Stratified sampling what is it

Similar to a weighted average, this method of sampling produces characteristics in the sample that are proportional to the overall population. Stratified random sampling works well for populations with a variety of attributes but is otherwise ineffective if subgroups cannot be formed.

Stratification gives a smaller error in estimation and greater precision than the simple random sampling method. The greater the differences between the strata, the greater the gain in precision. Unfortunately, this method of research cannot be used in every study. The method's disadvantage is that several conditions must be met for it to be used properly.

Researchers must identify every member of a population being studied and classify each of them into one, and only one, subpopulation.

As a result, stratified random sampling is disadvantageous when researchers can't confidently classify every member of the population into a subgroup. Also, finding an exhaustive and definitive list of an entire population can be challenging. Overlapping can be an issue if there are subjects that fall into multiple subgroups. When simple random sampling is performed, those who are in multiple subgroups are more likely to be chosen. The result could be a misrepresentation or inaccurate reflection of the population.

The above examples make it easy: undergraduate, graduate, male, and female are clearly defined groups. In other situations, however, it might be far more difficult. Imagine incorporating characteristics such as race, ethnicity, or religion. The sorting process becomes more difficult, rendering stratified random sampling an ineffective and less than ideal method.

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I Accept Show Purposes. Your Money. Personal Finance. Your Practice. Choose a target population. List all elements in the target population. According to the theme of the study, choose a stratification variable by which the population can be divided into homogeneous subgroups, or stratas. List all elements in the target population according to chosen stratifications. Choose a sample size for the study. 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.

Download Market Research Toolkit. 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. Advantages of Stratified Random Sampling. Allows to draw comparisons between subgroups of a population as the population is divided into homogeneous stratas based on shared characteristics.

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. Smaller sampling sizes can be used as stratified random sampling has high accuracy. Disadvantages of Stratified Random Sampling. 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. The sampling fraction , which refers to the size of Show page numbers Download PDF. Search form icon-arrow-top icon-arrow-top. Page Site Advanced 7 of In a stratified sample , researchers divide a population into homogeneous subpopulations called strata the plural of stratum based on specific characteristics e.

Every member of the population studied should be in exactly one stratum. Each stratum is then sampled using another probability sampling method, such as cluster or simple random sampling, allowing researchers to estimate statistical measures for each sub-population. Table of contents When to use stratified sampling Define your population and subgroups Separate the population into strata Decide on the sample size for each stratum Randomly sample from each stratum Frequently asked questions about stratified sampling.

To use stratified sampling, you need to be able to divide your population into mutually exclusive and exhaustive subgroups. That means every member of the population can be clearly classified into exactly one subgroup. It has several potential advantages:. A stratified sample includes subjects from every subgroup, ensuring that it reflects the diversity of your population. It is theoretically possible albeit unlikely that this would not happen when using other sampling methods such as simple random sampling.

If you want the data collected from each subgroup to have a similar level of variance , you need a similar sample size for each subgroup. Although your overall population can be quite heterogeneous, it may be more homogenous within certain subgroups. For example, if you are studying how a new schooling program affects the test scores of children, both their original scores and any change in scores will most likely be highly correlated with family income.

The scores are likely to be grouped by family income category. In this case, stratified sampling allows for more precise measures of the variables you wish to study, with lower variance within each subgroup and therefore for the population as a whole. For example, in order to lower the cost and difficulty of your study, you may want to sample urban subjects by going door-to-door, but rural subjects using mail.

Therefore, you decide to use a stratified sample, relying on a list provided by the university of all its graduates within the last ten years. Step 1: Define your population and subgroups Like other methods of probability sampling , you should begin by clearly defining the population from which your sample will be taken.