Why choose stratified sampling

The clusters are randomly selected, and then sampling occurs within these selected clusters. Two-stage cluster sampling first randomly selects the cluster, then the participants are randomly selected from within that cluster.

Multi-stage cluster sampling is a more complex process which involves dividing the population into groups before one or more clusters are chosen at random and sampled. The main difference between stratified sampling and cluster sampling is that with cluster sampling, there are natural groups separating your population. In cluster sampling, the sampling unit is the whole cluster. Instead of sampling individuals from each group, a researcher will study whole clusters.

In stratified random sampling, however, a sample is drawn from each strata using a random sampling method like simple random sampling or systematic sampling. Elements of each of the samples will be distinct, giving the entire population an equal opportunity to be part of these samples. Typically, natural groups do not exist, so you divide your target population into groups stratum.

Simple random sampling involves randomly selecting data from the entire population so each possible sample is likely to occur. There are no constraints with this method and therefore no bias. Stratified random sampling, on the other hand, divides the population into smaller groups strata based on shared characteristics. A random sample is then taken from each in direct proportion to the size of the stratum compared to the population and combined to create a random sample.

With stratified random sampling, you will end up with a sample that is proportionally representative to the population based on the stratum used. In most cases, this will work well. For instance, using our wage example from above, the sample has 5 randomly selected participants, which will be split by gender made up of 2 women and 3 men. Either, adjust the sample ratio to include more men — e.

Or, increase the sample size to include more of the population, to better reflect the wage range in the male proportion of the sample — e. Hopefully, you now have a good idea of how to use this probability sampling technique to aid your research and surveys. Market Research. Just a minute! It looks like you entered an academic email. This form is used to request a product demo if you intend to explore Qualtrics for purchase.

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Solutions for Market Research Tackle the hardest research challenges and deliver the results that matter with market research software for everyone from researchers to academics. Solutions for CX Professional Monitor and improve every moment along the customer journey; Uncover areas of opportunity, automate actions, and drive critical organizational outcomes. 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. Financial Analysis. Marketing Essentials. Your Privacy Rights. To change or withdraw your consent choices for Investopedia. At any time, you can update your settings through the "EU Privacy" link at the bottom of any page. These choices will be signaled globally to our partners and will not affect browsing data.

We and our partners process data to: Actively scan device characteristics for identification. I Accept Show Purposes. Your Money. Personal Finance. Your Practice. Popular Courses. Financial Analysis How to Value a Company. What Is Stratified Random Sampling? Learn more: Cluster Sampling.

Researchers and statisticians use stratified random sampling to analyze relationships between two or more strata. Learn more: Quantitative Market Research. Following is a classic stratified random sampling example:. For the survey to deliver precise results, the ideal manner is to divide each grade into various strata. Learn more: Convenience Sampling. Learn more: Cluster Sampling vs Stratified Sampling. Though you're welcome to continue on your mobile screen, we'd suggest a desktop or notebook experience for optimal results.

Survey software Leading survey software to help you turn data into decisions. Research Edition Intelligent market research surveys that uncover actionable insights. Customer Experience Experiences change the world. Deliver the best with our CX management software. Workforce Powerful insights to help you create the best employee experience. Stratified Random Sampling: Definition Stratified random sampling is a type of probability sampling 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.

Select your respondents Age, socioeconomic divisions, nationality, religion, educational achievements and other such classifications fall under stratified random sampling.

Learn more: Demographic Segmentation 8 Steps to select a stratified random sample: Define the target audience. 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. Every additional information decides the stratification variables. 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. You must also choose the characteristic that you will use to divide your groups.

This choice is very important: since each member of the population can only be placed in only one subgroup, the classification of each subject to each subgroup should be clear and obvious.

You can choose to stratify by multiple different characteristics at once, so long as you can clearly match every subject to exactly one subgroup. In this case, to get the total number of subgroups, you multiply the numbers of strata for each characteristic.

See an example.