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

作者：Lopez Sepulcre Andres^*; De Bona Sebastiano; Valkonen Janne K; Umbers Kate D L; Mappes Johanna

来源：Behavioral Ecology, 2015, 26(5): 1268-1273.

DOI：10.1093/beheco/arv091

• behavioral analysis
• categorical data
• escalation
• ethology
• GLMM
• item response theory
• ordinal data
• predator-prey interactions
• R
• repeatability
• response trees

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.

• 出版日期2015-10