business research methods chap007
TRANSCRIPT
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7-2McGraw-Hill/Irwin © 2003 The McGraw-Hill Companies, Inc.,All Rights
Reserved.
Part TwoTHE DESIGN OF RESEARCH
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Chapter SevenSAMPLING DESIGN
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Selection of Elements
• Population
• Population Element
• Sampling
• Census
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What is a Good Sample?
• Accurate: absence of bias
• Precise estimate: sampling error
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Types of Sampling Designs
• Probability
• Nonprobability
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Steps in Sampling Design
• What is the relevant population?
• What are the parameters of interest?
• What is the sampling frame?
• What is the type of sample?
• What size sample is needed?
• How much will it cost?
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Concepts to Help Understand Probability Sampling
• Standard error
• Confidence interval
• Central limit theorem
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Probability Sampling Designs
• Simple random sampling
• Systematic sampling
• Stratified sampling– Proportionate– Disproportionate
• Cluster sampling
• Double sampling
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Designing Cluster Samples
• How homogeneous are the clusters?
• Shall we seek equal or unequal clusters?
• How large a cluster shall we take?
• Shall we use a single-stage or multistage cluster?
• How large a sample is needed?
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Nonprobability Sampling
Reasons to use
• Procedure satisfactorily meets the sampling objectives
• Lower Cost
• Limited Time
• Not as much human error as selecting a completely random sample
• Total list population not available
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Nonprobability Sampling
• Convenience Sampling
• Purposive Sampling– Judgment Sampling– Quota Sampling
• Snowball Sampling