This document provides an overview of sampling techniques and sampling design. It discusses the significance of sampling and defines key terms like population, sample, sampling frame, representative sample, and sampling bias. It also describes different probability sampling techniques like simple random sampling, systematic random sampling, stratified random sampling, cluster sampling, and multi-stage sampling. Finally, it briefly covers non-probability sampling techniques and factors to consider in sample size and design.

1. DR. UROOJ A. SIDDIQUI
Sampling Techniques
and Sampling Design

2. Significance of Sampling
1. A large number of units can be studied with small
representative sample.
2. Economy of time and resources; It saves time, energy and
money
3. Useful for a study of homogenous universe.
4. Useful when data is unlimited and more
scattered/unaccessable
5. Higher degree of accuracy with use of scientific method on a
smaller number of units
6. Inferences more reliable with data collected by well trained
investigators.
7. Easier for tabulation and analysis.

3. Basic Terms
Population or Universe: represents the entire group of
units which is the focus of the study. It is group from which
the sample is to be selected.
Sample: The segment of the population that is selected for
investigation.
Sample Unit: Unit of analysis/research => units of samples
Sampling Frame: The listing of all units in the population
from which the sample will be selected.
Census: The enumeration of an entire population

4. Basic Terms (contd.)
Representative Sample: A sample that reflects the population
accurately.
Sampling Bias: A distortion in the representativeness of the
sample that arises when some members of the population stand
little or no chance of being selected for inclusion in the sample.
1. Sampling Error
2. Non-sampling Error (Systematic Bias)
Non-response: A source of non-sampling error that
occurs whenever some members of the sample refuse to
cooperate, cannot be contacted, or for some reason cannot
supply the required data

5. Basic Terms (contd.)
Sampling Types
Probability Sample: A sample that has been selected
using random selection so that each unit in the population
has a known/equal chance of being selected.
Non-probability Sample: A sample that has not been
selected using a random selection method.

6. Sample Design
 Type of Universe/Population: all objects to be studied.
Finite – population of city, factory workers, flats in colony
Infinite – TV/Radio audience; stars in sky
 Sampling Unit: city / village/ locality / family / individual
 Source List: Sampling Frame
 Sample Size: optimum; neither too large nor small
 Parameters of Interest: variables to be measured
 Budget: cost and convenience
 Sampling Procedure: Process of selecting units of sample –
sampling technique

7. Sampling
 When field of inquiry (population) is large – we
select a few units/items – respondents
 Selected respondents constitute sample
 Selection process of respondents is sampling
technique
 The survey so conducted is called as sample survey
 Population – N ; Sample – n
 Sample should be representative of population

8. Sampling Techniques
Probability Sample: Random/Chance Sampling Design
 Simple Random Sampling (RS)
 Systematic RS
 Stratified RS
 Cluster Sampling
 Multi-stage Sampling
Non-probability Sample: Non-Random Sampling Design
 Convenience
 Purposive / Judgmental
 Quota
 Snowballing

9. 1. Simple Random Sampling
Simple Random Sampling: It is a sample
randomly drawn from the population, in which
each unit has equal probability of inclusion in
the sample.
a. Define Population (N) (e.g DU students)
b. Devise a Comprehensive Sampling Frame
c. Decide your sample size (n)
d. List whole population assigning numbers 1 to N.
e. Using a table of random numbers (Manual or
computer generated), select (n) different random
numbers that lie between 1 and N
f. The units with the selected n random numbers
constitute the sample

10. Random Numbers
44 38 67 30 75 68 98 3 36 96 30 35 16 35 19 8 49 35
13 6 18 44 98 26 18 36 50 59 29 75 9 31 92 85 12 63
60 82 69 89 22 68 26 100 69 90 39 90 33 18 20 41 10
28 61 89 19 96 74 34 52 54 69 27 89 1 32 9 39 17
66 6 27 16 56 63 96 86 41 81 21 34 30 63 26 49 44
31 34 100 68 24 88 70 70 44 67 14 34 4

11. 2. Systematic Random Sampling
Systematic Random Sampling: It is a random sample
systematically drawn from the population, in which
each unit has equal probability of inclusion in the
sample.
a. Define Population (N) (e.g DU students)
b. Devise a Comprehensive Sampling Frame
c. Decide your sample size (n)
d. List whole population assigning numbers 1 to N.
e. Find out sampling fraction: s = N/n
f. Use random numbers to select the first sample (nₒ)
between 1 and s.
g. Select units in the following sequence: nₒ, nₒ+ s, nₒ+2s …..
.. [nₒ+(n-1)s]
h. The students with the selected n numbers constitute the
sample.

