A stratified sample draws from each group a sample and calls the groups strata (but usually not all units of a stratum are sampled). A cluster sample first draws a sample of groups from all groups and calls the groups clusters. Stratified Random Sampling when a population can be separated into non-overlapping groups, called strata, then selecting a simple random sample within each stratum is the sampling random procedure Strata - when a population can be separated into non-overlapping groups Sampling is the technique of selecting a representative part of a population for the purpose of determining the characteristics of the whole population. There are two types of sampling analysis: simple random sampling and stratified random sampling. Let’s look at both techniques in a bit more detail. Stratified sampling is when the population is divided into specific groups and then randomly sampled from those groups. Random Sampling is a vital part of psychological research. When the population is randomly sampled, it ensures that the study has more validity because there is no researcher Bias. What are the advantages 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. The researcher has a list of most of the schools in the state of each level that are using a database that the researcher has access to. In order to assess this question, the researcher takes a stratified random sample107, selecting nelementary = 100 n elementary = 100 schools from the population of 4421 elementary schools, nmiddle = 50 n Stratified sampling is a technique that divides a population into smaller groups, or strata, based on a common characteristic, such as age, gender, income, or education. Then, a random sample is 6Ign.

what is stratified random sampling