ABSTRACT
This chapter presents state of the art measures that capture dynamic changes and variances over time in typical command and control environments. Repeated measurement techniques are of imperative importance for predicting changes concerning complex and dynamic events in command and control environments. Techniques for transformation and smoothing of time series, modelling and forecasting are demonstrated; dynamic factor analysis is a statistical technique for analysis and reduction of a larger number of manifest time series variables to a smaller set of dynamic factors. Most statistical techniques used in human factors research are based on inter-individual variation generated by independent measures from a large number of participants; inferences at population level are made from differences between participants at sample level. Classical experimentation designs and static measures are, in many respects, insufficient tools for dynamic situations, where designs and measures adapted to natural environments are needed.
