The effects of self-disclosure on the communicative interaction between a person who stutters and a normally fluent speaker

PurposeThe purpose of this study was to investigate the effects of self-disclosure on the self-perception of stuttering severity, comfort, cognitive effort, and anxiety in a structured conversational interaction with a normally fluent speaker. The benefit of self-disclosure from the perspective of the person who stutters in a self-disclosed and in a non-disclosed condition was also studied. The total syllables produced and percent syllables stuttered were measured in both experimental conditions in order to evaluate the effects on the amount of speech produced and verbal fluency.MethodsTwenty-five adults who stutter were recruited in Philadelphia and New Jersey, through social media, university clinics, and National Stuttering Association (NSA) self-help groups. The participants engaged in a conversational task with a normally fluent speaker (NFS). The cognitive-affective variables (self-perception of stuttering severity, comfort, cognitive effort, anxiety and benefit) were measured using self-report questionnaires completed after each condition. The percent syllables stuttered and total syllables in each condition were also transcribed and analyzed.ResultsFrequency distributions of Likert Scale ratings indicated a slight preference for self-disclosure over non-disclosure, but there was a non-significant difference on the benefit variable for the disclosed and non-disclosed conditions. There were correlations between total syllables produced and percent syllables stuttered and comfort, in the non-disclosed state only. Other findings included non-significant differences for the cognitive-affective variables across conditions.ConclusionFrom the perspective of the person who stutters, self-disclosure at the outset of the communicative interaction did not have a decisive impact on the cognitive-affective or speech variables.     

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.     

情绪智力:多维度构思背后的信息

情绪智力作为影响个人发展与领导能力的关键变量已经得到许多关注。虽然许多研究都认为情绪智力更适合从一个多维度变量角度来探讨,但更多研究主要关注情绪智力的整体效应和差异,比较少从情绪智力的单一维度方面来进行研究。本研究以公务员群体来进行分析,运用多元方差分析和结构方程技术来分析人口统计特征对情绪智力多维度构思的影响,并特别关注情绪智力及各维度对个体绩效的影响。最后根据研究结果对与情绪智力相关的理论研究和管理实践提供一些建议。     

目击证人辨认研究

目击证人辨认对司法审判具有重要的意义,这使其成为心理学研究领域的热点之一。本文重点介绍了影响目击证人辨认准确性的因素,即估计者变量和系统变量,并分析了目击证人辨认研究的现存问题。     

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.     

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.     

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.