Understanding the social emotional practices of therapists

Emotions are central to the therapy process and skilful use by therapists of client emotion is an essential catalyst to client change. However, the contribution of emotion to the therapy process and how therapists’ social emotional skills are incorporated into psychological practice is still unclear. Using a statistical method for mapping psychological constructs, therapists’ social emotional skills were transformed into a “map” with three spatial dimensions, which was supported by comparative reliability checks. The nature of social emotional skills was further investigated by administering a Q-Sort of emotional practice items to 47 therapists. Ten highly applicable clusters of social emotional skills across seven style patterns with therapists were identified. Tentative links were drawn between demographic data and both clusters and therapist styles. These findings suggest therapists’ social emotional skills can be organised into meaningful clusters and that therapists can be styled according to their responses across these clusters. Furthermore, gaps identified in the model suggest possible “blind spots” in the literature. The implications of these findings are significant for training and practice.     

The Local Minima Problem in Hierarchical Classes Analysis: An Evaluation of a Simulated Annealing Algorithm and Various Multistart Procedures

Hierarchical classes models are quasi-order retaining Boolean decomposition models for N-way N-mode binary data. To fit these models to data, rationally started alternating least squares (or, equivalently, alternating least absolute deviations) algorithms have been proposed. Extensive simulation studies showed that these algorithms succeed quite well in recovering the underlying truth but frequently end in a local minimum. In this paper we evaluate whether or not this local minimum problem can be mitigated by means of two common strategies for avoiding local minima in combinatorial data analysis: simulated annealing (SA) and use of a multistart procedure. In particular, we propose a generic SA algorithm for hierarchical classes analysis and three different types of random starts. The effectiveness of the SA algorithm and the random starts is evaluated by reanalyzing data sets of previous simulation studies. The reported results support the use of the proposed SA algorithm in combination with a random multistart procedure, regardless of the properties of the data set under study. Eva Ceulemans is a post-doctoral fellow of the Fund for Scientific Research Flanders (Belgium). Iwin Leenen is a post-doctoral researcher of the Spanish Ministerio de Educación y Ciencia (programa Ramón y Cajal). The research reported in this paper was partially supported by the Research Council of K.U. Leuven (GOA/05/04).     

Hierarchical Classes Modeling of Rating Data

Hierarchical classes (HICLAS) models constitute a distinct family of structural models for N-way N-mode data. All members of the family include N simultaneous and linked classifications of the elements of the N modes implied by the data; those classifications are organized in terms of hierarchical, if–then-type relations. Moreover, the models are accompanied by comprehensive, insightful graphical representations. Up to now, the hierarchical classes family has been limited to dichotomous or dichotomized data. In the present paper we propose a novel extension of the family to two-way two-mode rating data (HICLAS-R). The HICLAS-R model preserves the representation of simultaneous and linked classifications as well as of generalized if–then-type relations, and keeps being accompanied by a comprehensive graphical representation. It is shown to bear interesting relationships with classical real-valued two-way component analysis and with methods of optimal scaling. The research reported in this paper was supported by the Research Fund of the University of Leuven (GOA/00/02 and GOA/05/04) and by the Fund for Scientific Research-Flanders (project G.0146.06). Eva Ceulemans is a Post-doctoral Researcher supported by the Fund for Scientific Research, Flanders. The authors gratefully acknowledge the help of Gert Quintiens and Kaatje Bollaerts in collecting the data used in Section 4 and of Jan Schepers in additional analyses of these data.     

On a test of dimensionality in redundancy analysis

Lazraq and Cléroux (Psychometrika, 2002, 411–419) proposed a test for identifying the number of significant components in redundancy analysis. This test, however, is ill-conceived. A major problem is that it regards each redundancy component as if it were a single observed predictor variable, which cannot be justified except for the rare situations in which there is only one predictor variable. Consequently, the proposed test leads to drastically biased results, particularly when the number of predictor variables is large, and it cannot be recommended for use. This is shown both theoretically and by Monte Carlo studies.The work reported in this paper was supported by Grant A6394 to the first author from the Natural Sciences and Engineering Research Council of Canada.     

Informational constraints shape emergent functional behaviours during performance of interceptive actions in team sports

Objectives

This study aimed to explain how defenders intercept the trajectory of a passing ball by understanding how they coupled their actions to critical information sources in a competitive performance setting in team sports.

Design

Time series data on movement displacements of fifteen senior male futsal performers were recorded and digitized during nine competitive futsal games.

Method

Performance was recorded by a digital camera and digitized with TACTO software. The spatial-temporal dynamics of performers during ten intercepted and ten non-intercepted passes were compared. Time to ball interception was calculated by the difference between the time of each defender to an interception point in ball trajectory and the time of the ball’s arrival at the same interception point. Initial distances between defenders and ball and velocity data of defenders and ball over time were also recorded.

