2. • All items in any field of inquiry constitute a
‘Universe’ or ‘Population’.
• The word ‘Universe’ denotes the aggregate
from which the sample is to be taken.
• A complete enumeration of all items in the
‘population’ is known as a Census inquiry.
Merits of Census Method:
• Each and Every unit of the population is
covered.
• Representative, accurate and reliable
results.
3. • Appropriate method of obtaining information
on rare events.
• Used as a basis for various surveys.
Demerits of Census Method:
• Difficult to adopt in case the universe is
infinite.
• Large amount of effort, money and time is
required.
Sampling:
• The process of understanding about the
universe on the basis of a sample drawn.
4. • The selected respondents constitute a
‘sample’, the selection process is ‘sampling
technique’, the survey is ‘sample survey’.
• From population size N, if a part of size n
(< N) of the population is selected, the group
consisting of these n units is ‘sample’.
Sample Design:
• Is a definite plan for obtaining a sample from
a given population.
• Technique or procedure involved in selecting
items for the sample.
• Determines the size of the sample.
7. Type of Universe:
• Defining the set of objects to be studied.
• Identifying the universe – finite or infinite.
• Have an idea about the nature and number
of items in the universe.
Sampling Unit:
• Determining the sampling unit before
selecting the sample – geographical one,
construction unit, social unit, individual etc.
• Important to decide one or more of such
units that researcher has to select for study.
8. Source List:
• Known as ‘sampling frame’ from which
sample is to be drawn.
• Contains the names of all items of a finite
universe.
• In case of non-availability, researcher has to
prepare the same.
• List must be comprehensive, correct, reliable
and appropriate.
Size of Sample:
• Refers to the number of items to be selected
from the universe.
9. • Size of the sample should be optimum –
representative, reliable and flexible.
• Based on the nature and size of the
universe, nature of the problem to be
studied, involvement of cost, etc.
Budgetary Constraint:
• Cost considerations have a major impact
upon the size and type of the sample.
• Can even lead to the use of a non-
probability sample.
10. Characteristics of a Good Sample Design:
• Must result in a truly representative sample.
• Must be such which results in a small
sampling error.
• Must be viable in the context of funds
available for the study.
• Must be such that systematic bias can be
controlled.
11. Types of Sample Design
Element selection basisRepresentation basis
Probability
(Random selection)
Non-Probability
( Non-Random)
Restricted
(Confined within)
Unrestricted
(Element is drawn from
population at large)
12. Basic Sampling DesignBasic Sampling Design
Element Selection
Technique
Representation basis
Probability Sampling Non-probability
Sampling
Unrestricted
Sampling
Simple Random
Sampling
Haphazard Sampling
or Convenience
Sampling
Restricted
Sampling
Complex Random
Sampling
Systematic Sampling
Stratified Sampling
Cluster Sampling
Purposive Sampling
or
Judgment Sampling
Quota Sampling
13. All respondents have an equal chance
of being selected.
Selection of items, matter of chance.
Technique-Lottery Method or Table
of Random Numbers.
Selecting one unit at random and then
Additional units at evenly spaced
Intervals.
K=N/n ( K=Sampling Interval,
N= Universe,
n= Sample Size)
SimpleRandomSystematic
14. Population is divided into different
groups ( Strata)
Sample is drawn from each stratum at
random
Formation of subgroups at first and a
number of these subgroups (clusters )
are randomly selected . All numbers in
each cluster are surveyed
Random selection is made of primary,
intermediate and final units.
Provinces Districts Towns items at
random
StratifiedClusterMultistage
15. Non-probability Sampling
Convenience Sampling:
• Is obtained by selecting ‘convenient’
population units.
• Is also called as chunk, which refers to the
fraction of population being investigated
which is selected neither by probability nor
by judgment but by convenience.
• Samples are prone to bias by their nature of
selection.
• Used frequently for making pilot studies.
16. • Questions may be tested and preliminary
information obtained by the chunk before the
final sampling design is decided upon.
Purposive Sampling:
• Type of non-random sampling, also known
as judgment or deliberate sampling.
• Choice of sample items depends exclusively
on the judgment of the researcher.
• Items selected are mostly typical
(representative) of the universe with regard
to the characteristics under investigation.
17. • Used in case of small size of the universe.
• Sample units may be affected by the
personal prejudice or bias of the universe.
Quota Sampling:
• Quotas are set up according to some
specified characteristics (age, income,
habitation..)
• Within the quotas, selection of sample items
depends on personal judgment.
• Probability of missing representative
samples due to personal prejudice and bias.
18. Random Sampling Methods
Simple Random Sampling
• Each and every unit of the population has
an equal opportunity of being selected in
the sample.
