Stratified sampling is a probability sampling method and a form of random sampling in which the population is divided into two or more groups (strata) according to one or more common attributes. These attributes can be sex, age, income, level of education etc. according to aims and objectives of the study.
Stratified random sampling intends to guarantee that the sample represents specific sub-groups or strata. Accordingly, application of stratified sampling method involves dividing population into different subgroups (strata) and selecting subjects from each strata in a proportionate manner. The figure below illustrates simplistic example where sample group of 10 respondents are selected by dividing population into male and female strata in order to achieve equal representation of both genders in the sample group.
Stratified sampling can be divided into the following two groups: proportionate and disproportionate. Application ofproportionate stratified random samplingtechnique involves determining sample size in each stratum in a proportionate manner to the entire population. For example, if the entire population for a research is 5000 people, in proportionate stratified random sampling the group can be divided into five strata with 1000 people in each stratum.
Indisproportionate stratified random sampling, on the contrary, numbers of subjects recruited from each stratum does not have to be proportionate to the total size of the population. If disproportionate stratified random sampling is applied in a research with 5000 people, the population can be divided into five strata with following unequal numbers of population in each stratum: 1000, 1500, 1200, 800 and 500.
Accordingly, the application of proportionate stratified random sampling generates more accurate primary data compared to disproportionate sampling.
Application of Stratified Sampling: an Example
Suppose, your dissertation aims to explore leadership styles exercised by medium-level managers at Bayerische Motoren Werke Aktiengesellschaft (BMW AG). You have selected semi-structured in-depth interviews with managers as the most appropriate primary data collection method to achieve the research objectives.
Application of stratified random sampling contains the following three stages.
1. Identification of relevant stratums and ensuring their actual representation in the population. Apart from gender as illustrated in example above, range of criteria that can be used to divide population into different strata include age, the level of education, status, nationality, religion and others. Specific patterns of categorization into different stratums depend on aims and objectives of the study.
In our case, BMW Group employees are employed across four business segments – automotive, motorcycles, financial services and other entities[1]. Accordingly, each segment can be adapted as stratum to draw sample group members.
2. Numbering each subject within each stratum with a unique identification number.
3. Selection of sufficient numbers of subjects from each stratum. It is critically important for samples from each stratum to be selected in a random manner so that the relevance of bias can be minimized. As it is illustrated in the table below, following the procedure described above results in the sample group of 16 respondents – BMW Group medium level managers that proportionately represent all four business segments of the company.
| Automotive | Motorcycles | Financial services | Other entities |
| N | Manager | ü | N | Manager | ü | N | Manager | ü | N | Manager | ü |
| 001 | Hudson | | 001 | Conrad | ü | 001 | Guzman | | 001 | Sparks | |
| 002 | Bass | ü | 002 | Braun | | 002 | Craig | | 002 | Atkinson | ü |
| 003 | Richmond | | 003 | Gentry | | 003 | Green | ü | 003 | Montes | |
| 004 | Tucker | | 004 | Hartman | ü | 004 | Ballard | ü | 004 | Mcguire | |
| 005 | Chavez | ü | 005 | Levine | | 005 | Cox | | 005 | Spencer | ü |
| 006 | Riddle | | 006 | Griffin | ü | 006 | Dunlap | ü | 006 | Davies | |
| 007 | Mckinney | | 007 | Valentine | | 007 | Patrick | | 007 | Bradford | ü |
| 008 | Terrell | ü | 008 | Mcdonald | | 008 | Gardner | ü | 008 | Collins | |
| 009 | Hayes | | 009 | Brown | ü | 009 | Carpenter | | 009 | Chen | |
| 010 | Escobar | ü | 010 | Kaufman | | 010 | Vasquez | | 010 | Hess | ü |
| | | | | | | | | | | | |
Advantages of Stratified Sampling
- Stratified random sampling is superior tosimple random samplingbecause the process of stratifying reduces sampling error and ensures a greater level of representation.
- This sampling method captures key characteristics of population in the sample.
- Thanks to the choice of stratified random sampling adequate representation of all subgroups can be ensured.
- When there is hom*ogeneity within strataand heterogeneity between strata, the estimates can be as precise (or even more precise) as with the use of simple random sampling.
Disadvantages of Stratified Sampling
- The application of stratified random sampling requires the knowledge of strata membership a priori. The requirement to be able to easily distinguish between strata in the sample frame may create difficulties in practical levels.
- Overlapping issues may occur in a way that some subjects may fall into different subgroups. This can result in misrepresentation of the population.
- Research process may take longer and prove to be more expensive due to the extra stage in the sampling procedure.
- The choice of stratified sampling method adds certain complexity to the analysis plan.
