Business Research Methods William G. Zikmund

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Business Research MethodsWilliam G. Zikmund

• Chapter 16
• Sample Designs and Sampling Procedures

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Sampling Terminology

• Sample subset of larger population
• Population or universe any complete group that
  share some set of characteristics (e.g., people,
  sales territories, stores, etc.)
• Population element individual member of
  population
• Census investigation of all individual elements
  that make up a population

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Why Sample?

• It works! Properly selected samples yield
  accurate and reliable results.
• If elements are similar smaller sample is needed
• May even be more accurate than census
• Bureau of Census uses samples to check accuracy
  of the U. S. Census
• It saves resources

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Stages in the Selection of a Sample
Define the target population
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
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Target Population

• Vitally important decision
• To Whom Do We Want to Talk?
• Relevant population
• Operationally define
• Can be a simple or difficult task
• See Exhibit 16.4 operationally defining a
  Household Member

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Sampling Frame

• A list of elements from which the sample may be
  drawn
• a.k.a. Working population
• Mailing lists - data base marketers
• Sampling frame error occurs when population is
  not accurately represented in the sampling frame.

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Sampling Units

• Group selected for the sample
• Primary Sampling Units (PSU)
• Secondary Sampling Units
• Tertiary Sampling Units

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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

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Systematic Errors

• Nonsampling errors
• Unrepresentative sample results (e.g., educated
  vs. uneducated respondents in mail survey)
• Not due to chance
• Due to study design or imperfections in execution

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Errors Associated with Sampling

• Sampling frame error
• Random sampling error
• Nonresponse error

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Two Major Categories of Sampling

• Probability sampling
• Known, nonzero probability for every element
• Nonprobability sampling
• Probability of selecting any particular member is
  unknown
• Technically, inappropriate to apply statistical
  techniques to project beyond the sample
• Still often used

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Nonprobability Sampling

• Convenience
• Judgment
• Quota
• Snowball

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Convenience Sampling

• Also called haphazard or accidental sampling
• The sampling procedure of obtaining the people or
  units that are most conveniently available

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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

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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.

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Snowball Sampling

• A variety of procedures
• Initial respondents are selected by probability
  methods if possible
• Additional respondents are obtained from
  information provided by the initial respondents

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Probability Sampling

• Simple random sample
• Systematic sample
• Stratified sample
• Cluster sample
• Multistage area sample

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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

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Systematic Sampling

• A simple process
• Every nth name from the list will be drawn

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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

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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

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Examples of Clusters
Population Element Possible Clusters in the
United States
U.S. adult population States Counties Met
ropolitan Statistical Area Census
tracts Blocks Households
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Examples of Clusters
Population Element Possible Clusters in the
United States
Airline travelers Airports Planes Sports
fans Football stadiums Basketball
arenas Baseball parks
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What is the Appropriate Sample Design?

• Representativeness is Always Important
• Degree of accuracy
• Resources
• Time
• Advanced knowledge of the population
• National versus local
• Need for statistical analysis

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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.

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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 population.
• target population - visitors to a particular Web
  site.
• Hard to reach subjects may participate

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Web Site Visitors

• Unrestricted samples are clearly convenience
  samples
• Randomly selecting visitors
• Questionnaire request randomly "pops up"
• Over- representing the more frequent visitors

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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

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Internet Samples

• Recruited Ad Hoc Samples
• Opt-in Lists