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Note that it would also be possible to use cues to predict outcome (dollar allocation) for the set of judges, giving one regression per agency. The beta weights would then reflect the relative importance of the cues for a given object (agency decision scenario) across all judges. This would be appropriate if the object of research were to cluster types of objects (decision scenarios) rather than to cluster types of subjects (decision-makers).
In the figure above, Analyst 1 is shown to allocate dollars to agencies exponentially as the GA cue has higher values. Note some authors, such as Willoughby & Finn (1996), omit the data points and just show the fit line on function forms, to simplify analytic comparisons.
As mentioned, there can be one such function form for each analyst for each cue, enabling the researcher on a graphical basis to compare the decision-making profiles of different analysts. One can also group analysts by using cluster analysis, then create group-level function forms in order to be able to compare group profiles. This is done by computing the dependent mean by group (ex., mean dollars allocated for each of 6 subgroups of analysts identified by cluster analysis), then using cue levels to predict the agency means using as subjects the members of each group in turn (for illustration, see Willoughby & Finn, 1996: 537, 539).