1. ThiQar college of Medicine Family & Community medicine dept
ThiQar college of Medicine Family & Community medicine dept. Biostatistics Lecture Third stage by: Dr. Muslim N. Saeed December 22nd ,2016

2. Sampling techniques

3. Common speech
Population : all the people living in an area, frequently of a country.
In statistics
Population is any collection of individuals in which we may interested, where these individuals may be anything.

4. Important statistical terms
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

5. If we are interested in:
population
Characteristics of Iraqi people
All people in Iraq
Treatment of diabetics
all diabeticsics
Failure rate in 3rd year of college of medicine
Height of males in 3rd year of college of medicine – ThiQar university

6. Imagine that we are going to make studies on:
Percentage of Iraqi population that had access to internet.
The population we would to ask is bigger than 30 million
- Time
Money
at time of interview we miss some people
It is better to choose sample in appropriate way so that we can obtain later conclusion

7. -- the selection methods for elements of population (sampling methods)
Sample size
- Reliability degree of the conclusions that we can obtain, this is, an estimation of an error that we are going to have (in term of probability).

8. A sample
A finite part of a statistical population whose
properties are studied to gain information
about the whole population.
– A set of respondents selected from a larger
population for the purpose of a survey or
experiment.

9. Sampling
The act, process, or technique of selecting a
suitable sample, or a representative part of a
population for the purpose of determining
parameters or characteristics of the whole
population.

10. Probability sampling
Random sampling
Stratified sampling
Cluster sampling
Systematic sampling
other types of sample technique
Non- probability sampling
Convenience sampling
Purposive sampling
snowball
Quota sample

11. Target Population:
The population to be studied/ to which the investigator wants to generalize his results
Sampling Unit:
smallest unit from which sample can be selected
Sampling frame
List of all the sampling units from which sample is drawn
Sampling scheme
Method of selecting sampling units from sampling frame

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

13. 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

14. Conclusions Probability samples are the best Ensure Representativeness
Precision

15. Simple random sample:
It requires:
Sample frame: a numerical list of all observations (or units) composing the population
Sample fraction: sample size to the total population
Lottery method
Computer generated random sampling
Random number table (random digit)

16. Simple random sampling

17. Systematic sampling
Systematic random sampling – samples according to a rule
E.g., every fifth person is chosen
Problems: same as simple random. Rule must not lead to bias.

18. Systematic sampling

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

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

21. Must have good knowledge of strata
Stratified sampling
Multi-stage sampling
Stratified sampling – break the sample into various subgroups or strata and sample from them.
Must have good knowledge of strata

23. Nonprobability sampling
Qualitative researchers are not as concerned about representativeness
Relevance to the research topic
Importance of context
Sample size does not have to be determined in advance.
Selection of cases gradually over time
Important: many statistics assume random sampling

24. Types of nonprobability sampling
Convenience sampling (haphazard, accidental) – sample whoever is available.
Used by both quantitative and qualitative researchers
Problems
no representativeness
It is haphazard, can be very biased
Not random.

25. Purposive sampling - Use judgment and deliberate effort to pick individuals who meet a specific criteria.
Especially good for exploratory or field research.
Appropriate for at least 3 situations.
1. select cases that are especially informative.
E.g., college coaches and championships
2. desired population for the study is rare or very difficult to locate.
E.g., prostitutes
3. case studies analysis – find important individuals and study them in depth.

27. Errors in sample
Systematic error (or bias)
Inaccurate response (information bias)
Selection bias
Sampling error (random error)