Simple random samples and stratified random samples differ in how the sample is drawn from the overall population of data. Simple random samples involve the random selection of data from the entire population so each possible sample is equally likely to occur. In contrast, stratified random sampling divides the population into smaller groups, or strata, based on shared characteristics. A random sample is taken from each stratum in direct proportion to the size of the stratum compared to the population. The sample subsets are then combined to create a random sample.
Simple random sampling and stratified sampling are both types of probability sampling where each sample has a known probability of being selected. This is different from judgmental sampling, where the units to be sampled are handpicked by the researcher.
The population is the total set of observations or data. A sample is a set of observations from the population. The sampling method is the process used to pull samples from the population. A simple random sample is a random sample pulled from the entire population with no constraints placed on how the sample is pulled. This method has no bias in selecting the sample from the population, so each population element has an equal chance of being included in the sample.
How Stratified Random Sampling Works
Stratified random samples group the population elements into strata based on certain criteria, then randomly choose elements from each stratum in proportion to the stratum’s size versus the population. The researchers must take care to ensure the strata do not overlap. Each point in the population must only belong to one stratum so each point is mutually exclusive. Overlapping strata would increase the likelihood that some data are included, thus skewing the sample.
Stratified sampling offers certain advantages and disadvantages compared to simple random sampling. A stratified sample can provide a more accurate representation of the population based on the characteristic used to divide the population into strata.
For populations with important distinguishing characteristics, stratified sampling can create a more representative sample. This often requires a smaller sample size, which can save resources and time. In addition, by including sufficient sample points from each stratum, the researchers can conduct a separate analysis on each individual stratum. (For related reading, see: What Are Some Examples of Stratified Random Sampling?)
A stratified sample can ensure representation of certain strata for inclusion in the population. Random sampling may not pull any data points from a smaller stratum, but a stratified sample includes those samples with a proportional representation. More work is required to pull a stratified sample than a random sample. Researchers must individually track and verify the data for each stratum for inclusion, which can take a lot more time compared with random sampling. (For related reading, see: What are the criteria for a simple random sampling?)