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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