A stratified random sample involves dividing the population of interest into several smaller groups, called "strata" and then taking a simple random sample from each of these smaller groups. This method is commonly used when we want to guarantee a large enough sample from each subgroup.
Stratified random sampling is more compatible with qualitative research but it can also be used in quantitative data collection. Conclusion Whether you opt for proportionate or disproportionate stratified sampling, the most important thing is creating sub-groups that are internally homogenous, and externally heterogeneous.
30 seconds. 1 pt. This sample is selected by dividing the population into subgroups and then taking a fixed number of units from each group using the simple random sample. stratified. cluster. judgment. experimental. Multiple Choice. Edit.
Therefore, a stratified random sampling procedure was created based on the Center for Disease Control and Prevention’s (CDC) Community Assessment for Public Health Emergency Responses (CASPER) sampling methodology . The CASPER approach was developed using cross-sectional epidemiological principles and is a form of a community needs assessment
Each subject has an equal probability of being chosen from the population to form a sample (subpopulation) of the overall population. So therefore, stratified random sampling is a sampling approach in which the population is separated into groups or strata depending on a particular characteristic.
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what is stratified random sampling
