7-1

7-2 McGraw-Hill/Irwin © 2003 The McGraw-Hill Companies, Inc.,All Rights Reserved. Part Two THE DESIGN OF RESEARCH

7-3 Chapter Seven SAMPLING DESIGN

7-4 Selection of Elements Population –Is the total collection of elements about which we wish to make some inferences Population Element –Is the subject on which the measurement is being taken –It is the unit of study Sampling –Selecting some of the elements in a population Census –Is a count of all the elements in a population

7-5 Reasons for Sampling Lower cost –The economic advantages Greater accuracy of results –The quality of a study I often better with sampling than with a census Greater speed of data collection –Reducing the time between the recognition of a need fro information and the availability of that information Availability of population elements –Some situations (material strength) require sampling –The population is infinite

7-6 Sampling versus Census The advantages of sampling over census studies are less compelling when population is small and the variability within the population is high Two conditions are appropriate for a census study: –When population is small (feasible) –When the elements are quite different from each other (necessary)

7-7 What is a Good Sample? The ultimate test of a sample design is how well it represents the characteristics of the population it purports to represent Validity of a sample depends on two considerations –Accurate: absence of bias –Precise estimate: sampling error

7-8 Accuracy Is the degree to which bias is absent from the sample Some sample elements underestimate and others overestimate the population values –Variation offset each other –Sample value close to population value Enough elements in the sample Must be drawn in a way to favor neither of them No systematic variance –The variation in measures due to some known or unknown influences that “cause” the scores to lean in one direction more than another

7-9 Precision No sample will fully represent its population in all respects The numerical descriptors that describe samples may be expected to differ from those that describe populations because of random fluctuations inherent in the sampling process –This is called sampling error and reflects the influences of chance in drawing the sample numbers Precision is measured by the standard error of estimate, a type of standard deviation measurement –The small the standard error of estimate, the higher is the precision of the sample

7-10 Types of Sampling Designs Probability –Based on the concept of random selection, a controlled procedure that assures that each population element is given a known nonzero chance of selection Nonprobability –Is arbitrary (nonrandom) and subjective –Each member does not have a known nonzero chance of being included

7-11 Types of Sample Design Element Selection Unrestricted Restricted Representation Basis Probability Nonprobability Simple randomConvenience Complex randomPurposive Judgment Quota Systematic Cluster Stratified Double Snowball

7-12 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?

7-13 Sample Frame The sample frame is closely realted to the population It is the list of elements from which the sample is actually drawn

7-14 Myths About Sample Size A sample must be large or it is not representative A sample should bear some proportional relationship to the size of the population from which it is drawn

7-15 Some Principles That Influence Sample Size The greater the dispersion or variance within the population, the larger the sample must be to provide estimation precision The greater the desired precision of the estimate, the larger the sample must be The narrower the interval range, the larger the sample must be The higher the confidence level in the estimate, the larger the sample must be The greater the number of subgroups of interest within a sample, the greater the sample size must be, as each subgroup must meet minimum sample size requirement If the calculated sample size exceeds 5 percent of the population, sample size may be reduced without sacrificing precision

7-16 Precision Is Measured By The interval range in which they would expect to find the parameter estimate The degree of confidence they wish to have in that estimate

7-17 Concepts to Help Understand Probability Sampling Standard error Confidence interval Central limit theorem

7-18 Impracticality of Simple Random Sampling It requires a population list that is often not available It fails to use all the information about a population, thus resulting in a design that may be wasteful It may be expensive to implement in both time and money

7-19 Probability Sampling Designs Simple random sampling Systematic sampling Stratified sampling –Proportionate –Disproportionate Cluster sampling Double sampling

7-20 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?

7-21 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

7-22 Nonprobability Sampling Convenience Sampling Purposive Sampling –Judgment Sampling –Quota Sampling Snowball Sampling