1. Population vs. Sample

2. Population: a set which includes all measurements of interest to the researcher (The collection of all responses, measurements, or counts that are of interest) Sample: A subset of the population

3. Why sampling? Get information about large populations  Less costs  Less field time  More accuracy i.e. Can Do A Better Job of Data Collection  When it’s impossible to study the whole population

4. Sampling Techniques

5. Samples Having clearly identified a thesis statement or question, as well as the population, variables and type of data involved, a researcher can begin to conduct his or her study; To conduct research, data from a sample must be collected, which could involve medical testing, laboratory analyses, surveys, etc.

6. Samples The sample must be: 1. representative of the population; 2. appropriately sized (the larger the better); 3. unbiased; 4. random (selections occur by chance); The above criteria are interrelated.

7. Samples To ensure that the four criteria are met, careful planning is needed (any errors in the sample will result in unreliable conclusions); One of several methods can be chosen to achieve randomness when selecting a sample.

8. Types of sampling  Non-probability samples  Probability samples

9. PROBABILITY SAMPLING A probability sampling scheme is one in which every unit in the population has a chance (greater than zero) of being selected in the sample, and this probability can be accurately determined.. When every element in the population does have the same probability of selection, this is known as an 'equal probability of selection' (EPS) design. Such designs are also referred to as 'self-weighting' because all sampled units are given the same weight.

10. Probability samples Random sampling –Each subject has a known probability of being selected Allows application of statistical sampling theory to results to: –Generalise –Test hypotheses

11. Conclusions Probability samples are the best Ensure –Representativeness –Precision

12. Methods used in probability samples  Simple random sampling  Systematic sampling  Stratified sampling  Multi-stage sampling  Cluster sampling

13. Non probability samples Any sampling method where some elements of population have no chance of selection (these are sometimes referred to as 'out of coverage'/'undercovered'), or where the probability of selection can't be accurately determined. It involves the selection of elements based on assumptions regarding the population of interest, which forms the criteria for selection. Hence, because the selection of elements is nonrandom, nonprobability sampling not allows the estimation of sampling errors.

14. Example: We visit every household in a given street, and interview the first person to answer the door. In any household with more than one occupant, this is a nonprobability sample, because some people are more likely to answer the door (e.g. an unemployed person who spends most of their time at home is more likely to answer than an employed housemate who might be at work when the interviewer calls) and it's not practical to calculate these probabilities.

15. Non probability samples  Convenience samples (ease of access) sample is selected from elements of a population that are easily accessible  Snowball sampling (friend of friend….etc.)  Purposive sampling (judgemental) You chose who you think should be in the study  Quota sample

16. Non probability samples Probability of being chosen is unknown Cheaper- but unable to generalise potential for bias

17. Random Sampling Methods Six methods are commonly employed. 1. Simple Random Sampling → all individuals in the population have an equal likelihood of being chosen; → for example, number all students and select the numbers from a hat (minimize the level of control that the researcher has).

18. Simple random sampling

19. Random Sampling Methods - Continued 2. Systematic Random Sampling → used when you are sampling a fixed percentage of the population; → randomly select a starting point, then select every n th individual; → n is referred to as the sampling interval (n = pop size/sample size); → for example, number all students in a list, randomly select a starting point in the list, and select every n th individual.

20. Systematic sampling

21. Random Sampling Methods - Continued 3.Stratified Random Sampling → population is divided into strata, or groups; → randomly select members of each stratum (the number selected is proportional to the stratum’s size); → for example, divide our population into 9’s, 10’s, 11’s and 12’s, and randomly select members in each grade.

22. Random Sampling Methods - Continued 4.Cluster Random Sampling → population is organized into groups; → groups are randomly selected, and all members of the group are sampled; → for example, divide our school into homerooms, randomly select homerooms, and sample all students in selected homerooms.

23. Cluster sampling Section 4 Section 5 Section 3 Section 2Section 1

24. Random Sampling Methods - Continued 5.Multi-Stage Random Sampling → population is organized into groups; → randomly select groups, and then randomly select members in these groups (an equal number selected per group); → for example, repeat the steps for Cluster Random Sampling, but then randomly select students in each selected homeroom.

25. Random Sampling Methods - Continued 6.Destructive Sampling → applicable to products only; → products chosen randomly, tested for quality control.

26. Random Sampling Methods - Continued The sampling method chosen depends on the population of interest; Sometimes, methods can be combined; Careful planning is the key to generating reliable results – always have contingency plans!

27. 1. Determine the type of sampling method used in each scenario. a) The Ontario government randomly selects five high schools in Ontario and surveys each teacher in those schools. Cluster random sampling

28. b) You wish to survey 100 employees at Trillium Shopping Plaza (contains 216 stores). You randomly select 10 stores, then randomly select 10 employees from each store. Multi-staged Random Sample

29. c) Every fiftieth family in the Unionville telephone book is surveyed by phone. Systematic Random Sample

30. d) Jonathon randomly selects three cards from a standard deck of cards. Simple Random Sample

31. 2. In a town of 120 000 people, smoking has been banned in all restaurants. A committee of students wants to find out what the whole town thinks of this new law. The committee wants to survey 1460 people. Which sampling technique is most appropriate?