Bidimensional regression: assessing the configural similarity and accuracy of cognitive maps and other two-dimensional data sets.
Bidimensional regression is a method for comparing the degree of resemblance between 2 planar configurations of points and, more generally, for assessing the nature of the geometry (Euclidean and non-Euclidean) between 2-dimensional independent and dependent variables. For example, it can assess the similarity between location estimates from different tasks or participant groups, measure the fidelity between cognitive maps and actual locations, and provide parameters for psychological process models. The authors detail the formal similarity between uni- and bidimensional regression, provide computational methods and a new index of spatial distortion, outline the advantages of bidimensional regression over other techniques, and provide guidelines for its use. The authors conclude by describing substantive areas in psychology for which the method would be appropriate and uniquely illuminating.
Friedman
Alinda
A
Department of Psychology, University of Alberta, Edmonton, Canada. alinda@ualberta.ca
Kohler
Bernd
B
D000328:Adult; D003071:Cognition; D004215:Distance Perception; D006801:Humans; D015233:Models, Statistical; D009949:Orientation; D010469:Perceptual Distortion; D011594:Psychometrics / Q000706:statistics & numerical data*; D012044:Regression Analysis; D012931:Social Environment; D013028:Space Perception