1. brian schnick

2. BASIC CONCEPTS IN SAMPLING  Advantages of Sampling  Sampling Error  Sampling Procedure

3. Advantages of Sampling  Sampling is a necessity in many geographic research problems Population may be simply too big for 100% contact  Sampling is an efficient and cost-effective method of collecting information Less time, cost, personnel, etc. required vs. collecting data on the entire population  Sampling can provide highly detailed information In-depth analysis of the few vs. cursory analysis of the many

4. Advantages of Sampling continued  Sampling allows repeated collection of information quickly and inexpensively Phenomena under study may frequently/rapidly change, requiring updates  Sampling can provide a high degree of accuracy Fewer data points allow for detailed study vs. superficial look at many points

5. Sampling Error  Randomness must be incorporated to represent population accurately But samples are never 100% accurate  Methodology may introduce systematic error Sampling elements other than the elements of interest

6. Sampling Error continued  Improper sampling design What method produces the required level of accuracy?  Operational, logistic, personnel problems Inconsistent collection practices, errors in recording data

7. Sampling Procedure  Step 1: Conceptually define target population and target area Target Population: the complete set of individuals from which information is to be collected Target Area: the entire region or set of locations from which information is to be collected

8. Sampling Procedure continued  Step 2: Designate sample population and sampled area from sampling frame Sampling frame: practical structure that contains the entire set of elements from which the sample will actually be drawn Sampled population: set of all individuals contained in the sampling frame, from which the sample is drawn Sampled area: set of all locations within the study area from which the sample is drawn

9. Sampling Procedure continued  Step 3: Select Sampling Design Probability sampling preferred over non-probability sampling Types of probability samples include random, systematic, stratified, cluster,and hybrid Spatial and non-spatial variations in sampling design exist

10. Sampling Procedure continued  Step 4: Design research instrument and operational plan Direct observation, field measurement, questionnaires, personal & telephone interviews Establish protocols for handling anticipated problems Logistic & procedural tasks completed before sampling

11. Sampling Procedure continued  Step 5: Conduct pretest Trial run/pilot survey of sample collection method Correct discovered problems Results may indicate sample size

12. Sampling Procedure continued  Step 6: Collect sample data Consistency in collection methods and procedures is essential Ensure high level of quality control

13. TYPES OF PROBABILITY SAMPLING  Simple Random  Systematic  Stratified  Cluster  Hybrid

14. Simple Random Sampling  All individuals in the sampling frame have an equal chance at selection  All individuals ordered sequentially and chosen by random number generator

15. Systematic Sampling  All individuals ordered sequentially and chosen by fixed interval

16. Stratified Sampling  Population divided into subgroups/areas then sampled  Stratified Sampling can be Proportional: population ratios maintained in subgroups Disproportional: population rations not maintained in subgroups 20% of a city live in apartments – do the subgroups sampled have 20% in apartments?

17. Cluster Sampling  Population divided into subgroups/areas  Some subgroups/areas selected  100% of selected subgroups/areas sampled

18. Hybrid Sampling  Combination of sampling designs Example: cluster of individuals sampled on a systematic time frequency

19. SPATIAL SAMPLING  Types  Point sampling in detail

20. Types of Spatial Sampling  Line Sampling  Area Sampling  Point Sampling

21. Point Sampling  Simple random point sampling  Systematic point sampling

22. Point Sampling continued  Stratified point sampling  Cluster point sampling

23. Point Sampling continued  Hybrids Stratified systematic unaligned point sample (most widely used) Disproportional stratified systematic aligned point sample

24. Point Sampling continued  Hybrids Cluster systematic point sample Disproportional stratified cluster point sample

25. Additional Material  Experiment-Resources.com Experiment-Resources.com  US Census US Census  Wiki Wiki  YouTube YouTube

26. Credits  Outline and scanned graphics from An Introduction to Statistical Problem Solving in Geography - 2d Edition, McGrew & Monroe, Waveland Press, Inc., 2009