1. LIS 570 Selecting a Sample

2. Summary • Sampling - the process of selecting observations • random; non-random • probability; non-probability You don’t have to eat the whole ox to know that the meat is tough

3. Aim • A representative sample • A sample which accurately reflects its population • Avoiding bias

4. Basic terminology • Population - the entire group of objects about which information is wanted • Unit - any individual member of the population • Sample - a part or subset of the population used to gain information about the whole • Sampling frame - the list of units from which the sample is chosen • Variable - a characteristic of a unit, to be measured for those units in the sample

5. Step 1: Identify the Population • The units of analysis about whom or which you want to know • Define the population concretely • Example • Adult Residents of Seattle

6. 2.Decide on a Census or a Sample • Census • Observe each unit • an “attempt” to sample the entire population • not foolproof • Sample • observe a sub-group of the population

7. 3. Decide on Sampling Approach Random Non-random Probability Non-probability

8. Random sampling • Random (Probability) Sampling • Each unit (element) has the same chance (probability) of being in the sample • Chance or luck of the draw determines who is in the sample (Random)

9. Random samples • Each unit has a known probability or chance of being included in the sample • An objective way of selecting units • Random Sampling is not haphazard or unplanned sampling

10. Types of random sampling • Simple random sample • Systematic sampling • Stratified sampling • Cluster sampling

11. How to choose The nature of the research problem Availability of a sampling frame Money Desired level of accuracy Data collection method

12. Simple random samples • Obtain a complete sampling frame • Give each case a unique number starting with one • Decide on the required sample size • Select that many numbers from a table of random numbers • Select the cases which correspond to the randomly chosen numbers

13. Systematic sampling • Sample fraction • divide the population size by the desired sample size • Select from the sampling frame according to the sample fraction • e.g sample faction = 1/5 means that we select one person for every five in the population • Must decide where to start

14. Stratified sampling • Premise - if a sample is to be representative then proportions for various groups in the sample should be the same as in the population • Stratifying variable • characteristic on which we want to ensure correct representation in the sample • Order sampling frame into groups • Use systematic sampling to select appropriate proportion of people from each strata

15. Cluster sampling • Involves drawing several different samples • draw a sample of areas • start with large areas then progressively sample smaller areas within the larger • Divide city into districts - select SRS sample of districts • Divide sample of districts into blocks - select SRS sample of blocks • Draw list of households in each block - select SRS sample of households

16. Random Samples • Advantages • Ability to generalise from sample to population using statistical techniques • Inferential statistics • High probability that sample generally representative of the population on variables of interest

17. Non-random Samples • Purposive • Quota • Accidental • Generalizability based on “argument” • Replication • Sample “like” the population

18. Selecting a sampling method • Depends on the population • Problem and aims of the research • Existence of sampling frame

19. Conclusion • The purpose of sampling is to select a set of elements from the population in such a way that what we learn about the sample can be generalised to the population from which it was selected • The sampling method used determines the generalizability of findings Random samples X Non-random sample