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Key Concepts and Theories of Research

Representativeness and the sample

  • 3 things to know
  • Component 2

Overview

Why a large sample can still misrepresent: biased frames, self-selection and volunteers, what a case study can and cannot support, and when a small random sample beats a big convenient one.

What you need to know

Answer each one before you reveal it.

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Key term

Why can a large sample still fail to represent a population?

Key term

What does representativeness mean?

Key term

On which characteristics should a sample be representative?

Questions from this subtopic

Each one with the model answer underneath.

  1. 1

    What does representativeness mean?

    Model answer

    Representativeness is the extent to which a sample reflects the characteristics of the population it came from. A representative sample is a miniature of the whole.

  2. 2

    On which characteristics should a sample be representative?

    Model answer

    A sample should match the population on the characteristics relevant to the topic, such as age, sex, class or region. Irrelevant characteristics do not matter.

  3. 3

    Why is representativeness a property of the sample rather than the method?

    Model answer

    Any method can be applied to a good sample or a poor one. Interviewing is neither representative nor unrepresentative in itself.

  4. 4

    How is a sample's representativeness checked?

    Model answer

    A sample's composition is compared with census or other population figures. Divergence shows which groups are over- or under-represented.

  5. 5

    What is weighting?

    Model answer

    A weight adjusts the influence of each case so that the sample's profile matches the population. Under-represented groups are made to count for more.

  6. 6

    Give one limitation of weighting.

    Model answer

    Weighting corrects the proportions but not the fact that few of that group were interviewed. A handful of cases is stretched to speak for many.

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