Suggest a new Definition Show
Proposed definitions will be considered for inclusion in the Economictimes.com Marketing
Definition: Stratified sampling is a type of sampling method in which the total population is divided into smaller groups or strata to complete the sampling process. The strata is formed based on some common characteristics in the population data. After dividing the population into strata, the researcher randomly selects the sample proportionally. Description: Stratified sampling is a common sampling technique used by researchers when trying to draw conclusions from different sub-groups or strata. The strata or sub-groups should be different and the data should not overlap. While using stratified sampling, the researcher should use simple probability sampling. The population is divided into various subgroups such as age, gender, nationality, job profile, educational level etc. Stratified sampling is used when the researcher wants to understand the existing relationship between two groups. The researcher can represent even the smallest sub-group in the population. There are two types of stratified sampling – one is proportionate stratified random sampling and another is disproportionate stratified random sampling. In the proportionate random sampling, each stratum would have the same sampling fraction. For example, you have three sub-groups with a population size of 150, 200, 250 subjects in each subgroup respectively. Now, to make it proportionate, the researcher uses one specific fraction or a percentage to be applied on its subgroups of population. The sample for first group would be 150*0.5= 75, 200*0.5=100 and 250*0.5= 125. Here the constant factor is the proportion ration for each population subset. The only difference is the sampling fraction in the disproportionate stratified sampling technique. The researcher could use different fractions for various subgroups depending on the type of research or conclusion he wants to derive from the population. The only disadvantage to that is the fact that if the researcher lays too much emphasis on one subgroup, the result could be skewed.
Published on September 19, 2019 by Shona McCombes. Revised on October 10, 2022. When you conduct research about a group of people, it’s rarely possible to collect data from every person in that group. Instead, you select a sample. The sample is the group of individuals who will actually
participate in the research. To draw valid conclusions from your results, you have to carefully decide how you will select a sample that is representative of the group as a whole. There are two types of sampling methods: You should clearly explain how you selected your sample in the methodology section of your paper or thesis. First, you need to understand the difference between a population and a sample, and identify the target population of your research. The population can be defined in terms of geographical location, age, income, and many other characteristics. It is important to carefully define your target population according to the purpose and practicalities of your project. If the population is very large, demographically mixed, and geographically dispersed, it might be difficult to gain access to a representative sample. Sampling frameThe sampling frame is the actual list of individuals that the sample will be drawn from. Ideally, it should include the entire target population (and nobody who is not part of that population). Example: Sampling frameYou are doing research on working conditions at Company X. Your population is all 1000 employees of the company. Your sampling frame is the company’s HR database which lists the names and contact details of every employee.Sample sizeThe number of individuals you should include in your sample depends on various factors, including the size and variability of the population and your research design. There are different sample size calculators and formulas depending on what you want to achieve with statistical analysis. Probability sampling methodsProbability sampling means that every member of the population has a chance of being selected. It is mainly used in quantitative research. If you want to produce results that are representative of the whole population, probability sampling techniques are the most valid choice. There are four main types of probability sample.  1. Simple random samplingIn a simple random sample, every member of the population has an equal chance of being selected. Your sampling frame should include the whole population. To conduct this type of sampling, you can use tools like random number generators or other techniques that are based entirely on chance. Example: Simple random samplingYou want to select a simple random sample of 100 employees of Company X. You assign a number to every employee in the company database from 1 to 1000, and use a random number generator to select 100 numbers.2. Systematic samplingSystematic sampling is similar to simple random sampling, but it is usually slightly easier to conduct. Every member of the population is listed with a number, but instead of randomly generating numbers, individuals are chosen at regular intervals. Example: Systematic samplingAll employees of the company are listed in alphabetical order. From the first 10 numbers, you randomly select a starting point: number 6. From number 6 onwards, every 10th person on the list is selected (6, 16, 26, 36, and so on), and you end up with a sample of 100 people.If you use this technique, it is important to make sure that there is no hidden pattern in the list that might skew the sample. For example, if the HR database groups employees by team, and team members are listed in order of seniority, there is a risk that your interval might skip over people in junior roles, resulting in a sample that is skewed towards senior employees. 