1. Sample-Sampling-Pengelompokan Data

2. Important statistical terms
Population:
a set which includes all
measurements of interest
to the researcher
Sample:
A subset of the population
(representative/ miniature of population)
Sample
Populasi
Sampling: the process of selecting a sample

3. The purpose for sampling……
To get information about large populations
Reasons for sampling:
Less costs
Less field time
Getting more accurate results
When it’s impossible to study the whole population

4. The sampling process…
POPULATION
INFERENCE
(kesimpulan)
SAMPLE

5. Regarding the sample… POPULATION (N) SAMPLE (n) IS THE SAMPLE
REPRESENTATIVE?
SAMPLE (n)

6. Regarding the inference…
POPULATION (N)
INFERENCE
IS THE
INFERENCE
GENERALIZABLE?
SAMPLE (n)

7. Steps in sampling... 1. Define population (N) to be sampled
2. Determine sample size (n)
3. Control for bias and error
4. Select sample

8. 1. Define population to be sampled...
Identify the group of interest and its characteristics  Inference should be generalizable
…called the “target” population (the ideal selection)
…often times the “accessible” or “available” population must be used (the realistic selection)

9. 2. Determine the sample size...
The size of the sample affects both the representativeness of the sample and the statistical analysis of the data
…larger samples are more likely to detect a difference between different groups
…smaller samples are more likely not to be representative

10. 3. Control for sampling bias and error...
Be aware of the sources of sampling bias and identify how to avoid it

11. 4. Select the sample...
A process by which the researcher attempts to ensure that the sample is representative of the population from which it is to be selected
…requires identifying the sampling method that will be used

12. Types of sampling
Non-probability samples
Probability samples

13. Non probability samples
Sample is not drawn randomly
Convenience samples (ease of access)
sample is selected from elements of a population that are easily accessible
Purposive sampling (judgemental)
We chose who we think should be in the study
In the case of non probability sample we usually cannot draw conclusions about the whole population

14. Non probability samples
Probability of being chosen is unknown
i.e
Cheaper sample - but unable to generalise
potential for bias

15. 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

16. Conclusions Probability samples are the best Ensure Representativeness
Precision

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

18. Simple Random
Population
Sample
Taken from simple population

19. Stratified Population Sample Sample
Guarantee that all groups are represented like in the population
Population
Sample
18-29
Proportional allocation
30-49
65+
50-64
Even allocation
Compare groups
Sample

20. Systematic Select picking interval e.g every fifth.
Choose randomly one among the first five (or whatever the picking interval is).
Pick out every fifth (or whatever the picking interval is) beginning from the chosen one.

21. Cluster Divide population into clusters (schools, districts,…)
Choose randomly some of the clusters.
Draw sample from chosen clusters using appropriate sampling method (or investigate chosen groups in whole).
Sample

22. Cluster sampling
Cluster: a group of sampling units close to each other i.e. crowding together in the same area or neighborhood

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

24. Sampling Error
Sample 1
mean 43,5
Population
mean 40,8
Sample 2
mean 40,3
Different samples from the same population give different results
Sample 3
mean 45,4

25. Errors in sampling
Systematic error (or bias)
Inaccurate response (information bias)
Sampling error (random error)