Encounters with the Universal and Cultural in Psychoanalytic Psychotherapy

The role played by socio-cultural factors in psychoanalytic psychotherapy from the vantage point of both the client and the therapist is elucidated. The position conferred upon culture throughout the history of psychoanalysis from classical to contemporary perspectives is briefly reviewed with special reference to the practice of psychoanalysis in India. A case study having relevance to the present theme is discussed where ignorance of factors salient to Indian culture adversely affected the outcome. Attention is given to internal and external pressures that prompted the therapist to take the stand. It is argued that the therapist may tread the difficult path between cultural universalism and cultural relativism rather than committing to either of them in order to do justice to psychoanalytical clinical work.     

The <Emphasis Type="Italic">K</Emphasis>-INDSCAL Model for Heterogeneous Three-Way Dissimilarity Data

A weighted Euclidean distance model for analyzing three-way dissimilarity data (stimuli by stimuli by subjects) for heterogeneous subjects is proposed. First, it is shown that INDSCAL may fail to identify a common space representative of the observed data structure in presence of heterogeneity. A new model that removes the rotational invariance of the classical multidimensional scaling problem and specifies K common homogeneous spaces is proposed. The model, called mixture INDSCAL in K classes, or briefly K-INDSCAL, still includes individual saliencies. However, the large number of parameters in K-INDSCAL may produce instability of the estimates and therefore a parsimonious model will also be discussed. The parameters of the model are estimated in a least-squares fitting context and an efficient coordinate descent algorithm is given. The usefulness of K-INDSCAL is demonstrated by both artificial and real data analyses.     

Cohen’s Linearly Weighted Kappa is a Weighted Average of 2×2 Kappas

An agreement table with n∈ℕ_≥3 ordered categories can be collapsed into n−1 distinct 2×2 tables by combining adjacent categories. Vanbelle and Albert (Stat. Methodol. 6:157–163, 2009c) showed that the components of Cohen’s weighted kappa with linear weights can be obtained from these n−1 collapsed 2×2 tables. In this paper we consider several consequences of this result. One is that the weighted kappa with linear weights can be interpreted as a weighted arithmetic mean of the kappas corresponding to the 2×2 tables, where the weights are the denominators of the 2×2 kappas. In addition, it is shown that similar results and interpretations hold for linearly weighted kappas for multiple raters.     

Factor Analysis via Components Analysis

When the factor analysis model holds, component loadings are linear combinations of factor loadings, and vice versa. This interrelation permits us to define new optimization criteria and estimation methods for exploratory factor analysis. Although this article is primarily conceptual in nature, an illustrative example and a small simulation show the methodology to be promising.     

A Sandwich-Type Standard Error Estimator of SEM Models with Multivariate Time Series

Structural equation models are increasingly used as a modeling tool for multivariate time series data in the social and behavioral sciences. Standard error estimators of SEM models, originally developed for independent data, require modifications to accommodate the fact that time series data are inherently dependent. In this article, we extend a sandwich-type standard error estimator of independent data to multivariate time series data. One required element of this estimator is the asymptotic covariance matrix of concurrent and lagged correlations among manifest variables, whose closed-form expression has not been presented in the literature. The performance of the adapted sandwich-type standard error estimator is evaluated using a simulation study and further illustrated using an empirical example.     

Kullback–Leibler Information and Its Applications in Multi-Dimensional Adaptive Testing

This paper first discusses the relationship between Kullback–Leibler information (KL) and Fisher information in the context of multi-dimensional item response theory and is further interpreted for the two-dimensional case, from a geometric perspective. This explication should allow for a better understanding of the various item selection methods in multi-dimensional adaptive tests (MAT) which are based on these two information measures. The KL information index (KI) method is then discussed and two theorems are derived to quantify the relationship between KI and item parameters. Due to the fact that most of the existing item selection algorithms for MAT bear severe computational complexity, which substantially lowers the applicability of MAT, two versions of simplified KL index (SKI), built from the analytical results, are proposed to mimic the behavior of KI, while reducing the overall computational intensity.     

“Why Am I so Anxious?” A Study of Attention Bias in Obsessive Compulsive Disorder

Previous research investigating attentional bias has produced intriguingly equivocal results. The present large-scale study explored attentional bias in subjects diagnosed with obsessive-compulsive disorder (OCD) and in a control group (n = 80) using the Emotional Stroop Test. Results clearly indicated poorer performances across the board amongst patients diagnosed with OCD compared to the control group, particularly in the case of anxiety-related stimuli. This paper discusses, in the light of current models of attentional bias towards threat in anxiety disorders, the processes whereby attentional bias towards anxiety stimuli might play a vital role in the causation and maintenance of OCD.