Question

Explain the advantages and disadvantages of a stratified random sample compared with a proportionate stratified random sample. Give an example of each kind of sample.

Answer 1

Stratified random sampling is a method of sampling in which small groups of people are formed on the basis of common features like income, education etc. and are named as strata.

The small subgroups are made to ensure that samples are studied from each group of population so that the sample can represent the whole population for Research and studies.

  Advantages of stratified random sampling Over proportionate stratified random sampling:

1. it helps in getting the key features of population in the sample.

2. it ensures that samples are randomly taken from every strata which in turn contributing a variety of qualities and characteristics of population.

3. It helps in assembling characteristics from sample that are proportional to the whole population.

4. stratified random sampling provide great preciseness and distinctiveness in the data and less chances of error.

disadvantages of stratified random sampling :

1. It cannot be used for every type of study.

2. it is hard to make a group with their division because overlapping of characteristics could happen.

3. integrating characteristics and selecting them can become difficult due to which stratified random sampling is not an ideal method.

For example, If you take a population of thousand people, then in stratified random sampling you first meet to convert them into subgroups on the basis of common characteristics and then samples are taken randomly from each subgroup.

                                                 1000 = 4 subgroups

                400 people + 300 people + 200 people + 100 people

Now to perform stratified random sampling in these four groups for 100 pieces of data , 40 samples must come from the first group, 30 samples must come from the second group, 20 samples from III group and 10 samples from the fourth group.

40+30+20+10=100 pieces of data

It also ensures that samples are taken from each subgroup for the statistical analysis.

Proportionate stratified random sampling - In this method of sampling we need to ensure that the Sample size must be proportional to the population size of the strata or subgroups.

IT Can be calculated from , (sample size/ population size ✖ subgroup size).

For example, If a researcher wanted to take 1000 samples of medical graduates using age range then,

1. 1000/10,000✖7000 = 700 2. 1000/10,000✖3000 = 300

Age group	23-27	28-32 total
No. of people	7000	3000 10,000
Sample size	700	300 1000

This is how you can prepare a sample for proportionate stratified random sampling.