1. Sampling January 9, 2007

2. Cardinal Rule of Sampling Never sample on the dependent variable! –Example: if you are interested in studying factors that lead to organizational failure, you cannot just study organizations that failed, you need to compare them with organizations which are successful

3. Types of Sampling  Probability Sampling –Basic principle: a sample will be representative of the population from which it is selected if all members of the population have an equal chance of being selected (EPSEM) –Allows estimation of sample’s representativeness

4. Types of Sampling  Probability Sampling –Basic principle: a sample will be representative of the population from which it is selected if all members of the population have an equal chance of being selected (EPSEM) –Allows estimation of sample’s representativeness  Nonprobability sampling

5. Sampling Concepts Element – unit about which information is collected

6. Sampling Concepts Element Universe – theoretical aggregation of all elements

7. Sampling Concepts Element Universe Population – theoretically specified aggregation of elements

8. Sampling Concepts Element Universe Population Survey population – aggregation of elements from which the survey sample is selected

9. Sampling Concepts Element Universe Population Survey population Sampling unit – element considered for selection at some stage of sampling

10. Sampling Concepts Element Universe Population Survey population Sampling unit Sampling frame – list of sampling units from which the sample is selected

11. Sampling Concepts Element Universe Population Survey population Sampling unit Sampling frame Observation unit – element from which data is collected

12. Sampling Concepts Element Universe Population Survey population Sampling unit Sampling frame Observation unit Variable – set of mutually exclusive characteristics

13. Sampling Concepts Element Universe Population Survey population Sampling unit Sampling frame Observation unit Variable Parameter – summary description of a given variable in the population

14. Sampling Concepts Element Universe Population Survey population Sampling unit Sampling frame Observation unit Variable Parameter Statistic – summary description of a given variable in a survey sample

15. Sampling Concepts Element Universe Population Survey population Sampling unit Sampling frame Observation unit Variable Parameter Statistic Sampling error – can be estimated using probability theory (standard error)

16. Sampling Concepts Element Universe Population Survey population Sampling unit Sampling frame Observation unit Variable Parameter Statistic Sampling error Confidence intervals – computing sampling error allows estimation of confidence that the parameter is within a specified range

17. Types of Sampling Designs Simple random sampling –Assign a number to each element in the sampling frame then use a table of random numbers to select elements for the sample Systematic sampling –Every kth element in the total list is included in the sample –Sampling interval, periodicity

18. Types of Sampling Designs Stratified sampling –Obtain greater representativeness and decrease probable sampling error –Organize the population into homogenous subsets (with heterogeneity between subsets) and select the appropriate number of elements from each subset

19. Types of Sampling Designs Multistage Cluster Sampling (general or stratified) –Repeat 2 basic steps: listing and sampling –List primary sampling units, (stratify if useful to do so), take a sample, use sample to create list of secondary sampling units (stratify), take sample, etc. –Each stage produces sampling error

20. Types of Sampling Design Probability Proportionate to Size (PPS) –Each cluster is given a chance of selection proportionate to its size –The same number of elements is chosen from each selected cluster –This allows for selection of more clusters, representation of elements in large clusters, each population element has an equal chance of selection

21. Types of Sampling Design Modifications to PPS designs –May decide to include all very large clusters, but then each element should be given the same chance of being selected as in other clusters –small clusters may contain fewer than the number of elements to be taken per cluster, but clusters can be combined to solve this problem (try to combine homogenous clusters)

22. Nonprobability Sampling Purposive or judgmental sampling Quota sampling Reliance on available subjects Snowball sampling