Sampling Methods1
It is incumbent on the researcher to clearly define the target population. There are no strict rules to follow,
and the researcher must rely on logic and judgment. The population is defined in keeping with the objectives
of the study.
Sometimes, the entire population will be sufficiently small, and the researcher can include the entire
population in the study. This type of research is called a census study because data is gathered on every
member of the population.
Usually, the population is too large for the researcher to attempt to survey all of its members. A small, but
carefully chosen sample can be used to represent the population. The sample reflects the characteristics of the
population from which it is drawn.
Sampling methods are classified as either probability or nonprobability. In probability samples, each member
of the population has a known non-zero probability of being selected. Probability methods include random
sampling, systematic sampling, and stratified sampling. In nonprobability or purposive sampling, members are
selected from the population in some nonrandom manner. These include convenience sampling, judgment
sampling, quota sampling, and snowball sampling. The advantage of probability sampling is that sampling error
can be calculated. Sampling error is the degree to which a sample might differ from the population. When
inferring to the population, results are reported plus or minus the sampling error. In nonprobability or
purposive sampling, the degree to which the sample differs from the population remains unknown.
PROBABILITY SAMPLING
The simplest form of random sampling is called simple random sampling. In this we select participants from a
given population such that each person in the population has an equal chance of being selected. If number of
participants is large (1000) true random selection will produce a participant group with very similar
demographic features to the total population from which it is selected. A randomly selected sample of 1000
will have no more than a 5% margin of error when used to predict categorical characteristics (35% Labour, 45%
National. 10% Greens, 10% don’t know) of a population.
Systematic sampling is often used instead of random sampling. It is also called an Nth name selection
technique. After the required sample size has been calculated, every Nth record is selected from a list of
population members. As long as the list does not contain any hidden order, this sampling method is as good as
the random sampling method. Its only advantage over the random sampling technique is simplicity. Systematic
sampling is frequently used to select a specified number of records from a computer file.
Stratified Random Sampling involves dividing your population into homogeneous subgroups based on one
factor and then taking a simple random sample in each subgroup. So, for example, you might want to compare
the perceptions of Maori and Samoans using a questionnaire. You would then randomly select participants
with each group. Or your subgroups might be based on culture (two), gender and age (two groups) which
would give 8 (2x2x2) equal subgroups within which you randomly select participants.
Quota Sample In this case also, the entire population is first divided into homogeneous sub-groups with
respect to the given characteristic such as culture. Then, you recruit a specified number of people from each
strata as you come across them rather than selecting them through random procedure. The resulting samples
are called quota samples.
PURPOSIVE SAMPLING 2
Purposive sampling starts with a purpose in mind and the sample is thus selected to include people of interest
and exclude those who do not suit the purpose. Subjects are selected because of some characteristic. Patton
1
Retrieved 29/04/2009 from http://www.statpac.com/surveys/sampling.htm
Patton, M. Q. (1990). Qualitative evaluation and research methods (2nd ed.). Newbury Park, CA: Sage
Publications
2
(1990) has proposed the following cases of purposive sampling. Purposive sampling is popular in qualitative
research.
Type
Random
Sampling
Definition
A systematic process of
selecting subjects or units
for examination and
analysis that does not take
contextual or local
features into account.
Convenience
Sampling
A process of selecting
subjects or units for
examination and analysis
that is based on
accessibility, ease, speed,
and low cost. Units are
not purposefully or
strategically selected.
Purposefully picking a
wide range of variation on
dimensions of interest documents unique or
diverse variations that
have emerged in adapting
to different conditions.
Identifies important
common patterns that cut
across variations.
The process of selecting a
small homogeneous group
of subjects or units for
examination and analysis.
The process of selecting a
small number of important
cases - cases that are likely
to "yield the most
information and have the
greatest impact on the
development of
knowledge" (Patton, 2001,
p. 236).
The iterative process of
selecting "incidents, slices
of life, time periods, or
people on the basis of
their potential
manifestation or
representation of
important theoretical
constructs"
Maximum
Variation
Sampling
Homogenous
Sampling
Critical Case
Sampling
Theory-based
or Theoretical
Sampling
When is it used?
Random sampling is typically used in experimental and quasiexperimental designs.
Random sampling typically involves the generation of large
samples.
Random sampling is used when researchers want their findings
to be representative of some larger population to which
findings can be generalized
This is the least desirable sampling method, and researchers
should typically avoid using it.
