4. Population
• A population is the total collection of elements
about which we wish to make some inferences.
• Any complete group
– People
– Sales territories
– Stores
5. Census
• Investigation of all individual elements that
make up a population
A census is a count of
all the elements in a
population.
6. Sampling
• The process of using a small number of
items or parts of larger population to make
a conclusions about the whole population
7. Selecting samples
Population, sample and individual cases
Source: Saunders et al. (2009)
Figure 7.1 Population, sample and individual cases
8. The need to sample
Sampling- a valid alternative to a census when
• A survey of the entire population is impracticable
• Budget constraints restrict data collection
• Time constraints restrict data collection
• Results from data collection are needed quickly
9. Overview of sampling techniques
Sampling techniques
Source: Saunders et al. (2009)
Figure 7.2 Sampling techniques
10. Stages in the Define the target population
Selection
of a Sample Select a sampling frame
Determine if a probability or nonprobability
sampling method will be chosen
Plan procedure
for selecting sampling units
Determine sample size
Select actual sampling units
Conduct fieldwork
12. Sampling Frame
• A sample frame is the listing of all population
elements from which the sample will be drawn.
13. Sampling Units
• Group selected for the sample
• Primary Sampling Units (PSU)
• Secondary Sampling Units
• Tertiary Sampling Units
14. Random Sampling Error
• The difference between the sample results
and the result of a census conducted using
identical procedures
• Statistical fluctuation due to chance
variations
15. Systematic Errors
• Nonsampling errors
• Unrepresentative sample results
• Not due to chance
• Due to study design or imperfections in
execution
16. Errors Associated with Sampling
• Sampling frame error
• Random sampling error
• Nonresponse error
17. Two Major Categories of
Sampling
• Probability sampling
• Known, nonzero probability for every
element
• Nonprobability sampling
• Probability of selecting any particular
member is unknown
19. Probability Sampling
• Simple random sample
• Systematic sample
• Stratified sample
• Cluster sample
• Multistage area sample
20. Convenience Sampling
• Convenience samples are nonprobability
samples where the element selection is
based on ease of accessibility. They are the
least reliable but cheapest and easiest to
conduct.
• Examples include informal pools of friends
and neighbors, people responding to an
advertised invitation, and “on the street”
interviews.
21. Judgment Sampling
• Also called purposive sampling
• An experienced individual selects the
sample based on his or her judgment about
some appropriate characteristics required
of the sample member
22. Quota Sampling
• Ensures that the various subgroups in a
population are represented on pertinent
sample characteristics
• To the exact extent that the investigators
desire
• It should not be confused with stratified
sampling.
23. Snowball Sampling
• A variety of procedures
• Initial respondents are selected by
probability methods
• Additional respondents are obtained from
information provided by the initial
respondents
24. Simple Random Sampling
• A sampling procedure that ensures that each
element in the population will have an equal
chance of being included in the sample
25. Simple Random
Advantages Disadvantages
•Easy to implement with •Requires list of
random dialing population elements
•Time consuming
•Larger sample needed
•Produces larger errors
•High cost
14-25
27. Systematic
Advantages Disadvantages
•Simple to design •Periodicity within
•Easier than simple population may skew
random sample and results
•Easy to determine •Trends in list may bias
sampling distribution of results
mean or proportion •Moderate cost
14-27
28. Stratified Sampling
• Probability sample
• Subsamples are drawn within different
strata
• Each stratum is more or less equal on some
characteristic
• Do not confuse with quota sample
29. Stratified
Advantages Disadvantages
•Control of sample size in •Increased error if subgroups
strata are selected at different rates
•Increased statistical efficiency •Especially expensive if strata
•Provides data to represent and on population must be created
analyze subgroups •High cost
•Enables use of different
methods in strata
14-29
30. Cluster Sampling
• The purpose of cluster sampling is to
sample economically while retaining the
characteristics of a probability sample.
