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7 - Categorical responses

Published online by Cambridge University Press:  04 June 2010

Piet de Jong
Affiliation:
Macquarie University, Sydney
Gillian Z. Heller
Affiliation:
Macquarie University, Sydney
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Summary

Categorical variables take on one of a discrete number of categories. For example, a person is either male or female, or a car is one of a number of models. Other examples are colors, states of wellbeing, employment status and so on.

The simplest example of a categorical variable is where the outcome is binary, meaning it can take on only one of two values such as claim or no claim on a policy, dead or alive. These outcomes are usually coded as 0 or 1, with the occurrence of the event of interest (claim, death) coded as 1, and nonoccurrence as 0. The terms “success” and “failure” are also often used for the occurrence and non-occurrence of the event.

Categorical variables fall into two distinct classes: those whose categories have a natural ordering or otherwise. For recording purposes, it is often convenient to code the categories numerically. For example 1 may denote Red, 2 Blue, and so on. In this case the numbers are purely “nominal” and for example 2 does not mean better than 1. In injury classification, however, 1 may denote minor injury, 2 more major up to 8 indicating catastrophic injury and 9 death. In this case the categories are ordered, although it is not necessarily the case that 4 is twice as bad as 2.

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Publisher: Cambridge University Press
Print publication year: 2008

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  • Categorical responses
  • Piet de Jong, Macquarie University, Sydney, Gillian Z. Heller, Macquarie University, Sydney
  • Book: Generalized Linear Models for Insurance Data
  • Online publication: 04 June 2010
  • Chapter DOI: https://doi.org/10.1017/CBO9780511755408.008
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  • Categorical responses
  • Piet de Jong, Macquarie University, Sydney, Gillian Z. Heller, Macquarie University, Sydney
  • Book: Generalized Linear Models for Insurance Data
  • Online publication: 04 June 2010
  • Chapter DOI: https://doi.org/10.1017/CBO9780511755408.008
Available formats
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Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Categorical responses
  • Piet de Jong, Macquarie University, Sydney, Gillian Z. Heller, Macquarie University, Sydney
  • Book: Generalized Linear Models for Insurance Data
  • Online publication: 04 June 2010
  • Chapter DOI: https://doi.org/10.1017/CBO9780511755408.008
Available formats
×