{"id":55167,"date":"2018-03-13T06:03:16","date_gmt":"2018-03-13T13:03:16","guid":{"rendered":"https:\/\/www.questionpro.com\/blog\/?p=55167"},"modified":"2023-08-30T11:39:37","modified_gmt":"2023-08-30T11:39:37","slug":"stratified-random-sampling","status":"publish","type":"post","link":"https:\/\/www.questionpro.com\/blog\/stratified-random-sampling\/","title":{"rendered":"Stratified Random Sampling: Definition, Method and Examples"},"content":{"rendered":"\n

Stratified random sampling is a widely used statistical technique in which a population is divided into different subgroups, or strata, based on some shared characteristics. The purpose of stratification is to ensure that each stratum in the sample and to make inferences about specific population subgroups. Subgroup analysis<\/a> is crucial for tailoring treatments to specific patient groups, optimizing healthcare outcomes.<\/p>\n\n\n\n

This technique is beneficial in cases where the population is heterogeneous, and a simple random sample may not provide accurate results. By dividing the population into strata, researchers can ensure that their sample is representative of the population and avoid sampling biases<\/a>.<\/p>\n\n\n\n

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Content Index<\/p>\n\n

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  1. What is Stratified Random Sampling?<\/a><\/li>\n \t
  2. 8 Steps to Conduct Stratified Random Sampling<\/a><\/li>\n \t
  3. Types of Stratified Random Sampling<\/a><\/li>\n \t
  4. Stratified Random Sampling Examples<\/a><\/li>\n \t
  5. Advantages of Stratified Random Sampling<\/a><\/li>\n \t
  6. When to use Stratified Random Sampling?<\/a><\/li>\n \t
  7. Conclusion<\/a><\/li>\n<\/ol>\n<\/div>\n\n\n\n

    What is Stratified Random Sampling?<\/strong><\/h2>\n\n\n\n

    Stratified random sampling is a type of probability method using which a research organization can branch off the entire population into multiple non-overlapping, homogeneous groups (strata) and randomly choose final members from the various strata for research which reduces cost and improves efficiency. Members in each of these groups should be distinct so that every member of all groups gets an equal opportunity to be selected using simple probability. This sampling method is also called “random quota sampling.”<\/p>\n\n\n\n

    Let’s consider a situation where a research team seeks opinions about religion among various age groups. Instead of collecting feedback from 326,044,985 U.S citizens, random samples of around 10000 can be selected for research. These 10000 citizens can be divided into groups according to age, i.e., 18-29, 30-39, 40-49, 50-59, and 60 and above. Each stratum will have distinct members and the number of members\u2014age, socioeconomic divisions, nationality, religion, educational achievements, and other classifications.<\/p>\n\n\n\n

    8 Steps to Conduct Stratified Random Sampling<\/b><\/strong><\/h2>\n\n\n\n

    The following are the steps to select a stratified random sample:<\/p>\n\n\n\n

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    1. Define the target <\/span>audience<\/span><\/a>. <\/span><\/li>\n\n\n\n
    2. Recognize the stratification variable or variables and figure out the number of strata to be used. These stratification variables should be in line with the objective of the research<\/a>. Every additional information decides the stratification variables. For instance, if the objective of the research is to understand all the subgroups, the variables will be related to the subgroups.<\/li>\n\n\n\n
    3. Use an already-existent sampling frame<\/a> or create a frame that\u2019s inclusive of all the information of the stratification variable for all the elements in the target audience.<\/span><\/li>\n\n\n\n
    4. Make changes after evaluating the sampling frame on the basis of lack of coverage, over-coverage, or grouping.<\/span><\/li>\n\n\n\n
    5. Considering the entire population, each stratum should be unique and should cover each and every member of the population. Within the stratum, the differences should be minimum, whereas each stratum should be extremely different from one another. Each element of the population should belong to just one stratum.<\/span><\/li>\n\n\n\n
    6. Assign a random, unique number to each element.<\/span><\/li>\n\n\n\n
    7. Figure out the size of each stratum according to your requirement. The numerical distribution amongst all the elements in all the strata will determine the type of sampling<\/a> to be implemented. It can either be proportional or disproportional stratified sampling<\/a>.<\/span><\/li>\n\n\n\n
    8. The researcher can select random elements from each stratum to form the sample. A minimum of one piece must be chosen from each stratum so that there\u2019s representation from every stratum, but if two elements from each stratum are selected, quickly calculate the error margins<\/a> of the calculation of collected data<\/a>.<\/li>\n<\/ol>\n\n\n\n

      Using this method helps ensure that the sample is representative of the population and reduces sampling error<\/a>, leading to more accurate results.<\/p>\n\n\n\n

      Learn more: <\/span>Simple Random Sampling<\/span><\/a> & Purposive Sampling<\/a><\/p>\n\n\n\n

      Types of Stratified Random Sampling<\/strong><\/h2>\n\n\n\n