Personality, career decision self-efficacy and commitment to the career choices process among Chinese graduate students

This study examined the mediating effect of career decision self-efficacy on the relationship between the Five-Factor Model of personality and the career commitment process (i.e., vocational commitment and the tendency to foreclose) in a sample of 785 Chinese graduate students. The multiple regression analyses showed that neuroticism and conscientiousness related significantly to progress in vocational commitment both directly and indirectly through career decision self-efficacy. High agreeableness related to less premature foreclosure. In addition, career decision self-efficacy associated with greater progress in vocational commitment but also a strong tendency to foreclose. The implications for career development theory and practice are discussed.     

Sex differences when searching for a mate: A process‐tracing approach

Sex differences in the extent of mate search were investigated using two sequential choice processes. In Study 1, attribute search, prior to choosing a mate or rejecting two competing mates, for either a short‐ or long‐term relationship, was explored. Men (women) seeking short‐term relationships were the least (most) likely to reject both suitors. Men acquired a greater number of attributes prior to rejecting mates. The length of the relationship solely affected men's search behaviors. Study 2 utilized a nested sequential model namely participants decided how many suitors to sample prior to choosing a short‐term mate, and how many attributes to acquire on each sampled suitor. Women sampled a greater number of suitors prior to choosing a mate. Biological sex was found to be a greater moderator of mate search than were three personality traits linked to search. Across both studies less information was acquired prior to rejecting versus choosing a mate. Copyright © 2008 John Wiley & Sons, Ltd.     

Cognitive errors,coping patterns,and the therapeutic alliance: A pilot study of in‐session process

Aim: This exploratory study examined the association between clients’ assessment of the therapeutic alliance and their cognitive errors (CE) and coping action patterns (CAP). Method: Selected therapy sessions of clients (N = 26) were rated for cognitive errors and coping action patterns using the CERS and CAPRS methods (Drapeau, Perry, & Dunkley, 2008; Perry, Drapeau, & Dunkley, 2005). The therapeutic alliance was assessed using the Working Alliance Inventory (WAI; Horvath & Greenberg, 1989). Results: Following Bonferroni corrections, no significant relationship was found between clients’ CEs and their ratings of the WAI. However, the ‘Negotiation’ CAP was associated with the total alliance score, and with the Task and Goal subscales. Implications: A better understanding of the cognitive processes presented by clients in session can enable clinicians to address these factors early on when the alliance is most critical.     

A Latent Gaussian process model for analysing intensive longitudinal data

Intensive longitudinal studies are becoming progressively more prevalent across many social science areas, and especially in psychology. New technologies such as smart-phones, fitness trackers, and the Internet of Things make it much easier than in the past to collect data for intensive longitudinal studies, providing an opportunity to look deep into the underlying characteristics of individuals under a high temporal resolution. In this paper we introduce a new modelling framework for latent curve analysis that is more suitable for the analysis of intensive longitudinal data than existing latent curve models. Specifically, through the modelling of an individual-specific continuous-time latent process, some unique features of intensive longitudinal data are better captured, including intensive measurements in time and unequally spaced time points of observations. Technically, the continuous-time latent process is modelled by a Gaussian process model. This model can be regarded as a semi-parametric extension of the classical latent curve models and falls under the framework of structural equation modelling. Procedures for parameter estimation and statistical inference are provided under an empirical Bayes framework and evaluated by simulation studies. We illustrate the use of the proposed model though the analysis of an ecological momentary assessment data set.     

Revisiting dispersion in count data item response theory models: The Conway–Maxwell–Poisson counts model

Count data naturally arise in several areas of cognitive ability testing, such as processing speed, memory, verbal fluency, and divergent thinking. Contemporary count data item response theory models, however, are not flexible enough, especially to account for over- and underdispersion at the same time. For example, the Rasch Poisson counts model (RPCM) assumes equidispersion (conditional mean and variance coincide) which is often violated in empirical data. This work introduces the Conway–Maxwell–Poisson counts model (CMPCM) that can handle underdispersion (variance lower than the mean), equidispersion, and overdispersion (variance larger than the mean) in general and specifically at the item level. A simulation study revealed satisfactory parameter recovery at moderate sample sizes and mostly unbiased standard errors for the proposed estimation approach. In addition, plausible empirical reliability estimates resulted, while those based on the RPCM were biased downwards (underdispersion) and biased upwards (overdispersion) when the simulation model deviated from equidispersion. Finally, verbal fluency data were analysed and the CMPCM with item-specific dispersion parameters fitted the data best. Dispersion parameter estimates indicated underdispersion for three out of four items. Overall, these findings indicate the feasibility and importance of the suggested flexible count data modelling approach.     

