Stratified sampling is a statistical method that involves dividing a population into subgroups, known as strata, before drawing samples from each. This technique ensures that specific segments within your data receive focused attention, leading to more representative and accurate results.
How Stratified Sampling Works
Step 1: Define the population: Clearly identify the entire group you wish to study. This is the overall set of individuals, items, or data points you are interested in analyzing.
Step 2: Choose stratification variables: Select the characteristics that will divide your population into distinct, non-overlapping subgroups. Common variables include age, gender, income level, or geographic location.
- If the population is already divided in a way that makes other forms of stratified sampling ineffective or inefficient, you can simply perform random sampling on each group.
Step 3: Divide the population into strata: Create homogeneous subgroups (strata) based on the stratification variables you’ve chosen. Each member of the population should belong to only one stratum.
Step 4: Determine sample size: Decide how many samples you need to draw from each stratum. This can be done using proportionate or disproportionate allocation (explained below).
Step 5: Select samples: Employ random sampling within each stratum to select your sample. This ensures that every member of each subgroup has an equal chance of being selected.
Advantages
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Improves representation of minority subgroups.
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Increases precision for a given sample size.
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Allows for different sampling techniques in different strata.
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Enables analysis of inter-strata variations.
Types
Proportionate Stratified Sampling
In proportionate stratified sampling, the sample size for each stratum is proportional to its size in the overall population. For example, if a population of 1,000 has 200 people in Stratum A, and 800 people in Stratum B, a sample of 100 would include 20 people from Stratum A and 80 people from Stratum B.
Disproportionate Stratified Sampling
In disproportionate stratified sampling, sample sizes are not proportional to stratum size. This approach might be used when certain strata are more variable or analytically important than others. Researchers adjust the sample sizes to ensure adequate representation and statistical power for these crucial subgroups.
When to Use Stratified Sampling
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The population has distinct, non-overlapping subgroups.
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Subgroup proportions in the population are known.
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You need to study specific subgroups in detail.
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There’s high variability between subgroups but low variability within them.
Limitations
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Requires knowledge of appropriate stratification variables.
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Can be complex and time-consuming.
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May be challenging if population members belong to multiple strata.
Video Tutorial
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Stratified sampling allows you to gain a more precise and representative sample from a population with distinct subgroups, enhancing the reliability of your research across various disciplines.