Census and Sampling
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WHAT IS A CENSUS?
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WHAT IS SAMPLING?
AC 1.2 PRESENT THE SURVEY METHODOLOGY AND
SAMPLING FRAME USED
ASSIGNMENT TASK 1.2
Learning Outcomes
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 By the end of the session, learners would be able to
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describe:
Sampling and sampling terms
Types of sampling methods
Rationale for sampling
Sampling methods
Sampling errors and biasness
POPULATION & SAMPLING
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 Census or population
 The sampling method
refers to the entire
market segment we want
to get information from.
 Sampling is a
representative of the
market segment or
population
selected must be fair &
accurate for the
information to be
statiscally reliable
 If the sampling method is
incomplete and
inaccurate, it is said to be
biased.
What exactly is a “sample”?
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A subset of the population, selected
by either “probability” or “nonprobability” methods. If you have a
“probability sample” you simply
know the Probablity of any member
of the population being included (not
necessarily that it is “random.”)
Sampling
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Who do you want to
generalize to?
The Theoretical
Population
What population can
you get access to?
The Study
Population
How can you get
The Sampling
Frame
access to them?
Who is in your study?
The Sample
Sample Design
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Aim and Stages of Sampling
Benefits of Sampling
 Aims at avoiding bias and
 Saves time
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achieving maximum precision
from a given outlay of time and
money
Stages in sample design:
Decide the objective of the
research
Define the study population
Choose a sample method
Decide sample size
 Faster results
 Saves money
 Enables more surveys to be
carried out
 Can concentrate on a small
number of units
 A carefully chosen sample can
yield statistically valid
information about the
population as a whole
Sampling Methods
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 Nature of the
Population
Sample
population, time and
money
 Unbiased (each object
in the population to be
equally chosen as part
of the sample)
 Representative of the
population (e.g. more
female than male …)
Sampling frame
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 Random sampling requires a sampling frame.
 The means of identifying sampling units.
 “A list of members of a population…” (Morris,
2000, p41) from which a sample can be chosen. Eg:
Electoral Roll, telephone directory, etc.
 It should be comprehensive, complete, accurate and
up to date.
 Care should be taken for any ‘Bias’: eg: ownership of
telephone may indicate certain social class only.
Sample Bias
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 A Sample is Biased if it
 Selection Bias (exclude
differs from the
population in a
systematic way
 To be able to generalize
from a sample
population without bias,
select random sample
or under-represent part
of the population)
 Measurement or
response bias
(measurement faulty )
 Non response bias
Choice of Sampling Method
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 Is a sample frame available? If
 Available budget
so, random sampling is
possible
 Is the population homogeneous
or heterogeneous?
 What degree of precision is
required? To lower the margin
of error the sample size must
be increased
 What is the budget for the
exercise?
 Accuracy required
 How quickly the information is
needed
 Accessibility of the population
Types of Sampling
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Probability Methods
 Random sampling
 Does not involve human
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
judgement
Requires a sampling
frame
Simple random sampling
Systematic sampling
Stratified random
sampling
Cluster sampling
Non-probability Methods
 Non random sampling
 Involves human
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judgement
No sampling frame
required
Quota sampling
Convenience sampling
Judgement sampling
RANDOM SAMPLING METHODS
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SIMPLE RANDOM
SAMPLING: This
means selecting a sample
with every item or
respondent in the
sampling frame having
an equal chance of being
selected.
SYSTEMATIC
SAMPLING: selecting
items from the list at
regular intervals e.g.
every 5th customer that
buys hamburger or every
10th car that buys petrol.
Cluster Sampling
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 Population is divided into
clusters then chosen at
random (e.g. department of
a business)
 Within a cluster all the
objects are included in the
sample
 If clusters are different
from each other regarding
to the element we measure
it can introduce bias or non
representative.
 More convenient than
simple random sampling
Stratified Sampling
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 Similar to Cluster
 Complex to administer
 To make the sample more
Students
Females
Males
Strata
Random subsamples of n/N
representative the population is
divided into a number of strata
(groups or levels). If the list of 600
students consists of 360 males and
240 females, the sample of 50
students is more representative if it
reflects these proportions as
follows:
Number of males 360/600x50 =30
Number of females 240/600x50 = 20
Stratified Sampling
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Advantages
Disadvantages
• Every unit in a stratum has the same
chance of being selected.
• The sampling frame of the
entire population has to be
prepared separately for each
stratum.
• Using the same sampling fraction for
all strata ensures proportionate
representation in the sample of the
characteristic being stratified.
• Adequate representation of minority
subgroups of interest can be ensured
by stratification and by varying the
sampling fraction between strata as
required.
• Varying the sampling fraction
between strata, to ensure
selection of sufficient numbers
in minority subgroups for study,
affects the proportional
representativeness of the
subgroups in the sample as a
whole.
Non-probability Methods
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Convenience Sampling
This involves gathering
information from anyone
available for the interview,
no matter their
background, this is not a
very reliable method
because the respondents
may not really be the ideal
people to provide the
information we require.
Quota Sampling
 Getting information
only from respondents
exhibiting certain
characteristics e.g. sex,
age, socio-economic
group or other
demographic details up
to a maximum quota or
number
Judgement Sampling
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This means the researcher using his or her own
judgement to select respondents based on his belief
that they fit quite into the profile of the people he
wishes to get information from
HOW TO HAVE A GOOD SAMPLE
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 Have an accurate and
comprehensive sample
frame or records such as
electoral or census data,
sales data etc.
 The sample population
must be potential
customers
 Choose a suitable sample
size
 Identify the person who
buys the product (always
differentiate between
customers & consumers)
Errors in research
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 Sampling error
 Non response error
 Data collection error
 Data analysis error
Sampling Error
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 The difference between
the estimate of value
obtained from a sample
and the actual value
 Arises because a sample
cannot exactly represent
the population as a whole
 Bias as a consequence of
the way in which a sample
is structured or the way it
was selected
 Sampling error can be
reduced either by
increasing the size of the
sample or by improving
the amount of knowledge
on the structure of the
market prior to drawing
up the sample
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Data Collection Errors
Errors in Analysis
 Leading questions
 Omission of important factors
 Clerical errors
 Misrepresentation
 Inarticulate respondents
 Ambiguous questions
 Unintended interviewer
bias
 Errors in completing precoded answers
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in questionnaire design
Error in statistical analysis
Drawing the wrong conclusion
Confusion between cause and
correlation
Misrepresentation of the data
False definitions