Explications causales normatives dans la distribution de sanctions professionnelles injustes

This research explores the degree of acceptability of the reasons given by a team leader to explain the unfair sanctions he/she distributes to his/her subordinates. Fifteen male high level managers leading team leaders, and 61 employees (30 women and 31 men) responded to a questionnaire in which they had to assess the desirability of the reasons given by a team leader for which he/she unfairly punished his subordinates: it might be either getting angry with them despite their good work (negative sanction) or, on the contrary, refraining intervention when they made mistakes (positive sanction). The results indicate a disaccountability of both the receiver and the distributor of the sanction, with, instead, the highlighting of contextual determinants, especially in case of negative sanction. There is also an influence of the direction of the sanction (positive vs. negative) on the stable/unstable component of the instance chosen as explanatory. These results are discussed on a theoretical level and perspectives of applications are proposed.     

Education and Understanding Structural Causes for Group Inequalities

Group inequalities in the United States are most often attributed to the characteristics of the individuals who belong to these groups; thinking about structural causes of group inequalities is rare. This paper reviews cognitive, cultural, and systemic reasons for this bias. The efficacy of education as a way to increase structural thinking was investigated in two studies of college students' causal thinking about group inequalities. Both studies involved a course on intergroup relations that covered structural sources of racial or ethnic inequalities. Results supported hypotheses that the course would increase structural thinking about racial or ethnic inequality, and that structural thinking would generalize to inequalities not explicitly covered in the course. Both course content and active learning pedagogy were related to structural thinking about inequalities. Active learning was also related to applying structural thinking to targets of change.     

FREE SPEECH

Recognition of the harms done by free speech is a function of the social ontology presupposed. An atomist ontology implies that the harms suffered are restricted to individual people. This paper suggests an alternate ontology—one that describes systems established by the causal reciprocities of their proper parts. It proposes a consequentialist moral theory, and considers the harms suffered by these systems (a) when speech exposes their internal, otherwise private, behaviors or features, (b) when speech is malicious and false, and (c) when speech is monopolistic. Does the proposed ontology have objectionable implications for public policy? Alternative answers are considered briefly.     

Diversity of ideas about diversity measurement

Measurement of diversity is important in several sciences but has evolved mainly within ecology. An ecological community with many species is more diverse than one with few but the way the total number of individuals is distributed among the species also influences the degree of diversity or heterogeneity. Although not always explicitly used, it can be shown that diversity is a concept of some importance in psychology, too. In general terms quantification of diversity is related to the apportionment of some quantity into a number of well-defined classes. The dual-concept type of diversity index reflects both the number of classes and the degree of evenness (homogeneity) of the apportionment. The quantity that is distributed has commonly been a population of elements and apportionment homogeneity measures population heterogeneity. The statistical context has dominated the thinking about the measurement of diversity, heterogeneity, and homogeneity. Some well-known indices of diversity and the ideas behind them are discussed. It is concluded that the general geometric representation of homogeneity (similarity) and heterogeneity (dissimilarity) (Junge, 1978; 1991) offers a more general approach to the measurement of diversity.     

Meta‐CART: A tool to identify interactions between moderators in meta‐analysis

In the framework of meta‐analysis, moderator analysis is usually performed only univariately. When several study characteristics are available that may account for treatment effect, standard meta‐regression has difficulties in identifying interactions between them. To overcome this problem, meta‐CART has been proposed: an approach that applies classification and regression trees (CART) to identify interactions, and then subgroup meta‐analysis to test the significance of moderator effects. The previous version of meta‐CART has its shortcomings: when applying CART, the sample sizes of studies are not taken into account, and the effect sizes are dichotomized around the median value. Therefore, this article proposes new meta‐CART extensions, weighting study effect sizes by their accuracy, and using a regression tree to avoid dichotomization. In addition, new pruning rules are proposed. The performance of all versions of meta‐CART was evaluated via a Monte Carlo simulation study. The simulation results revealed that meta‐regression trees with random‐effects weights and a 0.5‐standard‐error pruning rule perform best. The required sample size for meta‐CART to achieve satisfactory performance depends on the number of study characteristics, the magnitude of the interactions, and the residual heterogeneity.     

