11 Basic Sampling Issues
Concept of Sampling
• Sampling: Process of obtaining information from
subset (a sample) of a larger group (the universe
or population).
• We then take the results from the sample and
project them to the larger group.
• The motivation for sampling is to be able to make
these estimates more quickly and at a much
lower cost than would be possible by other
means.
Concept of Sampling
• Population: Entire group of people about
whom information is needed; also called
universe or population of interest.
• Defining the population of interest is usually
the 1st step in the sampling process and often
involves defining the target market for the
product/service in question.
Population
Example: The product concept test for a new nonprescription
cold symptom-relief product such as Contac.
• The population of interest might includes everyone,
because everyone gets colds from time to time.
• Although this is true, not everyone buys a nonprescription
cold symptom relief product when he/she get cold.
• In this case, the 1st task in the screening process would be
to determine whether people have purchased/used one or
more of a number of competing brands during some time
period.
• Only those who had purchased/used one of these brands
would be included in the population of interest.
(Even this new product is really innovative, sales will have
to come from current buyers in the product category.)
Concept of Sampling
Sample vs. Census
• Census: Collection of data obtained from or about every
member of the population of interest.
• Sample: Subset of all members of a population of interest.
The popular belief that a census provides more accurate
results than a sample is not necessary true. The researcher
may not be able to obtain a complete and accurate list of the
entire population, or certain members of the population may
refuse to provide info. or be difficult to find.
Developing a Sampling Plan
Step 1 Define the
population of interest
Step 2 Choose a data
collection method
Step 3 Identify a
sampling frame
Step 6 Develop
operational procedures
for selecting sample
elements
Step 5 Determine
sample size
Step 4 Select a
sampling method
Step 7 Execute the
operational sampling
plan
Developing a Sampling Plan
Step 1: Define the Population of Interest
• The 1st issue in developing a sampling plan is to specify
the characteristics of those individuals/things (i.e.
customers, companies, stores) from whom or about
whom info. is needed to meet the research objectives.
• The population of interest is often specified in terms of
geographic area, demographic characteristics,
product/service usage characteristics, brand awareness
measures, or other factors.
Developing a Sampling Plan
Step 1: Define the Population of Interest
• In addition to defining who will be included in the population of
interest, researchers should define the characteristics of individuals
who should be excluded.
• For example: most commercial marketing research surveys exclude
some individuals for so-called security reasons. Very frequently, one
of the first questions on a survey asks whether the respondent or
anyone in the respondent’s immediate family works in marketing
research, advertising, or product/service area at issue in the survey.
If the individual answers yes to this question, the interview is
terminated. This type of question is called a security question
because those who work in the industries in question are viewed as
security risks (they may be competitors/ work for competitors).
Developing a Sampling Plan
Step 2: Choose a Data-Collection Method
Samples of implications we need to consider:
• Mail surveys suffer from biases associated with low response
rates
• Telephone surveys have a less significant but growing problem
with nonresponse, and suffer from call screening technologies
used by potential respondents and increasing percentage of
people have mobile phones only.
• Internet surveys have problems with professional respondents
and the fact that the panel or e-mail lists used often do not
provide appropriate representation of the population of
interest.
Developing a Sampling Plan
Step 3: Identify a Sampling Frame
• Sampling frame: List of population elements from which
units to be sampled can be selected or a specified
procedure for generating such a list.
• For example, a telephone book might be used as the
sample frame for a telephone survey sample in which the
population of interest is all households in a particular city.
However, the telephone book does not include households
that do not have telephones and those with unlisted
numbers. Therefore, the researchers should use procedures
that will that will produce samples including appropriate
proportions of the households with unlisted numbers.
Developing a Sampling Plan
Step 4: Select a Sampling Method
Systematic
Cluster
Probability
sampling
Simple random
Stratified
Sampling
methods
Convenience
Judgment
Nonprobability
sampling
Quota
Snowball
“Classification of sampling methods”
Developing a Sampling Plan
Step 4: Select a Sampling Method
The major alternatives in sampling methods can be
grouped under 2 headings:
• Probability samples: Samples in which every element
of the population has a known, nonzero likelihood of
selection.
• Nonprobability samples: Samples in which specific
elements from the population have been selected in a
nonrandom manner.
Developing a Sampling Plan
Step 4: Select a Sampling Method
Advantages of probability samples:
• The researcher can be sure of obtaining info. From a
representative cross section of the population of interest.
