Finding the correct sample for your research study is a hectic job, is not it? Yes, it is a very challenging and arduous task. It is easy to do when you are working with a small group of people, and your area of research is not too broad. However, when you are dealing with large population sizes, you need a more effective way to collect samples, and one of those effective ways is stratified random sampling.
Do you know what this sampling is? Do you have an idea of its types? Most probably not, because if you knew about this, you would not be here reading this article. Well, there is no need to worry because you have come to the right place. In today’s article, we will define the stratified random sampling technique, explain its types, and discuss the examples to strengthen our knowledge about it. So, let’s get started.
What is stratified random sampling?
Stratified random sampling is a type of probability sampling. In this sampling method, the research splits the whole target population into different groups (strata). When forming the groups, it is important to note that all the groups must be non-overlapping and homogeneous. The word random here tells that all the respondents are selected randomly so that there is no element of bias in the research study. Hence, it is all about the definition of this probability sampling method.
What are examples of stratified random sampling?
After reading the information above, you have set the theoretical base for this method. To have an idea of how things work practically, it is time to look into some examples of this sampling method. Hence, the two examples of this method are as follows:
Example no. 1
Let’s suppose a researcher wants to conduct research on the education level of women. To research this, he chooses the method of stratified random sampling to sample different women from the population. As this method asks for some homogeneous groups, the research may divide the whole population into different groups based on ethnicity, religion, gender, etc. Hence, this is how he will study the whole population and present his results.
Example no. 2
Let’s talk about another example. Suppose a researcher wants to know how many MBA graduate students have secured a job within 3 months after the completion of their degrees. The researcher finds out the number of students exceeds 50,000. This is quite a large number to study. So, he divides the participants into different groups based on gender, race, and colour and then studies them one by one. When making groups, it is important that you make no duplications and ensure that all the groups are homogeneous.
Hence, these are examples of stratified random sampling. After reading them, you must have got an idea of how things work in this sampling method. If not, you can still visit any dissertation writing service website and ask for help.
What are the types of stratified random sampling?
The golden rule of this sampling method is that all the groups formed must possess unique characteristics, and they must be homogenous. So, based on its working, there are two types of this important sampling method. A brief description of those two types is as follows:
1. Disproportionate stratified sampling
A disproportionate or disproportional sampling method is a method in which each group or stratum is not proportional to its size in the population. In simple words, all the subgroups of strata do not get an equal opportunity to participate in the research process. In this sampling method, each subgroup within a group will have a different sampling fraction. The success of this method mainly depends on how accurately the researcher measures the sampling fraction. The reason is that there are high chances of bias in this method.
2. Proportionate stratified sampling method
The proportionate or proportional sampling method is the second type of stratified random sampling. In this sampling method, the sample size is directly proportional to the size of each group of the entire population. This means that each group in this method has the same sampling fraction. This sampling method is often a more precise and accurate representation of the whole population. The reason is that all the strata in the population have an equal chance of participating in the research. Hence, this is what a proportionate sampling method is. However, if you still feel any ambiguity, you can get help from dissertation writers UK.
Conclusion
Conclusively, the stratified random sampling method has two types based on its working. But whether you choose a disproportionate or proportionate sampling method, the most important thing is to create non-overlapping groups. All the groups must be homogenous, and each unit in the samples must get an equal opportunity to take part in the research.
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