This document discusses different types of sampling designs used in census and survey research. It describes probability sampling designs like simple random sampling, stratified random sampling, systematic random sampling, and complex random sampling techniques. It also covers non-probability sampling designs such as quota sampling and deliberate sampling. The key aspects of sample design discussed are representation, accuracy, feasibility given constraints, and generalizability of results.

1. SAMPLING DESIGN
J. JANARDHAN

2. CENSUS AND SAMPLE SURVEY
 In any field of inquiry constitute a ‘Universe’ or ‘Population’
 A complete enumeration of all items in the ‘population’ is known
as a census inquiry
 many a time it is not possible to examine complete population
 Sometimes, it is possible to obtain sufficiently accurate results by
study only a part of total population

3. STEPS IN SAMPLE DESIGN
 The researcher must pay attention in Sample design
(i) Type of universe (ii) Sampling unit (Sta, Dis, Md, Vi)
(iii) Source list (iv) Size of sample:
(v) Parameters of interest (vi) Budgetary constraint:
(vii) Sampling procedure

4. CHARACTERISTICS OF
SAMPLE DESIGN
 (a) Sample design must result in a truly representative sample
 (b) It must be such which results in a small sampling error
 (c) it must be viable in the context of funds
 (d) It can be controlled in a better way
 (e) The results of the sample study can be applied, in general, for
 the universe with a reasonable level of confidence

5. DIFFERENT TYPES
 There are different types of sample designs
probability sampling
non-probability sampling
 Probability sampling is based on the concept of random selection
 non-probability sampling is ‘non-random’ sampling
 non-probability sampling is also having different types
deliberate sampling,
purposive sampling
judgment sampling
Quota sampling

6. PROBABILITY SAMPLING
 Every item of the universe has an equal chance
 By a lottery, individual units are picked up whole group
 we can measure the errors of estimation or the significance
 of results
 This fact brings out the superiority of random sampling design
 (a) It gives equal priority to each element in the population
 (b) It gives each possible sample combination an equal probability
of being chosen

7. SIMPLE RANDOM SAMPLING
 It is also known as chance sampling
 Each and every item in the population has an equal chance
 For instance, you want to select 100 clients to survey
 If there were 1000 clients over the past 12 months
 Then, the sampling fraction is f = n/N = 100/1000 = .10 or 10%

8. STRATIFIED RANDOM SAMPLING
 It is used when the population does not constitute a homogeneous
group
 Under this sampling the population is divided into several sub-
populations ( ‘Strata’)
 We select items from each stratum to constitute a sample
 Since each stratum is more homogeneous than the total population
 We are able to get more precise estimates for each stratum
 Stratified sampling results in more reliable and detailed
information

9. SYSTEMATIC RANDOM SAMPLING
 (i) Systematic random sampling:
 The most practical way of sampling is to select every ith item on a
list.
 For instance, if a 4 per cent sample is desired,
 the first item would be selected randomly from the first twenty-
five
 Thereafter every 25th item would automatically be included in the
sample

10. SYSTEMATIC RANDOM SAMPLING
 (i) Systematic random sampling:


11. COMPLEX RANDOM SAMPLING
(iii) Cluster sampling:
 If the total area of interest happens to be a big one
 Sample can be taken is to divide the area into a number of smaller
non-overlapping areas (clusters)
 Cluster sampling, no doubt, reduces cost
 Cluster sampling is used only because of the economic advantage
 But, certainly it is less precise than random sampling
(iv) Area sampling: If clusters happen to be some geographic sub-
divisions
(v) Multi-stage sampling: Banking Sector in India

12. COMPLEX RANDOM SAMPLING
 (vii) Sequential sampling:
 This sampling design is some what complex sample design.
 The ultimate size of the sample under this technique is not fixed
in advance,
 But is determined according to mathematical decision rules on the
basis of information yielded as survey progresses

13. QUOTA SAMPLING
 Taking random samples from individual strata is often so
expensive
 that interviewers are simply given quota to be filled from different
strata
 The actual selection is left to the interviewer’s judgment
 The size of the quota for each stratum is generally proportionate
to the size of that stratum in the population
 Quota samples generally happen to be judgment samples rather
than random samples

14. DELIBERATE SAMPLING
 Deliberate sampling is also known as purposive or non-
probability sampling
 This sampling method involves purposive or deliberate selection
of particular units

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