In this chapter we consider some particular aspects of the analysis of behavioural data. We begin with the crucial dimension of time. We consider a number of ways in which order can be extracted from the observed stream of behaviour. We go on to consider how best to treat the data obtained from choice tests as described in Chapter 8 and conclude with some ways of dealing with social behaviour.

Bout length

An estimate of bout length may be required when behavioural acts recur in temporal clusters (a bout of events) or when the same, relatively prolonged behaviour pattern occurs continuously for a period (a bout of a single behavioural state). If behaviour patterns are neatly clumped into discrete bouts separated by uninterrupted gaps, then one bout can be distinguished from the next with relative ease. Often, though, bouts are not obviously discrete, in which case a statistical criterion must be used to define a single bout of behaviour. One commonly used technique is log survivorship analysis. This is a simple graphical method for specifying objectively the minimum interval separating successive bouts: the bout criterion interval (BCI). Any gap between successive occurrences of the behaviour that is less than the BCI in length is treated as a within-bout interval, while all gaps greater than the BCI are treated as between-bout intervals. To estimate the BCI, the cumulative frequency of gap lengths (on a logarithmic scale) is plotted against gap length (on a linear scale).