Outcomes as affirmation of membership value: Material compensation as an administrative response to procedural injustice

The current line of research suggests that the provision of compensation by group representatives may be an effective way to address the identity concerns resulting from procedural violations because compensation serves to reaffirm the victim’s membership value, protecting his or her identity. A series of five studies is presented, demonstrating that compensation can function symbolically as a legitimate act of concern for the injustice victim. Results showed that offers of compensation by group representatives resulted in more favorable evaluations of the group and higher identification than when no compensation was offered, but only when the compensation was construed as a benevolent gesture and only when the injustice was identity relevant. Even unsuccessful attempts to compensate the victim resulted in positive reactions towards the group. Consistent with relational models of procedural justice, these effects were mediated by perceptions of membership value.     

MODELING RELATIONS

In the ordinary way of representing relations, the order of the relata plays a structural role, but in the states themselves such an order often does not seem to be intrinsically present. An alternative way to represent relations makes use of positions for the arguments. This is no problem for the love relation, but for relations like the adjacency relation and cyclic relations, different assignments of objects to the positions can give exactly the same states. This is a puzzling situation. The question is what is the internal structure of relations? Is the use of positions still justified, and if so, what is their ontological status? In this paper mathematical models for relations are developed that provide more insight into the structure of relations “out there” in the real world.     

Latent Class Models for Diary Method Data: Parameter Estimation by Local Computations

The increasing use of diary methods calls for the development of appropriate statistical methods. For the resulting panel data, latent Markov models can be used to model both individual differences and temporal dynamics. The computational burden associated with these models can be overcome by exploiting the conditional independence relations implied by the model. This is done by associating a probabilistic model with a directed acyclic graph, and applying transformations to the graph. The structure of the transformed graph provides a factorization of the joint probability function of the manifest and latent variables, which is the basis of a modified and more efficient E-step of the EM algorithm. The usefulness of the approach is illustrated by estimating a latent Markov model involving a large number of measurement occasions and, subsequently, a hierarchical extension of the latent Markov model that allows for transitions at different levels. Furthermore, logistic regression techniques are used to incorporate restrictions on the conditional probabilities and to account for the effect of covariates. Throughout, models are illustrated with an experience sampling methodology study on the course of emotions among anorectic patients. Frank Rijmen was partly supported by the Fund for Scientific Research Flanders (FWO).     

Computer simulation through an error-statistical lens

After showing how Deborah Mayo’s error-statistical philosophy of science might be applied to address important questions about the evidential status of computer simulation results, I argue that an error-statistical perspective offers an interesting new way of thinking about computer simulation models and has the potential to significantly improve the practice of simulation model evaluation. Though intended primarily as a contribution to the epistemology of simulation, the analysis also serves to fill in details of Mayo’s epistemology of experiment.     

Re-conceptualizing the past: Historical data in vocational interest research

Noteworthy progress has been made in the development of statistical models for evaluating the structure of vocational interests over the past three decades. It is proposed that historically significant interest datasets, when combined with modern structural methods of data analysis, provide an opportunity to re-examine the underlying assumptions of J.L. Holland’s [Holland, J. L. (1959). A theory of vocational choice. Journal of Counseling Psychology, 6, 35–45; Holland, J. L. (1997). Making vocational choices (3rd ed.). Odessa, FL: Psychological Assessment Resources] RIASEC model. To illustrate this potential, data obtained from J. P. Guilford’s study of interest structure were re-analyzed using modern circumplex and hierarchical clustering techniques to evaluate Holland’s and I. Gati’s [Gati, I. (1979). A hierarchical model for the structure of interests. Journal of Vocational Behavior, 15, 90–106; Gati, I. (1991). The structure of vocational interests. Psychological Bulletin, 109, 309–324] interest structures. Obtained results indicate that a circumplex model can be used to effectively represent the structure underlying Guilford’s interest measures. However, hierarchical clustering results suggest that Holland’s RIASEC types may not be the most effective categories for grouping specific interest measures into broader interest areas. The current findings provide support for the continued investigation of alternatives to Holland’s interest categories using modern measures of basic interests.     

