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Sampling, Ethics and Practical Issues

Stratified random and quota sampling

  • 3 things to know
  • Component 2

Overview

Stratified random sample, disproportionate stratification and quota sample.

What you need to know

Answer each one before you reveal it.

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Key term

How does a quota sample differ from a stratified random one?

Key term

What is a stratified random sample?

Key term

How are the sizes of the strata decided in a stratified random sample?

Questions from this subtopic

Each one with the model answer underneath.

  1. 1

    What is a stratified random sample?

    Model answer

    A stratified random sample divides the population into strata such as age, sex or class. A random sample is then drawn from within each stratum.

  2. 2

    How are the sizes of the strata decided?

    Model answer

    Each stratum is normally sampled in proportion to its share of the population. A group forming a fifth of the population supplies a fifth of the sample.

  3. 3

    Why is a stratified sample usually more representative than a simple random one?

    Model answer

    Stratification guarantees that each group appears in its correct proportion. Chance cannot leave a group out.

  4. 4

    What must a researcher already know in order to stratify a sample?

    Model answer

    The researcher must know the proportions of each relevant characteristic in the population. Census figures usually supply them.

  5. 5

    What is disproportionate stratification, and why use it?

    Model answer

    Disproportionate stratification deliberately over-samples a small group so that enough of its members are studied. A minority forming two per cent of the population would otherwise yield too few cases to analyse.

  6. 6

    Give two costs of stratified sampling.

    Model answer

    Stratified sampling needs accurate population data and a frame carrying the stratifying characteristics. A separate draw within every stratum also takes longer.

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