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    Stratified Sampling – How It Works? Advantages and Disadvantages

    Stratified sampling is a very famous method of collecting samples randomly. This method is widely used to study the differences between several groups in an area. It differs from simple random sampling as it involves treating all groups in a population the same. This article will guide you properly on stratified sampling and how it works. It will also explain all the pros and cons of this method.

    Stratified Sampling:

    Stratified sampling is an approach to random sampling which deals with the division of a certain group from a population into smaller groups. These subgroups are called strata. In this type of sampling, the strata are created on the basis of characteristics or features of the members, such as educational attainment or income. There are many benefits and applications of using this sampling method, such as it can help in effectively studying life expectancy and population demographics. This method is also called quota random sampling or proportional random sampling. This method is also widely used to study the differences between several groups in a place which is different from doing simple random sampling. It is because, in simple random sampling, you treat all groups in a population the same with an equal chance of being observed.

    How does it work?

    Many times researchers find that the population size is very large to complete the research on the number of people with similar features. To save money and time, an analyst might need to consider a more practical method of sampling by choosing just a small group of a population. This small group will be considered a subset of a population which will be used to show the whole population. You might need to select a sample from a population by using various methods. One method can be the stratified sampling method. In this sampling method, you will divide the whole population into similar subgroups which will be called strata. After this, you will select random samples from each stratum.

    For instance, take an example of research that will like to know the number of engineering students in the last two years who had received good jobs after graduating from the universities of the UK. The research found that more than 150,000 students got good job offers after graduation in the UK. He might then decide to take randomly choose fifty thousand graduates for his survey. However, he can take an even better approach by dividing the whole population into subgroups and then taking random samples. For this, they will have to create strata based on similar characteristics such as career background, nationality, race, range, age or gender. After this, they will select random samples from strata in a number based on the size of the strata compared to the size of the entire population. The researchers will then pool these subsets from strata to make random samples.

    Advantages of Stratified Sampling:

    Many researchers use this type of approach for sampling as it offers many advantages such as:

    Precise Estimation for the subgroups:

    When participants from subgroups are homogenous compared to the whole population, stratified sampling can be used to give more accurate Estimations about the subgroups instead of just a simple random sampling method. It is because, in this method, subgroups have a very low standard deviation as compared to the whole population. In the research, strata will be considered subpopulations. Accuracy in the strata is very important while assessing the characteristics of groups. Moreover, accuracy and precision can help you do better analysis with better insights and statistical power to detect the difference between the subgroups. For instance, a standardised testing company may want to properly check how test scores differentiate by geographical region or household income.

    Greater efficiency in conducting the survey:

    A stratified sampling method can help you to decrease the costs of the survey. It can also help you to simplify the process of data collection. In several cases, dividing a large population into smaller groups can help the researcher to conduct the survey in a better way. Research and studies could become more practical and less expensive when you will divide a large group of people into smaller subgroups with similar characteristics. For instance, you can use this method to survey people from rural and urban areas.

    Represents all groups:

    By clearly using the subgroups in the research methodology, researchers can make sure that the whole strata represent all types of groups. By incorporating smaller groups in research, random sampling will only miss some of the members. This sampling method can help you retain the whole variety in a population. In this way, you will be able to produce meaningful results with this method. However, if you are facing any difficulties, you may hire UK dissertation writers to work on this.

    Disadvantages of Stratified Sampling:

    There are the following disadvantages of using this type of sampling method:

    • In this method, you need to come up with a plan for the subgroups so that each member of a population can fit into only one group. This can take a lot of time and effort.
    • The second drawback of this method is that you need to have enough information to assign the subjects to the correct subgroups. For this, you need to do a lot of planning and gather a lot of information.
    • Moreover, this method gives advantages only when you can create strata which are homogeneous as compared to the whole population. Without this, you will not be able to produce effective results in your study.
    • The last disadvantage of this method is that the feasibility to perform this method relies on the subgroups to some level. Many groups are very difficult and complex to identify, such as religion and ethnicity.


    The above guide can help you to clearly understand the concept of stratified sampling. This sampling method will divide the whole population into similar subgroups. In this way, you will be able to represent all types of groups in a sample by using this method.

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