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Chapter 7
The Logic of Sampling
Probability sampling is the primary method for selecting large, representative samples for
social science research.
Two types of Sampling
Nonprobability sample
Probability sample
Nonprobability Sampling
does not represent the population
qualitative based
study specific characteristics
poor reliability
good validity
Purposive or judgmental sampling
select
sample on the basis of your own knowledge of the population and the purpose of
the study
selecting deviant cases for study is another example of purposive sampling
Snowball sampling
form of accidental sampling
convenient sampling
appropriate when the members of a special population are difficult
E.g. : prostitutes, steroid users, rape victims, abusers, homeless
to locate
Snowball sampling
researcher collect data on the few members of the target population they can locate, and
then ask those individuals to provide the information needed to locate other members of
that population who they happen to know
 exploratory purposes
Quota sampling
address the issue of representativeness
begins with a matrix or table describing
the characteristics of the target population ->
you need to know what proportion of the population is for example male and female as
well as what proportions of each gender fall into various age categories, education levels,
ethnic groups, etc.
Quota sampling
then you collect the data from people having all the characteristics of a given cell
then assign weight to all the people in a given cell that is appropriate to their portion of
the total population
Informants
is a member of the group who can talk directly about the group
select informants some what typical of the groups you’re studying
informants will sometimes be marginal or atypical within their group
The Theory and Logic of Probability Sampling
generalize
representative
quantitative
bias
those
selected are not typical or representative of the larger population they have been
chosen from – can be unintended
representativeness and probability of selection
representative
-> sample will be representative of the population from which it is
selected if the aggregate characteristic of the sample closely approximates those same
aggregates characterized in the population
basic principle
all members of the population have an equal chance of being selected in the sample ->
EPSEM (Equal Probability of Selection Method)
2 Advantages
more representative than other types- biases are avoided
estimate the accuracy or representative of samples
based on random selection procedure
Element
unit about which information is collected and that provides the basis of analysis ->
people, certain types of people
element used for sample selection
unit of analysis used for data analysis
Population
theoretically specified aggregation of the elements in a study
Study population
aggregation of elements from which the sample is actually selected
Random selection
purpose
of sampling -> to select a set of elements from a population in such a way that
descriptions of those elements (stats) accurately portray the parameters of the total
population from which the elements are selected
Key: Random Selection
each element has an equal chance of selection independent of any other event in the
selection process
A sampling unit
element or set of elements considered for selection in some stage of sampling
serves as a check on bias
Parameter
probability theory -> basis for estimating the parameters of a population
parameters -> summary description of a given variable in a population
Normal curve
enables us to estimate the sampling error – the degree of error to be
expected for a given sample design -> standard deviation
Confidence level and interval
probability theory
-
statistics fall within a specified interval from the parameter
Sampling frame
the list or quasi list
Ex. : if a sample of
of elements from which a probability sample is selected
students is selected from a student roster, the roster is the frame
Types of Sampling Design
Simple random sampling
Basic sampling method assumed
Use sampling frame
↓
Researcher gives each element a number
↓
A table of random numbers used to select elements (Appendix B)
Systematic sampling
every
kth element in the total list is chosen (systematically) for inclusion in the sample
should select the first element at random
ex. Select a random number between 1 and 10
the element having that number is included in the sample, plus every tenth element
following it
systematic sample with a random start
sampling interval is the standard distance between elements selected in the sample
Stratified sampling
obtains greater degree of representativeness-decreasing the probable sampling error
rather than selecting a sample from the total population at large, the researcher ensures
that appropriate numbers of elements are drawn from homogeneous subsets of that
population
can stratify, for example, by class, gender
Stratified sampling
the ultimate function of stratification is to organize the population into homogeneous
subsets (with heterogeneity between subsets), and to select the appropriate number of
elements from each
choice of stratification variables typically depends on what variables are available –
you should be concerned primarily with those that are related to variables you want to
represent accurately
the
Multistage cluster sampling
cluster
sampling may be used when it’s either impossible or impractical to compile an
exhaustive list of the elements composing the target population
involves repeating two basic steps: listing and sampling
Multistage cluster sampling
the list of primary sampling units (churches, blocks) is compiled and stratified for
sampling
then a sample of those units is selected
the selected primary sampling units are then listed and perhaps stratified
the list of secondary sampling units is then sampled
Multistage cluster sampling
the general guideline for cluster design is to maximize the number of elements within
each cluster, this scientific guideline must be balanced against an administrative
constraint
the efficiency of cluster sampling is based on the ability to minimize the listing of
population elements
by initially selecting clusters, you need only list the elements composing the selected
clusters