Ch 11 Sampling
The Nature of Sampling
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Sampling
Population Element
Population
Census
Sampling frame
Why Sample?
Availability of
elements
Greater
speed
Lower cost
Sampling
provides
Greater
accuracy
When Is a Census Appropriate?
Feasible
Necessary
What Is a Valid Sample?
Accurate
Precise
Types of Sampling Designs
Element
Selection
Unrestricted
Probability
Nonprobability
Simple random
Convenience
Restricted
Complex random
Purposive
Systematic
Judgment
Cluster
Quota
Stratified
Double
Snowball
When to Use Larger Sample
Sizes?
Population
variance
Number of
subgroups
Confidence
level
Desired
precision
Small error
range
What is Sample
• Sampling
1. Any group on which information is obtained
2. Usually is representative of a larger group called a
population
3. Defining the population
--- The group of interest to the researcher
--- A group researcher would like to generalize
--- Usually has at least one characteristic that sets it off from other
populations
--- The target population may not have the characteristics one would like
to generalize from
--- The accessible population is the population one could generalize from
Random Sampling
• Definition
1. Obtaining a rather accurate representative view of a larger
group
2. Every individual in the population has an equal opportunity
to be selected
3. When each member of the population does not have a
chance of being selected because the researcher is looking for
specific criteria it is an example of a nonrandom sample or
purposeful sample
Random Sampling (Continued 1)
• Random Sampling Methods
1. Simple Random Sampling
--- Every member of the population has an equal and
independent chance of being selected
--- This done by using a table of random numbers which is a
large list of numbers that has no order or pattern
--- The list is usually focused in the back of statistics books
--- The purpose of random sampling is that if it is large
enough it should produce a representative sample
Simple Random
Advantages
• Easy to implement
with random dialing
Disadvantages
• Requires list of
population elements
• Time consuming
• Uses larger sample
sizes
• Produces larger
errors
• High cost
Random Sampling (Continued 2)
--- A random sample needs to be more than 20 to 30 individuals to be
large enough to be representative
a. Descriptive studies: 100
b. Correlational studies: 50
c. Experimental studies: 30
d. Causal-comparative: 30
2. Stratified Random Sampling
--- Strata-sub groups are selected for the sample in the same proportions
as they exist in the population
--- Selects a representative or equal percentage from each strata using the
table of random numbers
--- This will improve the likelihood that the key characteristics the
researcher is wising to make generalizations about will be included
proportionately in the sample
Stratified
Advantages
Disadvantages
• Control of sample size in
strata
• Increased statistical
efficiency
• Provides data to
represent and analyze
subgroups
• Enables use of different
methods in strata
• Increased error will result
if subgroups are selected
at different rates
• Especially expensive if
strata on population must
be created
• High cost
Selecting a Stratified Sample
In a population of 365 twelfthgrade American governments
The researcher identifies two
subgroups, or strata:
219 female students
(60%)
146 male students
(40%)
From these, she randomly selects
a stratified sample of:
66 female students
(60%)
and
44 male students
(40%)
Cluster
Advantages
Disadvantages
• Provides an unbiased
estimate of population
parameters if properly
done
• Economically more
efficient than simple
random
• Lowest cost per sample
• Easy to do without list
• Often lower statistical
efficiency due to
subgroups being
homogeneous rather than
heterogeneous
• Moderate cost
Random Sampling (Continued 3)
3. Cluster Sampling
--- A randomized sample of group within the total population rather than
individuals
--- Everyone within the group will need to become part of the sample
--- Used when individuals are difficult to randomize
--- Can only make generalizations about the group and not the
individuals within the group. This is a common error researchers
make.
4. Two Stage Random Sampling
--- Combines cluster sampling with individual sampling
--- This would help to eliminate any problems with just cluster sampling
--- One would randomly select a cluster and than randomly select
individuals within the cluster
Stratified and Cluster Sampling
Stratified
• Population divided
into few subgroups
• Homogeneity within
subgroups
• Heterogeneity
between subgroups
• Choice of elements
from within each
subgroup
Cluster
• Population divided
into many subgroups
• Heterogeneity within
subgroups
• Homogeneity
between subgroups
• Random choice of
subgroups
Nonprobability Samples
No need to
generalize
Feasibility
Limited
objectives
Time
Cost
Nonprobability
Sampling Methods
Convenience
Judgment
Quota
Snowball
Random Sampling (Continued 4)
• Non-Random Sampling
1. System Sampling
--- Every individual in the population list is included in the
sample
--- Random Start-Draw a number from a hat and select
every individual who is within that sampling interval
2. Convenience Sampling
--- A group of individuals are available for the study
--- Such samples cannot be considered representative
--- Demographics and characteristics need to be discussed
--- To improve validity of the sample more than one study
should be used to overcome an one time occurace
Random Sampling (Continued 5)
3. Purposeful Sampling
--- Researcher uses personal judgment about the sample
based on the knowledge of the population and the specific
purpose of the research
• Generalizing from a sample
1. Population generalizability
--- The degree to which a sample represents the population
of interest and can be generalizable to a large group
--- A representative sample is the extent to which a sample
is identical in all characteristics to the intended population
Random Sampling (Continued 6)
---- Any researcher who loses over 10 percent of the original
ample would be well advised to acknowledge this limitation
and qualify their conclusions accordingly
---- If the sample is less than expected the researcher should
describe the sample as thoroughly as possible so that interested
persons can judge for themselves to which any of the findings
apply
• Ecological Generalizability
---- The degree to which the results of a study can be
extended to other settings or conditions
--- The environment and or the settings have to be the same