explore · Strategy · 30–60 min · 5-20 people · low energy

Stratified Random Sampling

Stratified random sampling divides the population into distinct strata (sections or segments) to ensure adequate representation before selecting a random sample from each. This method improves the accuracy and representativeness of the sample.

When to use Stratified Random Sampling

Use this method when you want to ensure that specific subgroups within a population are adequately represented in the sample.

What it solves

Underrepresentation of key subgroups in a simple random sample; Inaccurate conclusions due to biased sampling.

How to run Stratified Random Sampling, step by step

  1. Identify the relevant strata (subgroups) within the population. (10 min)
  2. Determine the proportion of each stratum in the overall population. (10 min)
  3. Select a random sample from each stratum, proportional to its representation in the population. (20 min)
  4. Analyze the combined sample. (20 min)

Materials needed

  • List of population characteristics
  • Calculator
  • Random number generator

Facilitator tips

  • Ensure strata are mutually exclusive and collectively exhaustive.
  • Accurately determine the proportion of each stratum in the population.

Common pitfalls

  • Inaccurate stratification leading to biased sampling. Carefully define and validate strata.
  • Disproportionate sampling from certain strata. Ensure proportional representation of each stratum.

Variations

  • Use disproportionate stratified sampling to oversample smaller strata.
  • Combine with cluster sampling for large, geographically dispersed populations.

Running it online or hybrid

Use online tools to manage strata and random selection within each stratum.

What it produces

A sample that accurately reflects the proportions of different subgroups within the overall population.

Origin

Adapted from BetterEvaluation.org — source

Build a session around Stratified Random Sampling

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