Can the REBT theory explain loneliness? Theoretical and clinical applications

Loneliness is a common psychological experience affecting a significant minority of the general population. Loneliness may in part be related to the existence of dysfunctional cognitive evaluations. To date, however, loneliness has yet to be explicitly assessed within a cognitive-behavioural theoretical framework. The current study sought to determine the association between negative cognitions, within the context of Rational Emotive Behaviour Therapy (REBT), and the experience of loneliness. A multinational sample of university students (n = 397) completed self-report assessments of rational and irrational beliefs, and loneliness. Structural equation modelling results found that the REBT model of psychopathology, and the REBT model of psychological health, provided satisfactory representations of loneliness, explaining 36% and 23% of variance in loneliness, respectively. Several dysfunctional (“Demandingness”, “Catastrophising” and “Self-Downing” beliefs) and functional (“Preferences” and “Self-Acceptance” beliefs) cognitions were directly and indirectly associated with loneliness. These results highlight that cognitions and loneliness are meaningfully related, and indicate that cognitive-behavioural models may be useful in understanding loneliness. More specifically, current results suggest that REBT may offer a viable psychotherapeutic approach to treating loneliness.     

The relation of modelling and the perception of parental care with adolescent smoking: a cross-sectional study

The smoking of tobacco among adolescents is due to several influential factors, both individual and social. The present study aimed at comparing the effects of different variables on adolescent cigarette smoking, specifically, peer and parent modelling, self-regulatory efficacy, and parental degree of care as perceived by the adolescent. Data were collected by means of a self-report questionnaire administered to a sample of 229 secondary school students aged between 15 and 20 years (mean = 16.69, SD = 1.14). To test the influence of different variables, we performed a binary logistic regression analysis. Results indicated that the father is a key figure in that his care and affection can prevent adolescents from becoming a smoker and his modelling is a risk factor. The significant influence of friends was also confirmed and indicated that peer modelling is a strong predictor of adolescent cigarette smoking. Finally, the results revealed that self-regulatory efficacy is a relevant psychological variable capable of preventing smoking initiation during adolescence.     

Two good reasons to say ‘change!’ – ensemble representations as well as item representations impact standard measures of VWM capacity

Visual working memory (VWM) is a central bottleneck in human information processing. Its capacity is most often measured in terms of how many individual‐item representations VWM can hold (k). In the standard task employed to estimate k, an array of highly discriminable colour patches is maintained and, after a short retention interval, compared to a test display (change detection). Recent research has shown that with more complex, structured displays, change‐detection performance is, in addition to individual‐item representations, supported by ensemble representations formed as a result of spatial subgroupings. Here, by asking participants to additionally localize the change, we reveal indication for an influence of ensemble representations even in the very simple, unstructured displays of the colour‐patch change‐detection task. Critically, pure‐item models from which standard formulae of k are derived do not consider ensemble representations and, therefore, potentially overestimate k. To gauge this overestimation, we develop an item‐plus‐ensemble model of change detection and change localization. Estimates of k from this new model are about 1 item (~30%) lower than the estimates from traditional pure‐item models, even if derived from the same data sets.     

A Comparison of Multilevel Mediation Modeling Methods: Recommendations for Applied Researchers

Multilevel structural equation modeling (MSEM) has been proposed as a valuable tool for estimating mediation in multilevel data and has known advantages over traditional multilevel modeling, including conflated and unconflated techniques (CMM & UMM). Recent methodological research has focused on comparing the three methods for 2-1-1 designs, but in regards to 1-1-1 mediation designs, there are significant gaps in the published literature that prevent applied researchers from making educated decisions regarding which model to employ in their own specific research design. A Monte Carlo study was performed to compare MSEM, UMM, and CMM on relative bias, confidence interval coverage, Type I Error, and power in a 1-1-1 model with random slopes under varying data conditions. Recommendations for applied researchers are discussed and an empirical example provides context for the three methods.     

