Transcript Marketing Research Aaker, Kumar, Day Seventh Edition

Marketing
Research
Aaker, Kumar, Day
Seventh Edition
Instructor’s Presentation
Slides
Chapter Fourteen
Sampling Fundamentals
Sampling Fundamentals
When Is Census Appropriate?
Population size itself is quite small
Information is needed from every individual in
the population
Cost of making an incorrect decision is high
Sampling errors are high
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Sampling Fundamentals
(Contd.)
When Is Sample Appropriate?
Sample size is large
Both cost and time associated with obtaining
information from the population is high
Quick decision is needed
In a given time period, more time can be
spent on each interview, thereby increasing
response quality
Marketing Research 7th Edition
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Sampling Fundamentals
(Contd.)
When Is Sample Appropriate? (Cont.)
Easier to manage surveys of smaller samples
and also exercise quality control in the
interview process
Population being dealt with is homogeneous
Used if census is impossible
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Error in Sampling
Total Error

Difference between the true value and the observed value of
a variable
Sampling Error

Error is due to sampling
Non-sampling Error

Error is observed in both census and sample

Measurement Error

Data Recording Error

Data Analysis Error

Non-response Error
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Sampling Process
Determining Target Population
Well thought out research objectives
Consider all alternatives
Know your market
Consider the appropriate sampling unit
Specify clearly what is excluded
Should be reproducible
Convenience
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Sampling Process (Contd.)
Determining Sampling Frame
Creating lists
Selection A Sampling Procedure
Choose between Bayesian and
traditional sampling procedure
Decide whether to sample with or
without replacement
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Identifying the Target Population
Determining the Sampling Frame
Reconciling the
Population, Sampling
Frame Differences
Selecting a Sampling Frame
Probability
Sampling
Non-Probability
Sampling
Determining the Relevant Sample Size
The Sampling
Process
Execute Sampling
Data Collection From Respondents
Handling the NonResponse Problem
Information for Decision-Making
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Sampling Techniques
Probability Sampling
All population members have a known probability
of being in the sample
Simple Random Sampling
Each population member, and each possible
sample, has equal probability of being selected
Stratified Sampling
The chosen sample is forced to contain units from
each of the segments or strata of the population
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Sampling Techniques
(Contd.)
Types of Stratified Sampling
Proportionate Stratified Sampling
Number of objects/sampling units chosen from each group
is proportional to number in population
Can be classified as directly proportional or indirectly
proportional stratified sampling
Disproportionate Stratified Sampling
Sample size in each group is not proportional to the
respective group sizes
Used when multiple groups are compared and respective
group sizes are small
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Sampling Techniques
(Contd.)
Cluster Sampling
Involves dividing population into subgroups
Random sample of subgroups/clusters is
selected and all members of subgroups are
interviewed
Very cost effective
Useful when subgroups can be identified that
are representative of entire population
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Sampling Techniques
(Contd.)
Systematic Sampling
Involves systematically spreading the sample
through the list of population members
Commonly used in telephone surveys
Non Probability Sampling
Costs and trouble of developing sampling frame
are eliminated
Results can contain hidden biases and
uncertainties
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Sampling Techniques
(Contd.)
Types of Non Probability Sampling
Judgmental
"Expert" uses judgement to identify representative
samples
Snowball
Form of judgmental sampling
Appropriate when reaching small, specialized
populations
Each respondent, after being interviewed, is asked to
identify one or more others in the appropriate group
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Sampling Techniques
(Contd.)
Convenience
Used to obtain information quickly and
inexpensively
Quota
Minimum number from each specified
subgroup in the population
Often based on demographic data
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Non Response Problems
Respondents may:
Refuse to respond
Lack the ability to respond
Be inaccessible
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Non Response Problems
(Contd.)
Sample size has to be large enough to
allow for non response
Those who respond may differ from non
respondents in a meaningful way, creating
biases
Seriousness of nonresponse bias depends
on extent of non response
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Solutions to Nonresponse
Problem
Improve research design to reduce the
number of nonresponses
Repeat the contact one or more times
(call back) to try to reduce
nonresponses
Attempt to estimate the nonresponse
bias
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Shopping Center Sampling
20% of all questionnaires completed or
interviews granted are store-intercept
interviews
Bias is introduced by methods used to
select
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Shopping Center Sampling
(Contd.)
Source of Bias:
Selection of shopping center
Point of shipping center from which
respondents are drawn
Time of day
More frequent shoppers will be more
likely to be selected
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Shopping Center Sampling
(Contd.)
Solutions to Bias:
Shopping Center Bias

Use several shopping centers in different neighborhoods

Use several diverse cities
Sample Locations Within a Center

Stratify by entrance location

Take separate sample from each entrance

To obtain overall average, strata averages should be combined by
weighing them to reflect traffic that is associated with each
entrance
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Time Sampling
Stratify by time segments
Interview during each segment
Final counts should be weighed
according to traffic counts
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Sampling in an International
Context
Major Problems:
Absence of information on sample
frames in other countries
Sampling equivalence
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Sampling Procedure
Decide whether research will be conducted
in all countries or is generalizable from one
country to another
Non-probability sampling is more frequently
used than probability sampling because of
lack of information
Snowball sampling technique is very popular
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Sampling Procedure (Cont.)
Two phase sampling is used to reduce costs
Researcher must decide whether to use the
same sampling procedure across all
countries
Consider relative cost, reliability and
accuracy
In determining sample size, researcher must
consider cost and availability of population
data
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