The Holy and Great Council of the Orthodox Church and Interreligious Dialogue

This article examines how, through a pan-Orthodox synodical decision, the Orthodox Church has for the first time officially asserted its obligation to enter into dialogue with people of other cultures and religious beliefs and emphasized the value of serious and clear interreligious dialogue to promote mutual trust, peace, and reconciliation. The Holy and Great Council of the Orthodox Church of Crete clarified that interreligious dialogue excludes both syncretism and proselytism, and that interreligious dialogue needs to be accompanied by the condemnation of fundamentalism, nationalism, and the religious justification of violence. Finally, this article makes reference to the basic principles of the Orthodox tradition regarding the promotion of interreligious dialogue and emphasizes that dialogue entails respect for other religions but also discernment.     

State and Trait Anger Predicting Creative Process Engagement—The Role of Emotion Regulation

Drawing on the specific emotion approach, and based on the emotional regulation theory and cognitive and activation perspectives on emotions, this study examined the differentiated impact of state and trait anger on creative process engagement (CPE) and the moderating influences of emotion reappraisal and suppression. Data were obtained from daily surveys (N = 422) of 98 employees from three consultancy companies. Hierarchical linear modeling analysis revealed that trait anger has a stronger impact on CPE than state anger does. Furthermore, the relationship between state anger and CPE is stronger when emotion reappraisal is lower, rather than higher, and the relationship between trait anger and CPE is also stronger when emotion suppression is lower, rather than higher.     

Change in school ethnic diversity and intergroup relations: The transition from segregated elementary to mixed secondary school for majority and minority students

This research examined the impact of a change in school diversity on school children's intergroup relations. A longitudinal survey tracked 551 White British and Asian British students (M_age = 11.32) transitioning from elementary (time 1) to secondary (time 2) school in an ethnically segregated town in the United Kingdom. We estimated a multivariate, multilevel model. A cross-sectional comparison of segregated schools and a mixed elementary school at time 1 revealed that both Asian and White British in the mixed school reported more positive intergroup relations. A longitudinal analysis found that the transition from segregated elementary to mixed secondary schools was associated with Asian British developing more positive intergroup relations. White British reported overall less positive intergroup relations, although only trust decreased; evidence from other measures remains inconclusive. The findings are important for understanding early stages of diversity exposure, and the impact of changing diversity levels on majority and minority groups.     

Searching hard versus searching smart: The role of search process quality in an internship context

Based on a self‐regulatory approach, we propose that students searching for an internship following a high‐quality process will show greater search success. In a sample of 191 Belgian final year students looking for an internship, the quality of students’ search process was positively related to both self‐reported and objective search outcomes, beyond the mere intensity of their search. Specifically, reflection related positively to students’ satisfaction and perceived fit with their internship, as well as to organizations’ assessment of students’ internship performance. Planning related positively to the speed of finding an internship. Furthermore, the four search process quality dimensions explained incremental variance in these outcomes beyond a unidimensional measure of metacognitive activities, supporting the added value of our multidimensional approach.     

Development of computational models of emotions: A software engineering perspective

Computational Models of Emotions (CMEs) are software systems designed to explain the phenomenon of emotions. The mechanisms implemented in this type of computational models are based on human emotion theories reported in the literature and designed to provide intelligent agents with affective capabilities and improve human-computer interaction. However, despite the growing interest in this type of models, the development process of CMEs does not seem to follow formal software methodologies. In this paper, we present an analysis of CMEs from a software engineering perspective. We aim to identify what elements of software engineering are used in the development process of CMEs and to demonstrate how some software engineering techniques may support and improve their development process. We discuss a series of challenges to be addressed in order to take advantage of software engineering techniques: (1) definition of guidelines to help decide which emotion theories should be implemented computationally, (2) homogenization of terms about human emotions, their components, phases, and cycles implemented in CMEs, (3) design of CMEs whose components can be reusable, (4) definition of standard criteria for comparative analysis between CMEs, (5) identification of software engineering principles, concepts, and design practices useful in the construction of CMEs, and (6) definition of standard frameworks to validate CMEs.