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Stratified Random Sampling: Definition, Examples & Types

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What is Stratified Random Sampling?

Stratified Random Sampling is a probability sampling method found in market research software that uses a two-step process to select the sample group. The population is first divided into homogeneous subpopulations, or stratas, that are mutually exclusive and collectively exhaustive. This means that every element in the population must be assigned to only one stratum, and there shouldn’t be any overlap of elements across the stratas. The population is divided into different stratas on the basis of some stratification variables, such as income or domicile, for example.

After the elements have been divided into their respective stratas, SRS (simple random sampling) can be employed in market research studies to choose elements from each stratum to be a part of the sample group. As these elements are selected probabilistically, each element in the population has an equal and known chance of being selected. This is why stratified random sampling is a type of probability sampling. In theory, only SRS should be used to select elements from stratas, however in practice, researchers sometimes use other sampling methods such as systematic random sampling.

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Stratified sampling

Definition

Stratified random sampling is a sampling method that divides the target population into homogeneous groups before using SRS to select elements from each strata to be a part of the sample group.

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Types of Stratified Random Sampling

Stratified Random Sampling is divided into two broad categories. They are as follows:

Proportionate Stratified Sampling

In proportionate stratified sampling, the sample size drawn from each stratum is proportionate to the stratum’s size in relation to the total population. Therefore, once the sample size is known, researchers calculate the percentage or proportion of each stratum in relation to the size of the target population. Once each stratum’s relative size is known, a sample size for each stratum can be determined. Once this is done, simple random sampling can be used to select random elements from each stratum. This method of sampling is easier, quicker, and more straightforward than disproportionate stratified sampling.

This method is used because larger stratas, or subpopulations, tend to have larger standard deviations (in regard to the characteristics of the stratified variables chosen) and hence to increase precision of the research, larger sampling sizes must be chosen from these stratas. 

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Disproportionate Stratified Sampling

In disproportionate stratified sampling, on the other hand, the sample size chosen from each stratum is proportionate to the relative size of the stratum and to the standard deviation in the distribution of the characteristics among the elements in that stratum. The sample units from each stratum are determined by the researcher and their study’s rationale.

Steps to conduct Stratified Random Sampling

  1. Choose a target population.
  2. List all elements in the target population.
  3. According to the theme of the study, choose a stratification variable by which the population can be divided into homogeneous subgroups, or stratas.
  4. List all elements in the target population according to chosen stratifications.
  5. Choose a sample size for the study.
  6. Calculate the sample size of each strata. This will depend on the type of stratified sampling that is being employed; whether it is proportionate or disproportionate.
  7. Use simple random sampling to choose elements from each stratum, keeping the stratum’s sample size in mind.

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When is Stratified Random Sampling used

  • When studies aim to find correlations or differences (or any sort of relationship) between different subgroups of a population.
  • When researchers are trying to study only specific stratas of the population.
  • When researchers want to save time by having a smaller sample size, stratified sampling can be used to pick a sample group as it’s a highly accurate method of sampling and hence a large sample size is not required.

Advantages of Stratified Random Sampling

  1. Allows to draw comparisons between subgroups of a population as the population is divided into homogeneous stratas based on shared characteristics.
  2. Most accurate and efficient probability sampling method compared to other sampling designs as elements are chosen from multiple distinct groups of a population, especially when aided by online survey tools.
  3. Smaller sampling sizes can be used as stratified random sampling has high accuracy. This saves researchers’ time while conducting the research.
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Disadvantages of Stratified Random Sampling

  1. A sampling frame for each stratum is required in order to use this sampling method. This may make it harder and more tedious to conduct sampling.
  2. More time consuming than other sampling methods, such as SRS or systematic sampling as more steps are required in the selection of the sample group.
  3. Is an expensive method of sampling as researchers need access to all elements of the target population in case they are selected to be a part of the sample group.
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