Qualitative and Quantitative
Sampling
Types of Nonprobability
Sampling
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Nonprobability sampling
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Typically used by qualitative researchers
Rarely determine sample size in advance
Limited knowledge about larger group or population
Types
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Haphazard
Quota
Purposive
Snowball
Deviant Case
Sequential
Populations and Samples
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A population is any well-defined set of units of
analysis.
The population is determined largely by the
research question; the population should be
consistent through all parts of a research project.
A sample is a subset of a population.
Samples are drawn through a systematic
procedure called a sampling method.
Sample statistics measure characteristics of the
sample to estimate the value of population
parameters that describe the characteristics of a
population.
Populations and Samples
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A population would be the first choice
for analysis.
Resources and feasibility usually
preclude analysis of population data.
Most research uses samples.
Haphazard Sampling
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Cheap and quick
Can produce ineffective, highly
unrepresentative samples
NOT recommended
Person-on-the-street interviews
Clip out survey from a newspaper and
mail it in
Quota Sampling
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First you identify relevant categories of
people
Then you figure out how many to
sample from each category
Ensures that some differences are in
the sample
Still haphazard sampling within the
category, however
Purposive Sampling
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Expert uses judgment in selecting
cases with a specific purpose in mind
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Especially informative cases
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Difficult-to-reach, specialized population
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Cultural themed magazines
Prostitutes
Particular types of cases
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Gamson study in the book
Snowball Sampling
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Identifying and sampling the cases in a
network
I find a prostitute to talk to, then ask
her for some more prostitutes I could
talk to, and it goes on and on and on
Deviant Case Sampling
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Seeks cases that differ from the
dominant pattern or that differ from the
predominant characteristics of other
cases
Selected because they are unusual
High school dropouts example
Sequential Sampling
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Researcher uses purposive sampling
until the amount of new information or
diversity of cases is filled
Gather info until the marginal utility of
new information levels off
Probability Sampling
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Saves time and cost
Accuracy
Sampling element: unit of analysis or
case in a population
Population is all of the possible
elements, specified for unit,
geographical location, and temporal
boundaries
Probability Sampling
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Sampling frame is specific list that
closely approximates all of the
elements in a population
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Can be extremely difficult because there
just aren’t good lists for some things
Frames are almost always inaccurate
Parameter v. Statistic
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Parameter: characteristic of an entire
population
Statistic: estimates of population
parameters based on sample
Literary Digest Poll Mishap
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Sampling frame was automobile
registrations and telephone directories
Accurate predictions in 1920, 24, 28,
and 32
Send postcard and respondents send
back
In 1936, sampled 10 million and
predicted massive victory for Landon
over FDR
Literary Digest Poll Mishap
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VERY, VERY wrong
Frame did NOT represent the target
population (all voters)
Excluded as much as 65% of voters,
including most of FDR’s supporters
during the Depression
Why Random Sampling?
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Each element has an equal probability
of selection
Can statistically calculate the
relationship between sample and the
population—sampling error
Types:
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Simple Random
Systematic
Stratified
Cluster
Simple Random Sample
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Number all of the elements in a
sampling frame and use a list of
random numbers to select elements
(or pull from a hat etc.)
Pulling marbles out of a jar
Random chance can make it so we’re
off on the actual population, but over
repeated independent samples, the
true number will emerge
Simple Random Sample
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We will end up with a normal bell curve
the more we sample
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Random sampling does NOT mean that every
random sample will perfectly represent the
population
Confidence intervals are ranges around a specific
point used to estimate a parameter
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I am 95% certain that the population parameter lies
between 2,450 and 2,550 red marbles in the jar
Systematic Sampling
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Simple random sampling with a
shortcut for selection
Number each element in the sampling
frame
Calculate a sampling interval—tells
researcher how to select elements by
skip pattern
Systematic Sampling
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I want to sample 500 names from a list
of 1000
Sampling interval is 2
I select a random starting point and
choose every other name to give me
500
Big problem when elements in a
sample are organized in some kind of
cycle or pattern
Stratified Sampling
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First divide the population into
subpopulations on basis of
supplemental info and then do a
random sample from each
subpopulation
Guarantees representation
This can allow for oversampling as
well for specific research purposes
Cluster Sampling
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Useful when there is no good sampling
frame available
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All high school basketball players, for
example
First you random sample clusters of
information then draw a random
sample of elements from within the
clusters you selected
Cluster Sampling
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Example
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Want to sample individuals from
Cleveland
Randomly select city blocks, then
households within blocks, then
individuals within households
Less expensive, but also less precise
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Error shows up in each sample drawn
How Large Should a Sample
Be?
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It depends
Smaller the population, the bigger your
sampling ratio will need to be to be
accurate
< 1,000 = 30%
10,000 = 10%
> 150,000 = 1%
> 10,000,000 = .025%
How Large Should a Sample
Be?
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For small samples, small increases in
sample size produce big gains in
accuracy
Decision about best sample size
depends on:
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Degree of accuracy required
Degree of variability in population
Number of variables measured
simultaneously
Inference
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The goal of statistical inference is to
make supportable conclusions about
the unknown characteristics, or
parameters, of a population based on
the known characteristics of a sample
measured through sample statistics.
Any difference between the value of a
population parameter and a sample
statistic is bias and can be attributed to
sampling error.
Inference
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On average, a sample statistic will equal the
value of the population parameter.
Any single sample statistic, however, may
not equal the value of the population
parameter.
Consider the sampling distribution: When
the means from an infinite number of
samples drawn from a population are
plotted on a frequency distribution, the
mean of the distribution of means will equal
the population parameter.
Inference
Inference
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By calculating the standard error of the
estimator (or sample statistic), which
indicates the amount of numerical
variation in the sample estimate, we
can estimate confidence.
More variation means less confidence
in the estimate.
Less variation means more
confidence.
Inference
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One way to increase confidence in an
estimate is to collect a larger, rather
than a smaller, sample.
Measures of variability get smaller with
larger samples:
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But the value of a larger sample may be
offset by the increased cost; this is yet
another tradeoff in research design.
To reduce sampling error by half, a
sample must quadruple in size.