Population and Sampling_ppt (1).pptx

Population and Sampling
methods
Abebe Tolera (BSC, MPH in Epidemiology)
1/23/2023 1

Sampling and data collection
methods
 Unit objectives
 At the end of this unit, students are
expected to:
1. Understand concept of sampling and
types of sampling methods
2. Differentiate the Strength and Limitation
of each sampling techniques
1/23/2023 2

Sampling and data collection
methods
 What is sampling?
 Why is it important?
 What are the different types sampling?
 What are the advantages and limitations of
the different sampling techniques?
1/23/2023 3

Population in Statisticians
 In statistics the term “population” has a slightly
different meaning from the one given to it in
ordinary speech.
 It need not refer only to people or to animate
creatures
 Statisticians also speak of a population of
objects, or events, or procedures, or
observations, including such things as the
quantity of lead in urine, visits to the doctor, or
surgical operations.
 A population is thus an aggregate of
creatures, things, cases and so on.
1/23/2023 4

Population in Statisticians
 Generally, population refers to the people who live in
a particular area at a specific time.
 In statistics, population is the entire set of items or
data on your study of interest.
 It can be a group of individuals, objects, events,
organizations, etc.
 Although a statistician should clearly define the
population he or she is dealing with, they may not be
able to enumerate it exactly.
 A population commonly contains too many
individuals to study conveniently, so an investigation
is often restricted to one or more samples drawn
from it.
1/23/2023 5

Sampling…
 Researchers often use sample survey
methodology to obtain information
about a larger population by selecting
and measuring a sample from that
population.
 Since population is too large, we rely on
the information collected from the
sample.
 Thus, for Cost minimization
1/23/2023 6

Sampling…
 Sampling: is a process used in statistical
analysis in which a predetermined
number of observations are taken from a
larger population.
 The methodology used to sample from a
larger population depends on the type of
analysis being performed
1/23/2023 7

Sampling…
 It is the process of selecting a portion of
the population to represent the entire
population.
 A main concern in sampling:
1. Ensure that the sample represents the
population, and
2. The findings can be generalized.
1/23/2023 8

Samples are used when :
◦ The population is too large to collect data.
◦ The data collected is not reliable.
◦ The population is hypothetical and is
unlimited in size.
◦ Take the example of a study that
documents the results of a new medical
procedure. It is unknown how the
procedure will affect people across the
globe, so a test group is used to find out
how people react to it.
1/23/2023 9

Sampling…
Steps needed to select a sample and
ensure that this sample will fulfill its
goals.
1. Establish the study's objectives
 –Clarifying the aims of the survey is critical
to its ultimate success
2. Define the target population
 –The target population is the total population
for which the information is required
1/23/2023 10

Sampling…
 Reference population (or target
population): the population of interest to
whom the researchers would like to make
generalizations.
 Sampling population: the subset of the
target population from which a sample will be
drawn.
 Study population: the actual group in which
the study is conducted = Sample
 Study unit: the units on which information
will be collected: persons, housing units, etc.
1/23/2023 11

Sampling…
3. Decide on the data to be collected
The data requirements of the survey
must be established.
To ensure that the requirements are
operationally sound, the necessary
data terms and definitions also need
to be determined
1/23/2023 13

Sampling…
4. Set the level of precision
There is a level of uncertainty associated
with estimates coming from a sample.
Researchers can estimate the sampling
error associated with a particular sampling
plan, and try to minimize it.
Sample-to-sample variation causes
sampling error
Acceptable precision is important
1/23/2023 14

Sampling…
5. Decide on the methods of
measurement
 Choose measuring instrument and
method of approach to the population
1/23/2023 15

Sampling…
6. Preparing Frame
 List of all members of the population
from which the sample will be taken
 The elements must not overlap
1/23/2023 16

A sample should generally :
◦ Satisfy all different variations present
in the population as well as a well-
defined selection criterion.
◦ Be utterly unbiased on the properties
of the objects being selected.
◦ Be random to choose the objects of
study fairly.
1/23/2023 17

