Thank you Dr Yousef for an excellent lecture on sampling

1. @kaimrc_edu
1

2. SAMPLING TECHNIQUES
Oyindamola B. YUSUF
Biostatistician
KAIMRC-WR

3. LECTURE OUTLINE
• Introduction
• Determining number of subjects
• Total population Surveys
• What is sampling?
• Reasons for taking a sample
• Principle of sampling
• Types of sampling

4. DETERMINING NUMBER OF SUBJECTS
• Statistical considerations
• Practical Considerations

5. PRACTICAL CONSIDERATIONS
• AVAILABILITY OF SUBJECTS
• RESOURCES- TIME, MONEY,
PERSONELL

6. STATISTICAL CONSIDERATIONS
• Purpose of study
• Primary outcome measure
• How small a difference is to be detected
• Type 1 error: To find treatments
significantly different if treatments don’t
really differ
• Drop out rate if study is prospective
• Power to detect an actual difference

7. TOTAL POPULATION SURVEY
• Every individual in the defined population
is included and studied.
• It has Advantages and Disadvantages

8. ADVANTAGES OF STUDYING TOTAL
POPULATION
• (a) The estimate is accurate and
without error since no unit is left out
• (b) There is no need to worry about
selection procedure
• (c) And there are no feelings of
discrimination created in the
population.

9. DISADVANTAGES OF TOTAL
POPULATION STUDY
• (a) It is expensive
• (b) It takes time to complete
• (c) Demands a lot of personnel
• (d) It may not be feasible
• (e) It may be less accurate.

10. WHAT IS A SAMPLE ?
• Part of a population selected for
study
• May be able to infer the
characteristics of the population
from those of the sample.

11. ADVANTAGES OF TAKING A SAMPLE
• Advantages
• (a) Less expensive
• (b) Quick results guaranteed
• (c) Demands on personnel is less
• (d) Possibility of obtaining more accurate data because of the smaller
number of units involved

12. DISADVANTAGES OF TAKING A SAMPLE
• Estimate obtained from the sample is likely to be different from
that would have been obtained if the total population have been
studied.
– - this discrepancy is called sampling error and it is always
present.
• It is sometimes difficult to select a good sample i.e. a
representative sample.

13. PRINCIPLE OF SAMPLING
• AVOIDANCE OF BIAS

14. GENERAL CAUSES OF BIAS
• a. Lack of proper knowledge of the population from
which the sample is selected.
• b. Inadequacy of sampling frame.
• c. Personal prejudice - i.e. when personal feelings is
allowed to influence sample selection-observer error.

15. HOW TO AVOID BIAS
• TAKE A PROBABILITY
SAMPLE
• THIS IS KNOWN AS A
RANDOM SAMPLE
• SAMPLE HAS A KNOWN
CHANCE OF BEIGN
SELECTED

16. DEFINITIONS OF TERMS NEEDED TO
TAKE A PROBABILITY SAMPLE
• (i) Sample Size
• (ii) Sampling Fraction
• (iii) Sampling Frame
• (iv) Sampling Unit
• (v) Unit of Enquiry
• (vi) Sampling Error
• (vii) Good or Representative Sample

17. EXAMPLES OF PROBABILITY SAMPLES
• SIMPLE RANDOM SAMPLE
• SYSTEMATIC SAMPLE
• STRATIFIED RANDOM
SAMPLE
• CLUSTER RANDOM SAMPLE
• MULTI-STAGE RANDOM
SAMPLE

18. SIMPLE RANDOM SAMPLE
• Simple random sample: A sampling procedure in which each unit in the
population has the same (equal) chance of being selected. However the
population must be finite and a sampling frame must exist.
• Each unit must have an assigned number in the sampling frame. Without
a proper sampling frame, it is impossible to take a simple random sample.

19. SELECTION PROCEDURE OF SIMPLE
RANDOM SAMPLE
• 1. Lottery method
• 2. Use of table of Random numbers.
• 3. Use of computer facilities.
• Lottery
• 1. Construct a frame of all the sampling
units.
• 2. Use ballots to select the required
number of units.

20. SYSTEMATIC RANDOM SAMPLE
• Unit selected in any one sample
occupied related position to each
other in the sampling frame
• Determine sampling fraction and
sampling interval-k
• The first unit to be selected is selected
at random between 1 and k.
• Thereafter every kth unit is selected.

21. EXAMPLE ON SYSTEMATIC SAMPLE
• Suppose a sample of 50 patients is required
from the register of 1,000 patients available in
the records section of a teaching hospital. The
sample fraction here will be 50/1000 = 1/20
, thus k = 20.
• The first member in the register is selected
randomly between 1 and 20.
• The first and every 20th member is
subsequently selected as sample members.

22. STRATIFIED RANDOM SAMPLE
• Population is divided into homogenous strata according to some
relevant characteristics of the population
• A random sample is selected from each stratum
• The sample size may be sub-divided in proportion to the
population size in each stratum. This is called a proportional
allocation.
• For example to select 200 units from a population of 6000 units of
which 2000 units are females and 4000 units males. The number
chosen in each sex stratum will be 68 and 132 respectively if there
is a proportionate allocation of the sample numbers in the strata.

23. MULTISTAGE RANDOM SAMPLE
• Multistage Sample
• Sampling in stages
• Final sample obtained after more than one stage
• Ex. Selection sample of students from the university
• 1st Stage Selections: Select 50 depts at random out of the existing 160
(for example).
• 2ND Stage: from each selected depts, list all the students then select
• students in each of these.

24. CLUSTER SAMPLE
• The sampling unit is a cluster of units
• Units could be households, streets, or villages.
• The approach is useful in rural areas where
there are no sampling frames.
• Multi stage sample and cluster sample are the
most popular method in a rural area.
• In cluster sample, selection takes place only
once.

25. EXAMPLE OF CLUSTER SAMPLE
• Study of attitudes of medical students to
HIV/AIDS pandemic.
• Define each department as a cluster of students
• May select a specified number of departments
at random out of the total number of
departments in the University of Ibadan
• Study all students in the departments selected.

26. EXAMPLES OF NON-PROBABILITY
SAMPLES
• Volunteer
• Judgmental
• Quota
• Purposive
• Convenience

27. SUMMARY
• Feasibility of probability samples
• Endeavour to always take a probability sample
• Always report sampling procedures

28. THANK YOU.