This presentation discusses different types of random or probability sampling methods. There are five main types discussed: simple random sampling, systematic random sampling, stratified random sampling, cluster random sampling, and multistage random sampling. For each method, examples are provided, the steps to implement the method are outlined, and the advantages and disadvantages are summarized. The presentation aims to define and explain these common probability sampling techniques.

1. Presentation
on
Random Or Probability
Sampling

2. A.T.M. Sazzad Hossain
sazzaddesh77@gmail,com

3. Types of Sampling
Random Or Probability Sampling definition
Types of Random Sampling
Define Of them with example
Application, Advantage & Disadvantage
*Outlines

4. 1- Probability
samples
2- Non
Probability
samples
There are two types of
Sampling

5. A probability sampling method is any method of sampling that
utilizes some form of random selection.
*Random Or Probability
Sampling

6. *For an example:
*If we want to collected the information about socio-economic
background of the students studying at the Department of
Statistics in BRUR.
*Here we can use Probability Sampling.

7. 1. Simple Random Sampling
2. Systematic Random Sampling
3. Stratified Random Sampling
4. Cluster Random Sampling
5. Multistage Random Sampling
* There are Five Methods used
in Probability Sampling

8. Simple random sampling is the technique or method of drawing a
sample in such a way that each unit of the population has equal and
independence chance of being included in the sample.
*Simple random sampling

9. *For an example:
It can be used to know the socio-economic background of the students
studying at the Department of Statistics in BRUR over a specified
period of time.
For this, we must contact every studying student. If there are recorded
of 500 students’ to obtain the information we desire. Then we can take
the sample at random by the use of simple random sampling from the
recoded 500 student’s.

11. 1. Defining the population;
2. Choosing our sample size;
3. Listing the population;
4. Assigning numbers to the units;
5. Finding random numbers; and
6. Selecting our sample.
*There are six steps to
create a simple random
sampling :

12. *The selection of simple random
sampling (SRS) has two types :
1. Simple random sampling with replacement,
2. Simple random sampling without replacement.

13. *Simple random sampling with
replacement:
If a unit is selected and noted then it is returned back to the
population before the next drown is made is called simple
random sampling with replacement (SRSWR).
The calculated function : 𝑁 𝑛

14. *Simple random sampling without
replacement
*If a unit is selected and noted then it is not returned back to the
population for any unit of population is called simple random
sampling without replacement (SRSWOR).
*The calculated function : 𝑁𝐶 𝑛

15. Use
Advantages
1. Easy to conduct & conceptualize,
2. High probability of achieving a representative sample,
3. Meets assumptions of many statistical procedures,
4. No need of prior information of population,
5. Equal and independent chance of selection to every
element.

16. Disadvantages
1. Identification of all members of the population can be
difficult,
2. Contacting all members of the sample can be difficult,
3. Expensive and time consuming,
4. Low frequency of use,
5. Larger risk of random error.

17. *Systematic random sampling
Systematic random sampling is the random sampling
method that requires selecting samples based on a
system of intervals in a numbered population.
It’s also known as interval sampling.

18. *For an example:
If we have a population (like, the teacher of our
department) total of 12 individuals and need 4 subjects.
We first picks our starting number, 2.
Then the researcher picks our interval, 3. The members
of our sample will be individuals.

20. *There are four steps to create a
Systematic random sampling
1. Create a list of population,
2. Select a beginning number,
3. Select an interval,
4. Gather a list of employees based on the interval
number.

21. Use
Advantages
1. Simple to draw sample,
2. Moderate cost & usage,
3. Easy to verify.
4. Suitable sampling frame can be identified easily
5. Sample evenly spread over entire reference population

22. Disadvantages
1. Periodic ordering required,
2. Contacting
3. Sample may be biased if hidden periodicity in
population coincides with that of selection.
4. Difficult to assess precision of estimate from one
survey.

23. *Stratified random sampling
A population divided into sub-groups, called strata
and a sample is selected from each stratum in such a
way that units within strata are homogeneous and
between strata they are heterogeneous.

