This document provides an overview of different sampling methods, including probability and non-probability sampling. It defines key terms like population, sample, and frame. It then describes various probability sampling techniques like simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multi-stage sampling. Examples are provided for each. Advantages and disadvantages of each method are also outlined. The document concludes by describing non-probability sampling techniques like convenience sampling, purposive sampling, quota sampling, and snowball sampling.

1. Unit III

5. Calculator for sample size and
confidence interval
Sample size Confidence interval

6. Classification of Sampling
Probability sampling
Non probability sampling

7. Probability sampling
 Every unit of the population has an equal chance of
being selected for the sample
 Probability sampling techniques
Simple Random Sampling
Systematic Sampling
Stratified Sampling
Cluster Sampling
Multi-stage Sampling

8. SIMPLE RANDOM SAMPLING
It is applied when the method of selection
assures each individual element in the
universe an equal chance of being chosen.
Selection is free from bias
Can calculate the probability- sample
size(n) and population size(N) Therefore, the
probability is =n/N

9. Ways of selecting a Simple Random
Sample
 Lottery draw: The name or identifying
number of each item in the population is
recorded on a slip of paper and placed in a
box – shuffled – randomly choose required
sample size from the box.
 Random number draw: Each item is
numbered and a table of random numbers is
used to select the members of the sample.

10. Example 1 : simple random sampling
 Imagine that you own a movie theatre and you are offering a
special horror movie film festival next month. To decide which
horror movies to show, you survey moviegoers asking them
which of the listed movies are their favourites. To create the list
of movies needed for your survey, you decide to sample 100 of
the 1,000 best horror movies of all time.
 A. Horror movie population is divided evenly into classic
movies(those filmed in or before 1969) and modern movies
(those filmed in or later than 1970).
 Write out all of the movie titles on slips of paper and place them
in an empty box.
 Draw out 100 titles and you will have your sample
By using his approach, you will have ensured that each movie had
an equal chance of selection.

11. Example 2 Simple Random sampling
 Suppose your college has 500 students(population)
and you need to conduct a short survey on the quality
of the food served in the cafeteria. You decide that a
sample of 70 students(sample) should be sufficient for
your purposes
List of clients: In order to get your sample
Random sub sample
1) Assign a number from 001 to 500 to each students
2) Use of table of randomly generated numbers(Random
Number Tables)
3) Randomly pick a starting point in the table and look
at the random number appear there.

12. Example 2 conts
4)(In this case) The data run into three digits(500), the
random number would need to contain three digits as
well
5)Ignore all random numbers greater than 500 because
they do not correspond to any of the students in the
college
6)Remember!! Sample is without replacement, so if the
number recurs, skip over it and use the next random
number.
7)The first 70 different numbers between 001 to 500
make up your sample

14. Advantage and disadvantage for simple
Random Sampling
Advantage Disadvantage
 Easiest method &
commonly used.
Nots require any
additional info. On the
frame(such as gender,
geographical area etc).
 Analysis of data is
reasonably easy and has
a sound mathematics
basis
 Make no use of auxillary
info.
 Can be expensive and
unfeasible for large
population (to identified
and reach) or if the
personal interview
required.
 Not be representative of
the whole population

15. Systematic Random sampling

16. Systematic random sampling
 There is a gap or interval, between each selected unit
in the sample
 Selection of units is based on sample interval, k
starting from a determined point, where k=N/n
 Steps
1) Number the units on your frame from 1 to N
and the population are arranged in some way
2) First sample drawn between 1 and k randomly
(determining point/ the random start).
3) Afterwards, every k th must be drawn until the
total sample has been drawn.

17. Example
Suppose your college has 500 students(population) and you
need to conduct a short survey on the quality of the food
served in the cafeteria. You decide that a sample of 70
students(sample) should be sufficient for your purposes
Number the units on your frame(students) frin 1 to N
(population) In this case N=500
Determine the sample interval, k=N/n, k=500/70
k=7.1 k=8(rounding up)
you will need to select one unit(student) of every 8th units
to end up with a total of 70 students as your sample

18. Example (conts)
 Select a number between 1 and 8 at random(random
start)
 Example if you choose numbers 5. then the 5th
student on your frame would be the first unitincluded
in your sample.
 Select every k th unit after that first number
Eg. 5(the random start), 13(5+8), 21(13+8), 29(21+8)….
up to 500 (where the total sample needed are
obtain).

19. Advantages and Disadvantages of System
Random Sampling
Advantages Disadvantages
 Easier to draw
without mistakes
 More precise than
SRS as more evenly
spread over
population
 Easy to use
 If it has periodic
arrangements then
sample collected may
not be an accurate
representation of the
entire population
 Over representation of
several group is greater

20. Stratified Random sampling
 A population is divided into homogenous mutually
exclusive subgroups called strata and a sample is
selected from each stratum
 Goal: to guarantee that all groups ihn the populations
are adequately represented.
 Within stratum –Uniformly (homogenous),
 Between strata – differences ( heterogenenous).
 Can be stratified by any variable that is available
e.g.Gender(Male & female), Education Level (sslc,hsc,
diploma, 1st degree,...),etc.
 Number of sample from each stratum-select randomly
 (no of element in the stratum/ no of population)* no
of samples

21. Example
 You were select a simple random sample of 70 students
from the frame you might be end up with just a little
over 350 female and 150 students in your college in the
total of 500 students
 Stratifying the population by gender. (Male and female)
 Calculate the exact sample size from each strata
Male = (150/500)*70 = 21 male students
Female =(350/500)*70 =49 students
total sample is 49+21=70 students

