1. Lecture 9 Prof. Development and Research Lecturer: R. Milyankova
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. Sampling Techniques 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. The Theoretical Population
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. The Theoretical Population
Groups in Sampling
The Theoretical Population
The Study Population

16. The Theoretical Population How can you get access to them?
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. The Theoretical Population
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. The Theoretical Population
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
Margin of error
Population 5% 3% 2% 1% 50 44 48 49
100 79 91 96 99
150
108
132
141
148
200
168
185
196
250
151
203
226
244
300
234
267
291
400
334
384
500
217
340
414
475
750
254
440
571
696
1000
278
516
706
906
2000
322
1091
1655
5000
357
879
1622
3288
10000
370
964
1936
4899
100000
383
1056
2345
8762
1066
2395
9513

22. Sampling Techniques 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

26. Statistical Terms in Sampling
Variable
self esteem
Statistic
Average = 3.72
sample

27. Statistical Terms in Sampling
Variable
self esteem
Statistic
Average = 3.72
sample
Parameter
Average = 3.75
population

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