PRESENTATION ON STRATIFED SAMPLING

SOCH COLLEGE OF IT1st semester Bsc.CSIT

Ranipauwa

Presented by: Krishna Bharati

DEFINITION OF STRATIFIED SAMPLING

A stratified sample is a probability sampling technique in which the researcher divides the entire target

population into different subgroups, or strata, and then randomly selects the final subjects proportionally from the different strata. This type of sampling is used when the researcher wants to highlight specific

subgroups within the population.

• For example, to obtain a stratified sample of university students, the researcher would first organize the population by college class and then select appropriate numbers of freshmen, sophomores, juniors, and seniors. This ensures that the researcher has adequate amounts of subjects from each class in the final sample.

• It is important to note that the strata used in stratified sampling must not overlap. Having overlapping subgroups will give some individuals a higher chance of being selected as subjects in the sample. If this happened, it would not be a probability sample.

• Some of the most common strata used in stratified random sampling are age, gender, religion, educational attainment, socioeconomic status, and nationality.

When To Use Stratified Sampling

• There are many situations in which researchers would choose stratified random sampling over other types of sampling. First, it is used when the researcher wants to highlight a specific subgroup within the population. Stratified sampling is good for this because it ensures the presence of key subgroups within the sample.

• Researchers also use stratified random sampling when they want to observe relationships between two or more subgroups. With this type of sampling, the researcher is guaranteed subjects from each subgroup are included in the final sample, whereas simple random sampling does not ensure that subgroups are represented equally or proportionately within the sample.

TYPES OF STRATIFIED SAMPLING 1.Proportionate Stratified Random Sample

In proportional stratified random sampling, the size of each strata is proportionate to the population size of the strata when looked at across the entire population. This means that each stratum has the same sampling fraction.

For example, let’s say you have four strata with population sizes of 200, 400, 600, and 800. If you choose a sampling fraction of ½, this means you must randomly sample 100, 200, 300, and 400 subjects from each stratum respectively. The same sampling fraction is used for each stratum regardless of the differences in population size of the strata.

Disproportionate Stratified Random Sample

In disproportionate stratified random sampling, the different strata do not have the same sampling fractions as each other. For instance, if your four strata contain 200, 400, 600, and 800 people, you may choose to have different sampling fractions for each stratum. Perhaps the first strata with 200 people has a sampling fraction of ½, resulting in 100 people selected for the sample, while the last strata with 800 people has a sampling fraction of ¼, resulting in 200 people selected for the sample.

ADVANTAGES OF STRATIFIED SAMPLING

• If population density varies greatly within a region, stratified sampling will ensure that estimates can be made with equal accuracy in different parts of the region, and that comparisons of sub-regions can be made with equal statistical power.

• For example, in Ontario a survey taken throughout the province might use a larger sampling fraction in the less populated north, since the disparity in population between north and south is so great that a sampling fraction based on the provincial sample as a whole might result in the collection of only a handful of data from the north.

• Randomized stratification can also be used to improve population representativeness in a study.

DISADVANTAGES OF STRATIFIED SAMPLING

• One main disadvantage of stratified random sampling is that is can be difficult to identify appropriate strata for a study.

• A second disadvantage is that it is more complex to organize and analyze the results compared to simple random sampling.