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Random Or Probability Sampling

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

Types of Sampling

Random Or Probability Sampling definition

Types of Random Sampling

Define Of them with example

Application, Advantage & Disadvantage

*Outlines

1- Probability samples

2- Non Probability

samples

There are two types of Sampling

A probability sampling method is any method of sampling that utilizes some form of random selection.

*Random Or Probability Sampling

*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.

1. Simple Random Sampling2. Systematic Random Sampling3. Stratified Random Sampling4. Cluster Random Sampling5. Multistage Random Sampling

* There are Five Methods used in Probability Sampling

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

*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.

https://en.wikipedia.org/wiki/File:Simple_random_sampling.PNG

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 :

*The selection of simple random sampling (SRS) has two types :

1. Simple random sampling with replacement,2. Simple random sampling without replacement.

* 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 :

* 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 :

Use

It’s used when the target group is sufficiently large & defined.

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.

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.

*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.

*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.

https://en.wikipedia.org/wiki/File:Systematic_sampling.PNG

*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.

Use

It’s used when it is easier to select every nth item.

Advantages

1. Simple to draw sample,2. Moderate cost & usage,3. Easy to verify.4. Suitable sampling frame can be identified easily5. Sample evenly spread over entire reference population

Disadvantages

1. Periodic ordering required,2. Contacting3. Sample may be biased if hidden periodicity in

population coincides with that of selection.4. Difficult to assess precision of estimate from one

survey.

* 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 withi n strata are homogeneous and between strata they are heterogeneous.

*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.

* There are seven steps to create a Stratified random

sampling1. 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.

Use

When the population is heterogeneous and contains several different groups, some of which are related to the topic of the study.

Advantages

1. More accurate sample,2. Can be used for both proportional and non-

proportional samples,3. Representation of subgroups in the sample

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.

* 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.

*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.

https://en.wikipedia.org/wiki/File:Cluster_sampling.PNG

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

Use It is uses when the sampling frame of the elements may not to be readily available

Advantages1. 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

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

*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.

*For an example:

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

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.

*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.

Advantages1. It’s has Cost & Time-effectiveness,2. High level of flexibility,3. Fewer investigators are needed4. Normally more accurate than cluster sampling for

the same size sample

UseIt’s used when it is costly or impossible to form a list of all the units in the target population.

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.

That’s All