Exploring Marketing Research William G. Zikmund Chapter 20: Basic Data Analysis.
Research Methods William G. Zikmund, Ch16
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Transcript of Research Methods William G. Zikmund, Ch16
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Business Research Methods
William G. Zikmund
Chapter 16:
Sample Designs and Sampling Procedures
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Sampling Terminology
• Sample
• Population or universe
• Population element
• Census
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Sample
• Subset of a larger population
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Population
• Any complete group– People– Sales territories– Stores
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Census
• Investigation of all individual elements that make up a population
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Define the target population
Select a sampling frame
Conduct fieldwork
Determine if a probability or nonprobability sampling method will be chosen
Plan procedure for selecting sampling units
Determine sample size
Select actual sampling units
Stages in the Selectionof a Sample
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Target Population
• Relevant population
• Operationally define
• Comic book reader?
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Sampling Frame
• A list of elements from which the sample may be drawn
• Working population
• Mailing lists - data base marketers
• Sampling frame error
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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
• 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
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Nonprobability Sampling
• Convenience
• Judgment
• Quota
• Snowball
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Probability Sampling
• Simple random sample
• Systematic sample
• Stratified sample
• Cluster sample
• Multistage area sample
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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
• Additional respondents are obtained from information provided by the initial respondents
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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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Population Element Possible Clusters in the United States
U.S. adult population StatesCountiesMetropolitan Statistical AreaCensus tractsBlocksHouseholds
Examples of Clusters
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Population Element Possible Clusters in the United States
College seniors CollegesManufacturing firms Counties
Metropolitan Statistical AreasLocalitiesPlants
Examples of Clusters
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Population Element Possible Clusters in the United States
Airline travelers AirportsPlanes
Sports fans Football stadiumsBasketball arenasBaseball parks
Examples of Clusters
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What is the Appropriate Sample Design?
• 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 populations. – 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