Stratified Random Sampling, PDF

What Is a Stratified Random Sampling?

Stratified random sampling is a probability sampling in which the entire population is separated into homogenous groups (strata) for the sampling process. Each stratum is based on shared features or attributes, such as education level, income, and gender. Then, samples are taken randomly from each stratum and compared with one another to conclude. Researchers frequently employ stratified random sampling to examine data from distinct subgroups or strata. It enables them to rapidly collect a sample representative of the total population under study. Stratified sampling is one of four probability sampling approaches, along with simple random, systematic, stratified, and cluster sampling. Your selection of sampling method will rely on your objectives, budget, and required level of precision. With this in mind, establish what you wish to accomplish and experiment with several strategies to see which will work best for your research. Modern technologies have dramatically improved access to information. As a result, this has contributed to the development of significant academic research. A report from the National Science Foundation indicates that the global output of research in science and engineering has increased by 4% each year.

Benefits of Stratified Random Sampling

The sampling procedure divides a population into subpopulations before using random sample procedures. A disadvantage is that researchers need to segment the population into smaller groups. In contrast to simple random sampling, which involves choosing information randomly from the entire population to guarantee that each prospective sample has an equal chance of occurring, stratified sampling is distinct and different. On the other hand, stratified random sampling divides the population into specific groups, or strata, depending on shared characteristics. Here are some benefits and drawbacks of using stratified sampling:

Unbiased: In a well-designed stratified random sampling plan, the provided population is divided into some homogenous groups according to purposive characteristics. The technique of random selection is used to gather samples from each stratum. This avoids the drawbacks of purposeful and random sampling while enjoying both methods’ advantages. Using stratified random sampling eliminates the possibility that any critical section of the population is completely ignored and guarantees that each stratum or a substantial percentage of the population is appropriately represented. Stratified random sampling delivers a more representative sample of the population than other sampling strategies, which results in less unpredictability.Superior Accuracy: Stratified random sampling provides more accurate estimates than traditional random sampling since the variability within each stratum is decreased. In stratified random sampling, for instance, the sampling estimate of the population percentage is more accurate than the equivalent estimate generated from essential random sampling in proportionate and Neyman’s allocations of the sampling to various strata.Effectiveness in Survey Conduct: Stratified sampling can facilitate data collection and reduce survey costs. When a survey’s administrators divide the entire population into strata, they frequently gain an advantage. When researchers divide a large population into small subgroups with similar members, surveys can become more cost-effective and beneficial. These benefits come when specialist individuals, skills, or areas of knowledge can survey a specific division more effectively. For instance, you can engage multiple people to compare urban and rural areas. The segmentation of the population into somewhat homogenous groups facilitates administration. Unlike random samples, stratified samples are intended to be regionally specific. This eventually reduces the cost and time associated with data collection, respondent interviews, and fieldwork supervision.Source Reliable for Sampling: For different demographic groupings, obtaining distinct levels of precision is sometimes desirable. Stratified random sampling is the only method that allows us to receive results with known accuracy for each stratum. Frequently, the sampling challenges for distinct demographic segments vary significantly. When this occurs, the problem can be effectively resolved by stratified sampling by treating each component of the population as a specific stratum and addressing each stratum separately during sampling.

Types of Data Collection Methods

As our culture shifts away from factory work, lifelong education becomes increasingly necessary. Technology and data have become integral to our working methods. This transformation has placed greater expectations than ever before on educators. You must understand how to collect and handle data, whether teaching in a classroom or a boardroom. If you’re still interested, below are several data collection methods.

1. Interviews

If you asked a person with little knowledge of data analysis the best way to acquire information from individuals, the most popular response would likely be interviewed. Anyone may generate a list of questions, but the key to doing productive interviews is understanding what to ask. Person-to-person interviews can be the most expensive primary data collection method. Occasionally, though, an in-person interview might be worthwhile, as the interviewer can design follow-up questions based on the candidate’s responses in real time. Additionally, interviews permit open-ended questioning. Interviews are more adaptable and flexible than primary data collection techniques, such as surveys.

