Understanding Stratified Random Sampling - Explanation.
Stratified sampling. Stratified sampling is used to select a sample that is representative of different groups. If the groups are of different sizes, the number of items selected from each group.
Stratified sampling example In statistical surveys, when subpopulations within an overall population vary, it could be advantageous to sample each subpopulation (stratum) independently. Stratification is the process of dividing members of the population into homogeneous subgroups before sampling.
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Stratified sample size (1) Identification and definition of the population. Ex. To compare the efficiency of two methods of training the psychosocial competence in management according to the level of self-esteem, the population consists of the 300 top managers from a random city. (2) Determining the sample size (calculating sample size) Ex.
Stratified random sampling intends to guarantee that the sample represents specific sub-groups or strata. Accordingly, application of stratified sampling method involves dividing population into different subgroups (strata) and selecting subjects from each strata in a proportionate manner. Stratified Random Sampling: Definition, Method and Examples.
Stratified random sampling is used when the researcher wants to highlight a specific subgroup within the population. This technique is useful in such researches because it ensures the presence of the key subgroup within the sample. Researchers also employ stratified random sampling when they want to observe existing relationships between two or.
You could random sample equally from within each sugroup in a stratified sample, but in certain situations, you may want to use unequally sized samples. About the Book Author Deborah J. Rumsey, PhD, is a professor of statistics and the director of the Mathematics and Statistics Learning Center at the Ohio State University.