Results

Time to ball interception revealed positive values when passes were not intercepted, and negative to zero values when passes were intercepted. At the moment of pass initiation defenders’ distances to the ball constrained their possibilities for successful interception. Analysis of defenders’ adaptations to the environment revealed that continuous changes in the defenders’ velocities constrained their success of the interception.

Conclusions

Intercepted passes seemed to be influenced by the continuous regulation of a defender’s velocity relative to the ball’s trajectory. Time to ball interception is a variable that captured the emergent functional behaviours of players attempting to intercept the trajectory of a pass in the team sport of futsal.     

Semantic clustering as a neuropsychological predictor for amnestic-MCI

ABSTRACT

Recent research has demonstrated that patients with Alzheimer's disease (AD) show deficits in semantic processing when compared to cognitively healthy individuals. This difference is thought to be attributed to losses in higher cortical systems that are predominantly associated with executive functioning. The first aim of the study will be to determine if differences in semantic clustering can accurately differentiate patients with amnestic mild cognitive impairment (aMCI) from cognitively normal (CN) individuals. The second aim will be to determine the extent to which semantic processing might be associated with executive functions. Data from 202 (134 CN, 68 aMCI) participants were analyzed to quantify differences in semantic clustering ratios on the HVLT-R. Study participants ages ranged from 51 to 87 with education ranging from 6 to 20 years. ANCOVA revealed statistically significant differences on semantic clustering ratios (p < .001). Moderate correlations between semantic clustering Category Fluency Test (r = .45) were also found. Statistically significant group differences were also present on Trails-B and WAIS-R Digit Symbol performance (p < .001). Overall, these data indicate that deficits in semantic clustering are present in aMCI patients.     

Generalized multilevel structural equation modeling

A unifying framework for generalized multilevel structural equation modeling is introduced. The models in the framework, called generalized linear latent and mixed models (GLLAMM), combine features of generalized linear mixed models (GLMM) and structural equation models (SEM) and consist of a response model and a structural model for the latent variables. The response model generalizes GLMMs to incorporate factor structures in addition to random intercepts and coefficients. As in GLMMs, the data can have an arbitrary number of levels and can be highly unbalanced with different numbers of lower-level units in the higher-level units and missing data. A wide range of response processes can be modeled including ordered and unordered categorical responses, counts, and responses of mixed types. The structural model is similar to the structural part of a SEM except that it may include latent and observed variables varying at different levels. For example, unit-level latent variables (factors or random coefficients) can be regressed on cluster-level latent variables. Special cases of this framework are explored and data from the British Social Attitudes Survey are used for illustration. Maximum likelihood estimation and empirical Bayes latent score prediction within the GLLAMM framework can be performed using adaptive quadrature in gllamm, a freely available program running in Stata.gllamm can be downloaded from http://www.gllamm.org. The paper was written while Sophia Rabe-Hesketh was employed at and Anders Skrondal was visiting the Department of Biostatistics and Computing, Institute of Psychiatry, King's College London.     

Psychometrics in Psychological Research: Role Model or Partner in Science?

This is a reaction to Borsboom's (2006) discussion paper on the issue that psychology takes so little notice of the modern developments in psychometrics, in particular, latent variable methods. Contrary to Borsboom, it is argued that latent variables are summaries of interesting data properties, that construct validation should involve studying nomological networks, that psychological research slowly but definitely will incorporate latent variable methods, and that the role of psychometrics in psychology is that of partner, not role model. Requests for reprints should be sent to Klaas Sijtsma, Department of Methodology and Statistics, FSW, Tilburg University, PO Box 90153, 5000 LE, Tilburg, The Netherlands.     

The general factor of personality: Substance or artefact?

While it is now widely recognized that a general factor (GFP) can be extracted from most personality data, this finding has been subject to numerous critiques: (1) that the GFP is an artefact due to socially desirable responding; (2) that it is factorially indeterminate; (3) that it can be more parsimoniously modelled using blended variables; (4) that it shows less genetic variance due to dominance than should be true of a fitness trait; (5) that it correlates more weakly with g than would be predicted from Life History theory; (6) that it cannot be recovered across personality inventories. We present new evidence and argument to show that each of these critiques is open to reasonable doubt.     

The Joint Multivariate Modeling of Multiple Mixed Response Sources: Relating Student Performances with Feedback Behavior

The present study concerns a Dutch computer-based assessment, which includes an assessment process about information literacy and a feedback process for students. The assessment is concerned with the measurement of skills in information literacy and the feedback process with item-based support to improve student learning. To analyze students’ feedback behavior (i.e. feedback use and attention time), test performance, and speed of working, a multivariate hierarchical latent variable model is proposed. The model can handle multivariate mixed responses from multiple sources related to different processes and comprehends multiple measurement components for responses and response times. A flexible within-subject latent variable structure is defined to explore multiple individual latent characteristics related to students’ test performance and feedback behavior. Main results of the computer-based assessment showed that feedback-information pages were less visited by well-performing students when they relate to easy items. Students’ attention paid to feedback was positively related to working speed but not to the propensity to use feedback.