*EBL 101*

**Research Methods: Sampling **

Virginia
Wilson

Librarian,
Murray Library

University
of Saskatchewan

Saskatoon,
Saskatchewan, Canada

Email:
virginia.wilson@usask.ca

**Received:** 16
June 2014 **Accepted:** 19 June 2014

2014 Wilson. This is an Open Access article
distributed under the terms of the Creative Commons-Attribution-Noncommercial-Share
Alike License 2.5 Canada (http://creativecommons.org/licenses/by-nc-sa/2.5/ca/), which permits unrestricted use,
distribution, and reproduction in any medium, provided the original work is
properly attributed, not used for commercial purposes, and, if transformed, the
resulting work is redistributed under the same or similar license to this one.

Research
often involves choosing a certain number of people or items in order to answer
a question. A frequent question is how does one choose a sample? Another
question is how many individuals or items is enough? What sample size will give
the best results for the question at hand? Well, the answer to the latter is:
“it depends”. It depends on the question, on the method one uses to administer
the research project, on what kind of results one is hoping for.

Sampling
is a consideration in both qualitative and quantitative research. Survey
methodology, interviews, focus groups, bibliometrics, content analysis,
usability testing, etc., all rely on an appropriate number of people or items
being selected and examined. For the purposes of this brief column, I’ll look
at sampling as it pertains to survey methodology, as much of this information
can be applied to other types of research methodologies. A valid sample must be
considered in order to obtain generalizability in quantitative research and
trustworthiness in qualitative research.

There
are various types of sampling methods, including nonprobability sampling and
probability sampling. Below is a very brief examination of the methods under
each, adapted from Basic research methods for librarians, 5th ed. (Connaway
& Powel, 2010). Sampling is a complex exercise, depending on the type. As
usual, the brevity of this column necessitates only the briefest overview of
the topics.

Nonprobability
sampling: the researcher cannot be sure of a “specific element of the
population [i.e. the particular grouping that is being looked at] being
included in the sample” (p. 117). The weakness of a nonprobability sample is
that it “does not permit generalizing from the sample to the population because
the researcher has no reassurance that the sample is representative of the
population” (p. 117). Still, these types of samples are easier and often
cheaper to obtain than the alternative (which we will get to later), and they
can be adequate depending upon the research question.

There
are various types of nonprobability samples:

• Accidental (or convenience) sampling:
selecting the cases at hand until the desired number of people/items is
reached.

• Quota sample: the same as accidental
sampling, except that “it takes steps to ensure that the significant, diverse
elements of the population are included” (p. 118).

• Snowball sample: a cumulative sample is
generated by starting with a few people and asking them to recommend more
people.

• Purposive sample: based on the researcher’s
“knowledge of the population and the objectives of the research” (p. 119).

• Self-selected sample: people self-identify
with the desired population criteria and select themselves to participate in
the study.

Probability
sampling: this type of sampling comes closer to the objective of sampling, that
is, “to select elements that accurately represent the total population from
which the elements were drawn” (p. 119). The critical piece in probability
sampling is that “every element in the population has a known probability of
being included in the sample” (pp. 119-120).

There
are various types of probability samples:

• Simple random sampling: this is the basic
sampling method in survey research and it “gives each element in the population
an equal chance of being included in the sample” (p. 120). The simple random
sample is generated most often by using a table of random numbers. There are
variations of the random sample, differentiated by the way the samples are
generated.

Ø systematic
sample: involves “taking the every nth element from a list until the total list
has been sampled” (p. 123).

Ø stratified
random sample: the population elements
are divided into categories, then independent random samples are selected from
each category.

Ø cluster
sample: the population (not the population’s elements as in stratified random
sampling) are divided up into clusters and samples are drawn from the clusters.
This is particularly helpful when a population cannot be easily listed for
sampling purposes.

This
has been a whirlwind trip through types of sampling, as the other main point I
would like to address is the “how many” question. How many people/items are
enough to be representative of any given population? The rule of thumb for
sample sizes is the larger the better. However, time, funding, and a host of
other issues also have a role to play in determining how big to go. Connaway
and Powell state that there are four criteria that you can think about to help
determine the necessary sample size:

1. The degree of precision required (the less
accuracy needed, the smaller the sample you can get away with)

2. The variability of the population (the
greater the variability, the larger the sample size)

3. The method of sampling (i.e. “stratified
sampling requires fewer cases to achieve a specified degree of accuracy” (p.
129).

4. How the results are to be analyzed (small
samples have limitations in terms of the types of statistical analyses that can
be used)

There
are formulas that can be used to determine the ideal number. Luckily, for the
mathematically challenged (like me) there are tables and calculators that
researchers can use that already have the formulas applied:

·
Table: http://www.research-advisors.com/tools/SampleSize.htm

·
A sample size calculator: http://www.raosoft.com/samplesize.html

·
Calculator: http://www.surveysystem.com/sscalc.htm

·
Simple random sample calculator: http://www.nss.gov.au/nss/home.nsf/pages/Sample+size+calculator

Other
resources related to sampling:

Beck, S.E.
& Manuel, K. (2008). Practical research methods for librarians and
information professionals. New York: Neal-Schuman. (includes sampling across a
variety of research methods).

Bouma, G. D.,
Ling, R., & Wilkinson, L. (2009). The research process, Canadian edition.
Don Mills, ON: Oxford University Press. (includes a chapter on selecting a
sample and a table of random numbers).

Cochran, W.G.
(1977). Sampling techniques. 3rd ed. New York: Wiley.

Kish, L.
(1995). Survey sampling. New York: Wiley.

**References**

Connaway, L.S.
& Powell, R.R. (2010). Basic research methods for librarians, 5th ed. Santa
Barbara, CA: Libraries Unlimited.

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