What are the advantage of sampling?
Advantages of sampling:
1. Feasibility: Sampling may be the only
feasible method of collecting information.
2. Reduced cost: Sampling reduces demands
on resource such as finance, personnel, and
material.
3. Greater accuracy: Sampling may lead to
better accuracy of collecting data
4. Sampling error: Precise allowance can be
made for sampling error
5. Greater speed: Data can be collected and
summarized more quickly
1/23/2023 18

Disadvantages of sampling:
 The following are some of the
limitations of sampling.
1. Estimates based on sample are subjected to
sampling error.
2. Bias, if sample are not representative.
1/23/2023 19

Sampling…
While selecting a SAMPLE, there are
basic questions:
◦ What is the group of people (STUDY
POPULATION) from which we want to
draw a sample?
◦ How many people do we need in our
sample?
◦ How will these people be selected?
1/23/2023 20

Sampling Methods
Two broad divisions:
1. Probability sampling methods
2. Non-probability sampling methods
1/23/2023 21

Probability sampling
 It gives each element a known non-zero
chance of being included in the sample.
 Samples are randomly selected and their
probability of inclusion can be calculated.
o Let sampling frame be N & sample size be n. Every
individual has a known chance of n/N being
selected.
 This method is closer to a true representation
of the population.
1/23/2023 22

Probability sampling
 It can be difficult to use due to size of
the sample and cost to obtain,
 but the generalizations that come from
it are more likely to be closer to the a
true representation of the population.
1/23/2023 23

Probability sampling…
 The following are the most common
probability sampling methods:
1. Simple random sampling
2. Systematic random sampling
3. Stratified random sampling
4. Cluster sampling
5. Multi‐stage sampling
6. Sampling with probability proportional to size
1/23/2023 24

Simple Random Sampling
 SRS is the most basic scheme and simplest form
of probability/random sampling.
 Involves the selection of a sample from a
population, based on chance
 Each measurement or count in the population
has the same or known chance (probability) of
being selected.
 Representativeness is also ensured.
1/23/2023 25

Step is conducting simple random
sampling
1. Make a list
2. Assign sequential number
3. Use random number generator
4. Choose sample
1/23/2023 26

Simple Random Sampling…
 Select the required number of study units, using
random methods like:
A.Use of “lottery’ methods
B.Table of random numbers
C.Computer programs
 Standard statistical software (e.g. STATA, SPSS)
have commands for automated random sample
selection.
1/23/2023 27

 Advantages:
 Each unit in the sampling frame has an equal
chance of being selected.
 It does not require additional information on the
frame (such as geographic areas)
 Easy to apply to small populations.
 Limitations:
 Expensive and unfeasible for large dispersed
populations
 Requires a sampling frame.
 Minority subgroups may not be selected.
1/23/2023 29
Simple Random Sampling…

Systematic Random Sampling
 Also interval sampling, systematic
sampling where there is a gap, or interval,
between each selected unit in the sample.
 Selection of individuals from the sampling
frame systematically.
 In systematic sampling, only the first unit is
selected at random.
 Less time consuming and easier to
perform than Simple RS.
1/23/2023 30

Systematic Random Sampling…
 Steps in systematic random sampling:
1. Number the units on your frame from 1 to N (where N is the total
population size).
2. Determine the sampling interval (K) by dividing the number of
units in the population (N) by the desired sample size (n).
3. Select a number between one and K at random. This number is
called the random start and would be the first number included
in your sample.
4. Select every Kth unit after that first number
1/23/2023 31

 Example:
 To select a sample of 100 from a population of 400, you would
need a sampling interval of 400 ÷ 100 = 4. Therefore, K = 4.
 You will need to select one unit out of every four units to end
up with a total of 100 units
 Select a number between 1 and 4 from a table of random
numbers.
 If you choose 3, the third unit on your frame would be the first
unit included in your sample;
 The sample might consist of the following units to make up a
sample of 100: 3 (the random start), 7, 11, 15, 19...395, 399 (up to
N, which is 400 in this case).
1/23/2023 32

 Advantages:
 Less time consuming and easier to perform than
simple RS.
 Important when the population from which sample
is to be drawn is homogeneously distributed.
 Can be conducted without a sampling frame (useful
in some situations where a sampling frame is not
readily available).
 Disadvantages:
 Can not be used when a cyclic repetition is inherent
in the sampling frame. In which case, the sample
will not be representative of the population.
1/23/2023 33

