1. Sampling

2. Why, when?
If we would like to draw conclusions on a target population but examining the total of it is not reasonable or not feasible:
Too expensive (cost, time, other resources)
Impossible (e.g. the population is changing fast; some parts of the population is not available etc.)
Measurement destroys or modifies the subjects.

3. Definitions
Target population: the part of the total population that is in the focus of the research.
Sampling technique/strategy/method: the process of selecting the sample elements.
Sample: a subgroup of the target population that is actually involved in the research. The sample represents the target population through the analysis.
Sampling frame: the list of the cases in the target populaion from which the sample is drawn.

4. Source: Saunders et al. 2016

5. Representativeness
A sample is representative if its structure (considering every possible dimensions) is the same as the target population’s.
In theory total representativeness is impossible to reach. However, we call a sample practically representative, if it is representative to all the important (or traditionally considered) dimensions.
Biased sample: the sample is not representative (not typical) of the target population.

6. Representativeness
If the target sample and the sampling frame do not match completely, than it is impossible to even know if the sample is representative or not.
It is not always important to have a representative sample: it depends on the research questions.
However, be aware how to formulate your conclusions. If the sample is not representative, you can make statements only about the sample.

7. If the sample is not representative (and it should be)…
Gather additional data if possible
If the difference is considered to be small, weight your sample
Conduct a full new sampling
Change your research questions

8. Process of sampling Identifying the target population
Identifying the sampling frame
Choosing the sampling technique
Decision on the sample size
Identifying the sample elements we would like to measure/collect
Data collection
Checking on the collected sample (representativeness?)

9. Sampling techniques
Probability sampling: each element of the population has the same chance (probability) to be selected into the sample.
In reality no true random sampling is possibe, only quasi-random.
Non-probability sampling: the chances are not equal.

10. Source: Saunders et al. 2016

11. Simple random sampling
Selecting the sample at random (with the same probability) from the sampling frame. Random generated numbers are required.
A large sample is necessary.

12. Systematic random sampling
Sample elements selected with regular intervals. The first element is selected randomly.
Not even a frame is necessary.
Easy to use.
Prerequisite: it should be possible to arrange the elements in an order.

13. Stratified sampling
Target population (or the frame) is divided into discrete strata (subgroups that are homogeneous inside and heterogeneous outside).
A random sample is drawn from each strata.
It is more likely to get a representative sample even from smaller samples.

14. Cluster sampling
Target population (or the frame) is divided into discrete clusters (natural subgroups that are heterogeneous inside and similar to each other).
First,a random sample is selected from the clusters, then the selected clusters are examined (every element within them or you can use a random sampling)
Can consist one or two (or more) stages of clustering

15. Convenience/availability sampling
Selecting subjects haphazardly only based on their availability.

16. Quota sampling
Precalculated quotas are used to ensure the representativeness in a small number of dimensions (like sex, age category, geographical area…).
Outsde of these dimensions it is considered to be a convenience sample (thus if there is any other important factors not covered by the quotas, than it will be highly non-representative).

17. Purposive sampling
Selecting the cases (based on prior information and/or knowledge) that serves your research golas the best.
The best in the case of very small samples.
It is not representative (in the statistical sense), but it can be evean more informative per case.

18. Volunteer sampling Participants volunteer rather than being chosen.
Snowball sampling: predecessor participant provides access to new ones. It is very good in collecting a sample when it is hard for the researcher to get into contact with the participants.
Self-selection sampling: participants volunteer due to advertisements.

19. Selection biases Researcher’s selection: Self-selection Selecting in
Closing out
Self-selection

20. Decision on the sample size
Probability sampling or not
Confidence that the research can tolerate
Margin of error the research can tolerate
Type of analysis (e.g. necessary sizes of the subcategories)
Size of the target population
Expected response rate
Feasibility (cost, time, resources, ethics etc.)

22. Source: Saunders et al. 2016