3. Stratified samplingStratified sampling involves dividing the population into subpopulations that may differ in important ways. It allows you draw more precise conclusions by ensuring that every subgroup is properly represented in the sample. To use this sampling method, you divide the population into subgroups (called strata) based on the relevant characteristic (e.g. gender, age range, income bracket, job role). Based on the overall proportions of the population, you calculate how many people should be sampled from each subgroup. Then you use random or systematic sampling to select a sample from each subgroup. Example: Stratified samplingThe company has 800 female employees and 200 male employees. You want to ensure that the sample reflects the gender balance of the company, so you sort the population into two strata based on gender. Then you use random sampling on each group, selecting 80 women and 20 men, which gives you a representative sample of 100 people.4. Cluster samplingCluster sampling also involves dividing the population into subgroups, but each subgroup should have similar characteristics to the whole sample. Instead of sampling individuals from each subgroup, you randomly select entire subgroups. If it is practically possible, you might include every individual from each sampled cluster. If the clusters themselves are large, you can also sample individuals from within each cluster using one of the techniques above. This is called multistage sampling. This method is good for dealing with large and dispersed populations, but there is more risk of error in the sample, as there could be substantial differences between clusters. It’s difficult to guarantee that the sampled clusters are really representative of the whole population. Example: Cluster samplingThe company has offices in 10 cities across the country (all with roughly the same number of employees in similar roles). You don’t have the capacity to travel to every office to collect your data, so you use random sampling to select 3 offices – these are your clusters.What can proofreading do for your paper?Scribbr editors not only correct grammar and spelling mistakes, but also strengthen your writing by making sure your paper is free of vague language, redundant words and awkward phrasing. See editing example Non-probability sampling methodsIn a non-probability sample, individuals are selected based on non-random criteria, and not every individual has a chance of being included. This type of sample is easier and cheaper to access, but it has a higher risk of sampling bias. That means the inferences you can make about the population are weaker than with probability samples, and your conclusions may be more limited. If you use a non-probability sample, you should still aim to make it as representative of the population as possible. Non-probability sampling techniques are often used in exploratory and qualitative research. In these types of research, the aim is not to test a hypothesis about a broad population, but to develop an initial understanding of a small or under-researched population. 1. Convenience samplingA convenience sample simply includes the individuals who happen to be most accessible to the researcher. This is an easy and inexpensive way to gather initial data, but there is no way to tell if the sample is representative of the population, so it can’t produce generalizable results. Example: Convenience samplingYou are researching opinions about student support services in your university, so after each of your classes, you ask your fellow students to complete a survey on the topic. This is a convenient way to gather data, but as you only surveyed students taking the same classes as you at the same level, the sample is not representative of all the students at your university.2. Voluntary response samplingSimilar to a convenience sample, a voluntary response sample is mainly based on ease of access. Instead of the researcher choosing participants and directly contacting them, people volunteer themselves (e.g. by responding to a public online survey). Voluntary response samples are always at least somewhat biased, as some people will inherently be more likely to volunteer than others. Example: Voluntary response samplingYou send out the survey to all students at your university and a lot of students decide to complete it. This can certainly give you some insight into the topic, but the people who responded are more likely to be those who have strong opinions about the student support services, so you can’t be sure that their opinions are representative of all students.3. Purposive samplingThis type of sampling, also known as judgement sampling, involves the researcher using their expertise to select a sample that is most useful to the purposes of the research. It is often used in qualitative research, where the researcher wants to gain detailed knowledge about a specific phenomenon rather than make statistical inferences, or where the population is very small and specific. An effective purposive sample must have clear criteria and rationale for inclusion. Always make sure to describe your inclusion and exclusion criteria. Example: Purposive samplingYou want to know more about the opinions and experiences of disabled students at your university, so you purposefully select a number of students with different support needs in order to gather a varied range of data on their experiences with student services.4. Snowball samplingIf the population is hard to access, snowball sampling can be used to recruit participants via other participants. The number of people you have access to “snowballs” as you get in contact with more people. Example: Snowball samplingYou are researching experiences of homelessness in your city. Since there is no list of all homeless people in the city, probability sampling isn’t possible. You meet one person who agrees to participate in the research, and she puts you in contact with other homeless people that she knows in the area.Frequently asked questions about samplingWhat is sampling? A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students. In statistics, sampling allows you to test a hypothesis about the characteristics of a population. Why are samples used in research? Samples are used to make inferences about populations. Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable. What is multistage sampling? In multistage sampling, or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage. This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from. Cite this Scribbr articleIf you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.
Is this article helpful?You have already voted. Thanks :-) Your vote is saved :-) Processing your vote... Which of the following is a sampling method in which the population is first divided into strata and then samples?After dividing the population into strata, the researcher randomly selects the sample proportionally. Description: Stratified sampling is a common sampling technique used by researchers when trying to draw conclusions from different sub-groups or strata.
In which sampling method population is divided into?4. Cluster sampling. Cluster sampling also involves dividing the population into subgroups, but each subgroup should have similar characteristics to the whole sample.
Which sampling method we first divide the population area into sections the random We select of those clusters and choose all the members from those selected clusters?In cluster sampling, researchers divide a population into smaller groups known as clusters. They then randomly select among these clusters to form a sample. Cluster sampling is a method of probability sampling that is often used to study large populations, particularly those that are widely geographically dispersed.
|
