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 Avoiding bias
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
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