Chapter Eleven

1
Chapter Eleven

• Sampling Fundamentals 1

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Sampling Fundamentals

• Population
• Sample
• Census
• Parameter
• Statistic

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The One and Only Goal in Sampling!!

• Select a sample that is as representative as
  possible.

So that an accurate inference about the
population can be made goal of marketing
research
4
Sampling Fundamentals

• When Is Census Appropriate?
• When Is Sample Appropriate?

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Error in Sampling

• Total Error
• Difference between the true value (in the
  population) and the observed value (in the
  sample) of a variable
• Sampling Error
• Error due to sampling (depends on how the sample
  is selected, and its size)
• Non-sampling Error (dealt with in chapter 4)
• Measurement Error, Data Recording Error, Data
  Analysis Error, Non-response Error

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Sampling Process Identify Population

• Question For a toy store in Charlotte (be as
  specific as possible)
• Question For a small bookstore in RH
  specializing in romance novels

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Sampling Process Determine sampling frame

• List and contact information of population
  members used to obtain the sample from
• Example to address a population of all
  advertising agencies in the US, the sampling
  frame would be the Standard Directory of
  Advertising Agencies
• Availability of lists is limited, lists may be
  obsolete and incomplete

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Problems with sampling frames

• Subset problem
• The sampling frame is smaller than the population
• Another sampling frame needs to be tapped
• Superset problem
• Sampling frame is larger than the population
• A filter question needs to be posed
• Intersection problem
• A combination of the subset and superset problem
• Most serious of the three

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Problems with sampling frames
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Sampling Process Sampling Procedure

• Probability Sampling
• Each member of the population stands an equal
  chance of getting into the sample
• Preferred due to greater representativeness
• Nonprobability Sampling
• Convenience sampling some members stand a
  better chance of being sampled than others

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Sampling Procedure
-Simple Random Sampling -Systematic
Sampling -Stratified Sampling -Cluster Sampling
Probability Sampling
Heres the difference!
Sampling Procedures
-Convenience Sampling -Judgmental
Sampling -Snowball Sampling -Quota Sampling
Non-Probability Sampling
Probability Sampling Each subject has the same
non-zero probability of getting into the sample!
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Probability Sampling Techniques

• Simple Random Sampling
• Each population member has equal, non-zero
  probability of being selected
• Equivalent to choosing with replacement

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Probability Sampling Techniques

• Accuracy cost trade off
• Sampling Efficiency Accuracy/Cost
• Sampling efficiency can be increased by either
  reducing the cost, increasing the accuracy or
  doing both
• This has led to modifying simple random sampling
  procedures

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Probability Sampling Techniques

• Stratified Sampling
• The chosen sample is forced to contain units from
  each of the segments or strata of the population
• Sometimes groups (strata) are naturally present
  in the population
• Between-group differences on the variable of
  interest are high and within-group differences
  are low
• Then it makes better sense to do simple random
  sampling within each group and vary within-group
  sample size according to
• Variation on variable of interest
• Cost of generating the sample
• Size of group in population
• Increases accuracy at a faster rate than cost

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Stratified Sampling what strata are naturally
present
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Directly Proportionate Stratified Sampling
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Inversely Proportional Stratified Sampling

• 600 consumers in the population
• 200 are heavy drinkers
• 400 are light drinkers.
• If heavy drinkers opinions are valued more and
  a sample
• size of 60 is desired, a 10 percent inversely
  proportional
• stratified sampling is employed. Selection
  probabilities are computed as follows

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Probability Sampling Techniques

• Cluster Sampling
• Involves dividing population into subgroups
• Random sample of subgroups/clusters is selected
  and all members of subgroups are interviewed
• Advantages
• Decreases cost at a faster rate than accuracy
• Effective when sub-groups representative of the
  population can be identified

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Cluster Sampling

• Geography knowledge of all middle school children
  in the US
• Attitudes to cell phones amongst all college
  students in the US
• Knowledge of credit amongst all freshman college
  students in the US
• Combine cluster and stratified sampling

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A Comparison of Stratified and Cluster Sampling
Stratified sampling Homogeneity within
group Heterogeneity between groups All groups are
included Random sampling in each group Sampling
efficiency improved by increasing accuracy at a
faster rate than cost
Cluster sampling Homogeneity between
groups Heterogeneity within groups Random
selection of groups Census within the
group Sampling efficiency improved by decreasing
cost at a faster rate than accuracy.
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Probability Sampling Techniques

• Systematic Sampling
• Systematically spreads the sample through the
  entire list of population members
• E.g. every tenth person in a phone book
• Bias can be introduced when the members in the
  list are ordered according to some logic. E.g.
  listing women members first in a list at a dance
  club.
• If the list is randomly ordered then systematic
  sampling results closely approximate simple
  random sampling
• If the list is cyclically ordered then systematic
  sampling efficiency is lower than that of simple
  random sampling

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Non-Probability Sampling

• Benefits
• Driven by convenience
• Costs may be less
• Common Uses
• Exploratory research
• Pre-testing questionnaires
• Surveying homogeneous populations
• Operational ease required

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Non-Probability Sampling Techniques

• Judgmental
• Selected according to expert judgment
• Snowball
• Each sample member is asked to recommend another
• Used when populations are highly specialized /
  niched
• Convenience
• whosoever is convenient to find
• Quota
• Judgment sampling with a stipulation that the
  sample include a minimum number from each
  specified sub-group