There is no randomness and the likelihood of bias is high. You
can't draw any meaningful conclusions from the results you
obtain. Saves time, money, and effort. Poorest rational; lowest
credibility. Yields information-poor cases
Often, researchers want to understand how a phenomenon is
seen and understood among different people, in different
settings and at different times.
When using a maximum variation sampling method the
researcher selects a small number of units or cases that
maximize the diversity relevant to the research question
Homogeneous sampling is used when the goal of the research is
to understand and describe a particular group in depth
This is a good method to use when funds are limited. Although
sampling for one or more critical cases may not yield findings
that are broadly generalisible they may allow researchers to
develop logical generalizations from the rich evidence produced
when studying a few cases in depth.
Permits logical generalization and maximum application of
information to other similar cases because if it's true of this
once case it's likely to be true of all other cases
This method is best used when the research focuses on theory
and concept development and the research team's goal is to
develop theory and concepts that are connect to, grounded in
or emergent from real life events and circumstances.
Theoretical sampling is an important component in the
development of grounded theories.
This sampling approach has the goal of developing a rich
understanding of the dimensions of a concept across a range of
settings and conditions.
Type
Confirming
and
disconfirming
cases
Extreme or
deviant cases
Typical cases
Intensity
sampling
Politically
important
cases
Purposeful
Random
Sampling
Definition
Identification of
confirming and
disconfirming case occurs
after some portion of data
collection and analysis has
already been completed
The process of selecting or
searching for highly
unusual cases of the
phenomenon of interest
or cases that are
considered outliers, or
those cases that, on the
surface, appear to be the
'exception to the rule' that
is emerging from the
analysis. E.g. such as
outstanding
success/notable failures,
top of the class/dropouts,
exotic events, crises.
The process of selecting or
searching for cases that
are not in anyway atypical,
extreme, deviant or
unusual.
The process of selecting or
searching for rich or
excellent examples of the
phenomenon of interest.
These are not, however,
extreme or deviant cases –
e.g. such as good
students/poor students,
above average/below
average.
The process of selecting or
searching for a politically
sensitive site or unit of for
analysis
The process of identifying
a population of interest
and developing a
systematic way of
selecting cases that is not
based on advanced
knowledge of how the
outcomes would appear.
The purpose is to increase
credibility not to foster
representativeness
When is it used?
Identifying confirming and disconfirming cases is a sampling
strategy that occurs within the context of and in conjunction
with other sampling strategies.
Researchers seek out confirming and disconfirming cases (that
do not fit emergent patterns and allow the research team to
evaluate rival explanations) in order to develop a richer, more in
depth understanding of a phenomenon and to lend credibility
to one's research account.
Identifying extreme or deviant cases is a sampling strategy that
occurs within the context of and in conjunction with other
sampling strategies.
The process of identifying extreme or deviant cases occurs after
some portion of data collection and analysis has been
completed.
Researchers seek out extreme or deviant cases in order to
develop a richer, more in-depth understanding of a
phenomenon and to lend credibility to one's research account.
Identifying typical cases can help a researcher identify and
understand the key aspects of a phenomenon as they are
manifest under ordinary circumstances.
Providing a case summary of a typical case can be helpful to
those not famililar with a culture or social setting.
Intensity sampling can allow the researcher to select a small
number of rich cases that provide in depth information and
knowledge of a phenomenon of interest. Intensity sampling
requires prior information and exploratory work to be able to
identify intense examples.
One might use intensity sampling in conjunction with other
sampling methods. For example, one may collect 50 cases and
then select a subset of intense cases for more in depth analysis.
Attracts attention to the study (or avoids attracting undesired
attention by purposefully eliminating from the sample politically
sensitive cases).
The use of a randomized sampling strategy, even when
identifying a small sample, can increase credibility.
It adds credibility to sample when potential purposeful sample
is larger than one can handle. Reduces judgment within a
purposeful category. (Not for generalizations or
representativeness.)
Type
Stratified
Purposeful or
Quota
Sampling
Criterion
Sampling
Opportunistic
or emergent
sampling
Snowball or
chain
sampling
Definition
Purposeful samples are
stratified or nested by
selecting particular units
or cases that vary
according to a key
dimension. For example,
researching primary care
practices, we stratify this
purposeful sample by
practice size (small,
medium and large) and
practice setting (urban,
suburban and rural).
