A general theoretical taxonomy of career decision-making difficulties, based on decision theory, has been developed. To examine the proposed taxonomy empirically, a questionnaire was constructed in which the various possible difficulties in the theoretical model were represented by respective statements. The questionnaire was administered to a sample of 259 young Israeli adults who were at the beginning of their career decision-making process and to an American sample of 304 university students. The observed relations among the 10 scales, which represent the 10 theoretical categories of difficulties, and those among the items within 2 selected categories, were similar in the 2 samples and compatible with the expected relations derived from the theoretical model. The implications for career counseling and research are discussed.

This research focuses on developing a theoretical framework for analyzing the emotional and personality-related aspects of career-decision-making difficulties. The proposed model is comprised of three major clusters: pessimistic views, anxiety, and self-concept and identity. In Study 1, the Emotional and Personality Career Difficulties Scale (EPCD) was developed, refined, and used to empirically test the model with an Israeli Internet sample (N = 728). Study 2 (N = 276) provided evidence for the cross-cultural validity of the proposed model, using an American college student sample. The relations between the cognitive and emotional components of career-decision-making difficulties are discussed, and theoretical, research, and counseling implications are explored.

Geometric models of proximity data that underlie multidimensional scaling assume the triangle inequality, D(i, j) + D(j, k) > D(i, k), and segmental additivity, D(i, j) + D(j, k) = D(i, k) if i, j, and k lie on a straight line. An alternative analysis, based on a feature-matching model, leads to the (coincidence) hypothesis that the dissimilarity between objects that differ on two separable dimensions is larger than predicted from their unidimensional differences on the basis of the triangle inequality and segmental additivity. A series of studies of two-dimensional stimuli with separable attributes (including house plants, parallelograms, schematic faces, and histograms), using judgments of similarity and dissimilarity, classification, inference, and recognition errors, all support the coincidence hypothesis. The size of the effect is determined by the separability of the stimuli, the transparancy of the dimensional structure, and the discriminability of the levels within each dimension. Applications of the coincidence effect to inductive inference are investigated, and its relations to selective attention and spatial density are discussed. We conclude that the triangle inequality and segmental additivity cannot be jointly satisfied for separable attributes and discuss the implications of this result for multidimensional scaling.

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