2. Observation

Observation is the practice of gathering data without asking questions. This method is more subjective because the researcher or observer must apply judgment to the collected data. In some instances, however, the risk of prejudice is negligible. Generally speaking, observation may determine the dynamics of a situation, which other data collection methods cannot measure. Observation can also be integrated with supplementary data, such as video.

3. Documents and Records

Occasionally, you can obtain a substantial amount of information without asking anyone. Document- and record-based research utilizes existing data to conduct an investigation. Examples of this type of study include attendance logs, meeting minutes, and financial documents. Using documents and records can be economical because you rely primarily on previously conducted research. However, because the researcher has less control over the results, documents, and papers might be an unreliable sources of information.

4. Focus Groups

A blend of interviewing, surveying, and observation, a focus group is a form of data collection involving numerous persons who share a common characteristic. A focus group is intended to give a collective dimension to individual data collection. Participants in a focus group study may be asked to view a presentation, debate its content, and answer surveys or interview-style questions.

5. Oral Histories

On the surface, oral history may resemble an interview. Both methods of data collection require asking questions. Oral history is more accurately described as the recording, preservation, and interpretation of historical knowledge based on individuals’ opinions and personal experiences during events. Unlike interviews and surveys, oral histories are related to a specific phenomenon. For instance, a researcher may wish to examine the impact of a flood on the town. Oral history can offer light on what transpired precisely. It is a holistic evaluation strategy that employs a range of methodologies.

How to Perform Stratified Random Sampling

As a researcher or statistician, learning to employ stratified random sampling will help you evaluate strata relationships more simply. The sampling is more accurate and representative by obtaining a proportional random sample of a stratified population. For the sake of their research, researchers establish strata based on shared features or attributes, such as gender, race, geography, education level, or socioeconomic status. Each population associate can only be assigned to one subgroup, or stratum, before being randomly sampled using a probability sampling method such as random selection. This allows you to estimate the statistical measures for each subset and ensures that strata are represented proportionally in the sample. Four steps are required to do a stratified random sampling:

1. Specify the Population and Subpopulations.

Define the population from which you intend to draw your sample. Then, divide this population into subgroups with distinct characteristics. You can use various factors, such as race and gender, to define subgroups. Subgroups must be mutually exclusive, include no overlap, and consist of the entire population. Remember that each person can only belong to one subgroup.

2. Divide the People into Smaller Groups.

After identifying the population and subgroups, compile an information sheet about each population member. Assign each individual to a distinct grouping. Ensure that there is no overlap, that each stratum is mutually exclusive, and that each stratum contains the entire population.

3. Choose the Subgroup Sample Size.

The next step is to guarantee that the number of people in each subgroup’s sample is about the same as in the whole population. Subgroups that are less common in the entire population are also less common in the sample. On the other hand, subgroups that are more common in the whole population are also more common in the sample. Choose a total sample size that is big enough to draw statistical conclusions for each stratum.

4. Take Random Samples of the Subgroups

Choose a sampling method based on probability, such as systematic sampling or random selection. Utilize this sampling technique on each subgroup to generate a sample. This represents a random stratified sample of the original population.


What is the limitation of stratified sampling?

An essential disadvantage of stratified sampling is that it might be challenging to pick the appropriate strata for a sample. The second disadvantage is that organizing and assessing the data is more challenging than a simple random sample.

Why is stratified better than random?

Both have advantages and downsides. Because it uses specific features to divide the population into subsets, it can provide a more accurate picture of the people.

When should you use stratified sampling?

It would help if you used stratified sampling when you can split your sample into mutually exclusive and exhaustive subgroups that you assume will have different mean values for the variable you are examining.

Moreover, this is stratified random sampling. Hopefully, you now understand how to implement this probability sampling method in your research and survey analysis. Are you prepared to implement your own Stratified Random Sampling? Check out our various templates above!