One of the major advantages of stratified sampling is it allows you to create a diverse research sample that represents every group in your population of interest. Read: Coefficient of Variation: Definition, Formula, Interpretation, Examples & FAQs Advantages of Stratified Sampling Next, she uses ⅕ as her sampling fraction and selects 800 male students and 1,200 female students for the sample population. First, she splits the population of interest into two strata based on gender so that we have 4,000 male students and 6,000 female students. Example of Proportionate Stratified SamplingĪs part of a research to know how many students want to pursue a career in the sciences. With disproportionate stratified sampling, every unit in a stratum stands the same chance of getting selected for the systematic investigation. This sampling fraction is always the same across all strata, regardless of their sizes.
Typically, the researcher derives a sampling fraction and uses this fraction to determine how the variables are selected for the sample. This means that the probability of choosing a variable from a stratum for the sample depends on the relative size of the stratum in its population of interest. In proportionate stratified sampling, the researcher selects variables for the sample based on their original distribution in the population of interest. There's no clear-cut method for choosing the variables for the research sample.Ī key advantage of disproportionate sampling is it allows you to collect responses from minority subsets whose sample size would otherwise be too low to allow you to draw any statistical conclusions. So, you could have 60,000 participants from the first group and 20,000 and 17,000 from others, respectively. Example of Disproportionate Stratified SamplingĪ researcher splits the population of interest into three subsets based on their age groups:ĭisproportionate stratified sampling means the researcher randomly chooses members of the sample from each group. The implication is that the members of different subgroups do not have an equal opportunity to be a part of the research sample. Disproportionate Stratified Sampling Methodĭisproportionate stratified sampling is a stratified sampling method where the sample population is not proportional to the distribution within the population of interest. Proportionate Stratified Sampling Methodġ.To achieve this, researchers rely on two methods of stratified sampling namely The golden rule of stratified sampling is that every stratum should have distinct characteristics that differentiate it from the others. The whole idea is to preserve the homogeneity within each group, so that no subset is excluded from the eventual sample. When splitting the population into smaller groups, the researcher relies on the naturally-occuring divisors such as geographical location, gender, education level, and age, to mention a few.įor example, when conducting research on the level of education amongst women in a community, one can identify different population groups based on ethnicity, gender, religion, and income level. This method often comes to play when you're dealing with a large population, and it's impossible to collect data from every member. Stratified sampling is a selection method where the researcher splits the population of interest into homogeneous subgroups or strata before choosing the research sample.
#STRATA DEFINITION HOW TO#
In this article, we'd show you how to do this, also touch on the different types of stratified sampling. To get the most reliable results, you need to map out heterogeneous divisors in your population, so you can have truly diverse strata. Stratified sampling can help you achieve this.
However, when you're dealing with a larger audience, you need a more effective way to gather relevant and unbiased feedback from your sample. If you're researching a small population, it might be possible to get representative data from every unit or variable in the target audience.
0 kommentar(er)
