Researchers first identify a method of selecting a sample from each subgroup, or stratum, based on a shared characteristic such as age or gender. They then randomly select members from within each stratum to form the final sample.
These include gender, age, sex, race, education, or income.
The process of classifying the population into groups before sampling is called stratification. The strata must be mutually exclusive. Every population member falls into only one stratum.
When stratifying, researchers tend to use proportionate sampling, where they maintain the correct proportions to represent the population as a whole.
For example, if the larger population contains 40% history majors and 60% English majors, the final sample should reflect these percentages.
Researchers typically use disproportionate sampling only when studying an underrepresented group.
Applications
- Political Polling: Useful for estimating opinions across demographic groups or predicting election outcomes.
- Income Comparisons: Comparing income levels across different populations or job types.
- Time or Budget Limits: Stratified sampling is quicker and cheaper than many other sampling methods.
- Highly Varied Populations: Dividing a highly varied population into strata helps organize the sample.
- No Full Population Access: Useful when researchers cannot access an entire population.
How to Conduct Stratified Sampling
- Define your population of interest and choose the characteristic(s) that you will use to divide your groups.
- Divide your sample into strata depending on the relevant characteristic(s). Each stratum must be mutually exclusive, but together, they must represent the entire population.
- Define the sample size for each stratum and decide whether your sample will be proportionate or disproportionate. Each stratum’s sample size should ideally match its share of the target population, the whole group the study is about. It should also match the sampling frame, the actual, accessible list of people the sample is drawn from, such as an enrolment register.
- Draw a random sample from each stratum and combine them to form your final sample.
Selecting randomly within each stratum, rather than choosing groups by hand, is what makes stratified sampling a probability method. Neyman (1934) first formalized this idea, showing that random selection lets researchers estimate how far a sample is likely to depart from the true population value.
Example Situations
- Public Health Studies: To understand the incidence of disease across different age groups, the population could be stratified into different age brackets (e.g., 0-18, 19-35, 36-50, 51+).
- Investigating the relationship between average travel frequency, trip mode structure, and the characteristics of residential areas (Shi, 2015).
- Examining the prevalence and psychological sequelae of childhood sexual and physical abuse in adults from the general population (Briere & Elliott, 2003).
- Evaluating the usefulness of personality traits in explaining and predicting entrepreneurship (Llewellyn & Wilson, 2003).
- Examining women’s involvement in multiple roles in relation to 3 stress indices: role overload, role conflict, and anxiety (Barnett & Baruch, 1985).
- Studying perceptions of drinking water quality at four locations in Western Australia (Syme & Williams, 1993).
Advantages
Efficiency and Cost
Grouping the population by a shared characteristic also saves time and money. Because each stratum is more homogeneous than the whole population, researchers often need a smaller total sample to reach the same precision as simple random sampling. So less data needs to be collected overall.
This saving is on top of the basic advantage any sample has over surveying the whole population: it is cheaper and more manageable. The trade-off is the extra planning needed to build accurate strata in the first place, covered under Planning below.
Accuracy
Stratified sampling can produce more precise estimates than simple random sampling when members of the subpopulations are homogeneous relative to the entire population. This gives a study more statistical power.
It also allows more precise comparisons between different strata than a simple random sample of the same size achieves. Because no single group is over- or under-represented, results generalize with more confidence: the sample mirrors the population on exactly the variables the strata were built around.
Limitations
Too many differences within the population
A population can’t be organized into meaningful strata when too many different characteristics matter at once, or when the population isn’t well understood.
The reason is homogeneity. The method’s precision advantage depends on each stratum staying fairly similar inside. If several variables, such as age, gender, and income, all matter at once, a stratum forms for every combination, and each one gets too small or too mixed to help.
Planning
Researchers must ensure that every member of the population fits into only one stratum and that all the strata collectively contain every member of the greater population. This involves extra planning and information gathering that simple random sampling does not require.
Because of this extra work, stratified sampling is used less often in psychology than simpler methods like opportunity sampling.
Sampling errors
A sampling error is the normal, random gap between a sample’s result and the true population value. It shrinks as the sample grows.
This is different from sampling bias, a systematic distortion that persists no matter how large the sample gets. If a stratified sample uses inaccurate population proportions, the result is bias, not sampling error, and the strata need correcting before continuing.
Contemporary Research
A major reform since the replication crisis is pre-registration. Researchers publicly state a study’s design, sample size, and analysis plan before collecting any data. Nosek, Ebersole, DeHaven, and Mellor (2018) describe this as tightening the link between the sample a study claims and the sample it actually used.
That matters for stratified sampling especially.
