Multivariate stochastic processes compatible with “aspect” models of similarity and choice

Various recent works have developed feature or aspect models of similarity and preference. These models are more concerned with the fine detail of the judgment process than were prior models, but nevertheless they have not in general developed an underlying stochastic process compatible with the assumed structure. In this paper, we show that a particular class of multivariate stochastic processes, namely those associated with the Marshall-Olkin multivariate exponential distribution, generates several of these models. In particular, such stochastic processes (appropriately interpreted) yield Tversky's elimination by aspects model, Edgell and Geisler's (normal) additive random aspects model, and Shepard and Arabie's additive cluster model.This work was supported by Natural Science and Engineering Research Council of Canada Grant A8124 to A.A.J. Marley.     

Models of best–worst choice and ranking among multiattribute options (profiles)

We develop and characterize new representations for the maxdiff model (Marley & Louviere, 2005) for best–worst choice between multiattribute options; in particular, we state conditions under which the scale value of a multiattribute option is a product of independent ratio scales on each attribute. We show that there is a vector of simple “scores” that are sufficient for the model, with each score a closed-form function of the maximum likelihood estimates of the model’s parameters. Several related models are developed for rank orders obtained by repeated best and/or worst choices, and two of these models are tested on stated preferences between mobile phones.     

Prior distributions for random choice structures

We study various axioms of discrete probabilistic choice, measuring how restrictive they are, both alone and in the presence of other axioms, given a specific class of prior distributions over a complete collection of finite choice probabilities. We do this by using Monte Carlo simulation to compute, for a range of prior distributions, probabilities that various simple and compound axioms hold. For example, the probability of the triangle inequality is usually many orders of magnitude higher than the probability of random utility. While neither the triangle inequality nor weak stochastic transitivity imply the other, the conditional probability that one holds given the other holds is greater than the marginal probability, for all priors in the class we consider. The reciprocal of the prior probability that an axiom holds is an upper bound on the Bayes factor in favor of a restricted model, in which the axiom holds, against an unrestricted model. The relatively high prior probability of the triangle inequality limits the degree of support that data from a single decision maker can provide in its favor. The much lower probability of random utility implies that the Bayes factor in favor of it can be much higher, for suitable data.     

Bifactor Modeling and the Estimation of Model-Based Reliability in the WAIS-IV

Previous confirmatory factor analytic research that has examined the factor structure of the Wechsler Adult Intelligence Scale–Fourth Edition (WAIS-IV) has endorsed either higher order models or oblique factor models that tend to amalgamate both general factor and index factor sources of systematic variance. An alternative model that has not yet been examined for the WAIS-IV is the bifactor model. Bifactor models allow all subtests to load onto both the general factor and their respective index factor directly. Bifactor models are also particularly amenable to the estimation of model-based reliabilities for both global composite scores (ω _h ) and subscale/index scores (ω _s ). Based on the WAIS-IV normative sample correlation matrices, a bifactor model that did not include any index factor cross loadings or correlated residuals was found to be better fitting than the conventional higher order and oblique factor models. Although the ω _h estimate associated with the full scale intelligence quotient (FSIQ) scores was respectably high (.86), the ω _s estimates associated with the WAIS-IV index scores were very low (.13 to .47). The results are interpreted in the context of the benefits of a bifactor modeling approach. Additionally, in light of the very low levels of unique internal consistency reliabilities associated with the index scores, it is contended that clinical index score interpretations are probably not justifiable.     

Self‐help strategies to reduce emotional distress: What do people do and why? A qualitative study

Background: Some people with psychological distress do not seek professional help but opt instead for self‐help strategies to reduce their symptoms. Little is known about these strategies. Aim: To investigate which self‐help approaches might be employed to reduce the effects of emotional distress, and the reasons for these choices. Method: Semi‐structured interviews with 11 clerical employees generated data analysed using grounded theory. Findings: Managing distress is a complex and multi‐dimensional process unique to each individual. The use of self‐help options is determined by the interconnection between the person's core beliefs, their social networks, and ideas about coping. Such beliefs may hinder help‐seeking. People tend to engage in activities that are familiar to them already, rather than attempt new ones. The purpose of self‐help strategies is to distract the person from their problems and to contribute to physical and mental well‐being. Implications for practice are discussed.