SAMPLING AND COLLECTING QUANTITATIVE AND QUALITATIVE DATA It is often not possible or practical to study an entire population, so researchers draw samples 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-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

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.

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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

samples.

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

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.

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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

interest.

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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

questions

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

cases/units

To address specific purposes

related to research questions

The researcher selects cases she

or he can learn the most from

Representativeness

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

research.4

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

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