12. 3. Stratified Random Sampling
Stratified Random Sampling is a sample drawn
randomly and/or systematically from the
population, after stratification using a stratifying
criterion, in which each unit has equal
probability of inclusion in the sample.
a. Define Population (N) (e.g DU students)
b. Decide your sample size (n)
c. Find out sampling fraction: s = N/n
d. Stratify the population based on a stratifying
criterion
e. List whole population in each strata.
f. Use random or systematic random sampling method
to select units from each strata, such that the selected
units constitute the sample (n).

13. 4. Cluster Sampling
Cluster Sampling is a sample drawn from all
units of randomly selected clusters out of a
population divided into clusters using a
clustering criterion.
a. Divide the population into clusters using a clustering
criterion
b. Use random or systematic random sampling method
to select (c) clusters from the total number of clusters.
c. The sample constitutes all the units from each
selected cluster z = c₁ + c₂ + ….+cn

14. 5. Multi-Stage Sampling
Multi-stage sampling refers to sampling plans where
the sampling is carried out in stages using a particular
sampling method to select smaller and smaller
sampling units at each stage.
In a two-stage sampling design, a sample of primary
units is selected using a particular sampling method
and then a sample of secondary units is selected within
each primary unit using the same or some other
sampling method.

15. e.g. Multi-Stage Cluster Sampling
Multi-Stage Cluster Sampling is a sample drawn
randomly and/or systematically from randomly
selected clusters out of a population divided into
clusters using a clustering criterion.
a. Divide the population into clusters using a clustering
criterion
b. Use random or systematic random sampling method to
select (c) clusters from the total number of clusters.
c. Decide your sample size (n)
d. The sample selected from each cluster will be z = n/c
e. Use random or systematic random sampling method to
select z units from each cluster, such that the selected
units constitute the sample (n).

16. Non-Probability Sampling Techniques
1. Convenience Sampling : It is a sample constituted of
units simply available to the researcher by virtue of its
accessibility.
2. Purposive or Judgmental Sampling : when sampled
units are selected on basis of some specific purpose
relevant to the research.
3. Quota Sampling: It is a sample generated from a fixed
quota of units from different categories of the population
– gender / caste / group / area
4. Snowball Sampling: It is a sample generated from the
contacts of a small group of people relevant to the
research topic. Every contact gives 1 or 2 other contacts

17. Criteria of Selecting Sample
Two costs in sampling analysis
 Cost of data collection
 Cost of incorrect inference
Causes of Incorrect Inferences
 Sampling Errors
 Systematic Bias (non-sampling errors)

18. Systematic Bias
Error due to deficiencies in the sampling approach, Causes:
1 ) Inappropriate Sampling Frame
2) Non Response
3) Defective Measurement – problems in measurer / device /
items / interviewer / improper analysis
4) Indeterminacy Principle – people behave differently when
kept under observation
5) Natural Bias – people tend to give what they think should be
correct instead of revealing truth – Upward / Downward Bias
 Systematic Bias can be reduced by detecting and
correcting the cause
 Larger the sample size larger the Systematic Bias

19. Sampling Error
Error in the findings due to random variations in sample
statistic around the true population parameter
(mean/variance)
 Random variations – chance variations equally likely in
both directions
 Sampling error can be calculated for a given sample
design and size
 Sampling error decreases with increase in sample size
 It is smaller in homogenous population
 Measurement of SE is precision of sample

20. Sampling Error
 Inference meaning – Estimate population
mean/variance with sample mean
 Are chances of catching a fish with a spear
higher or with using a net?
 Sample – Point Estimate: X
 Sample – Range: X ± a
 Precision – Confidence Level & Confidence Interval

21. Sampling Error
 Confidence Level (CL) – Selected / Chosen
 How much are we confident that population mean will
under this range?
 Confidence Interval (CI) – Calculated (distribution)
 Generally accepted CL Level – 95% (90% , 99%)
 95% CL means if we collect 100 samples then 95 samples
will have population mean within a particular range of
sample means i.e. Confidence Interval (range of mean)
 Margin of Error/ Significance Level = 100 – CL
 If CL = 95% then Error = 5%
 Generally selected SL = 5% (10% , 1%)

