![]() You must also choose the characteristic that you will use to divide your groups. Choosing characteristics for stratification ![]() Like other methods of probability sampling, you should begin by clearly defining the population from which your sample will be taken. Step 1: Define your population and subgroups Therefore, you decide to use a stratified sample, relying on a list provided by the university of all its graduates within the last ten years. Research exampleYou are interested in how having a doctoral degree affects the wage gap between gender identities among graduates of a certain university.īecause only a small proportion of this university’s graduates have obtained a doctoral degree, using a simple random sample would likely give you a sample size too small to properly compare the differences between men, women, and those who do not identify as men or women with a doctoral degree versus those without one. Sometimes you may need to use different methods to collect data from different subgroups.įor example, in order to lower the cost and difficulty of your study, you may want to sample urban subjects by going door-to-door, but rural subjects using mail. Allowing for a variety of data collection methods.In this case, stratified sampling allows for more precise measures of the variables you wish to study, with lower variance within each subgroup and therefore for the population as a whole. The scores are likely to be grouped by family income category. Lowering the overall variance in the populationĪlthough your overall population can be quite heterogeneous, it may be more homogenous within certain subgroups.įor example, if you are studying how a new schooling program affects the test scores of children, both their original scores and any change in scores will most likely be highly correlated with family income.With other methods of sampling, you might end up with a low sample size for certain subgroups because they’re less common in the overall population. If you want the data collected from each subgroup to have a similar level of variance, you need a similar sample size for each subgroup. It is theoretically possible (albeit unlikely) that this would not happen when using other sampling methods such as simple random sampling. It has several potential advantages:Ī stratified sample includes subjects from every subgroup, ensuring that it reflects the diversity of your population. Stratified sampling is the best choice among the probability sampling methods when you believe that subgroups will have different mean values for the variable(s) you’re studying. That means every member of the population can be clearly classified into exactly one subgroup. To use stratified sampling, you need to be able to divide your population into mutually exclusive and exhaustive subgroups. Frequently asked questions about stratified sampling.Step 4: Randomly sample from each stratum.Step 3: Decide on the sample size for each stratum.Step 2: Separate the population into strata.Step 1: Define your population and subgroups.This helps with the generalizability and validity of the study, as well as avoiding research biases like undercoverage bias. ![]() Researchers rely on stratified sampling when a population’s characteristics are diverse and they want to ensure that every characteristic is properly represented in the sample. Every member of the population studied should be in exactly one stratum.Įach stratum is then sampled using another probability sampling method, such as cluster sampling or simple random sampling, allowing researchers to estimate statistical measures for each sub-population. In a stratified sample, researchers divide a population into homogeneous subpopulations called strata (the plural of stratum) based on specific characteristics (e.g., race, gender identity, location, etc.). Try for free Stratified Sampling | Definition, Guide & Examples non-probability samplingĮliminate grammar errors and improve your writing with our free AI-powered grammar checker.
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