Criteria instability and the isolated option effect

In two-stage choices, decision makers often compare a new (isolated) option with the winner from the first stage. Previous research has identified a choice advantage for an isolated option, ostensibly due to loss aversion. We propose an alternative mechanism suggesting that instability of the criteria used in each choice stage is the main driver of the isolated option effect. Results from a series of experiments support the criteria instability account and not loss aversion as the explanation for the isolated option effect.     

Contributions of Neuroscience to Our Understanding of Cognitive Development

ABSTRACT— One major contribution of neuroscience to understanding cognitive development has been in demonstrating that biology is not destiny—that is, demonstrating the remarkable role of experience in shaping the mind, brain, and body. Only rarely has neuroscience provided wholly new insights into cognitive development, but often it has provided evidence of mechanisms by which observations of developmental psychologists could be explained. Behavioral findings have often remained controversial until an underlying biological mechanism for them was offered. Neuroscience has demonstrated promise for detecting cognitive problems before they are behaviorally observable—and, hence, promise for early intervention. In this article, we discuss examples drawn from imitation and mirror neurons, phenylketonuria (PKU) and prefrontal dopamine, maternal touch and stress reactivity, and nongenetic (behavioral) intergenerational transmission of biological characteristics.     

The Finite-Dimensional Freeman Thesis

I suggest a modification—and mathematization—of Freeman’s thesis on the relations among “perception”, “the finite brain”, and “the world”, based on my recent proposal that the theory of finite topological spaces is both an adequate and a natural mathematical foundation for human psychology.

Optimal Scoring of the Multidimensional Pain Inventory in a Chronic Pain Sample

The Multidimensional Pain Inventory (MPI) is one of the most commonly used self-report instruments in pain settings. The MPI can be used to classify patients into three clusters or its nine scales can be treated as dimensions in efforts to understand patient heterogeneity. Previous research suggests the existence of a fourth cluster, whose members have been labeled ‘repressors,’ that emerges with the addition of a defensiveness scale to the MPI. The current paper compared the abilities of MPI cluster and dimensional models with and without a measure of defensiveness to capture variability in validating variables related to personality, psychopathology, physical functioning, and treatment outcome in a chronic pain sample. Results suggest that dimensional models consistently outperform cluster models in explaining variance in outcome variables, and that the addition of a measure of defensiveness increments the validity offered by the MPI scales. Implications for the assessment of pain patients are discussed.

使用验证性补偿多维IRT模型进行认知诊断评估

随着人们对测验反馈结果精细化的需求逐渐提高, 具有认知诊断功能的测量方法逐渐受到人们的关注。在认知诊断模型(CDMs)闪耀着光芒的同时, 另一类能够在连续量尺上提供精细反馈的多维IRT模型(MIRTMs)似乎受到些许冷落。为探究MIRTMs潜在的认知诊断功能, 本文以补偿模型为视角, 聚焦于分别属于MIRTMs的多维两参数logistic模型(M2PLM)和属于CDMs的线性logistic模型(LLM); 之后为使两者具有可比性, 可对补偿M2PLM引入验证性矩阵(Q矩阵)来界定题目与维度之间的关系, 进而得到验证性的补偿M2PLM (CC-M2PLM), 并通过把潜在特质按切点划分为跨界属性, 以期使CC-M2PLM展现出其本应具有的认知诊断功能; 预研究表明logistic量尺上的0点可作为相对合理的切点; 然后, 通过模拟研究对比探究CC-M2PLM和LLM的认知诊断功能, 结果表明CC-M2PLM可用于分析诊断测验数据, 且认知诊断功能与直接使用LLM的效果相当; 最后, 以两则实证数据为例来说明CC-M2PLM在实际诊断测验分析中的可行性。