Measurement and structure of pathological gambling behavior

The serious effects of pathological gambling on both gamblers and their families warrants research on the disorder. This study aimed at identifying components of pathological gambling with the goal of developing methods for early detection and preventive strategies. A factor analysis of behaviors hypothesized to relate to pathological gambling resulted in a multidimensional model of the disorder. Eight factors were identified, including distinct neurotic, psychopathic, and impulsive components, and used in a comparative analysis of 83 admitted compulsive gamblers and 61 nongamblers. Five of the factors differentiated the two groups even when sex, age, and education were controlled. Implications of these findings are discussed.     

Type A behavior pattern and the judgment of noncontingency: mediating roles of mood and perspective

Past research indicates that Type A's and B's differ in their behavioral responses to lack of control. The present study examined perceptual judgments of noncontingency in an attempt to clarify further the role of a control dynamic in Type A-B differences. Type A's and B's assumed the role of either an actor or an observer on a standard contingency judgment task. Consistent with previous research, both Type A's and B's exhibited an illusion of control when in the role of actor. Only Type B's exhibited an illusion of control when observing another person perform the task. Additional analyses indicated that the absence of an illusion of control by Type A observers reflected accuracy rather than a motivational distortion. Mood was also found to mediate control judgments, but only for actors. The plausibility of a memory-based interpretation for the mood effects is discussed.     

On the self-serving function of social anxiety: shyness as a self-handicapping strategy

We tested the hypothesis that socially anxious or shy individuals use their anxiety symptoms as a strategy to control attributions made about their performances in social-evaluative settings (i.e., self-handicapping strategies). Specifically, we predicted that trait-socially anxious or shy persons would report more symptoms of social anxiety in an evaluative setting in which anxiety or shyness could serve as an excuse for poor performance than would individuals in (a) an evaluative setting in which shyness was precluded as an excuse or (b) a nonevaluative setting. Furthermore, we predicted that this self-protective pattern of symptom reporting would not occur for individuals who were not trait-socially anxious because these persons would not commonly use such symptoms as a self-handicapping strategy. Results supported these predictions for male subjects, but not for female subjects. Sex differences in the strategic use of shyness are discussed in relation to other research on sex differences in the etiology and correlates of social anxiety.     

Three-way metric unfolding via alternating weighted least squares

Three-way unfolding was developed by DeSarbo (1978) and reported in DeSarbo and Carroll (1980, 1981) as a new model to accommodate the analysis of two-mode three-way data (e.g., nonsymmetric proximities for stimulus objects collected over time) and three-mode, three-way data (e.g., subjects rendering preference judgments for various stimuli in different usage occasions or situations). This paper presents a revised objective function and new algorithm which attempt to prevent the common type of degenerate solutions encountered in typical unfolding analysis. We begin with an introduction of the problem and a review of three-way unfolding. The three-way unfolding model, weighted objective function, and new algorithm are presented. Monte Carlo work via a fractional factorial experimental design is described investigating the effect of several data and model factors on overall algorithm performance. Finally, three applications of the methodology are reported illustrating the flexibility and robustness of the procedure.We wish to thank the editor and reviewers for their insightful comments.     

Combinatorial data analysis: Association and partial association

A combinatorial data analysis strategy is reviewed that is designed to compare two arbitrary measures of proximity defined between the objects from some set. Based on a particular cross-product definition of correspondence between these two numerically specified notions of proximity (typically represented in the form of matrices), extensions are then pursued to indices of partial association that relate the observed pattern of correspondence between the first two proximity measures to a third. The attendant issues of index normalization and significance testing are discussed; the latter is approached through a simple randomization model implemented either through a Monte Carlo procedure or distributional approximations based on the first three moments. Applications of the original comparison strategy and its extensions to partial association may be developed for a variety of methodological and substantive tasks. Besides rank correlation, we emphasize the topics of spatial autocorrelation for one variable and spatial association between two and mention the connection to the usual randomization approach for one-way analysis-of-variance.