This document discusses sampling design and different sampling methods. It defines key terms like population, sample, and sampling unit. It explains the purposes of sampling like enabling the study of large populations in an economical, speedy, and accurate manner. The document differentiates between probability and non-probability sampling. It describes various probability sampling techniques like simple random sampling, systematic sampling, stratified sampling, and cluster sampling. It also outlines non-probability sampling methods such as accidental sampling, purposive sampling including quota and judgement sampling.

1. SAMPLING DESIGN
PROBABILITY SAMPLING
NON-PROBABILITY SAMPLING
 Muhammad Bilal, R.No.06
 Ali Hussnain syed R.No18
 Abbas Ali
 R.No.31

2.  A population is the set of data of all possible
measurements (or observations) of individuals or items.
 E.g.. the heights of all students in a junior college, the
lengths of life of all the light bulbs produced by a
manufacturer.
 A sample is a set of data chosen from a population and
is a subset of the population.
 A sampling unit is an individual member of a sample.

3. Definition of Sampling:
 Measuring a small portion of something and then
making a general statement about the whole thing.
 Process of selecting a number of units for a study in
such a way that the units represent the larger group
from which they are selected.

4. Why We Need Sampling
(Purposes and Advantages of Sampling)
 Sampling makes possible the study of a large, (different
characteristics) population.
 Sampling is for economy
 Sampling is for speed.
 Sampling is for accuracy.
 Sampling saves the sources of data from being all
consumed.

5. SAMPLING DESIGN
1. What is the target population?
- Target population is the aggregation of elements (members
of the population) from which the sample is actually
selected.
2. What are the parameters of interest?
- Parameters are summary description of a given variable in
a population.
3. What is the sampling frame?
- Sampling frame is the list of elements from which the
sample is actually drawn. Complete and correct list of
population members only.
4. What is the appropriate sampling method?
- Probability or Non-Probability sampling method

6. SAMPLING DESIGN
5. What size sample is needed?
There are no fixed rules in determining the size of a sample
needed. There are guidelines that should be observed in
determining the size of a sample.
 When the population is more or less homogeneous
and only the typical, normal, or average is desired to
be known, a smaller sample is enough. However, if
differences are desired to be known, a larger sample is
needed.
 When the population is more or less heterogeneous
and only the typical, normal or average is desired to
be known a larger sample is needed. However, if
only their differences are desired to be known, a
smaller sample is sufficient.

7. SAMPLING DESIGN
 The size of a sample varies inversely as the size of
the population. A larger proportion is required of a
smaller population and a smaller proportion may
do for a bigger population.
 For a greater accuracy and reliability of results, a
greater sample is desirable.
 In biological and chemical experiments, the use of
few persons is more desirable to determine the
reactions of humans.
 When subjects are likely to be destroyed during
experiment, it is more feasible to use non-humans.

8. General Types of Sampling
1. Probability sampling
2. Non-probability sampling

9. PROBABILITY
SAMPLING
 The sample is a proportion (a certain percent) of the
population and such sample is selected from the
population by means of some systematic way in which
every element of the population has a chance of being
included in the sample.
 Randomization is a feature of the selection process
rather than an assumption about the structure of the
population.
 More complex, time consuming and more costly

10. Non-probability sampling
 The sample is not a proportion of the population and
there is no system in selecting the sample. The
selection depends upon the situation.
 No assurance is given that each item has a chance of
being included as a sample
 There is an assumption that there is an even
distribution of characteristics within the population,
believing that any sample would be representative.

11. TYPES OF PROBABILITY
SAMPLING

12. A. PURE RANDOM SAMPLING
 This type of sampling is one in which every one in
the population of the inquiry has an equal chance of
being selected to be included in the sample.
 Also called the lottery or raffle type of sampling.
 This may be used if the population has no
differentiated levels, sections, or classes.
 Done with or without replacement

13. PURE RANDOM SAMPLING
 main advantage of this technique of sampling is
that, it is easy to understand and it is easy to apply
too.
 disadvantage is that, it is hard to use with too large
a population because of the difficulty encountered
in writing the names of the persons involved.

14. B. SYSTEMATIC SAMPLING
 A technique of sampling in which every name (old
system of counting off) in a list may be selected to be
included in a sample.
 Also called as interval sampling, there is a gap or
interval, between each selected unit in the sample.
 Used when the subjects or respondents in the study
are arrayed or arranged in some systematic or logical
manner such as alphabetical arrangement and
geographical placement from north to south.

15. SYSTEMATIC SAMPLING
 Main advantage is that it is more convenient, faster,
and more economical
 Disadvantage is that the sample becomes biased if
the persons in the list belong to a class by
themselves whereas the investigation requires that
all sectors of the population are to be involved.

16. C. STRATIFIED SAMPLING
 The process of selecting randomly, samples from the
different strata of the population used in the study.
 Advantage is that it contributes much to the
representative of the sample

17. D. CLUSTER SAMPLING
 Also called as multistage cluster sampling
 Used when the population is so big or the geographical area of
the research is so large.
 Advantage : efficiency
 Disadvantage: reduced accuracy or representativeness, on the
account of the fact that in every stage there is a sampling error.

18. TYPES OF NON-PROBABILITY
SAMPLING

19. A. ACCIDENTAL SAMPLING
/CONVENIENCE SAMPLING
 No system of selection but only those whom the researcher or
interviewer meet by chance are included in the sample.
 Process of picking out people in the most convenient and
fastest way to immediately get their reactions to a certain hot
and controversial issue.

20. ACCIDENTAL / CONVENIENCE SAMPLING
 Not representative of target population because sample
are selected if they can be accessed easily and
conveniently.
 Advantage : easy to use
 Disadvantage: bias is present
 It could deliver accurate results when the population is
homogeneous.

21. B. PURPOSIVE SAMPLING
 The respondents are chosen on the basis of their
knowledge of the information desired.

22. TYPES OF PURPOSIVE SAMPLING
1. QUOTA SAMPLING
 Specified number of persons of certain types are included in
the sample.
 Advantage over accidental sampling is that many sectors of
the population are represented. But its representativeness is
doubtful because there is no proportional representation and
there are no guidelines in the selection of the respondents.

23. PURPOSIVE SAMPLING
2. JUDGEMENT SAMPLING
 Sample is taken based on certain judgements about the
overall population.
 Critical issue: objectivity “how much can judgement be relied
upon to arrive at a typical sample?”
 Advantage: reduced cost and time involved in acquiring the
sample

24. Acknowledgment
 We are very thankful to:
Sir. Dr. Iftikhar Hussain