Improving behaviour classification consistency: a technique from biological taxonomy

Quantitative behaviour analysis requires the classification of behaviour to produce the basic data. In practice, much of this work will be performed by multiple observers, and maximising inter-observer consistency is of particular importance. Another discipline where consistency in classification is vital is biological taxonomy. A classification tool of great utility, the binary key, is designed to simplify the classification decision process and ensure consistent identification of proper categories. We show how this same decision-making tool - the binary key - can be used to promote consistency in the classification of behaviour. The construction of a binary key also ensures that the categories in which behaviour is classified are complete and non-overlapping. We discuss the general principles of design of binary keys, and illustrate their construction and use with a practical example from education research.

The effectiveness of using a simple ARIA based geographical classification to identify road crash patterns in rural and urban areas of Queensland

Research has noted a ‘pronounced pattern of increase with increasing remoteness' of death rates in road crashes. However, crash characteristics by remoteness are not commonly or consistently reported, with definitions of rural and urban often relying on proxy representations such as prevailing speed limit. The current paper seeks to evaluate the efficacy of the Accessibility / Remoteness Index of Australia (ARIA+) to identifying trends in road crashes. ARIA+ does not rely on road-specific measures and uses distances to populated centres to attribute a score to an area, which can in turn be grouped into 5 classifications of increasing remoteness. The current paper uses applications of these classifications at the broad level of Australian Bureau of Statistics' Statistical Local Areas, thus avoiding precise crash locating or dedicated mapping software. Analyses used Queensland road crash database details for all 31,346 crashes resulting in a fatality or hospitalisation occurring between 1st July, 2001 and 30th June 2006 inclusive. Results showed that this simplified application of ARIA+ aligned with previous definitions such as speed limit, while also providing further delineation. Differences in crash contributing factors were noted with increasing remoteness such as a greater representation of alcohol and ‘excessive speed for circumstances.' Other factors such as the predominance of younger drivers in crashes differed little by remoteness classification. The results are discussed in terms of the utility of remoteness as a graduated rather than binary (rural/urban) construct and the potential for combining ARIA crash data with census and hospital datasets.