 It is done when the population is known to be have
heterogeneity with regard to some characteristics
and those characteristics are used for stratification.
 The population is divided into homogeneous, non-
over lapping mutually exclusive groups called
strata according to certain characteristics of
interest (e.g., age, sex, province of residence,
income, etc.).
 The population (elements) should be homogenous
within stratum and heterogeneous between the
strata.
1/23/2023 34
Stratified Random Sampling

Stratified Random Sampling…
 The principal objective of stratification is to reduce
sampling errors.
 Then, independent samples are selected from each
stratum sing any of the sampling methods discussed
above.
 The sampling method can vary from one stratum to
another.
 Each stratum becomes an independent population and
you will need to decide the sample size for each stratum.
1/23/2023 35

Stratified Random Sampling…
 Proportionate Allocation:
 Each member of the survey population has a chance of being included
in the sample, but it does not require that this chance be the same for
everyone.
 With this method, the bigger the size of the unit, the higher the
chance it has of being included in the sample.
 E.G. Your study area has 3 villages with total population 1000. Village A
has 200, village B has 500 and village C has 300 populations.
 Determine sample size for each village if total sample size is 120.
A. Village A=120/1000 X 200 = 24
B. Village B =120/1000 X 500 = 60
C. Village C =120/1000 X 300 = 36
1/23/2023 37

Stratified Random Sampling
 Advantage:
 More efficient sampling strategy.
 Adequate representation of minority subgroups of interest
can be ensured.
 Improves the accuracy of estimation
 Disadvantages
 Sampling frame for the entire population has to be prepared
separately for each stratum.
 Not suitable if there are no homogeneous subgroups
 Can be expensive
 Requires accurate information about the population, or
introduces bias.
1/23/2023 38

 Sometimes it is too expensive to spread a sample
across the population as a whole.
 Travel costs can become expensive if interviewers
have to survey people from one end of the country to
the other.
 To reduce costs, researchers may choose a cluster
sampling technique.
 The clusters should be homogeneous unlike stratified
sampling where the strata are heterogeneous.
 The sampling unit is a cluster, and the sampling
frame is a list of these clusters.
1/23/2023 39
Cluster Sampling

Steps in Cluster Sampling
o Cluster sampling divides the population into groups
or clusters.
o A number of clusters are selected randomly to
represent the total population, and then all units
within selected clusters are included in the sample.
o No units from non‐selected clusters are included in
the sample—they are represented by those from
selected clusters.
o This differs from stratified sampling, where some
units are selected from each group.
 Example:
◦ In a school based study, we assume students of the same
school are homogeneous. We can select randomly sections
and include all students of the selected sections only.
1/23/2023 40

Cluster Sampling …
 Cost reduction is a reason for using cluster
sampling.
 It creates 'pockets' of sampled units instead of
spreading the sample over the whole territory and
reduce cost.
 Another reason is that sometimes a list of all units
in the population is not available, while a list of all
clusters is either available or easy to create.
 Mostly used for widely scattered population and if
there is no sampling frame under well defined
clusters.
1/23/2023 41

 The main drawback is a loss of efficiency when
compared with simple RS.
 It is usually better to survey a large number of
small clusters instead of a small number of large
clusters.
 Sampling error is usually higher than for a simple
random sample of the same size.
 It is based on the assumption that the
characteristic to be studied is uniformly
distributed throughout the reference population,
which may not always be the case.
 You do not have total control over the final
sample size. The final size may be larger or
smaller than you expected.
1/23/2023 42
Cluster Sampling …

 Appropriate when the reference population is large
and widely scattered.
 This type of sampling requires at least two stages.
1. The primary sampling unit (PSU) is the sampling
unit in the first sampling stage.
◦ In the first stage, large groups or clusters are identified and
selected. These clusters contain more population units than
are needed for the final sample.
2. The secondary sampling unit (SSU) is the sampling
unit in the second sampling stage, etc.
◦ In the second stage, population units are picked from
within the selected clusters (using any of the possible
probability sampling methods) for a final sample.
1/23/2023 43
Multistage Sampling

