Sampling Defintion , reguirements , sampling process, types of sampling , probability sampling , non probability sampling

1. SAMPLING
Mohammed Ashraf Zuhairy1

2. Technical Terminology
 An element is an object on which a
measurement is taken.
 A population All objects that have particular
characteristics of interest to researcher
 Sampling units : A sampling unit is a single
member of the sample

3. Technical Terms
 A sampling frame is a list or a map of population
identifying each sampling unit by a number. It is
essential for adopting any sampling procedure.
 A sample is a collection of sampling units chosen to
represent the population.

4. Population, sample and elements
Source: Saunders et al. (2009)
Selecting samples
Figure 7.1 Population, sample and individual cases

5. Census
 A census study occurs if the entire population is very small
or it is reasonable to include the entire population (for other
reasons).
A census is a count of
all the elements in a
population.

6.  The process of using a small number of items or parts of
larger population to make a conclusions about the whole
population
 The population of interest is usually too large to attempt to survey
all of its members.
 It reduces cost of investigation, the time required and the number of
personnel involved
 Gives results with known accuracy that can be calculated
mathematically
Sampling
Why sampling?

7.  Efficiency: It is the ability of sample to yield the desired
information.
 Representativeness: A sample should be representative of the
parent population so that inferences drawn from the sample can be
generalized to that population with measurable precision and
confidence
 Measurability: The design of the sample should be such that valid
estimates of its variability can be made
IDEAL REQUIREMENTS OF A SAMPLE

8.  Size: A sample should be large enough to minimize sample
variability and to allow estimates of the population
characteristics to be made with measurable precision.
 Coverage: Adequate coverage is essential if the sample has to
remain representative
 Goal Orientation: Sample selection should be oriented
towards the study objectives and research design
IDEAL REQUIREMENTS OF A SAMPLE

9.  Feasibility: The design should be sample enough to be
carried out in practice.
 Economy and cost-efficiency: Sample should be such that it
should yield the desired information with appreciable savings
in time and cost .
IDEAL REQUIREMENTS OF A SAMPLE

10. The actual sample selection can
be accomplished in two basic
ways
Purposive Selection Random Selection
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11.  The selection of a sample primarily aims at representing the
population as a whole. Hence, there can be a great temptation to
deliberately or purposively select the individuals who seem to
represent the population under study.
 For instance, in a study on oral hygiene in an urban school, 30
representative students may be picked, examined and assessed
for poor oral hygiene.
 It is easy to carry out and does not need to preparation of
sampling frame.
 However, it can substantially under-represent the rates of the
population under study.
PURPOSIVE SELECTION

12.  Here a sample of units is selected in such a way that all the
characteristics of the population are reflected in the sample.
 This is possible by selecting the units of sample at random.
 A sample in which each individual in the population has an equal
chance of appearing is a random sample
 Ex- in an experiment, where the experimental animal is chosen as the
investigator can catch the animal from the cage
RANDOM SELECTION

13. Sampling Process
 It is the procedures or steps of selecting final sample units .
Define the population
Specify the sampling frame
Specify sampling units
Selection of sampling method
Determine of the sample size
Specify the sampling plan
Select the sample

14. Types of Samples
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15. PROBABILITY SAMPLING
 IT is the most recommended method because all sampling units
have the same probability of being selected and included in the
study
 This method assures that the sample is a representative of the
whole population
 Therefore generalization of results to the whole population can
be made
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16. SIMPLE RANDOM SAMPLING
• Applicable when population is small, homogeneous & readily available
• This is a sampling technique in which each and every unit in the population
has an equal chance of being included in the sample.
• The basic procedure is:
• Prepare a sampling frame
• Decide on the size of the sample
• Select the required number of units
• To ensure randomness one may choose
any one of the following methods:
 Lottery method
 Table of random numbers
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17. Hat
Simple Random Sampling
Example: Out of a CLASS of 50
students 15 are to be selected to
take part in a FEST.
1. Assign ROLL number from 1 to 50 to
each student.
2. Write each number on a piece of paper
(or use raffle tickets), place in a hat and
mix up.
3. Draw the 15numbers from the hat.
1. Lottery Method

18. Simple Random Sampling
In this example, members of a population are numbered and put into a hat.
Tommy randomly picks two numbers from the hat. He then returns them and
chooses again. Because of this, the population has an equal chance of being
selected
1. Lottery Method

19. SIMPLE RANDOM SAMPLING
• To ensure randomness one may choose any one of the following methods:
2) The table of random numbers consist of random arrangements
of digits from 0 to 9 in rows and columns, arranged in a cunning
manner to eliminate personal selection. The selection is done
either in a horizontal or vertical direction. This method assures
randomness and eliminates personal bias.
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21. SYSTEMATIC SAMPLING
 This Sample is chosen by selecting an element of the population at the
beginning with a random start and then every Kth element is selected
until the appropriate size is selected.
 Population size = N, desired sample size = n, sampling interval k=N/n.
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22. SYSTEMATIC SAMPLING22