22. Advantages and disadvantages of
Stratified Random Sampling
Advantages Disadvantages
 Ensure an adequate sample
size for subgroups in the
population of interest
 Almost certainly produce a
gain in precision in the
estimates of the whole
population, because
heterogeneous population is
split into fairly homogeneous
strata
 Problem if strata not clearly
defined.
 Analysis is(or can be) quite
complicated
 Requires more efforts
 Needs a larger sample size
 Strata are overlapping,
chances of bias

23. Cluster sampling

24. Steps in Cluster Random sampling
 Steps
 Divides the population into groups or clusters
 within cluster – differences(heterogeneous)
 Between cluster- uniformity(homogenous)
 Select cluster at random
 all units within selected clusters are included in the
sample
 No units from non-selected clusters are included in
the sample

25. Advantages and disadvantages of
cluster random sampling
Advantages Disadvantages
 Reduced field costs
 Applicable where no
complete list of units is
available (special lists only
need be formed for
clusters)
 Easier to apply larger
Geographical area
 Save time of travelling
 Clusters may not be
representative of whole
population but may be too
alike
 Analysis more complicated
than for SRS
 Not good representative of
the population

26. Multi stage sampling
 Combination of all the methods described above
 Involves selecting a sample in at least two stages.
 Eg. 1: Stage 1. Stratified sampling
 Stage 2. Systematic sampling
 Eg 2: Stage 1. Cluster sampling
Stage2. Stratified sampling
Stage 3. Simple Random sampling

27. Advantages and Disadvantages of
Multi-stages sampling

28. NON PROBABILITY SAMPLING
 Sampling techniques one cannot estimate beforehand the
chanced of each elements being element being included in
the sample
 Non –probability sampling is a sampling techniques where
the odds of any member being selected for a sample cannot
be calculated. it’s the opposite of probability sampling,
where you can calculate the odds.
 For example, One person could have a 10% chance of being
selected and another person could have a 50% chance of
being selected. It’s non-probability sampling when you
can’t calculate the odds at all

29. When? Why? To use Non
probability sampling
 This type of sampling can be used when
demonstrating that a particular trait exists in the
population.
 It can also vbe used when the r esearcher aims to do a
qualitative, pilot or exploratory study.
 It can be used when the research does not aim to
generate results that will be used to create
generalizations pertaining to the entire population
 It is also useful when the researcher has limited
budget, time and workforce.

30. Advantages and disadvantages of
Non probability samplings
Advantages Disadvantages
 Possibility to reflect the
descriptive comments
about the sample
 Cost-effectiveness and
time effectiveness
 Effective when it is
unfeasible or impractical
tos conduct probability
sampling
 Possible Unknown
proportion i.e lack of
representation of the entire
population
 Lower level of generalization
of research findings
compared to probability
sampling
 Difficulties in estimating
sampling variability and
identifying bias

31. Non probability sampling
techniques

32. Convenience
sampling
In convenience
sampling no
inclusion criteria
identified prior to
the selection of
subjects. The
sample is selected in
anyway for the sake
of easiness and
convenience

33. Examples
Facebook
polls
Pepsi
challenge
Feedback
system in big
companies
Ad vantage Disadvantage
Simplicity of
sampling and the
ease of research
Helpful for
pilot studies and or
hypothesis
generation
Data collection
can be done in short
duration of time
Cheapest to
implement
Highly
vulnerable to
selection bias and
influences beyond
the control of the
researcher
High level of
sampling error
Studies that uses
convenience
sampling have little
credibility due to
reasons above

34. Purposive
sampling
The researchers
or experts obtain
a representative
sample by using a
sound judgment,
which will result
in saving time
and money.

35. Examples
Tv reporters
stopping cvertain
individuals on the
street in order to ask
their opinions abnout
GST on insurance(this
can be asked to only to
an educated man)
A study of
importance of exposure
in colleges ( this can be
asked to only those
students who has
experienced exposure
in college)
Advantages Disadvantages
Cost-effective
anytime effective
Requires
limited number
of primary data
sources
One of the
most time-
effective sampling
methods available
Vulnerability
to errors in
judgment by
researcher
Low level of
reliability and
high levels of bias
Inability to
generalize
research findings

36. Quota sampling
A method of
gathering
representative
data from a group
to ensure that
sample group
represents
certain of the
population
chosen by the
researcher

37. How to perform
quota sampling
Dividing the
population into
specific groups
Calculating a
quota for each
group
Determine
Specific
condition(s) to be
met and quota in
each group
Advantages Disadvantages
Quota sampling is
good when you are
pressed for time, since
primary data collection
can be done in shorter
time
This sampling
method can save costs
and time
It can also be done
in absence of sampling
frame
We can’t
calculate sampling
error and the
projection of the
research findings
There is
disproportionately
represented in the
final sample group
It may suffer
from researcher
incompetency
and/or lack of
experience

38. Comparison of stratified and quota
sampling

39. Snowball
Sampling
Snowball
sampling is
where research
participants
recruit other
participants for a
test or study. It is
used where
potential
participants are
hard to find

41. Examples
A study on
investigating
cheating on exams
Company that
involves primary
data collection from
employees of that
company
Mostly used in
taking surveys with
the help of
questionnaire
Advantages Disadvantages
The ability to
recruit hidden
populations
The possibility to
collect primary data in
low cost
It can be
completed in a short
duration of time
A very little
planning is required to
start sampling process
Oversampling can
be done
Respondents may
be hesitant to ask all
questions
It is not possible
to determine the actual
pattern of distribution
of population
It is not possible
to determine the
sampling error