This document outlines key concepts related to sampling, including: 1. It defines sampling as obtaining information from a subset of a larger population. 2. It describes the sampling process which involves defining the population, identifying a sampling frame, selecting a sampling design, determining sample size, and drawing the sample. 3. It discusses different types of sampling designs including probability (simple random, systematic, stratified, cluster) and non-probability (convenience, judgmental, snowball, quota) sampling. 4. It explains sampling error, differentiating between random and non-sampling errors. Sample size impacts random error, while non-sampling error is not controlled by sample size.

1. OUTLINE
SAMPLING.
 SAMPLING PROCESS.
 SAMPLING TYPES.
 SAMPLING ERROR.
SAMPLE SIZE

2. SAMPLING
The process of obtaining information from a sample of a larger group
(population).
 A sample is “a smaller (but hopefully representative) collection of units from a
population used to determine truths about that population”.

3. SAMPLING

4. Characteristics of Good Samples
Representative
Accessible
Low cost

5. SAMPLING PROCESS
1. Define the population :
The Element ..... individuals
families
seminar groups
Sampling Unit…. individuals over 20
families with 2 kids
seminar groups at ”new” university
Extent ............ individuals who have bought “one”
families who eat fast food
seminar groups doing MR
Timing ......... bought over the last seven days

6. SAMPLING PROCESS
2. Identify the sampling frame :
Select “sample units”
 Individuals
 Household
 Streets
 Telephone numbers
 Companies

7. SAMPLING PROCESS
1. Select a sampling design or procedure :
 PROBABILITY
 NON- PROBABILITY
2. Determine the sample size.
3. Draw the sample.

8. SAMPLING PROCESS

9. SAMPLING TYPES
Probability sampling - equal chance of being included in the sample (random)
◦ -simple random sampling
◦ -systematic sampling
◦ -stratified sampling
◦ -cluster sampling
Non-probability sampling - unequal chance of being included in the sample (non-random)
◦ -convenience sampling
◦ -judgment sampling
◦ -snowball sampling
◦ -quota sampling

10. Probability sampling

11. SIMPLE RANDOM SAMPLING
A sampling procedure in which every element in the population has a known and
equal chance of being selected as a subject (e.g., drawing names out of a hat).

12. SYSTEMATIC SAMPLING
If a sample size of n is desired from a population containing N elements, we might sample one
element for every n/N elements in the population.

13. STRATIFIED SAMPLING
Population is divided on the basis of characteristic of interest in the population e.g. male and
female may have different consumption patterns.

14. Cluster or Area Random Sampling
Clusters of population units are selected at random by dividing the population into clusters
(usually along geographic boundaries) and then all or some randomly chosen units in the
selected clusters are studied.

15. Non-probability
sampling

16. CONVENIENCE SAMPLING
Sometimes known as grab or opportunity sampling or accidental or haphazard sampling.
A type of non probability sampling which involves the sample being drawn from that part of
the population which is close to hand. That is, readily available and convenient.

17. JUDGMENTAL SAMPLING
 The researcher chooses the sample based on who they think would be appropriate for the
study.
 This is used primarily when there is a limited number of people that have expertise in the
area being researched.

18. SNOWBALL SAMPLING
 Selection of additional respondents is based on referrals from the initial respondents.
◦ - friends of friends
 Used to sample from low incidence or rare populations.

19. QUOTA SAMPLING
Quota sampling is a method for selecting survey participants.
In quota sampling, a population is first segmented into mutually exclusive sub-groups, just as in
stratified sampling.
Then judgment is used to select the subjects or units from each segment based on a specified
proportion.
For example, an interviewer may be told to sample 200 females and 300 males between the age
of 45 and 60.
This means that individuals can put a demand on who they want to sample (targeting).

20. SAMPLING ERROR

21. Random Sampling Error
 Random error- the sample selected is not representative of the population due to chance.
 The level of it is controlled by sample size.
 A larger sample size leads to a smaller sampling error.

22. Non-sampling Error
Systematic Error
◦
 The level of it is not controlled by sample size.

23. The basic types of non-sampling error
A non-response error occurs when units selected as part of the sampling procedure do not
respond in whole or in part .
A response or data error is any systematic bias that occurs during data collection, analysis or
interpretation, like:
◦ Respondent error (e.g., lying, forgetting, etc.).
◦ Interviewer bias.
◦ Recording errors.
◦ Poorly designed questionnaires

24. Probability Vs. Non-Probability Sampling
 Non-probability sampling is less time consuming and less expensive.
 The probability of selecting one element over another is not known and therefore the
estimates cannot be projected to the population with any specified level of confidence.

25. SAMPLE SIZE
Size of sample should be determined by a researcher keeping in view:
1. Nature of universe: homo (small sample)
hetero (large sample).
2. No. of classes proposed: directly proportional to the sample size .
3. Nature of study: general (large)
intensive (small).
4. Type of sampling: small random sample is better than a large but bad one.

26. SAMPLE SIZE
5. Standard of accuracy: high level of precision large sample.
6. Availability of finance: sample size =amount of money available.
7. Other considerations: size of population,
size of questionnaire,
nature of units,
conditions.