22. Sampling Error
Confidence Interval (CI) calculation
 CI is calculated as per the distribution that the sample
statistic follows
 Sample characteristic - statistic
 Population characteristic - parameter
 Sample statistic may be mean / variance / correlation
Distributions
 Mean – z (normal) or t distribution
 Difference of mean – z or t distribution
 Variance – chi square distribution
 Ratio of Variance – F distribution (ANOVA)

23. Sample Size
Sample Size (n) Considerations
 Population Size (N) – proportionate
 Population Variance (square of Standard Deviation)
 Homogenous – small size
 Heterogeneous – large size
 Sampling error (decreases with n)
 Systematic Bias (increases with n)
 Cost/Budget

24. Sample Size Finite N
n = Nx
(N-1)E² + x
x = Z(c/100)² . r(100-r)
E = √(N-n)x/n(N-1)
n is sample size,
N is population size,
E is Error;
r is fraction of responses;
Z(c/100) is the critical value
for the confidence level c.
E.g. Population Size = 20,000
Confidence Level = 95%
Margin of Error = 5%
Response Distribution = 50%
Recommended Sample
Size = 377
For 90% n will be 263
For 99% CL n will be 643
(Source: Raosoft sample size
calculator)

25. Sample Size Infinite Population
Experts calculate the sample size on the basis of techniques
employed in data analysis
 In research studies where various abstract concepts /
unobserved variables are measured the sample size is
calculated in accordance with the number of total number
of indicators / items in the study
 Thumb rule n = 5 to 10 times the number of indicators
 E.g. 5 Concepts/unobserved variables with 4 items each;
the total items will be – 20
 n = 5x20 to 10x20 = 100 to 200

26. Observational Design / Data Collection
 Sources of Data Collection
1. Secondary Data – already existing data /
information collected in past by some other/s
person for some purpose (other than our research)
 Secondary Data Sources
 Earlier researches / papers / articles in journals, thesis,
books, magazines, news papers / project reports
 Organizational Publications like news letters, annual
reports, special reports, periodicals etc.
 Govt. publications like census, economic surveys etc

27. Observational Design / Data Collection
 Sources of Data Collection
2. Primary Data – fresh / first hand data collected
by the researcher for one’s own purpose
Sources of Primary Data
 Observation
 Interview
 Experiment
 Survey - collection of data on many variables (many
questions) from large set of respondents

28. Observational Design / Data Collection
 Survey Tools
1. Questionnaire – the questions are given in
printed form / online to the respondents
 Respondents select/ write the answers
2. Schedule (Interview Schedule)– questions are
asked verbally by researcher/ enumerator to the
respondents
 Enumerator marks / write the answers

29. Questionnaire
1. Open ended question: What was your experience of RM
course? _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
2. Closed ended questions : Fixed answers from which
answers are to be chosen. Eg: What is your education
Level?
a) Less than X/XII b) X/XII c) UG d) PG
3. Converting open ended into closed (Coding of variables)
Employment: ______________
Agriculture – 1, Teacher – 2, Any other - 3

30. Types of Questions
1. Personal factual questions – age, occupation,
marital status, income, social group, religion etc
2. Factual questions about others – Household
income, organisation practices
3. Questions about attitudes – Likert Scale is best
(psychological)
4. Questions about beliefs – True or false / Yes or no
5. Questions about knowledge – Awareness, facts

31. Rules for designing questions (Rules of thumb)
1. Remember in mind your research question
(Do not miss out critical questions)
2. What is it that you want to know?
(Do you have a computer? – can mean own or
access)
3. How would you answer it?
(Put yourself in the place of respondent)

32. Avoid the followings
 Avoid ambiguous terms in questions - How often do
you usually visit the cinema?: Very often; quite often,
not very often , not at all (This is ambiguous) - More
than once a week, once a week, 2 or 3 times a month
 Avoid long questions – Keep it short and to one point
 Avoid double barrelled questions (How satisfied are
you with the RM course and ISI atmosphere)
 Avoid very general questions – General questions
lack frame of reference – How satisfied are you with
the course? (Content, methodology, presentation etc)

33.  Avoid leading questions – Would you agree to cutting
taxes though it might reduce the welfare programmes
of the govt?
 Avoid questions that are actually asking same things
twice – Which political party did you vote in the last
general elections? – Did you vote in the last election?
Yes – No; If yes, which political party did you vote?
 Avoid questions that include negatives – sometimes
unavoidable – try not to use - easy to miss out ‘not’ –
Do you agree that students should not carry mobile to
the schools?
 Avoid technical terms – Do you say ISI is a good
research agency?