Diagnostic causal reasoning with verbal information

In diagnostic causal reasoning, the goal is to infer the probability of causes from one or multiple observed effects. Typically, studies investigating such tasks provide subjects with precise quantitative information regarding the strength of the relations between causes and effects or sample data from which the relevant quantities can be learned. By contrast, we sought to examine people’s inferences when causal information is communicated through qualitative, rather vague verbal expressions (e.g., “X occasionally causes A”). We conducted three experiments using a sequential diagnostic inference task, where multiple pieces of evidence were obtained one after the other. Quantitative predictions of different probabilistic models were derived using the numerical equivalents of the verbal terms, taken from an unrelated study with different subjects. We present a novel Bayesian model that allows for incorporating the temporal weighting of information in sequential diagnostic reasoning, which can be used to model both primacy and recency effects. On the basis of 19,848 judgments from 292 subjects, we found a remarkably close correspondence between the diagnostic inferences made by subjects who received only verbal information and those of a matched control group to whom information was presented numerically. Whether information was conveyed through verbal terms or numerical estimates, diagnostic judgments closely resembled the posterior probabilities entailed by the causes’ prior probabilities and the effects’ likelihoods. We observed interindividual differences regarding the temporal weighting of evidence in sequential diagnostic reasoning. Our work provides pathways for investigating judgment and decision making with verbal information within a computational modeling framework.     

Two-Way Regularized Fuzzy Clustering of Multiple Correspondence Analysis

Multiple correspondence analysis (MCA) is a useful tool for investigating the interrelationships among dummy-coded categorical variables. MCA has been combined with clustering methods to examine whether there exist heterogeneous subclusters of a population, which exhibit cluster-level heterogeneity. These combined approaches aim to classify either observations only (one-way clustering of MCA) or both observations and variable categories (two-way clustering of MCA). The latter approach is favored because its solutions are easier to interpret by providing explicitly which subgroup of observations is associated with which subset of variable categories. Nonetheless, the two-way approach has been built on hard classification that assumes observations and/or variable categories to belong to only one cluster. To relax this assumption, we propose two-way fuzzy clustering of MCA. Specifically, we combine MCA with fuzzy k-means simultaneously to classify a subgroup of observations and a subset of variable categories into a common cluster, while allowing both observations and variable categories to belong partially to multiple clusters. Importantly, we adopt regularized fuzzy k-means, thereby enabling us to decide the degree of fuzziness in cluster memberships automatically. We evaluate the performance of the proposed approach through the analysis of simulated and real data, in comparison with existing two-way clustering approaches.     

A rating measure of ADHD-related neuropsychological impairment in children and adolescents: Data from the Cognition and Motivation in Everyday Life (CAMEL) Scale from population and clinical samples

A new parent-completed questionnaire, the Cognition and Motivation in Everyday Life (CAMEL) scale, was developed to provide a comprehensive assessment of neuropsychological impairment in children related to attention-deficit/hyperactivity disorder (ADHD) across diverse cognitive, motivational and energetic domains. Its psychometric properties were investigated. A total of 60 items were generated to cover a wide range of putative ADHD-related neuropsychological processes. A clinical (n = 142) and community (n = 810) sample of parents with children between 6 and 16 years of age completed the questionnaire. Data on ADHD symptoms were also collected with a commonly-used, validated parent rating scale to explore the associations between CAMEL scores and dimensional measures of child ADHD and conduct problems. Factor analysis identified six factors which we labeled (i) Cognition, (ii) Self-Direction and Organization, (iii) Effort Engagement, (iv) Arousal Regulation, (v) Motivational Responsiveness, and (vi) Cautiousness. Self-Direction and Organization and Arousal Regulation were the strongest predictors for ADHD symptomatology. Self-Direction and Organization was strongly associated with inattention and Arousal Regulation with hyperactivity-impulsivity symptoms. Parents distinguished between broad neuropsychological domains in reliable and plausible ways, making distinctions between key aspects of functioning. However, the boundaries between these domains did not map directly onto the distinctions drawn within traditional models of ADHD deficits. Further research is required to examine the predictive validity and cost-effectiveness of the CAMEL scale compared to direct objective testing using laboratory measures in predicting prognosis and treatment outcome.