• Sampling error can be computed.
• The survey results can be projected to the total population.
Disadvantages of probability samples: more expensive (high
interview costs due to rules for selection, professional time
spent in designing and executing the sample design)
Developing a Sampling Plan
Step 5: Determine Sample Size
• Sample size: The identified and selected
population subset for the survey, chosen because
it represents the entire group.
– Nonprobability samples rely on factors: available
budget, rules of thumb, and number of subgroups.
– Probability samples require formulas to calculate the
sample size
Developing a Sampling Plan
Step 6: Develop Operational Procedures for
Selecting Sample Elements
• The procedures are much more critical to the
successful execution of a probability sample,
in which case they should be detailed, clear,
and unambiguous and should eliminate any
interviewer discretion (alternative) regarding
the selection of specific sample elements.
Developing a Sampling Plan
Step 7: Execute the Operational Sampling Plan
• This step requires adequate checking to
ensure that specified procedures are followed:
Sampling and Nonsampling Errors
• Sampling error: Error that occurs because the sample
selected is not perfectly representative of the
population.
– Administrative: problems in the execution of the sample
plan. This can be avoided/minimized by careful attention
to design and execution of the sample.
– Random: due to chance and cannot be avoided
• Nonsampling error: All errors other than sampling
error may cause inaccuracy and bias in the survey
results; also called measurement error.
Probability Sampling Methods
1) Simple random sampling
• Probability sample selected by assigning a number to every
element of the population and then using a table of
random numbers to select specific elements for inclusion in
the sample.
Probability of selection
=
Sample size
Population size
• Example: if the population size is 10,000 and the sample
size is 400, the probability of selection is 4 percent.
• Simple random sampling guarantees that every member of
the population has a known and equal chance of being
selected for the sample. It begins with a current and
complete listing of the population.
Probability Sampling Methods
2) Systematic sampling
• Probability sampling in which the entire populations is numbered and
elements are selected using a skip interval.
• The researcher first numbers the entire population, as in simple random
sampling. Then determines a skip interval and selects names based on this
interval.
• Example: if you were using a local telephone directory and had computed
a skip interval of 100, every 100th name would be selected for the sample.
• The skip interval can be computed through use of the following formula:
Skip interval
=
Population size
Sample size
Probability Sampling Methods
3) Stratified sampling
• Probability sample that is forced to be more representative through
simple random sampling of mutually exclusive and exhaustive
subsets.
• This may be appropriate in certain cases. For example, if a political
poll is being conducted to predict who will win an election, a
difference in the way men and women are likely to vote would
make gender an appropriate basis for stratification.
• Procedural steps:
1.
2.
The original, or parent, population is divided into two or more
mutually exclusive and exhaustive subsets (e.g. male and female).
Simple random samples of elements from the two or more subsets
are chosen independently of each other.
Probability Sampling Methods
4) Cluster sampling
• Probability sample in which the sampling units
are selected from a number of small geographic
areas to reduce data collection costs.
• Two basic steps:
1. The population of interest is divided into mutually
exclusive and exhaustive subsets such as geographic
areas.
2. A random sample of subsets (e.g. geographic areas)
is selected.
Nonprobability Sampling Methods
1) Convenience samples
• Nonprobability samples based on using people
who are easily accessible.
• Example: Frito-Lay often use their own
employees for preliminary tests of new product
formulations developed by their R&D
departments. They are asking employees to
provide gross sensory evaluations of new product
formulations (e.g. saltiness, crispness,
greasiness).
Nonprobability Sampling Methods
2) Judgment samples
• Nonprobability samples in which the selection
criteria are based on the researcher’s judgment
about representativeness of the population
under study.
• Most test markets and many product tests
conducted in shopping malls are essentially
judgment sampling, one or a few markets are
selected based on judgment that they are
representative of the population as a whole.
Nonprobability Sampling Methods
3) Quota samples
• Nonprobability samples in which quotas, based on
demographic or classification factors selected by the
researcher, are established for population subgroups.
• Differences between a quota sample and a stratified
sample:
– Respondents for a quota sample are not selected
randomly, as they must be for a stratified sample.
– The demographic or classification factors of interest in a
quota sample are selected on the basis of researcher
judgment, while a stratified sample are selected based on
the existence of correlation between the factor and
behavior of interest.
Nonprobability Sampling Methods
4) Snowball samples
• Nonprobability samples in which additional
respondents are selected based on referrals
from initial respondents.