|| Checking for direct PDF access through Ovid
In ecological and behavioral research, drawing reliable conclusions from statistical models with multiple predictors is usually difficult if all predictors are simultaneously in the model. The traditional way of handling multiple predictors has been the use of threshold-based removal-introduction algorithms, that is, stepwise regression, which currently receives considerable criticism. A more recent and increasingly propagated modelling method for multiple predictors is the information theoretic (IT) approach that quantifies the relative suitability of multiple, potentially non-nested models based on a balance of model fit and the accuracy of estimates. Here, we examine three shortcomings of stepwise regression, subjective critical values, model uncertainty, and parameter estimation bias, which have been suggested to be avoided by applying information theory. We argue that, in certain circumstances, the IT approach may be sensitive to these issues as well. We point to areas where further testing and development could enhance the performance of IT methods and ultimately lead to robust inferences in behavioral ecology.