Contributions to factor analysis of dichotomous variables

A new method is proposed for the factor analysis of dichotomous variables. Similar to the method of Christoffersson this uses information from the first and second order proportions to fit a multiple factor model. Through a transformation into a new set of sample characteristics, the estimation is considerably simplified. A generalized least-squares estimator is proposed, which asymptotically is as efficient as the corresponding estimator of Christoffersson, but which demands less computing time.This research was supported by the Bank of Sweden Tercentenary Foundation under project Structural Equation Models in the Social Sciences, project director Karl G. Jöreskog.     

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.     

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。     

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.     

Dyadic processes of relational turbulence theory

This study tested dyadic processes of relational turbulence theory (RTT) in heterosexual marriages. We tested propositions 1, 2, and 5 of RTT, which propose that uncertainty about the marriage biases cognitive appraisals, and that interference from a partner heightens negative emotions, both of which culminate in relational turbulence for spouses. Guided by these propositions, husbands' and wives' (N = 510; 255 marital dyads) dyadic cognitive and emotional processes were estimated using the actor-partner interdependence mediation model. Consistent with theoretical propositions, we found evidence for actor-actor indirect effects; for both husbands and wives (a) the effect of spouses' relationship uncertainty on their own relational turbulence was mediated by their own biased cognitive appraisals, and (b) the effect of spouses' experienced interference on their own relational turbulence was mediated by their own anger from communicating in the marriage. However, controlling for actor-actor indirect effects, partner-defined processes (i.e., actor-partner and partner-actor indirect effects) uniquely explained husbands' and wives' relational turbulence.