Transcript Chapter 7 The Logic Of Sampling

Chapter 7
The Logic Of Sampling
Observation and Sampling
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Polls and other forms of social research
rest on observations.
The task of researchers is to select the
key aspects to observe (sample).
Generalizing from a sample to a larger
population is called probability sampling
and involves random selection.
Types of Sampling
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Purposive or judgmental sampling
• Selecting a sample based on
knowledge of a population, its
elements, and the purpose of the study.
• Used when field researchers are
interested in studying cases that don’t
fit into regular patterns.
Types of Sampling
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Snowball sampling
• Appropriate when members of a
population are difficult to locate.
• Researcher collects data on members
of the target population she can locate,
then asks them to help locate other
members of that population.
Probability Sampling
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Precise statistical descriptions of large
populations.
A sample of individuals from a population
must contain the same variations that
exist in the population.
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Representativeness: Quality of a sample having
the same distribution of characteristics as the
population from which it was selected
EPSEM
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Equal probability of selection method.
A sample design in which each member
of a population has the same chance of
being selected into the sample.
• Is that possible?
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Telephone
Face-to-face
Parameter
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Summary description of a given variable
in a population.
Sampling Error
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The degree of error to be expected of a
given sample design.
Confidence Level
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The estimated probability that a population
parameter lies within a given confidence
interval.
Thus, we might be 95% confident that between
35 and 45% of all voters favor Candidate A.
Confidence interval - The range of values
within which a population parameter is
estimated to lie.
Simple Random Sampling
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Feasible only with the simplest sampling
frame.
Not the most accurate method available.
Systematic Sampling
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Slightly more accurate than simple
random sampling.
Arrangement of elements in the list can
result in a biased sample.
Stratified Sampling
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Rather than selecting sample for
population at large, researcher draws
from homogenous subsets of the
population.
Results in a greater degree of
representativeness by decreasing the
probable sampling error.
Cluster Sampling
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A multistage sampling in which natural
groups are sampled initially with the
members of each selected group being
subsampled afterward.
Weighting
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Giving some cases more weight than others.
Assigning different weights to cases that were
selected into a sample with different
probabilities of selection.
In the simplest scenario, each case is given a
weight equal to the inverse of its probability o
selection.
When all cases have the same chance of
selection, no weighting is necessary.