Simultaneous classification and multidimensional scaling with external information

For the exploratory analysis of a matrix of proximities or (dis)similarities between objects, one often uses cluster analysis (CA) or multidimensional scaling (MDS). Solutions resulting from such analyses are sometimes interpreted using external information on the objects. Usually the procedures of CA, MDS and using external information are carried out independently and sequentially, although combinations of two of the three procedures (CA and MDS, or multidimensional scaling and using external information) have been proposed in the literature. The present paper offers a procedure that combines all three procedures in one analysis, using a model that describes a partition of objects with cluster centroids represented in a low-dimensional space, which in turn is related to the information in the external variables. A simulation study is carried out to demonstrate that the method works satisfactorily for data with a known underlying structure. Also, to illustrate the method, it is applied to two empirical data sets.     

Winnicott, Balint, and the Independent Tradition

This article compares and contrasts two main aspects of the work of Winnicott and Balint: their theories of infantile development and their theoretical and clinical work on the use of regression as a therapeutic agent. The relationship of their thinking to aspects of the British Independent Group's theories and clinical work is noted vis-à-vis the basic acceptance of classical theory and technique, acknowledgment and use of some of Klein's contributions, the influence of trauma and the external environment on psychic development and psychopathology, the importance of holding and the setting, and the reintegration of previously split off and lost parts of the self.     

Breaking the Solid Ground of Common Sense: Undoing “Structure” with Michael Balint

Balint's great merit was to question what, in the classical perspective, was assumed as a prerequisite for analysis and thus located beyond analysis: the maturity of the ego. A fundamental premise of his work was Ferenczi's distrust for the structural model, which praised the maturity of the ego and its verbal, social, and adaptive abilities. Ferenczi's view of ego maturation as a trauma derivative was strikingly different from the theories of all other psychoanalytic schools and seems to be responsible for Balint's understanding of regression as a sort of inverted process that enables the undoing of the sheltering structures of the mature mind. Balint's understanding of the relation between mature ego and regression diverged not only from the ego psychologists, who emphasized the idea of therapeutic alliance, but also from most of the authors who embraced the object-relational view, like Klein (who considered regression a manifestation of the patient's craving for oral gratification), Fairbairn (who gave up the notion of regression), and Guntrip (who viewed regression as a schizoid phenomenon related to the ego weakness). According to Balint, the clinical appearance of a regression would depend also on the way the regression is recognized, is accepted, and is responded to by the analyst. In this respect, his position was close to Winnicott's reformulation of the therapeutic action. Yet, the work of Balint reflects the persuasion that the progressive fluidification of the solid structure could be enabled only by the analyst's capacity for becoming himself or herself [unsolid].     

不同年级学生阅读科技文章的眼动研究

本实验使用４２００Ｒ眼动仪对小学三年级、初二、高二和大学生阅读科技文章的眼动特征进行研究。结果发现;１．随年龄的提高,被试阅读课文的总注视次数显著减少;２．高年级学生能够根据阅读内容的重要性来灵活地调节自己的眼动策略;３．随着年级的提高,各年级被试使用前进式回视的比例逐渐减少,而使用选择式回视的比例逐渐增加。     

Linear relations amongk sets of variables

A family of solutions for linear relations amongk sets of variables is proposed. It is shown how these solutions apply fork=2, and how they can be generalized from there tok3.The family of solutions depends on three independent choices: (i) to what extent a solution may be influenced by differences in variances of components within each set; (ii) to what extent the sets may be differentially weighted with respect to their contribution to the solution—including orthogonality constraints; (iii) whether or not individual sets of variables may be replaced by an orthogonal and unit normalized basis.Solutions are compared with respect to their optimality properties. For each solution the appropriate stationary equations are given. For one example it is shown how the determinantal equation of the stationary equations can be interpreted.     

The asymptotic distribution of commonality components

Commonality components have been defined as a method of partitioning squared multiple correlations. In this paper, the asymptotic joint distribution of all 2 ^k – 1 squared multiple correlations is derived. The asymptotic joint distribution of linear combinations of squared multiple correlations is obtained as a corollary. In particular, the asymptotic joint distribution of commonality components are derived as a special case. Simultaneous and nonsimultaneous asymptotic confidence intervals for commonality components can be obtained from this distribution.This work was supported in part by the Spencer Foundation and the National Science Foundation.The authors are grateful to Bryna Siegel-Gorlick for her help in obtaining the data used in Example 4.3, and to the referees for their comments and suggestions.     

Openness relates to COVID-19 vaccination rates across 48 United States but politics trump personality

Does geographic variation in personality across the United States relate to COVID-19 vaccination rates? To answer this question, we combined multiple state-level datasets: (a) Big Five personality averages (i.e., extraversion, agreeableness, conscientiousness, neuroticism, and openness; Rentfrow et al., 2008), (b) COVID-19 full-vaccination rates (CDC, 2021a), (c) health-relevant demographic covariates (population density, per capita gross domestic product, and racial/ethnic data; Webster et al., 2021), and (d) political and religiosity data. Analyses showed openness as the strongest correlate of full-vaccination rates (r = 0.51). Controlling for other traits, demographic covariates, and spatial dependence, openness remained significantly related to full-vaccination rates (r_p = 0.55). Adding political and religiosity data to this model diminished openness effects for full-vaccination rates to non-significance (r_p = 0.26); however, extraversion emerged as a significant correlate of full-vaccination rates (r_p = 0.37). Although politics are paramount, we suspect that states with higher average openness scores are more conducive to novel thinking and behavior—dispositions that may be crucial in motivating people to take newly-developed vaccines based on new technologies to confront a novel coronavirus.     

The Unreal Child1

Unrepresented mental states lead to an impaired ability to feel emotions and trust in oneself, one’s history and in the world. The article explores the question of how representations of oneself and the relevant other, the mother, become possible in the course of therapy when dissociative processes previously made this impossible, and what role unconscious communication plays in the analytic realm. This question will be explored by examining the theories of André Green, Philip Bromberg, and Howard Levine.     

Effect sizes in ANCOVA and difference-in-differences designs

It is common practice in both randomized and quasi-experiments to adjust for baseline characteristics when estimating the average effect of an intervention. The inclusion of a pre-test, for example, can reduce both the standard error of this estimate and—in non-randomized designs—its bias. At the same time, it is also standard to report the effect of an intervention in standardized effect size units, thereby making it comparable to other interventions and studies. Curiously, the estimation of this effect size, including covariate adjustment, has received little attention. In this article, we provide a framework for defining effect sizes in designs with a pre-test (e.g., difference-in-differences and analysis of covariance) and propose estimators of those effect sizes. The estimators and approximations to their sampling distributions are evaluated using a simulation study and then demonstrated using an example from published data.     

Rotation in the dynamic factor modeling of multivariate stationary time series

A special rotation procedure is proposed for the exploratory dynamic factor model for stationary multivariate time series. The rotation procedure applies separately to each univariate component series of aq-variate latent factor series and transforms such a component, initially represented as white noise, into a univariate moving-average. This is accomplished by minimizing a so-called state-space criterion that penalizes deviations of the rotated solution from a generalized state-space model with only instantaneous factor loadings. Alternative criteria are discussed in the closing section. The results of an empirical application are presented in some detail.This research was supported by the Institute for Developmental and Health Research Methodology, University of Virginia.