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When should a researcher use stratified random sampling?
While using stratified sampling, the researcher should use simple probability sampling. The population is divided into various subgroups such as age, gender, nationality, job profile, educational level etc. Stratified sampling is used when the researcher wants to understand the existing relationship between two groups.
Under what circumstances is a stratified random sample preferred to a simple random sample?
Stratified sampling works best when a heterogeneous population is split into fairly homogeneous groups. Under these conditions, stratification generally produces more precise estimates of the population percents than estimates that would be found from a simple random sample.
What is the first step in conducting stratified sampling?
The first step in stratified random sampling is to split the population into strata, i.e. sections or segments. The strata are chosen to divide a population into important categories relevant to the research interest.
What are the disadvantages of stratified random sample?
Pros and Cons of Stratified Random Sampling Stratified Random Sampling: An Overview. Stratified Random Sampling Example. Advantages of Stratified Random Sampling. Disadvantages of Stratified Random Sampling. Key Takeways: Stratified random sampling allows researchers to obtain a sample population that best represents the entire population being studied.
What are the advantages of stratified sampling?
Stratified Random Sampling provides better precision as it takes the samples proportional to the random population.
What is the difference between random sampling and convenience sampling?
With random sampling, every member of the population has an equal chance of being selected, thus the sample is a good representation of the population. A convenience sample is not representative of the population, and the method is not as structured or rigorous as probability methods.