Criterion sampling
involves selecting cases
that meet some
predetermined criterion of
importance. For example,
we will only interview
students whose Sequals
are less than “3”.
This occurs when the
researcher makes
sampling decisions during
the process of collecting
data. This commonly
occurs in field research.
As the observer gains
more knowledge of a
setting, he or she can
make sampling decisions
that take advantage of
unexpected events as they
unfold.
Snowball or chain
sampling involves using
well informed people to
identify critical cases informants who have a
great deal of information
about a phenomenon.
The researcher follows this
chain of contacts in order
to identify and accumulate
critical cases.
When is it used?
A stratified purposeful sampling approach is often used in
market research. Interviewers are required to find cases with
particular characteristics. They are given quota of particular
types of people to interview and the quota are organised so
that final sample should be representative of population. So,
for example if we want our sample to represent the age of our
population and 20% are between 20 and 30, and sample is to be
50 then 10 of sample (20%) must be in this age group.
Complex quotas can be developed so that several
characteristics (e.g. age, sex, marital status) are used
simultaneously. When enough information is known to identify
characteristics that may influence how the phenonmenon is
manifest, then it may make sense to use a stratified purposeful
sampling approach
A disadvantage of quota sampling - interviewers choose who
they like (within above criteria) and may therefore select those
who are easiest to interview, so bias can result. Also, it is
impossible to estimate accuracy because this is not a random
sample.
Criterion sampling can be useful for identifying and
understanding cases that are information rich. Criterion
sampling can provide an important qualiative component to
quantitative data. Criterion sampling can be useful for
identifying cases from a standardized questionnaire that might
be useful for follow-up.
A flexible research and sampling design is an important feature
of qualitative research, particularly when the research being
conducted is exploratory in nature.
When little is known about a phenomenon or setting, a priori
sampling decisions can be difficult.
In such circumstances, creating a research design that is flexible
enough to foster reflection, preliminary analysis, and
opportunistic or emergent sampling may be a good idea.
This method can be useful for identifying a small number of key
cases that are identified by a number of key or expert
informants as important cases or exemplars.
Identifies cases of interest from people who know people who
know people who know what cases are information-rich, that is,
good examples for study, good interview subjects.
Quota Sampling for the Research Methods project
Data from the 2006 NZ Census - Waitakere City
Occupation
Managers
Professionals
Technicians and Trades Workers
Community and Personal Service Workers
Clerical and Administrative Workers
Sales Workers
Machinery Operators and Drivers
Labourers
Employed Not Elsewhere Included
Unemployed
Full-time and part-time Tertiary Students
Not in the Labour Force (house persons,
retired, disability, illness)
Work and Labour Force Status Unidentifiable
Age
16-34 years
35-54
55-74
75 and older
Family context
Couple Without Children
Couple With Child(ren)
One Parent With Child(ren)
Not a parent
Total
Culture4
% European
% Maori
% Pacific Peoples
% Asian
% MELAA (3)5
% Other (including New Zealander)
Gender
Men
Women
Qualifications
No qualification
Any qualification up to level 6
Level 7 qualification or higher Degree or
Graduate Diploma
Adult
% of adult
population3 population
participants
rounded
participants
12,645
16,167
13,035
6,798
12,570
9,258
5,622
7,362
4,689
5,349
13,987
27,494
9%
11%
9%
5%
9%
7%
4%
5%
3%
4%
10%
19%
3.29
4.20
3.39
1.77
3.27
2.41
1.46
1.91
1.22
1.39
3.64
7.15
3
4
3
2
3
2
2
2
1
2
4
7
7,311
142,287
5%
100%
1.90
37
2
37
53618
54749
26382
7538
142287
38%
38%
19%
5%
100%
13.94
14.24
6.86
1.96
14
14
7
2
37
31,980
45,990
10,479
53,838
142,287
22%
32%
7%
38%
100%
8.31
11.95
2.72
13.99
8
12
3
14
37
83949
18497
21343
22766
2846
11383
160784
59%
13%
15%
16%
2%
8%
113%
21.83
4.81
5.55
5.92
0.74
2.96
16
5
6
6
1
3
37
68023
74264
142287
48%
52%
100%
17.69
19.31
18
19
37
30088
93737
18462
21%
66%
13%
7.82
24.38
4.80
8
24
5
142287
3
People 16 years and over
People can choose to identify with more than one ethnic group, therefore percentages do not add up to 100.
5
MELAA - Middle Eastern, Latin American and African
4
37