Download Babbie ch 5

Text Notes – Babbie ch 5
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The Logic of Probability Sampling
Homogeneity and Heterogeneity
1) Homogeneous population: all members of the population are identical on a given characteristic.
From such a population, a sample size of 1 would be representative of the population parameter for
the characteristic of interest. (e.g., the physical sciences)
2) Heterogeneous population: members of the population are varied on the variable of interest (e.g.,
household income). In order for a sample to be representative of population, controlled sampling
must be undertaken.
Sources of Bias:
1) Conscious Bias: when a researcher knowingly over- or under-samples certain members of the
population because of some characteristic. Egregious, intentional error. Ruins the representativeness
of the sample.
2) Unconscious Bias: when a researcher unknowingly over- or under-samples certain members of the
population. For example, a researcher may be less likely to sample houses with “beware of dog”
signs, even if he or she doesn’t consciously realize it. Also a grave threat to sample
representativeness.
Representativeness and Probability of Selection:
1) “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” (p. 71).
2) Representativeness: an adequate sample is one that is representative (i.e., an accurate portrayal) of
the population on the characteristic of interest to the researcher.
3) Probability of Selection: the probability that a certain element within a population has of being
selected for the sample.
Sampling Concepts and Terminology
The terms discussed herein (I will only define a few): element, universe, population, survey
population, sampling unit, sampling frame, observation unit, variable, parameter, statistic, sampling
error, confidence levels and intervals.
Element: the object/unit from which information is gathered; is the basis for analysis
Population: theoretically specified aggregation of survey elements
Survey population: aggregation of elements from which the survey sample is actually selected (i.e.,
often, the total from which survey elements are selected is not a completely congruent to the
theoretical population; therefore, there is often a discrepancy between the theoretical population and
the actual (or survey) population).
Sampling Unit: the element or set of elements that are considered for sampling at any stage in the
sampling procedure. For example, in clustered sampling, the clusters themselves are sampling units
as are the elements selected from within those clusters.
Sampling Frame: The actual list of sampling units from which the sample, or some stage of the
sample, is selected. For example, in a single-stage design where the population is University of
Baltimore students, the sampling frame would be the student roster at UB.
Populations and Sampling Frames
“Properly drawn samples will provide information appropriate for describing the population of
elements that comprise the sampling frame—nothing more” (p. 81).
1) That is, if elements from a population are missing from the sampling frame, the sample does not
describe those missing elements and is only accurate in describing the survey population provided by
the sampling frame.
Sampling Designs
Simple Random Sampling:
1) Obtain sampling frame
2) Assign number to each element of sampling frame
3) Randomly select numbers from the sampling frame
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Systematic Sampling:
1) Selecting every kth element from a total list (e.g., selecting every 10th element).
2) Periodicity: a possible problem with systematic sampling. If the list is arranged in such a way that
selecting every kth element selects only particular types of elements, then the sample will be biased.
For example, selecting every 5th person from a company roster which lists teams (5-member groups)
with the leader last.
Stratified Sampling:
1) Goal: to reduce sampling error
2) Draw a certain number of elements from homogenous subsets of the population (e.g., selecting equal
numbers of freshmen, sophomores, juniors, and seniors).
3) Variables that define subsets are those that are presumably related to the variable of interest.
4) Often select the number of elements from a subset in proportion to the number of people comprising
the population. For example, if the college is 75% female, then, you’d want 75% of your total
sample to be female, so if sampling 100, then, select 75 females from the female subset.
5) Using Systematic Sampling:
(a) If list is ordered according to subsets (e.g., ordered by year of school), then using systematic
sampling will yield a stratified sample (simple random sampling will not).
Multistage Cluster Sampling:
1) Cluster: a group of elements (e.g., church, school, business—all are groups of elements, i.e.,
individuals).
2) Process:
(a) List clusters, sample clusters
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For example, list all 4-year undergraduate universities in the U.S.
(ii)
Randomly sample this list.
(b) List elements within sampled clusters, sample elements
(i)
From randomly selected universities, obtain student rosters
(ii)
Sample from student rosters
3) Practical Considerations:
(a) Maximize number of clusters to the point of inefficiency
(b) Minimize number of elements selected from within each cluster.
Probability Proportionate to Size (PPS)
1) When clusters are grossly different sizes, may have to give larger clusters greater probability of
being selected (because there are fewer of them, even though they contain more elements than
smaller-sized clusters).
Disproportionate Sampling and Weighting
In order to take into account sampling that is unrepresentative of the population proportion (e.g., ¾
of your sample is females when only ½ of the population is females), you can weight the weight the
male cases (¼ of the sample) to count as three cases (making them ¾ of the original sample),
therefore having theoretically 6/4 of your original sample after weighting.
Non-Probability Sampling
Purposive or Judgmental Sampling: selecting a sample on the basis of one’s knowledge of the
population.
Quota Sampling: selecting sample elements based on element characteristics to reflect the
characteristics present in the population.