Multistage Sampling…
 If more than two stages are used, the process of
choosing population units within clusters
continues until there is a final sample.
 Finally sample size will be calculated by
multiplying by the number of stages used (which
we call the design effect).
 Benefit of a more concentrated sample for cost
reduction.
 More information is needed in this type of
sample than what is required in cluster
sampling.
 However, multi‐stage sampling still saves a
great amount of time and effort by not having to
create a list of all of the units in a population.
Because, No need of sampling frame for the
reference population.
1/23/2023 44

 Advantage
 Cuts the cost of preparing the sample frame
 Disadvantage
 Sampling error is high compared with simple
random sampling (so we need to use design effect)
 Less precise estimation than SRS for the same
sample but the reduction in cost outweighs this
and allow for a large sample size
1/23/2023 46
Multistage Sampling…

Sampling with probability
proportional to size
 Probability sampling requires that each
member of the survey population has a
chance of being included in the sample, but
it does not require that this chance be the
same for everyone.
 Requires that a sampling frame of clusters
with measures of size be available
 Read the detail approach of this method
1/23/2023 47

 Non-probability sampling
1/23/2023 48

Non-probability sampling
 Every element in the population [sampling
frame] does not have equal probability of being
chosen in the sample.
 Operationally convenient and simple in theory.
 When using non-probability sampling, sample
size is unrelated to accuracy, so cost benefit
consideration must be used.
49
1/23/2023

Non-probability sampling
when to use non-probability sampling
methods?
 Can not get a complete list of members of the
population
 When generalizability may not be of great
importance
 One OR a limited number of sites OR groups relevant
to the research
 Difficult to take samples from the possible study
population
1/23/2023 50

Convenience
sampling/haphazard sampling
 Drawn at the convenience of the researcher.
 The study units that happen to be available at
the time of data collection are selected.
 Or because the researcher is unable to employ
more acceptable sampling methods.
 Selected neither by probability nor by
judgment
51
1/23/2023

Convenience sampling ….
To sample employees in companies in a
nearby area, sample from a pool of friends or
neighbors.
 The people on the street interview
conducted by researcher or TV program
 Researcher studying on university students
might visit library, some department and
interview certain number of students.
1/23/2023 52

Convenience sampling….
Advantages
 Very easy method of sampling.
 Useful in pilot studies
 It is an economical method.
Disadvantages
 Not a representative of the population.
 Results usually biased & unsatisfactory
1/23/2023 53

Volunteer sampling
 As the term implies, this type of
sampling occurs when people volunteer
to be involved in the study.
 In these instances, the sample is taken
from a group of volunteers.
 •Sometimes, the researcher offers
payment to attract respondents.
 Sampling voluntary participants as
opposed to the general population may
introduce strong biases.
1/23/2023 54

Judgmental sampling
 Based on some judgment, gut-feelings or
experience of the researcher.
 Selection of a group from the population on the
basis of available information thought.
 Strategies for qualitative studies:
 Strategically select a limited number of
informants, so that their in-depth information is
used on an issue about which little is known.
1/23/2023 55

Judgmental sampling
Advantages
 Knowledge of the investigator can be best
used
 Economical.
 Urgent public policy & business decisions
 Study unknown traits/case sampling
Disadvantages
 personal prejudice and bias
 No objective way of evaluating reliability
of data
1/23/2023 56

Quota sampling
 Non-probability equivalent of stratified
sampling
 This differs from stratified sampling, where
the stratums are filled by random sampling.
 Quota set up according to some specified
characteristics
 In this method the investigator interviews as
many people in each category of study unit as
he can find until he has filled his quota
 Within the quota, selection depends on
personal judgment
1/23/2023 57

Quota sampling
Advantages
 Improvement over the judgment sampling.
 Easy sampling technique.
 Most frequently used in social surveys.
Disadvantages
 Not a representative sample.
 Not free from error.
1/23/2023 58

Snow ball sampling
 It is special non probability method used
when the desired sample characteristic is rare
 Relies on referrals from initial subjects to
generate additional subjects
 When we need to do in case of snowball
sampling is that first identify someone who
meets the criteria & then let him/her bring
the other he/she know
1/23/2023 59

Snow ball sampling
Advantage
 Access to difficult to reach population
Disadvantage
 Not representatives of the population
 Result in biased sample
1/23/2023 60

Population and Sampling_ppt (1).pptx

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