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Item Response Trees: a recommended method for analyzing categorical data in behavioral studies

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  • Andrés López-Sepulcre
  • Sebastiano De Bona
  • Janne K. Valkonen
  • Kate D.L. Umbers
  • Johanna Mappes

Abstract

Behavioral data are notable for presenting challenges to their statistical analysis, often due to the difficulties in measuring behavior on a quantitative scale. Instead, a range of qualitative alternative responses is recorded. These can often be understood as the outcome of a sequence of binary decisions. For example, faced by a predator, an individual may decide to flee or stay. If it stays, it may decide to freeze or display a threat and if it displays a threat, it may choose from several alternative forms of display. Here we argue that instead of being analyzed using traditional nonparametric statistics or a series of separate analyses split by response categories, this kind of data can be more holistically analyzed using a generalized linear mixed model (GLMM) framework extended to binomial response trees. Originally devised for the social sciences to analyze questionnaires with multiple-choice answers, this approach can easily be applied to behavioral data using existing GLMM software. We illustrate its use with 2 representative examples: 1) repeatability in the measurement of antipredator display escalation and 2) the analysis of predator responses to prey appearance.

Suggested Citation

  • Andrés López-Sepulcre & Sebastiano De Bona & Janne K. Valkonen & Kate D.L. Umbers & Johanna Mappes, 2015. "Item Response Trees: a recommended method for analyzing categorical data in behavioral studies," Behavioral Ecology, International Society for Behavioral Ecology, vol. 26(5), pages 1268-1273.
    • Handle: RePEc:oup:beheco:v:26:y:2015:i:5:p:1268-1273.
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    File URL: http://hdl.handle.net/10.1093/beheco/arv091
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    References listed on IDEAS

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