Lesson 2 of 8 · All lessons in this module
- Populations, samples and sampling error
- Stratified and cluster sampling
- Convenience and judgmental sampling, and choosing a method
- Sample size and mixing populations
- The central limit theorem
- The standard error of the sample meanVideo lesson
- Confidence intervals for the mean
- Bootstrap and jackknife resampling
Lesson 2 of 8 · 14 min
Stratified and cluster sampling
Stratified sampling draws randomly from every subgroup to guarantee representation and precision; cluster sampling picks whole subgroups to save time and money.
In short
- Stratified random sampling: divide the population into strata by one or more criteria, draw a simple random sample from each stratum in proportion to its size, then pool the subsamples.
- It guarantees that every subgroup of interest is represented and gives more precise estimates (smaller variance) than simple random sampling of the same size.
- Classic use: bond indexing. Cells are formed by duration, cash-flow pattern, sector, credit quality and call exposure. Number of cells = product of the categories, and each cell needs at least one bond.
- Cluster sampling: divide the population into clusters that are each a mini-version of the population, then randomly choose whole clusters. One-stage: include every member of the chosen clusters. Two-stage: randomly subsample within them.
- Cluster sampling is the most time- and cost-efficient probability method for a vast population, but for the same sample size it is usually less accurate.
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