Statistical inference based on ranks

This paper develops a unified approach, based on ranks, to the statistical analysis of data arising from complex experimental designs. In this way we answer a major objection to the use of rank procedures as a major methodology in data analysis. We show that the rank procedures, including testing, estimation and multiple comparisons, are generated in a natural way from a robust measure of scale. The rank methods closely parallel the familiar methods of least squares, so that estimates and tests have natural interpretations.This research was supported in part by grant MCS76-07292 from the National Science Foundation.     

Measuring nominal scale agreement between a judge and a known standard

Two designs for comparing a judge's ratings with a known standard are presented and compared. Design A pertains to the situation where the judge is asked to categorize each ofN subjects into one ofr (known) classes with no knowledge of the actual number in each class. Design B is employed when the judge is given the actual number in each class and is asked to categorize the individuals subject to these constraints. The probability distribution of the total number of correct choices is developed in each case. A power comparison of the two procedures is undertaken.     

Estimating the parameters of the latent population distribution

Under consideration is a test battery of binary items. The responses ofn individuals are assumed to follow a Rasch model. It is further assumed that the latent individual parameters are distributed within a given population in accordance with a normal distribution. Methods are then considered for estimating the mean and variance of this latent population distribution. Also considered are methods for checking whether a normal population distribution fits the data. The developed methods are applied to data from an achievement test and from an attitude test.     

A thurstonian pairwise choice model with univariate and multivariate spline transformations

A probabilistic choice model is developed for paired comparisons data about psychophysical stimuli. The model is based on Thurstone's Law of Comparative Judgment Case V and assumes that each stimulus is measured on a small number of physical variables. The utility of a stimulus is related to its values on the physical variables either by means of an additive univariate spline model or by means of multivariate spline model. In the additive univariate spline model, a separate univariate spline transformation is estimated for each physical dimension and the utility of a stimulus is assumed to be an additive combination of these transformed values. In the multivariate spline model, the utility of a stimulus is assumed to be a general multivariate spline function in the physical variables. The use of B splines for estimating the transformation functions is discussed and it is shown how B splines can be generalized to the multivariate case by using as basis functions tensor products of the univariate basis functions. A maximum likelihood estimation procedure for the Thurstone Case V model with spline transformation is described and applied for illustrative purposes to various artificial and real data sets. Finally, the model is extended using a latent class approach to the case where there are unreplicated paired comparisons data from a relatively large number of subjects drawn from a heterogeneous population. An EM algorithm for estimating the parameters in this extended model is outlined and illustrated on some real data.The first author is supported as Bevoegdverklaard Navorser of the Belgian Nationaal Fonds voor Wetenschappelijk Onderzoek. The authors are indebted to Ulf Böckenholt and Yoshio Takane for useful comments on an earlier draft of this paper.     

柯林斯语义层次网络模型的实验研究

应用语义验证法对柯林斯与奎利安的语义层次网络模型进行研究,结果表明:(1)柯林斯等人的语义层次网络模型不完全适用于人脑的信息加工;(2)被试对主语所指称客体的熟悉程度是决定语义验证时间的一个重要因素;(3)被试提取概念各属性的时间并非完全由属性储存的位置决定的。     

STIMULUS GENERALIZATION AND EQUIVALENCE CLASSES: A MODEL FOR NATURAL CATEGORIES

Two three-member classes were formed by training AB and BC using a conditional discrimination procedure. The A and B stimuli were nonsense syllables, and the C stimuli were sets of “short” or “long” lines. To test for equivalence, C1 or C2 was presented as a sample with A1 and A2 as comparisons. Once the class-related comparison was chosen consistently, different line lengths were substituted for the training lines in the CA tests. In general, the likelihood of choosing a given comparison was an inverse function of the difference in the length of the test line from the training line. Stimuli in an equivalence class became functionally related not only to each other but also to novel stimuli that resembled a member of the equivalence class. The combination of primary generalization and equivalence class formation, then, can serve as a model to account for the development of naturally occurring categories.     

A Brief History of Metacognitive Therapy: From Cognitive Science to Clinical Practice

Metacognitive therapy (MCT) is proving to be an effective treatment for anxiety and depression with effects that may exceed CBT. It has been described as a paradigm shift in psychotherapy in its theory-driven cognitive science approach and systematic development and evaluation. MCT was developed by Adrian Wells based on an information processing theory, the Self-Regulatory Executive Function model by Wells and Matthews. MCT theory formulates psychological disorders as sharing common causal factors under the influence of metacognition, representing a particular top-down model of biases in cognitive regulation. A key clinical implication was that a core set of interventions could be developed to impact a wide range of symptoms and disorders. In this paper, we trace the historical development of MCT and the major studies that informed theory and practice with the aim of introducing clinicians and researchers to this area and to understand why the metacognitive approach has developed into a treatment that is proving to be potentially more effective than current gold-standard treatments. In doing so, we will draw out the distinctive features of the approach and explore how this might offer a blueprint for scientific advancement in clinical psychology and psychotherapy.     

Self-compassion reduces parental burnout: Mediation roles of parental psychological control and support

This study investigated the mediating role of parental psychological control (PPC) and support in the relationship between self-compassion and parental burnout syndrome. The study participants included 1185 Vietnamese parents who completed questionnaires regarding demographic information, parental burnout, self-compassion, parental support of the child, and PPC. We used data analyses, including structural equation modeling, to test the mediation roles of PPC and parental support of the child. Our main findings include: (1) self-compassion had a significant negative effect on parental burnout; (2) the mediation roles of PPC and parental support of the child were also significant. These results suggest that parents with high self-compassion tend to provide more support for children in daily parenting, which helps reduce parental burnout. In contrast, parents with low self-compassion were more likely to use PPC, which exacerbated their burnout. The results from this study provide suggestions for creating effective interventions in the family context to reduce parental burnout and improve parenting effectiveness.