7-1 Chapter Seven SAMPLING DESIGN. 7-2 Sampling What is it? –Drawing a conclusion about the entire population from selection of limited elements in a.
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Presentation on theme: "7-1 Chapter Seven SAMPLING DESIGN. 7-2 Sampling What is it? –Drawing a conclusion about the entire population from selection of limited elements in a."— Presentation transcript:
7-2 Sampling What is it? –Drawing a conclusion about the entire population from selection of limited elements in a population –Terms: population, population element, census Why is it needed? –Low cost –Greater speed of data collection –Greater accuracy of results in some cases
7-3 What is a Good Sample? Accurate: absence of bias –Underestimator and overestimators are balanced Precise estimate –sampling error (difference of samples from population) is inevitable –Sampling error is typically measured by standard error of estimate –The smaller this error, the more precise sample
7-4 Types of Sampling Designs Probability Sampling –Random selection (giving each population element equal chance of selection) –Simple random vs. complex random Nonprobability Sampling –Arbitrary and subjective –It may be used for practical considerations.
7-5 Steps in Sampling Design What is the relevant population? What are the parameters of interest? What is the sampling frame? –Actual list of elements from which the sample is drawn What is the type of sample? What size sample is needed? (p.190) How much will it cost?
7-6 Probability Sampling Concepts to Help Understand –Standard error (standard deviation) –Confidence interval 68% interval, 95% internal –Central limit theorem The sample means will be distributed in a normal distribution around the population mean for the sample of n>30.
7-7 Probability Sampling Continued Simple vs Complex random sampling Systematic sampling Stratified sampling –Proportionate –Disproportionate Cluster sampling (e.g. area sampling) Double sampling (in order to reduce the sample size further)
7-8 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 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 of population not available
7-10 Nonprobability Sampling Continued Convenience Sampling –Maximum freedom in choosing the sample Purposive Sampling –Judgment Sampling –Quota Sampling Snowball Sampling