My e-book,The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step approach contains a detailed, yet simple explanation of sampling methods. The e-book explains all stages of the research process starting from the selection of the research area to writing personal reflection. Important elements of dissertations such as research philosophy, research approach, research design, methods of data collection and data analysis are explained in this e-book in simple words.
John Dudovskiy
[1] Annual Report (2020)
FAQs
What is stratified sampling? In stratified sampling, researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment). Once divided, each subgroup is randomly sampled using another probability sampling method.
Is stratified sampling qualitative or quantitative? ›
Stratified random sampling is more compatible with qualitative research but it can also be used in quantitative data collection. Whether you opt for proportionate or disproportionate stratified sampling, the most important thing is creating sub-groups that are internally hom*ogenous, and externally heterogeneous.
Is stratified sampling a research design? ›
Stratified random sampling is used when the researcher wants to highlight a specific subgroup within the population. This technique is useful in such researches because it ensures the presence of the key subgroup within the sample.
What is the sampling method in research methodology? ›
When doing a research study, we should consider the sample to be representative to the target population, as much as possible, with the least possible error and without substitution or incompleteness. The process of selecting a sample population from the target population is called the “sampling method”.
What are the advantages of stratified sampling method? ›
In short, it ensures each subgroup within the population receives proper representation within the sample. As a result, stratified random sampling provides better coverage of the population since the researchers have control over the subgroups to ensure all of them are represented in the sampling.
Can you use stratified sampling for qualitative research? ›
Researchers can also use statistical techniques such as stratified sampling or weighting to adjust for potential biases in the sample.
Can you use stratified sampling in quantitative research? ›
Then, a random sample is drawn from each stratum, proportionally or equally, to form the final sample. Stratified sampling is often used in quantitative research, which aims to measure and analyze numerical data and test hypotheses.
What are the strengths and limitations of stratified sampling? ›
Stratified random samples can be either proportional or disproportional to a subset's representatives in the general population. The advantages of stratified random samples include increased precision and lower costs. The disadvantages include difficulty in selecting appropriate strata and analyzing the results.
Is stratified sampling observational or experimental? ›
Stratification plays a similar role in observational studies as blocking does in randomized experiments and stratification helps us deal with confounders. With stratification, we can compare groups that are similar in the treatment group to groups that are similar in the control group.
What is an example of a stratified sample in research? ›
To give a quick example here: For research, the target market is split into two strata based on gender, where there are 2,000 males and 6,000 females. Then, for a sampling fraction of ¼, 500 males and 1,500 females will be selected in the final sample population.
Probability Sampling methods are further classified into different types, such as simple random sampling, systematic sampling, stratified sampling, and clustered sampling.
Which is the best sampling methodology? ›
Methods of sampling
In simple random sampling, every subject has an equal chance of being selected for the study. The most recommended way to select a simple random sample is to use a table of random numbers or a computer-generated list of random numbers.
What is the difference between systematic and stratified sampling? ›
In systematic sampling every nth number person is taken as the sample from a finite universe. But in stratified the heterogeneous universe is first divided into hom*ogeneous strata or layers and then the samples are selected randomly from these layers.
What are the pros and cons of stratified sampling? ›
The benefit of stratified sampling is that you obtain reasonably precise estimates for all subgroups related to your research question. The drawback is that analyzing these datasets is more complicated.
Is stratified sampling biased? ›
When using stratified sampling, you create subgroups based on certain characteristics of your population, such as age, gender, income, or education level. This ensures that each subgroup is a fair representation of the entire population, and that your sample is not biased towards any particular characteristic.
Is stratified sampling more efficient? ›
Efficiency: Stratification may increase efficiency of the estimates by forming strata in such a way that each stratum becomes hom*ogeneous with respect to the characteristic under study. Suitable sampling schemes to the respective strata may increase efficiencies of the estimators.
What type of sampling is stratified sampling? ›
Stratified random sampling is a method of sampling that involves the division of a population into smaller subgroups known as strata. In stratified random sampling, or stratification, the strata are formed based on members' shared attributes or characteristics, such as income or educational attainment.
What is stratified sampling in quantitative? ›
Stratified random sampling is a widely used statistical technique in which a population is divided into different subgroups, or strata, based on some shared characteristics. The purpose of stratification is to ensure that each stratum in the sample and to make inferences about specific population subgroups.
What is a stratified sample in a qualitative study? ›
Stratified Random sampling represents a sampling design in which a population is divided into sub-populations such that members of each sub-population are relatively hom*ogeneous with respect to one or more characteristics and relatively heterogeneous from members of all other sub-groups with respect to this/these ...
What type of sampling is qualitative? ›
Purposeful sampling is widely used in qualitative research for the identification and selection of information-rich cases related to the phenomenon of interest.
