Making Sense of
the Social World
4th Edition
Chapter 5: Sampling
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Population: The entire set of individuals or other
entities to which study findings are to be generalized.
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Sample: A subset of a population used to study the
population.
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Example: The United States.
Example: 10 states in the US.
Sampling Frame: A list of all elements or other units
containing the elements in a population.
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A list of all countries
Each country is an element on the list of countries in the
population.
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Sampling methods that allow us to know in advance
how likely it is that any element of a population will be
selected for the sample are termed probability
sampling methods.
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Simple Random Sampling
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Simple random sampling identifies cases strictly on the
basis of chance.
Each sampling unit has a known and equal chance of
being included in the sample.
“Coin Flip”
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Example
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Systematic Random Sampling
The first element is selected randomly from a list or from sequential files, and
then every nth element is selected.
•In almost all sampling situations, systematic random sampling
yields what is essentially a simple random sample.
•Beware when the sequence of elements is affected by
periodicity—that is, the sequence varies in some regular,
periodic pattern.
•Example: 100 people in the population. We need 20 people in
our sample. So sample every 100 / 20 = 5th person on a list.
• 5 is called the “skip interval” or “sampling interval”
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If the sampling interval is 8 for a study in this neighborhood,
every element of the sample will be a house on the northwest
corner—and thus the sample will be biased.
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Cluster Sampling
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Cluster sampling is useful when a sampling frame—a definite
list—of elements is not available, as often is the case for large
populations spread out across a wide geographic area or
among many different organizations.
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A cluster is a naturally occurring, mixed aggregate of
elements of the population, with each element (person, for
instance) appearing in one and only one cluster.
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Schools could serve as clusters for sampling students,
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City blocks could serve as clusters for sampling residents
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Counties could serve as clusters for sampling the general population
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Restaurants could serve as clusters for sampling waiters.
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Cluster Sampling
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Cluster sampling is at least a two-stage procedure.
First, the researcher draws a random sample of clusters.
Next, the researcher draws a census or random sample
of elements within each selected cluster.
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Because only a fraction of the total clusters are involved,
obtaining the sampling frame at this stage should be much
easier.
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Cluster Sampling
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Stratified Random Sample
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Stratified random sampling ensures that various
differing groups will be included in the sample.
First, all elements in the population (that is, in the
sampling frame) are distinguished according to their
value on some relevant characteristic (age, rank,
ethnicity). That characteristic forms the sampling strata.
Next, elements are sampled randomly from within these
strata
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Each element must belong to one and only one stratum.
Example: Freshman, Sophomores, Juniors, Seniors
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Proportionate Stratified Sampling
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Imagine that you plan to draw a sample of 500 from an ethnically
diverse neighborhood.
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The neighborhood population is 15% black, 20% Hispanic, 35%
Asian, and 30% white.
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If you drew a simple random sample, you might end up with
somewhat different percents of each group in your sample.
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But if you created sampling strata based on ethnicity proportions in
the population, you would randomly select cases from each stratum
in exactly the same proportions as in the neighborhood population.
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This is termed proportionate stratified sampling and it eliminates
any possibility of sampling error in the sample’s distribution of
ethnicity.
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Disproportionate Stratified Sampling
In disproportionate stratified sampling, the proportion of each stratum that is
included in the sample is intentionally varied from what it is in the population. In the
case of the sample stratified by ethnicity, you might select equal numbers of cases
from each racial or ethnic group:
•125 blacks (25% of the sample)
•125 Hispanics (25%)
•125 Asians (25%)
•125 whites (25%)
In this type of sample, the probability of selection of every case is known but
unequal between strata.
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Remember that…
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One of the main determinants of sample quality is sample
size.
Samples will be more representative of the population if
they are relatively large and selected through probability
sampling methods.
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Sometimes, a probability sample is not feasible or
generalizability is not desired.
•Nonprobability sampling methods are often used in qualitative
research
•They also are used in quantitative studies when researchers are
unable to use probability selection methods.
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Availability Sampling
Elements are selected for availability sampling
because they’re available or easy to find.
Thus this sampling method is also known as a(n)
haphazard, accidental, or convenience sample.
Examples:
•Interviewing people on a
street corner or at the mall
•Surveying students in a
classroom
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•Magazine surveys
•Observing conversations in
an online chat room
Quota Sampling
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Quota sampling is intended to overcome the most obvious flaw of
availability sampling—that the sample will just consist of whoever or
whatever is available, without any concern for its similarity to the
population of interest.
The distinguishing feature of a quota sample is that quotas are set to
ensure that the sample represents certain characteristics in proportion
to their prevalence in the population.
Similar to Proportionate or Disproportionate Stratified Sampling
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Quota Sampling, Continued
The problem is that even when we know that a quota sample is representative of
the particular characteristics for which quotas have been set, we have no way of
knowing if the sample is representative in terms of any other characteristics.
Here quotas have been set for gender only.
Under the circumstances, it’s no surprise that
the sample is representative of the population
only in terms of gender, not in terms of
ethnicity.
Interviewers are only human; they may avoid
potential respondents with menacing dogs in
the front yard, or they could seek out
respondents who are physically attractive or
who look like they’d be easy to interview.
That’s why quotas may be needed!!
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Purposive Sampling
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In purposive sampling, each sample element is
selected for a certain purpose.
Purposive sampling may involve studying the entire
population of some limited group (directors of shelters for
homeless adults) or a subset of a population (mid-level
managers with a reputation for efficiency).
Or a purposive sample may be a “key informant survey,”
which targets individuals who are particularly
knowledgeable about the issues under investigation (i.e.
“experts”).
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Snowball Sampling
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Snowball sampling is useful for hard-to-reach or hardto-identify populations for which there is no sampling
frame, but the members of which are somewhat
interconnected (or at least some members of the
population know each other).
It can be used to sample members of such groups as
drug dealers, prostitutes, practicing criminals, participants
in Alcoholics Anonymous groups, gang leaders, informal
organizational leaders, and homeless persons.
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More on Snowball Sampling
More systematic
versions of snowball
sampling can reduce
the potential for bias.
For example,
“respondent-driven
sampling” gives
financial incentives to
respondents to recruit
peers (Heckathorn,
1997).
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Conclusion
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Ultimately, one of the determinants of sample quality is
sample size.
Samples will be more representative of the population if
they are relatively large and selected through probability
sampling methods, but non-probability methods are also
an option and are frequently used.

Must disclose procedure used in your research report.
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