Toward theory-driven, quantitative performance measurement in ergonomics science: the abstraction hierarchy as a framework for data analysis

function/sub-system At this level of the AH, there are 12 variables that describe the state of the microworld: MO1, EI1, EO1, M1, E1, MI1, MO2, EI2, EO2, M2, E2 and MI2 (see Bisantz and Vicente 1994). With the addition of time, they form a 13-dimensional space. Multi-variance is de®ned in the same way as variance at the goal level, except thatthe variables are normalized with respect to their maximum possible scale values. This normalization process removes any arti®cial, di€ erential-weighting e€ ects caused by heterogeneous numerical scales across variables. There are actually two ways to calculate variance at this level of the AH, one that is context-sensitive and another that is not: (1) The ®rst way is by normalization with respect to the setpoint variables (D1, T1, D2 and T2), as well as scale (shown in table A1). (2) The second method is by normalization with respect to scale only (shown in