• Selection of items in the sample is a matter
of chance.
• All n items of the sample are selected
independently of one another and all N
items in the population have the same
chance of being included in the sample.
• To ensure randomness of selection – Lottery
method or table of random numbers.
19. • Lottery Method: A blindfold selection of the
number of slips (sample size) is made out of
the items of the universe.
• Slips should be of identical size, shape and
color and should be mixed thoroughly.
• Limited practical utility in case the size of
universe is large.
Table of Random Numbers:
• Several standard tables of Random
Numbers are available – Tippett (1927),
Fisher and Yates (1938), Kendall and Smith
(1939), Rao, Mitra and Mathai (1966).
20. • Tippett’s (1927) random number tables
consisting of 41,600 digits grouped into
10,400 sets of four-digited random numbers.
• The first forty sets from Tippett’s table are:
2952 6641 3992 9792 7969 5911 3170 5624
4167 9524 1545 1396 7203 5356 1300 2693
2370 7483 3408 2762 3563 1089 6913 7691
0560 5246 1112 6107 6008 8125 4233 8776
2754 9143 1405 9025 7002 6111 8816 6446
• For selecting 10 items out of 5000, the first
ten numbers up to 5000 should be selected.
21. • If the size of the universe is less than 1000,
for selecting 10 items out of 900, the
numbers from 0001 to 0900 will be selected.
• If the size of the universe is less than 100,
for selecting 10 items out of 90, after writing
down the number in pairs and reading either
horizontally or vertically and ignoring the
numbers greater than 90, the items may be
selected.
• Sample depends entirely on chance, hence
no possibility of personal bias affecting the
results.
22. • Difficult to have up-to-date lists of all the
items of the population to be sampled.
• Difficulty involved in studying samples
having widely dispersed geographically.
Complex Random Sampling Designs
Systematic Sampling:
• Is formed by selecting one unit at random
and then selecting additional units at evenly
spaced intervals until the sample has been
formed.
• Required a complete list of the population
from which sample is to be drawn.
23. • After the first item, subsequent items are
selected by taking every k th item from the
list.
• ‘k’ refers to the sampling interval or sampling
ratio, i.e., the ratio of population to the size
of the sample.
• k = N / n, N = universe size and n = sample
size.
• Is relatively a simple technique and more
efficient than simple random sampling.
• Also referred to as quasi-random sampling
method.
24. • An element of randomness is introduced to
pick up the unit with which to start and the
reminder of the items for the sample are pre-
determined by the sampling interval.
• Compared to simple random sample,
systematic sample spreads more evenly
over the entire population.
• In case of a fractional value of k, if it is < 0.5
it should be omitted, if it is > 0.5 it should be
taken as 1, and if it is 0.5 it should be
omitted if the number is even and taken as 1
if the number is odd.
25. • Example: If the number of households in a
village will be 102, 115 and 110 and the
sample size will be 20, then
k = 102 / 20 = 5.1 or 5
k = 115 / 20 = 5.75 or 6
k = 110 / 20 = 5.5 or 6
• The first household will be selected at
random between 1 to k and then every k th
household will be selected for the study.
26. Stratified Sampling:
• Population is divided into different groups
called strata.
• Sample is drawn from each stratum at
random.
• Accepted in obtaining a representative
sample from the heterogeneous universe by:
1. making as great homogeneity as possible
within each stratum, and
2. as marked a difference as possible
between the strata.
27. • Stratified sample may be either proportional
or disproportionate.
• Proportional – number of items drawn from
each stratum is proportional to the size of the
stratum.
Example: If a province is divided into five
parts and the percentages of population of
the respective five parts to the total
population are 10, 15, 20, 25 and 30 per
cent, to draw a sample of 500 households as
per the proportional stratified sample:
28. From Stratum (part) one : 500 (0.10) = 50
From Stratum (part) two : 500 (0.15) = 75
From Stratum (part) three : 500 (0.20) = 100
From Stratum (part) fourth : 500 (0.25) = 125
From Stratum (part) five : 500 (0.30) = 150
• The total sample will be 50 + 75 + 100 + 125 + 150
= 500.
• Disproportionate – An equal number of cases is
taken from each stratum regardless of how the
stratum is represented in the universe.
• A more representative sample, as little
possibility of any essential group of the
population being completely excluded.
29. Cluster Sampling:
• Divide the area into a number of smaller
non-overlapping areas.
• Randomly select a number of these smaller
areas (clusters).
• The ultimate sample consisting of all (or
samples of) units in these small areas or
clusters.
• Clusters should be as small as possible.
• Number of sampling units in each cluster
should approximately be same.
30. • Reduces Cost by concentrating surveys in
selected clusters.