• The primary sampling unit is no longer the
individual element in the population
• The primary sampling unit is a larger
cluster of elements located in proximity to
one another
31. Cluster
Advantages Disadvantages
•Provides an unbiased estimate •Often lower statistical
of population parameters if efficiency due to subgroups
properly done being homogeneous rather than
•Economically more efficient heterogeneous
than simple random •Moderate cost
•Lowest cost per sample
•Easy to do without list
14-31
32. Examples of Clusters
Population Element Possible Clusters in the United States
U.S. adult population States
Counties
Metropolitan Statistical Area
Census tracts
Blocks
Households
33. Examples of Clusters
Population Element Possible Clusters in the United States
College seniors Colleges
Manufacturing firms Counties
Metropolitan Statistical Areas
Localities
Plants
34. Examples of Clusters
Population Element Possible Clusters in the United States
Airline travelers Airports
Planes
Sports fans Football stadiums
Basketball arenas
Baseball parks
35. What is the
Appropriate Sample Design?
• Degree of accuracy
• Resources
• Time
• Advanced knowledge of the population
• National versus local
• Need for statistical analysis
36. Internet Sampling is Unique
• Internet surveys allow researchers to rapidly
reach a large sample.
• Speed is both an advantage and a
disadvantage.
• Sample size requirements can be met
overnight or almost instantaneously.
• Survey should be kept open long enough so
all sample units can participate.
37. Internet Sampling
• Major disadvantage
– lack of computer ownership and Internet
access among certain segments of the
population
• Yet Internet samples may be representative
of a target populations.
– target population - visitors to a particular Web
site.
• Hard to reach subjects may participate
38. Web Site Visitors
• Unrestricted samples are clearly
convenience samples
• Randomly selecting visitors
• Questionnaire request randomly "pops up"
• Over- representing the more frequent
visitors
39. Panel Samples
• Typically yield a high response rate
– Members may be compensated for their time
with a sweepstake or a small, cash incentive.
• Database on members
– Demographic and other information from
previous questionnaires
• Select quota samples based on product
ownership, lifestyle, or other characteristics.
• Probability Samples from Large Panels
In drawing a sample with simple random sampling, each population element has an equal chance of being selected into the samples. The sample is drawn using a random number table or generator. This slide shows the advantages and disadvantages of using this method. The probability of selection is equal to the sample size divided by the population size. Exhibit 14-6 covers how to choose a random sample. The steps are as follows: Assign each element within the sampling frame a unique number. Identify a random start from the random number table. Determine how the digits in the random number table will be assigned to the sampling frame. Select the sample elements from the sampling frame.
In drawing a sample with systematic sampling, an element of the population is selected at the beginning with a random start and then every K th element is selected until the appropriate size is selected. The kth element is the skip interval, the interval between sample elements drawn from a sample frame in systematic sampling. It is determined by dividing the population size by the sample size. To draw a systematic sample, the steps are as follows: Identify, list, and number the elements in the population Identify the skip interval Identify the random start Draw a sample by choosing every kth entry. To protect against subtle biases, the research can Randomize the population before sampling, Change the random start several times in the process, and Replicate a selection of different samples.
In drawing a sample with stratified sampling, the population is divided into subpopulations or strata and uses simple random on each strata. Results may be weighted or combined. The cost is high. Stratified sampling may be proportion or disproportionate. In proportionate stratified sampling, each stratum’s size is proportionate to the stratum’s share of the population. Any stratification that departs from the proportionate relationship is disproportionate.
In drawing a sample with cluster sampling, the population is divided into internally heterogeneous subgroups. Some are randomly selected for further study. Two conditions foster the use of cluster sampling: the need for more economic efficiency than can be provided by simple random sampling, and 2) the frequent unavailability of a practical sampling frame for individual elements. Exhibit 14-7 provides a comparison of stratified and cluster sampling and is highlighted on the next slide. Several questions must be answered when designing cluster samples. How homogeneous are the resulting clusters? Shall we seek equal-sized or unequal-sized clusters? How large a cluster shall we take? Shall we use a single-stage or multistage cluster? How large a sample is needed?
