility in the practical design of MM sampling schemes are crucial to

the success of the research study. The success of a MM research project in answering a

variety of questions is a function, to a large degree, of the combination of sampling strate-

gies that are employed. In conclusion, it is important to remember that ‘‘in research, sam-

pling is destiny’’ (Kemper et al., 2003, p. 275).


1. There are three general types of units that can be sampled: cases (e.g., individuals, institutions), materi-

als, and other elements in the social situation. The mixed methodologist should consider all three data sources

in drawing her sample.

2. External validity refers to the generalizability of results from a quantitative (QUAN) study to other

populations, settings, times, and so forth. Transferability refers to the generalizability of results from one spe-

cific sending context in a qualitative (QUAL) study to another specific receiving context (e.g., Lincoln &

Guba, 1985; Tashakkori & Teddlie, 1998).

3. Stratified sampling may be both a probability sampling technique and a purposeful sampling technique.

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 (stratified purposive sampling or quota sampling).

4. Combining QUAN and QUAL techniques often involves collaborative work between experts with dif-

ferent backgrounds (e.g., psychologists and anthropologists). Shulha and Wilson (2003) described examples

of such collaborative mixed methods research.

5. We use the term theoretical because the matrix is not based on empirical research examining the fre-

quency of sampling techniques by type of data generated. 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. The information contained in the other cells is based on informed speculation.

6. Other important factors in determining the QUAL sample size include the generation of a variation of

ranges, the creation of comparisons among relevant groups, and representativeness.

7. Collins, Onwuegbuzie, and Jiao (2006) presented their own typology of mixed methods sampling

designs. They then analyzed a sample of mixed methods studies from electronic databases and calculated the

prevalence rate for the designs in their typology.

8. Multilevel MM sampling is different from concurrent MM sampling, although they can both be used in

studies that combine MM sampling strategies. Concurrent MM sampling requires at least two strands and

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typically focuses on just one level or unit of analysis. On the other hand, multilevel MM sampling may be

employed within just one strand of a MM study and requires at least two levels or units of analysis.

9. MM studies may involve more than two strands (e.g., QUAN-QUAL-QUAN), but the discussion in this

article is limited to two strands for the sake of simplicity.


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