The method depends on fixing the strata’s proportions and sample sizes in advance, before any results come in. Simmons, Nelson, and Simonsohn (2011) showed that this kind of after-the-fact flexibility inflates false-positive rates.
The sample source has changed too. Much of today’s data collection has moved to online panels such as Prolific. These give researchers faster access to larger, more varied pools to draw strata from.
That still isn’t representativeness, though. Paolacci and Chandler (2014) found that crowdworkers remain non-representative of the wider population.
Cluster Sampling vs. Stratified Sampling
Stratified sampling and cluster sampling both involve dividing a large population into smaller groups and then selecting randomly among the subgroups to form a sample.
However, the two methods differ in how groups are chosen. In stratified sampling, researchers divide the population by characteristics like age, religion, ethnicity, or income, then randomly select participants from each stratum.
In cluster sampling, researchers first divide the population into naturally occurring groups, such as city blocks or school districts. They then randomly select whole clusters and study the members within them.
Stratified Sampling vs. Quota Sampling
Quota sampling and stratified sampling both involve dividing a population into mutually exclusive subgroups and sampling a predetermined number of individuals from each.
However, the most significant difference between these two techniques is that quota sampling is a non-probability sampling method, while stratified sampling is a probability sampling method.
In a stratified sample, individuals within each stratum are selected randomly, while in a quota sample, researchers choose the sample instead of randomly selecting it.
Key Terms
- A sample is the participants you select from a target population (the group you are interested in) to make generalizations about. As an entire population tends to be too large to work with, a smaller group of participants must act as a representative sample.
- Representative means the extent to which a sample mirrors a researcher’s target population and reflects its characteristics (e.g., gender, ethnicity, socioeconomic level). In an attempt to select a representative sample and avoid sampling bias (the over-representation of one category of participant in the sample), psychologists utilize a variety of sampling methods.
- Generalisability means the extent to which their findings can be applied to the larger population of which their sample was a part.
Key Takeaways
- Definition: Stratified random sampling divides a population into strata sharing a key characteristic, then randomly samples within each stratum.
- Proportional Representation: Researchers typically keep each stratum’s share of the sample equal to its share of the population.
- Key Strength: Because each stratum is more uniform than the whole population, comparisons between strata are more precise than simple random sampling of the same size.
- Key Limitation: Building accurate strata means knowing the population’s true proportions in advance, which is time-consuming and one reason the method is used less often in psychology.
- Not Quota Sampling: Selection within each stratum is random, unlike quota sampling, which fills subgroups non-randomly and is not a true probability method.
- Common Uses: Political polling, income and demographic research, and studies where budgets or timelines rule out sampling an entire population.
References
Barnett, R. C., & Baruch, G. K. (1985). Women’s involvement in multiple roles and psychological distress. Journal of Personality and Social Psychology, 49(1), 135–145.
Briere, J., & Elliott, D. M. (2003). Prevalence and psychological sequelae of self-reported childhood physical and sexual abuse in a general population sample of men and women. Child abuse & neglect, 27(10), 1205-1222.
How to use stratified random sampling to your advantage. Qualtrics. (n.d.). Retrieved from https://www.qualtrics.com/experience-management/research/stratified-random-sampling/
Llewellyn, D. J., & Wilson, K. M. (2003). The controversial role of personality traits in entrepreneurial psychology. Education+ Training.
Neyman, J. (1934). On the two different aspects of the representative method: The method of stratified sampling and the method of purposive selection. Journal of the Royal Statistical Society, 97(4), 558–625. https://doi.org/10.2307/2342192
Nickolas, S. (2021, May 19). How stratified random sampling works. Investopedia. Retrieved January 27, 2022, from https://www.investopedia.com/ask/answers/032615/what-are-some-examples-stratified-random-sampling.asp
Nosek, B. A., Ebersole, C. R., DeHaven, A. C., & Mellor, D. T. (2018). The preregistration revolution. Proceedings of the National Academy of Sciences, 115(11), 2600–2606. https://doi.org/10.1073/pnas.1708274114
Paolacci, G., & Chandler, J. (2014). Inside the Turk: Understanding Mechanical Turk as a participant pool. Current Directions in Psychological Science, 23(3), 184–188. https://doi.org/10.1177/0963721414531598
Shi, F. (2015). Study on a stratified sampling investigation method for resident travel and the sampling rate. Discrete Dynamics in Nature and Society, 2015.
Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological Science, 22(11), 1359–1366. https://doi.org/10.1177/0956797611417632
Syme, G. J., & Williams, K. D. (1993). The psychology of drinking water quality: an exploratory study. Water Resources Research, 29(12), 4003-4010.
