New methodology for identifying hierarchical relationships among performance measures: Concepts and demonstration in Parkinson's disease.
Many different tests that address aspects of human performance have been reported. Yet, critical issues remain. The hierarchical organization of tests, the degree of involvement of different human subsystems, and the relationship between measures are often unclear. General Systems Performance Theory provides the basis for a novel analytic method, termed Nonlinear Causal Resource Analysis, to determine task demands (i.e., analyze tasks) and predict performance in complex tasks using only measures of lower level subsystem performance capacities. Recently, we realized new insights and discovery of a new application of these concepts to address the issues noted. A quasi-objective methodology is presented to identify hierarchical relationships among performance measures. The method is applied to seven different performance measures in a study of Parkinson's Disease subjects (n = 197) exhibiting a wide range of disease severity. Resource economic interpretations of experimental data using performance theory concepts were used to define relationships between performance measures and to organize them hierarchically. This method is anticipated to have broad utility for identifying relationships between performance measures.
Kondraske
George V
GV
Human Performance Institute, University of Texas at Arlington, Arlington, TX 76019-0180, USA. kondraske@uta.edu
Stewart
R Malcolm
RM
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009483:Neuropsychological Tests; D010300:Parkinson Disease / Q000473:pathology*; D013647:Task Performance and Analysis