Sampling

1. Sampling Lecture 9 Prof. Development and Research Lecturer: R. Milyankova

2. Objectives of this session: • To understand the need for sampling in B&M research • To be aware of a range of probability and non-probability sampling techniques • To be able to select, to justify and to use a range of sampling techniques • To be able to assess the representativeness of respondents • To be able to apply the knowledge, skills and understanding gained to your own research project

3. Sampling terminology • Census – counting of the population • Population – the full set of cases from which a sample is taken • Sampling techniques – range of methods that enable you to reduce the amount of data you collect Population Case or element Sample

4. Need to sample Sampling provides a valid alternative when: • It would be impracticable for you to survey the entire population • Your budget constraints prevent you from surveying the entire population • Your time constraints prevent you from surveying the entire population • You have collected all the data but need the results very quickly

5. Major types of sampling methods • Probability or representative sampling - The probability for each case is known and is usually equal for all cases - Uses some form of random selection - Requires that each unit has a known (often equal) probability of being selected - Used more for survey-based than for experiment research • Non-probability or judgemental sampling - The probability of the separate cases is not known preliminary - Selection is systematic or haphazard, but not random - More frequently used for case study research

6. SamplingTechniques Extreme case Heterogeneous Homogeneous Critical case Typical case

7. Probability sampling: Stages • Identify a suitable sampling frame based on your research questions and/or objectives (unbiased, current and accurate) Checklist for selecting a sample frame • Are cases listed in the sampling frame relevant to your research topic, are they current? • Does the sampling frame include all cases, is it complete? • Does the sampling frame exclude the irrelevant cases, is it precise? • Can you establish control precisely how he sample will be selected? (when purchased lists of samples)

8. Probability sampling: Stages 2. Decide on a suitable sample size – the larger the sampling size, the lower the error (the sampling is a compromise between the accuracy of your findings and the amount of time and money you invest in collecting data) • The confidence you need to have in your data (the level of certainty) • The margin of error that you can tolerate • The types of analyses you are going to undertake • The size of the total population from which your sample is being drawn

9. Probability sampling: Stages • Minimum number of cases – 30 (The Economist). Less than 30 – use all cases + expert system • Level of certainty – 95 % • The margin of error depends on response rates (see Saunders, M. et all, 2003, Table 6.1, page 156)

10. Probability sampling: Stages Reasons for non-response: • Refusal to respond • Illegibility to respond • Inability to locate respondents • Respondent located but unable to make contact Total response rate = total number of responses total number of sample – ineligible Active response rate = total number of responses total number of responses–(ineligible+unreachable)

11. Probability sampling: Stages Select the most appropriate sampling technique and select the sample • Simple random – accurate and easily accessible, concentrate on face-to face contact otherwise does not matter, difficult to explain to support workers, high cost Close your eyes and choose the number • Systematic - accurate and easily accessible, suitable for all sizes, concentrate on face-to face contact otherwise does not matter, relatively easy to explain, low cost Every third case for example • Stratified random - accurate and easily accessible, suitable for all sizes, concentrate on face-to face contact otherwise does not matter, relatively difficult to explain, low cost Divide the population into strata (men-women, retail-corporate) • Cluster– as large as practicable, quick but reduced precision Discrete groups=clusters (geographical areas, town regions) • Multi-stage – substantial errors possible It is a development of the cluster sampling Sampling fraction = actual sample size total population

12. Probability sampling: Stages Checking the sample is representative for the population • Compare with samples, done for the needs of marketing or other sources for the population researched

13. Groups in Sampling The Theoretical Population How to identify the suitable sampling frame?

14. Groups in Sampling The Theoretical Population What population can you get access to? (Telephone directory)

15. Groups in Sampling The Theoretical Population The Study Population

16. Groups in Sampling The Theoretical Population The Study Population How can you get access to them? (methods of research)

17. Groups in Sampling The Theoretical Population The Study Population The Sampling Frame -complete list of all the cases in the population

18. Groups in Sampling The Theoretical Population The Study Population The Sampling Frame Who is in your study? The sample

19. Deciding on a suitable sampling size The larger your sampling size the lower the error • The confidence you need to have in your data – the level of certainty that the characteristics of data collected will represent the characteristics of the total population • The margin of error that you can tolerate – the accuracy you require for any estimates made from your sample • The types of analysis you are going to undertake – • The size of the total population from which your sample is being drawn

20. Where Can We Go Wrong? The Theoretical Population The Study Population The Sampling Frame The sample

21. Sample sizes for different sizes of population at a 95% level of certainty

22. SamplingTechniques Extreme case Heterogeneous Homogeneous Critical case Typical case

23. Non-probability sampling • Quota sampling – non-random, used for interview surveys, the population is divided into specific groups, stratified, less costly, can be set up very quickly • Purposive (judgmental) sampling – - extreme case or deviant sampling - heterogeneous or maximum variation sampling - homogeneous all sample members are similar - critical case sampling – selected either because they are important or because they are different - typical case sampling -

24. Non-probability sampling • Snowball sampling – when it is difficult to identify members of the desired population • Self-selection sampling – participate if they want • Convenience (haphazard) sampling – select those cases that are easier to obtain for your sample

25. Statistical Terms in Sampling Variable self esteem 1 2 3 4 5

26. Statistical Terms in Sampling Variable self esteem 1 2 3 4 5 Statistic Average = 3.72 sample

27. Statistical Terms in Sampling 1 2 3 4 5 self esteem Variable Statistic Average = 3.72 sample Parameter Average = 3.75 population

28. sample sample sample 5 5 5 0 0 0 5 5 5 0 0 0 3 . 0 3 . 2 3 . 4 3 . 6 3 . 8 4 . 0 4 . 2 4 . 4 3 . 0 3 . 2 3 . 4 3 . 6 3 . 8 4 . 0 4 . 2 4 . 4 3 . 0 3 . 2 3 . 4 3 . 6 3 . 8 4 . 0 4 . 2 4 . 4 1 5 1 0 5 0 3 . 0 3 . 2 3 . 4 3 . 6 3 . 8 4 . 0 4 . 2 4 . 4 The Sampling Distribution Average Average Average ...is the distribution of a statistic across an infinite number of samples The Sampling Distribution...