24. *For an example:
If we are interested to estimate the average amount of
income per household in a town the SRS may not give a
respective sample value. Since different classes of household
are in a town in this case the procedure of stratified random
sampling is used, since the household can be stratified into
high & low income stratum.

26. * There are seven steps to
create a Stratified random
sampling
1. Defining the population;
2. Choosing the relevant stratification;
3. Listing the population;
4. Listing the population according to the chosen
stratification;
5. Choosing your sample size;
6. Calculating a proportionate stratification; and
7. Using a simple random or systematic sample to
select your sample.

27. Use
Advantages
1. More accurate sample,
2. Can be used for both proportional and non-
proportional samples,
3. Representation of subgroups in the sample

28. Disadvantages
1. Identifying members of all subgroups can be
difficult,
2. Identification of all members of the population can
be difficult,
3. Stratified lists costly to prepare.

29. *Cluster Random Sampling
A cluster sample is a simple random sample, in which each
sampling unit is a collection or cluster of elements.
When the sampling unit is a Custer, the procedure is called
cluster sampling.

30. *For an example:
*Sometimes you may wish to undertake a survey of a population
spread over a large area , and find that the resources required to
travel to these areas would be too great. An example might be
where you wish to undertake a survey of school l in your
region and your survey requires you to use interviewers to
explain complex issues. You can not afforded to resource travel
across the entire region , but know that groups or clusters of
school have the same or similar characteristics e. g. urban ,
rural , size etc. You would simply select at random from these
clusters and undertake a census from those selected.

32. 1. Identify and define the population,
2. Determine the desired sample size,
3. Identify and define the logical cluster.
4. List all clusters (or obtain a list) that make up the population of
cluster.
5. Estimate the average number of population members per
cluster,
6. Determine the number of clusters needed by dividing the
sample size by the estimated size of cluster,
7. Randomly select the needed number of clusters by using a table
of random numbers,
8. Include in the study all population members in each selected
cluster.
* There are eight steps to create a
Cluster random sampling

33. Use
Advantages
1. It is easer , cheaper, faster and operationally
more convenient,
2. It can estimate characteristics of both cluster
and population.
3. Do not need the names of everyone in the
population

34. Disadvantages
1. It is generally less efficient than simple
random sampling ,
2. The cost to reach an element to sample
is very high,
3. It is very difficult to determine the
optimum cluster size.
4. Representation is likely to become an
issue

35. *Multistage sampling
Multi-stage sampling is like cluster sampling, but involves
selecting a sample within each chosen cluster, rather
than including all units in the cluster. Thus, multi-stage
sampling involves selecting a sample in at least two
stages. In the first stage, large groups or clusters are
selected. These clusters are designed to contain more
population units than are required for the final sample.
In the second stage, population units are chosen from
selected clusters to derive a final sample. If more than
two stages are used, the process of choosing population
units within clusters continues until the final sample is
achieved.

36. *For an example:
The following is an example of implementation of multi-stage
sampling method once a state has been chosen as cluster
sampling:

37. 1. Random number of districts within the state needs to be
selected as primary clusters.
2. Random number of villages within district needs to be
selected as secondary clusters.
3. Ultimately a number of houses need to be selected as
sampling unit to be used in the study.

38. * There are Three steps to
create a Multistage random
sampling
1. Electoral Subdivisions, Electoral subdivisions (clusters) are
sampled from a city or state.
2. Blocks, Blocks of houses are selected from within the electoral
subdivisions.
3. Houses, Houses are selected from within the selected blocks.

39. Advantages
1. It’s has Cost & Time-effectiveness,
2. High level of flexibility,
3. Fewer investigators are needed
4. Normally more accurate than cluster sampling for
the same size sample
 Use

40. Disadvantages
1. Further analysis is difficult,
2. High level of subjectivity,
3. Research findings can never be 100% representative
of population,
4. Less accurate than SRS of same size (but more
accurate for same cost),
5. There is the possibility of bias if, for example, only
if a small number of regions are selected.