The
Practice of Social Research
Chapter 7
Chapter Outline
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A Brief History of Sampling
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Nonprobability Sampling
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The Theory and Logic of Probability Sampling
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Populations and Sampling Frames
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Types of Sampling Designs
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Multistage Cluster Sampling
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Probability Sampling in Review
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The Ethics of Sampling
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Quick Quiz
A
Brief History of Sampling
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“President” Alf Landon
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Literary Digest poll, 1936
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Two million ballots mailed to people listed in telephone
directory
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Problems?
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“President” Thomas E. Dewey
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Gallup’s quota sampling
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Problems?
Nonprobability Sampling
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Nonprobability Sampling – any technique in which samples are
selected in some way not suggested by probability theory.
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Available subjects
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Purposive sampling
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Snowball sampling
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Quota sampling
Nonprobability Sampling
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Reliance on Available Subjects
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Convenience sampling
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Does not allow for control over representativeness.
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Only justified if less risky methods are unavailable.
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Researchers must be very cautious about generalizing when this
method is used.
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When might this method be appropriate?
Nonprobability Sampling
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Purposive or Judgmental Sampling – a type of nonprobability
sampling in which the units to be observed are selected on the basis of the
researcher’s judgment about which ones will be the most useful or
representative.
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Small subsets of a population
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Two-group comparison
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Deviant cases
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When might this method be appropriate?
Nonprobability Sampling
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Snowball Sampling – a nonprobability sampling method whereby each
person interviewed may be asked to suggest additional people for interviewing.
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Often used in field research, special populations
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When might this method be appropriate?
Nonprobability Sampling
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Quota Sampling – a type of nonprobability sampling in which units
are selected into a sample on the basis of pre-specified characteristics, so
that the total sample will have the same distribution of characteristics assumed
to exist in the population being studied.
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Similar to probability sampling, but has problems: quota frame
must be accurate, selection of sample elements may be biased
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When might this method be appropriate?
Nonprobability Sampling
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Selecting Informants
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Informant – someone who is well versed in the social phenomenon
that you wish to study and who is willing to tell you what s/he knows about it.
The
Theory and Logic of Probability Sampling
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Probability Sampling – the general term for samples selected in
accord with probability theory.
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Often used for large-scale surveys.
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If all members of a population were identical in all respects
there would be no need for careful sampling procedures. However, this is rarely
the same.
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A sample of individuals from a population must contain the same
variations that exist in the population.
Perfect Probability Sample
Less-Than-Perfect Probability Sample
The
Theory and Logic of Probability Sampling
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Conscious and Unconscious Sampling Bias
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Bias – those selected are not typical nor representative of the
larger population.
The
Theory and Logic of Probability Sampling
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Representativeness and Probability of Selection
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Representativeness – the quality of a sample of having the same
distribution of characteristics as the population from which it was selected.
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Samples need not be representative in all respects, only those
relevant to the research.
The
Theory and Logic of Probability Sampling
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A sample will be representative of the population from which it
is selected if all members of the population have an equal chance of being
selected in the sample.
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EPSEM (Equal Probability of Selection Method)
The
Theory and Logic of Probability Sampling
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Advantages of Probability Sampling
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Probability samples are typically more representative than other types of
samples because biases are avoided.
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Probability theory permits researchers to estimate the accuracy or
representativeness of the sample.
The
Theory and Logic of Probability Sampling
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Element – that unit of which a population is composed and which
is selected in a sample.
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Population – the theoretically specified aggregation of the
elements in a study.
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Study Population – a sampling method in which each element has an
equal chance of selection independent of any other event in the selection
process.
The
Theory and Logic of Probability Sampling
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Random Selection – each element has an equal chance of selection
independent of any other event in the selection process.
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Sampling Unit – that element or set of elements considered for
selection in some stage of sampling.
T
he
Theory and Logic of Probability Sampling
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Probability Theory, Sampling Distributions, and Estimates of
Sampling Error
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Parameter – a summary description of a given variable in a
population.
The
Theory and Logic of Probability Sampling
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The Sampling Distribution of Ten Cases
The
Theory and Logic of Probability Sampling
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Sample Size = 1
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Number of Samples = 10
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True Mean = $4.50
The
Theory and Logic of Probability Sampling
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Sample Size = 2
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Number of Samples = 45
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True Mean = $4.50
The
Theory and Logic of Probability Sampling
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Statistic – the summary description of a variable in a sample,
used to estimate a population parameter.
The
Theory and Logic of Probability Sampling
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Sampling Error – the degree of error to be expected of a given
sample design.
The
Theory and Logic of Probability Sampling
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Confidence Levels and Confidence Intervals
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Confidence Level – the estimated probability that a population
parameter lies within a given confidence interval.
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Confidence Interval – the range of values within which a
population parameter is estimated to lie.
Populations and Sampling Frames
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Sampling Frame – a list of units that compose a population from
which a sample is selected.
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If the sample is to be representative of the population, it is
essential that the sampling frame include all members of the population.
Populations and Sampling Frames
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Guidelines for Populations and Sampling Frames
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Findings based on a sample represent only the aggregation of
elements that compose the sampling frame.
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Sampling frames do not include all the elements their names might
imply. Omissions are inevitable.
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To be generalized, all elements must have equal representation in
the frame.
Types
of Sampling Designs
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Simple Random Sampling
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Systematic Sampling
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Stratified Sampling
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Implicit Stratification in Systematic Sampling
Types
of Sampling Designs
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Simple Random Sampling – a type of probability sampling in which
the units composing a population are assigned numbers. A set of random numbers
is generated and the units having those numbers are included in the sample.
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Not necessarily the most accurate sampling method.
Types
of Sampling Designs
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Systematic Sampling – a type of probability sampling in which
every kth unit in a list is selected for inclusion in the
sample.
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Slightly more accurate than simple random sampling.
Types
of Sampling Designs
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Sampling Interval – the standard distance between elements
selected from a population in the sample.
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Sampling Ratio – the proportion of elements in the population
that are selected to be in a sample.
Types
of Sampling Designs
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Stratified Sampling
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Stratification – the grouping of units composing a population
into homogenous groups (strata) before sampling.
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Slightly more accurate than simple random sampling.
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Stratification is a modification to simple random and systematic
sample methods.
Types
of Sampling Designs
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Implicit Stratification in Systematic Sampling
Types
of Sampling Designs
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Illustration: Sampling University Students
Multistage Cluster Sampling
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Cluster Sampling – a multistage sampling in which natural groups
are sampled initially with the members of each selected group being sub-sampled
afterward.
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Used when it is not practical or possible to create a list of all
elements that compose the target population.
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Highly efficient, but less accurate.
Multistage Cluster Sampling
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Stratification in Multistage Cluster Sampling
Multistage Cluster Sampling
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Probability Proportionate to Size (PPS) Sampling – a type of
multistage cluster sample in which clusters are selected not with equal
probabilities but with probabilities proportionate to their sizes—as measured by
the number of units to be sub-sampled.
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A more sophisticated form of cluster sampling.
Multistage Cluster Sampling
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Disproportionate Sampling and Weighting
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Weighting – assigning different weights to cases that were
selected into a sample with different probabilities of selection.