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```Sampling Distribution
Methods & Statistics
Utilizing B. Trochim’s Center for Social Research Methods, 1998
Probability Sampling
Key Terms
• Theoretical Population: group you are
interested in generalizing to
• Accessible Population: population that will
be accessible to you
• Sampling Frame: listing of the accessible
population from which you'll draw your
sample
Key Terms
• Sample: the group of people who you select
• Random Selection: how you draw the
sample of people for your study from a
population
• Random Assignment: how you assign the
sample that you draw to different groups or
Sampling Populations
More Key Terms
• Response: is a specific measurement value
that a sampling unit supplies
• Statistic: Mean, Median, Mode, etc. (applies
to samples)
• Parameter: measure the entire population
and calculate a value like a mean or average
constitutes a parameter, we don't refer to
this as a standard deviation
Sampling Terms Illustrated
Sampling Distribution
• Sampling Distribution: The distribution of an
infinite number of samples of the same size as
the sample in a given study
• Standard Error (AKA sampling error): The
standard deviation of the sampling distribution
– A standard deviation is the spread of the scores
around the average in a single sample. The standard
error is the spread of the averages around the
average of averages in a sampling distribution.
Sampling Distribution Illustrated
Sampling Error
• Standard Error: also called Sampling
Error
• The greater your sample size, the smaller
the standard error
• The smaller your sample size, the greater
the standard error
The 65, 95, 99 Percent Rule
Probability Sampling Method
Probability Sampling Method is any
method of sampling that utilizes some form
of random selection
Basic Sampling Terms
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Basic terms:
N = the number of cases in the sampling
frame
n = the number of cases in the sample
NCn = the number of combinations
(subsets) of n from N
f = n/N = the sampling fraction
Random Sampling
Simple Random Sampling: (Simplest Form)
•Objective: To select n units out of N such
that each NCn has an equal chance of being
selected.
•Procedure: Use a table of random
numbers, a computer random number
generator, or a mechanical device to select
the sample.
Random Sampling
Stratified Random Sampling
Stratified Random Sampling: Sometimes
called proportional or quota random
sampling, involves dividing the population
into homogeneous subgroups and then
taking a simple random sample in each
subgroup.
Stratified Random Sampling
• Objective: Divide population into nonoverlapping
groups (i.e., strata)
N1, N2, N3, ... Ni,
such that
N1 + N2 + N3 + ... + Ni = N.
Then do a simple random sample of f = n/N
in each strata.
Stratified Random Sampling
Illustrated
Systematic Random Sampling
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Systematic Random Sampling
number the units in the population from 1 to
N
decide on the n (sample size) that you want
or need
k = N/n = the interval size
randomly select an integer between 1 to k
then take every kth unit
Systematic Random Sampling
Illustrated
Cluster (Area) Random Sampling
Cluster (Area) Random Sampling: Used
for sampling a population that is disbursed
across a wide geographic
Cluster (Area) Random Sampling
• Divide population into clusters (usually
along geographic boundaries)
• Randomly sample clusters
• Measure all units within sampled cluster
Cluster Sampling
Multistage Sampling
• Multi-Stage Sampling: Combining Sample
Methods
Non - Probability Sampling
Designs
Probability Vs. Non - Probability
Sampling
Non - probability Sampling does not
involve random selection and probability
sampling does
Accidental, Haphazard or
Convenience Sampling
The traditional "man on the street" (of
course, now it's probably the "person on the
street") interviews conducted frequently by
television news programs to get a quick
(although non - representative) reading of
public opinion
Purposive Sampling
• Typically have one or more specific
predefined groups we are seeking.
• For instance, sampling people in a mall or
on the street who are carrying a clipboard
and stopping such people and asking if they
would be willing to be interviewed.
Modal Instance Sampling
• Sampling the most frequent case, or the "typical"
case
• In much informal public opinion polls, for
instance, pollsters interview a "typical" voter
• Problems:
– Who is typical?
– Are elements of defining typical inclusive?
Expert Sampling
• Involves the assembling of a sample of
persons with known or demonstrable
experience and expertise in some area
trying to defend your decisions -- you have
some acknowledged experts to back you.
• Disadvantage: Even the experts can be, and
often are, wrong.
Quota Sampling:
Proportional Quota Sampling
Proportional Quota Sampling: Want to
represent the major characteristics of the
population by sampling a proportional
amount of each.
Quota Sampling:
Proportional Quota Sampling
• For instance, if you know the population has 40%
women and 60% men, and that you want a total sample
size of 100, you will continue sampling until you get
those percentages and then you will stop. So, if you've
sixty men, you will continue to sample men but even if
legitimate women respondents come along, you will not
sample them because you have already"met your
quota.”
Quota Sampling:
Non - Proportional Quota Sampling
Specify the minimum number of sampled units
you want in each category. here, you're not
concerned with having numbers that match the
proportions in the population. Instead, you
simply want to have enough to assure that you
will be able to talk about even small groups in
the population
Heterogeneity Sampling
Sample for heterogeneity when we want to
include all opinions or views, and we aren't
proportionately. Another term for this is
sampling for diversity.
Snowball Sampling
Begin by identifying someone who
meets the criteria for inclusion in your