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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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    References listed on IDEAS

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    1. Holger Schielzeth & Wolfgang Forstmeier, 2009. "Conclusions beyond support: overconfident estimates in mixed models," Behavioral Ecology, International Society for Behavioral Ecology, vol. 20(2), pages 416-420.
    2. Hadfield, Jarrod D., 2010. "MCMC Methods for Multi-Response Generalized Linear Mixed Models: The MCMCglmm R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i02).
    3. Alice Guilleux & Myriam Blanchin & Jean-Benoit Hardouin & Véronique Sébille, 2014. "Power and Sample Size Determination in the Rasch Model: Evaluation of the Robustness of a Numerical Method to Non-Normality of the Latent Trait," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-7, January.
    4. De Boeck, Paul & Partchev, Ivailo, 2012. "IRTrees: Tree-Based Item Response Models of the GLMM Family," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(c01).
    5. De Boeck, Paul & Bakker, Marjan & Zwitser, Robert & Nivard, Michel & Hofman, Abe & Tuerlinckx, Francis & Partchev, Ivailo, 2011. "The Estimation of Item Response Models with the lmer Function from the lme4 Package in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 39(i12).
    6. Michael D. Jennions & Anders Pape Møller, 2003. "A survey of the statistical power of research in behavioral ecology and animal behavior," Behavioral Ecology, International Society for Behavioral Ecology, vol. 14(3), pages 438-445, May.
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