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# Rm7 sampling design

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### Transcript

• 1. Part Two THE DESIGN OF RESEARCH7-1
• 2. Chapter Seven SAMPLING DESIGN7-2
• 3. Selection of Elements • Population • Population Element • Sampling • Census7-3
• 4. What is a Good Sample? • Accurate: absence of bias • Precise estimate: sampling error7-4
• 5. Types of Sampling Designs • Probability • Nonprobability7-5
• 6. 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?7-6
• 7. Concepts to Help Understand Probability Sampling • Standard error • Confidence interval • Central limit theorem7-7
• 8. Probability Sampling Designs • Simple random sampling • Systematic sampling • Stratified sampling – Proportionate – Disproportionate • Cluster sampling • Double sampling7-8
• 9. 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?7-9
• 10. 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 available7-10
• 11. Nonprobability Sampling • Convenience Sampling • Purposive Sampling – Judgment Sampling – Quota Sampling • Snowball Sampling7-11