Sampling 180318081140

TOPIC : SAMPLING
Presented by :
Amna Javed
(Overview)
a) What is Population ?
b) What is Sample ?
c) Sampling Techniques
What is Probability sampling ?
What is Non-probability sampling ?
a) Advantages & Disadvantages sampling
b) Difference b/w Probability &
Non-Probability
c) Characteristics of sampling
What is a Population?
DEFINITION:
The group to which you want to generalize your findings.
IN OTHER WORDS:
The larger group you are representing with your sample.
OR
The larger group to which your results will apply.
What is a Sample?
DEFINITION
A subset of the population being studied from
which data is actually collected.
A good sample accurately represents all kinds of

elements/members in proportion to their presence in
the population.
Sampling Techniques
“Sampling techniques are the processes by which

the subset of the population from which you will
collect data are chosen”.
There are TWO general types of sampling techniques:
1. PROBABILITY SAMPLING
2. NON-PROBABILITY SAMPLING
Probability Sampling
DEFINITION
The process of selecting a sample from a population
using (statistical) probability theory.
IN PROBABILITY SAMPLING
 Each element/member of the population have an equal
chance of being included in the sample, and
 The researcher CAN estimate the error caused by
collecting data from all elements/members of the
population.
Types of Probability Sampling
1) Simple Random Sampling
2) Stratified Random Sampling
3) Cluster Sampling
4) Systematic Random Sampling
5) Multistage sampling
Simple Random Sampling
 The purest form of probability sampling.
 Assures each element in the population has an equal
chance of being included in the sample
 Random number generators
 Probability of Selection = (𝑆𝑎𝑚𝑝𝑙𝑒 𝑆𝑖𝑧𝑒)/(𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛
𝑆𝐼𝑧𝑒)
Stratified Random Sampling
 Population is divided into two or more groups called
strata
 Subsamples are randomly selected from each strata
Cluster Sampling
 The population is divided into subgroups (clusters) like
families.
 A simple random sample is taken from each cluster
Systematic Random Sampling
Each element has an equal probability of selection, but
combinations of elements have different probabilities.
 Population size N, desired sample size n,
 sampling interval k=N/n.
Multistage sampling
 Carried out in stages
 Using smaller and smaller sampling units at each stage
Non-Probability Sampling
DEFINITION
The process of selecting a sample from a population
without using (statistical) probability theory.
IN NON-PROBABILITY SAMPLING
• Each element/member of the population DOES NOT
have an equal chance of being included in the
sample, and
• The researcher CANNOT estimate the error caused
by not collecting data from all elements/members of
the population.
Types of Non-Probability Sampling
1. Convenience Sampling
2. Quota Sampling
3. Purposive (Judgmental) Sampling
4. Snowball Sampling
Convenient Sampling
DEFINITION;
• Selecting easily accessible participants with no
randomization.
For example;
In our example of the 10,000 university students, if we
were only interested in achieving a sample size of say
100 students. we may simply stand at one of the main
entrances to campus, where it would be easy to invite the
many students that pass by to take part in the research.
So, it is very easy (Convenient) to select.
Selection of Participants
TYPE OF SELECTION
PURPOSE
SAMPLING STRATEGY
Convenience Select cases Saves time,
based on their money and
availability for effort; but at the
the study. expense of
information and
credibility.
Quota Sampling
Definition:
Selecting participant in numbers proportionate
to their numbers in the larger population, no
randomization.
For example ;
The number of students from each group that we would
include in the sample would be based on the proportion
of male and female students amongst the 10,000
university students. (Proportion; 50 male & 50 Female
or 40 Female & 60 Male)
TYPE OF SELECTION
PURPOSE
SAMPLING STRATEGY
Quota; Select a sample Taking a set
that yields the number of cases
same proportions from each
as the population subgroup to raise
proportions on analytic
easily identified confidence and
variables. representativeness
.
Purposive (Judgmental) Sampling
Definition:
Purposive sampling, also known
as judgmental, selective or subjective
sampling, reflects a group of sampling
techniques that rely on the judgment of the
researcher; when it comes to selecting the units
that are to be studied.
For Example Specific People, Specific
cases/organizations, Specific events, Specific
pieces of data)
TYPE OF SELECTION
PURPOSE
SAMPLING STRATEGY
Purposive All cases that Useful for

meet some quality
criteria. assurance.
Snowball Sampling
DEFINITION
Selecting participants by finding one or two
participants and then asking them to refer you to
others.
For example; Meeting a homeless person,

interviewing that person, and then asking him/her
to introduce you to other homeless people you
might interview.
TYPE OF SELECTION
PURPOSE
SAMPLING STRATEGY
Snowball or chain Group members Identifies cases of
referral identify additional interest to people
members to be who know people,
included in the who know what
sample. cases are
information-rich.
Difference b/w Probability &
Non-Probability
Advantages of Sampling
 Very accurate.
 Economical in nature.
 Very reliable.
 High suitability ratio towards the different surveys.
 Takes less time.
 In cases, when the universe is very large, then the
sampling method is the only practical method for
collecting the data.
Disadvantages of sampling
 Inadequacy of the samples
 Chances for bias.
 Problems of accuracy.
 Difficulty of getting the representative sample.
 Untrained manpower.
 Absence of the informants.
 Chances of committing the errors in sampling.
Characteristics of the Sampling
 Much cheaper.
 Saves time.
 Much reliable.
 Very suitable for carrying out different surveys.
 Scientific in nature.
Difference Between sampling and
population
 The collection of all elements possessing common
characteristics that comprise universe is known as the
population. A subgroup of the members of population
chosen for participation in the study is called sample.
The population consists of each and every element of

the entire group. On the other hand, only a handful of
items of the population is included in a sample.
The characteristic of population based on all units is

called parameter while the measure of sample
observation is called statistic.
Questions ??
Thankyou
In a world where you can be anything, Be
Kind

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TOPIC : SAMPLING

A good sample accurately represents all kinds of

“Sampling techniques are the processes by which

There are TWO general types of sampling techniques:

2) Stratified Random Sampling

4) Systematic Random Sampling

3. Purposive (Judgmental) Sampling

Purposive All cases that Useful for

For example; Meeting a homeless person,

The population consists of each and every element of

The characteristic of population based on all units is

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