23. STRATIFIED SAMPLING
 A stratified random sample is obtained using the following procedure:
1) The population is subdivided into groups known as strata, such that each group
should homogeneous in its characteristic.
2) A simple random sample is then chosen from each stratum.
 This type of sampling is used when the population is heterogeneous with
regard to the characteristic under study. For e.g.; to determine the prevalence of
DMFT in different age groups
 Proper classification of the population into various strata and a suitable sample
size from each stratum are the two major points need to be considered in
stratified random sampling.
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24. STRATIFIED SAMPLING24
This example shows how the population is grouped together by some sort
of characteristic. Then Joe chooses one from each group to make up his
team. This is just one example of a stratified sampling

25. Cluster Sampling:
 This method is used when the population forms natural groups or
clusters, such as, villages, wards blocks or children of a school etc.
 In this sampling tchnique the population is divided into groups so
called clusters.
 Then a random sample of clusters is selected
 Cluster: a group of sampling units close to each other i.e. crowding
together in the same area or neighborhood
 The cluster include all types of characteristics in the population.

26. Cluster Sampling:
 The sampling units are clusters and the sampling frame is a list
of these clusters
 This sampling is extensively used, if the population
characteristics are heterogeneous and geographically varied.
 Each and every elementary unit of the selected cluster are
studied in this technique.

27. Cluster Sampling:
This example shows clusters of individuals separated by their street/avenue. Joe
then chooses one street/avenue to do his survey. This is just one example of a
cluster sampling

28. Cluster Sampling:

29. MULTISTAGE SAMPLING
 The first stage is to select the groups or clusters
 Then sub-samples are taken in as many subsequent stages as
necessary to obtain the desired sample size.
 E.g.: 1 st Stage : Choice of states within countries, 2 nd Stage :
Choice of towns within each state, 3rd stage: Choice of
neighborhoods within each town
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30. MULTI PHASE SAMPLING
 In this method, part of the information is collected from the
whole sample and a part from the sub-sample.
 For example, in a school health survey, all the children in
the school are examined.
 From these, only the ones with oral health problems are
selected in the second phase.
 A section needing treatment are selected in the third phase.
 The number of children in the sub-samples in the 3 rd and 4
th phase becomes smaller and smaller. This method may be
adopted when the interest is in any specific disease.
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31. NON PROBABILITY SAMPLING
 Non-probability samples are not truly representative and are therefore
less desirable than probability samples
 This method is used in cases where a researcher may not be able to
obtain a random or stratified sample, or it may be too expensive or
when it may not be necessary to generalize to a larger population.
 The validity of non-probability samples can be increased by trying to
approximate random selection, and by eliminating as many sources of
bias as possible.
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32. Nonprobability Samples
Cost
Feasibility
Time
No need to
generalize
Limited
objectives

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34. Convenience Sampling
Convenience sampling attempts to obtain a sample of convenient
elements. Often, respondents are selected because they happen to be in
the right place at the right time.
Sometimes known as grab or opportunity sampling or accidental
or haphazard sampling.
use of students, and members of social
organizations
mall intercept interviews without qualifying the
respondents
“people on the street” interviews

35. 35 CONVENIENCE SAMPLING…….
 Use results that are easy to get
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36. Judgment or purposive sampling
 In this method of sampling the choice of sample items depends
exclusively on the judgment of the investigators. That is, the
investigators exercises their judgment in the choice and includes
those items in the sample.
 For example, if sample of 25 students is to be selected from a class
of 90 students for analyzing the smoking habits of tobacco, the
investigators would select 25 students who, in his opinion, are
representative of the class.

37. QUOTA SAMPLING
 In quota sampling the selection of the sample is made by the
interviewer, who has been given quotas to fill from specified sub-groups
of the population
 begins with two decisions: What characteristics(quotas) ? + How many
people?
 EXAMPLE : In a study wherein the researcher likes to compare the
academic performance of the different high school class levels, its
relationship with gender and socioeconomic status, the researcher first
identifies the subgroups.
 Usually, the subgroups are the characteristics or variables of the study.
 The researcher divides the entire population into class levels, intersected
with gender and socioeconomic status. Then, he takes note of the
proportions of these subgroups in the entire population and then samples
each subgroup accordingly
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38. Snowball Sampling
In snowball sampling, an initial group of respondents is selected, usually
at random.
After being interviewed, these respondents are
asked to identify others who belong to the target
population of interest.
Subsequent respondents are selected based on
the referrals.

39. Errors In Sampling
Two types of errors that arise in sampling are:
 Sampling errors : The sampling errors : arise due to the sampling
process and arise because of faulty sample design or due to the
small size of the sample.
 Non-Sampling errors arise due to:
a) Coverage error:- Due to non-response / non-cooperation of
informant.
b) Observational error:- Due to interviewer’s bias / imperfect
experimental technique or both.
c) Processing error:- Due to errors in data entry or recording of
responses