A measurement model of women's behavioral risk taking.

The current study was designed to gain a better understanding of the nature of the relationship between substance use and sexual risk taking within a community sample of women (N = 1,004). Using confirmatory factor analysis, the authors examined the factor structure of sexual risk behaviors and substance use to determine whether they are best conceptualized as domains underlying a single, higher order, risk-taking propensity. A 2 higher order factor model (sexual risk behavior and substance use) provided the best fit to the data, suggesting that these 2 general risk domains are correlated but independent factors. Sensation seeking had large general direct effects on the 2 risk domains and large indirect effects on the 4 first-order factors and the individual indicators. Negative affect had smaller, yet still significant, effects. Impulsivity and anxiety were unrelated to sexual health risk domains.     

Hindsight bias: An interaction of automatic and motivational factors?

If subjects are asked to recollect a former response after having been informed about the correct response, their recollection tends to approach the correct response. This effect has been termedhindsight bias. We studied hindsight bias in an experiment requiring numerical responses to almanac-type questions for physical quantities. We varied (1) the time at which the correct information was provided, (2) the encoding of the original responses by asking/not asking subjects to give a reason for the respective response, and (3) the motivation to recall correctly. We found that hindsight is less biased if reasons are given and if the correct information is provided at an earlier time. Motivation had only interactive effects: (1) With high motivation to recall correctly, the time the correct information was provided had no influence. (2) With reasons given, the variation of motivation showed no effect. These results rule out purely motivational and purely automatic explanations.     

Hierarchical classes: Model and data analysis

A discrete, categorical model and a corresponding data-analysis method are presented for two-way two-mode (objects × attributes) data arrays with 0, 1 entries. The model contains the following two basic components: a set-theoretical formulation of the relations among objects and attributes; a Boolean decomposition of the matrix. The set-theoretical formulation defines a subset of the possible decompositions as consistent with it. A general method for graphically representing the set-theoretical decomposition is described. The data-analysis algorithm, dubbed HICLAS, aims at recovering the underlying structure in a data matrix by minimizing the discrepancies between the data and the recovered structure. HICLAS is evaluated with a simulation study and two empirical applications.This research was supported in part by a grant from the Belgian NSF (NFWO) to Paul De Boeck and in part by NSF Grant BNS-83-01027 to Seymour Rosenberg. We thank Iven Van Mechelen for clarifying several aspects of the Boolean algebraic formulation of the model and Phipps Arabie for his comments on an earlier draft.