Item Response Trees: A recommended method for analysing categorical data in behavioural studies — ASN Events

Item Response Trees: A recommended method for analysing categorical data in behavioural studies (#292)

Andres Lopez-Sepulcre 1 2 , Sebastiano De Bona 2 , Janne K Valkonen 2 , Kate DL Umbers 3 4 , Johanna Mappes 2
  1. Institute of Ecology and Environmental Sciences of Paris, CNRS - Université Pierre et Marie Curie, Paris, France
  2. Center of Excellence in Biological Interactions, University of Jyväskylä, Jyväskylä, KS, Finland
  3. School of Biological Sciences, University of Wollongong, Wollongong, Australia
  4. Centre for Evolutionary Biology, University of Western Australia, Perth, Australia
Behavioural data is 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 non-parametric 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 behavioural data using existing GLMM software. We illustrate its use with two representative examples: (1) repeatability in the measurement of anti-predator display escalation, and (2) the analysis of predator responses to prey appearance.