• Certainly less precise than random sampling
– not as much information in ‘n’ observations
within a cluster as there happens to be in ‘n’
randomly drawn observations.
• Mostly used for the economic advantage it
possesses; estimates based on cluster
samples are usually more reliable per unit
cost.
• Better known as Area sampling, if clusters
happen to be some geographic subdivisions.
31. Multi-stage Sampling:
• Is a further development of the principle of
cluster sampling.
• Random selection is made of primary,
intermediate and final units from a given
population or stratum.
• Example: A study on working of commercial
banks in Cambodia:
First Stage: To select large primary
sampling unit such as Provinces in the
country.
32. Second stage: To select certain districts and
study all banks in the chosen districts.
(Represents a two-stage sampling design)
Third stage: To select certain towns and
study all banks in the chosen towns.
(Represents a three-stage sampling design)
Fourth stage: To select randomly sample
banks from each selected towns.
(Represents a four-stage sampling design)
• Selection made at all stages randomly
referred to as ‘multi-stage random sampling
design’.
33. Selection of Appropriate Method
of Sampling
• A single method can not be considered as
best under all situations.
• Normally, simple random sampling should
be preferred because of its non-biasness.
• Purposive sampling is more appropriate
when the universe happens to be small and
a known characteristics of it is to be studied
intensively.
• Nature of problem, size of universe, size of
the sample, availability of funds, time etc.
influence the selection of a method.
34. Characteristics of Sample
• A sample should be so selected that it truly
represents universe. (Representativeness)
• To ensure representativeness the random
method of selection should be used.
• The size of sample should be adequate to
represent the characteristics of the universe.
(Adequacy)
• There should not be any difference in the
nature of units of the universe and sample.
(Homogeneity)
35. • All items of the sample should be selected
independently of one another.
(Independence)
• All items of the universe should have the
same chance of being selected in the
sample.
Sampling and Non-sampling Errors
• Error arising due to drawing inferences
about the population on the basis of few
observations – Sampling Error.
• Sampling Error in this sense is non-existent
in complete enumeration survey. (whole
population is surveyed).
36. • Error arising at the stages of ascertainment
and processing of data – non-sampling
errors.
• Non-sampling errors are common both in
complete enumeration and sample surveys.
• Will be of large magnitude in census than in
sample survey due to increase in the
number of units.
Sampling Errors
• Two types: Biased and unbiased
• Biased Errors: Arise from any bias in
selection, estimation etc.
37. • It forms a constant component of error which
does not decrease as the number in the
sample increases.
• Commonly known as cumulative or non-
compensating error.
Causes of Bias
• Faulty selection process of sample
• Faulty work during collection of information
• Faulty methods of analysis
38. Faulty selection process of sample:
• Deliberate selection of sample: Adopting
purposive sampling to select sample to
obtain a pre-determined results.
• Substituting/ Replacing chosen sample:
Replacing a chosen sample to a convenient
sample without any proper plan.
• Non-response: If all the items to be included
in the sample are not covered, even though
no substitution has been attempted. (mostly
occurs in mailed Questionnaires)
39. Faulty collection of Data
Biased observations may result from:
• Poorly designed Questionnaire
• Ill-trained interviewer
• Failure of a respondent’s memory
• Unorganized collection procedure
• Faulty editing and coding of responses
Bias in Analysis
• Acceptance of wrong methods of analysis
• Improper way of interpretation of data
40. • Unbiased Errors: Arise due to chance
differences between the members of
population included in the sample and those
not included.
• Random sampling error decreases as the
size of sample increases.
• Also known as non-cumulative or
compensating error.
Non-sampling Errors
• Occur at every stage of planning and
execution of the census or survey.
• Errors involved in observation, processing of
data, tabulation errors etc.
41. Factors for Non-sampling Errors:
• With respect to the objectives of the census
or survey, if data specification being
inadequate and inconsistent.
• Inappropriate statistical unit.
• Inaccurate methods of interview, observation
with inadequate or ambiguous schedules,
definitions or instructions.
• Lack of trained and experienced
investigators.
• Errors due to non-response (incomplete
coverage in respect of units).
42. • Errors in data processing operations –
editing, coding, classification etc.
• Errors during presentation and printing of
tabulated results.
Control of Sampling Errors
• Sample should be drawn either entirely at
random or at random subject to restrictions.
• Size of the sample should be increased for
increasing the accuracy level.
Control of Non-sampling Errors
• Having adequate training before conduction
of the survey.
43. • Employing better statistical techniques in
processing and analysis of data.
• Pre-testing or conducting a pilot survey.
• Undertaking complete and thorough editing
work.
• Effective follow-up of non-response cases.
