What is a stratified sample in a survey?
Stratified random sampling is a method of sampling that involves dividing a population into smaller groups–called strata. The groups or strata are organized based on the shared characteristics or attributes of the members in the group. The process of classifying the population into groups is called stratification.
When would you use a stratified sample?
Stratified random sampling is used when your population is divided into strata (characteristics like male and female or education level), and you want to include the stratum when taking your sample.
How do you find a stratified sample?
To create a stratified random sample, there are seven steps: (a) defining the population; (b) choosing the relevant stratification; (c) listing the population; (d) listing the population according to the chosen stratification; (e) choosing your sample size; (f) calculating a proportionate stratification; and (g) using …
How do you stratify a population?
A sample may be selected from a population through a number of ways, one of which is the stratified random sampling method. A stratified random sampling involves dividing the entire population into homogeneous groups called strata (plural for stratum). Random samples are then selected from each stratum.
Why is stratified sampling good?
Stratified sampling offers several advantages over simple random sampling. A stratified sample can provide greater precision than a simple random sample of the same size. Because it provides greater precision, a stratified sample often requires a smaller sample, which saves money.
What is the purpose of stratification?
Stratification (or blocking) of the study population is often performed prior to sampling in order to increase the precision of the estimate of the quantity of interest. A procedure similar to the simple random sampling case requires knowledge of the variability within each stratum.
How do you stratify data?
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, etc). Once divided, each subgroup is randomly sampled using another probability sampling method.
What are the benefits of stratified sampling?
Stratified sampling offers several advantages over simple random sampling.
- A stratified sample can provide greater precision than a simple random sample of the same size.
- Because it provides greater precision, a stratified sample often requires a smaller sample, which saves money.
What does stratify data mean?
Data stratification is the separation of data into smaller, more defined strata based on a predetermined set of criteria. A simpler way to view data stratification is to see it as a giant load of laundry that needs to be sorted.
Why is stratification used?
Stratification can be used to ensure equal allocation of subgroups of participants to each experimental condition. This may be done by gender, age, or other demographic factors.