Part Two THE DESIGN OF RESEARCH

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Presentation transcript:

Part Two THE DESIGN OF RESEARCH McGraw-Hill/Irwin © 2003 The McGraw-Hill Companies, Inc.,All Rights Reserved.

Chapter Seven SAMPLING DESIGN

Selection of Elements Population Population Element Sampling Census

What is a Good Sample? Accurate: absence of bias Precise estimate: sampling error

Types of Sampling Designs Probability Nonprobability

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?

Concepts to Help Understand Probability Sampling Standard error Confidence interval Central limit theorem

Probability Sampling Designs Simple random sampling Systematic sampling Stratified sampling Proportionate Disproportionate Cluster sampling Double sampling

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?

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

Nonprobability Sampling Convenience Sampling Purposive Sampling Judgment Sampling Quota Sampling Snowball Sampling