Higher investment levels into pre-planned routes increase the adherence of pedestrians to them

Extensive research has focused on identifying the principles in pedestrian route choice, often assuming that pedestrians follow an optimal route measured by factors, such as route length and busyness. However, the question of the extent to which pedestrians adhere to their planned route has not been convincingly resolved. Here, we form the hypothesis that the more pedestrians invest into a planned route by walking further along it, the bigger their tendency to stick to this route, even when it becomes less attractive than other options due to congestion, for example. We term this behaviour “route commitment effect” and conduct an online survey with over 300 participants to test and establish the existence of this effect. We propose a novel model to formalise this effect. Using simulations of our model, we give illustrative examples for the consequences of the route commitment effect. Our findings suggest that the route commitment effect can significantly influence pedestrian route choice in a simple scenario with only two possible routes. In more complex scenarios with many routes, the impact of the route commitment effect on the overall dynamics is much weaker, as pedestrians are distributed across routes. In general, we find that the route commitment effect reduces the efficiency of pedestrian flow and leads to more predictable pedestrian dynamics. Our study sheds light on the role of cognitive bias in pedestrian decision-making and may thus be helpful for facility design or operations.     

Screens across the pond: Findings from longitudinal screen time research in the US and UK

Digital media are increasingly pervasive in the lives of young children. This increase in the availability of digital media might have long-run implications for child development; however, it is too soon to definitively conclude the direction of effects. In part due to this lack of certainty, leading health organizations have chosen to make different recommendations to parents of young children: Many international health organizations (e.g., the American Academy of Pediatrics, World Health Organization) recommend very young children be limited to under one hour of screen time daily, whereas others (e.g., Royal College of Paediatrics and Child Health) have intentionally opted not to make recommendations about specific limits. These guidelines might contribute to parents in different countries making meaningfully different choices about children’s use of digital media. Using a sample of N = 303 families recruited in Cambridgeshire, England and New York City prior to the birth of couples’ first child, we explore predictors of digital media use across the first two years of life. Data were collected when children were 4, 14, and 24 months of age. Results of latent growth curve analyses show that generally, children spend more time engaging with digital media as they grow older; however, growth mixture models reveal most children fit into one of two classes: One group of children (High Media Users; 52.2 %) engages with a substantial amount of digital media, whereas the other (Low Media Users; 48.8 %) engages with relatively little. Children in the US were approximately 30 % more likely to be in the Low Media Users group and there were no differences in group membership on the basis of parents’ psychosocial wellbeing. While these differences could be due to a number of factors, these findings may reflect the power of pediatric recommendations.     

Robust Bayesian growth curve modelling using conditional medians

Growth curve models have been widely used to analyse longitudinal data in social and behavioural sciences. Although growth curve models with normality assumptions are relatively easy to estimate, practical data are rarely normal. Failing to account for non-normal data may lead to unreliable model estimation and misleading statistical inference. In this work, we propose a robust approach for growth curve modelling using conditional medians that are less sensitive to outlying observations. Bayesian methods are applied for model estimation and inference. Based on the existing work on Bayesian quantile regression using asymmetric Laplace distributions, we use asymmetric Laplace distributions to convert the problem of estimating a median growth curve model into a problem of obtaining the maximum likelihood estimator for a transformed model. Monte Carlo simulation studies have been conducted to evaluate the numerical performance of the proposed approach with data containing outliers or leverage observations. The results show that the proposed approach yields more accurate and efficient parameter estimates than traditional growth curve modelling. We illustrate the application of our robust approach using conditional medians based on a real data set from the Virginia Cognitive Aging Project.     

Work–family conflicts,cognitive appraisal,and burnout: Testing the mediation effect with structural equation modelling

Work–family conflict constitutes an important source of occupational stress predicting teachers’ burnout, and cognitive variables have shown to be core structures in explaining human adaptation to stress. Nevertheless, the role of cognitive appraisal needs to be fully analysed to comprehend how it can mediate the relationship between stress and burnout. In order to understand the potential mediation of cognitive appraisal in the relationship between stress and burnout, we adopted conceptual models of stress that highlighted the value of cognitive appraisal on positive and negative reactions to work demands. Also, we analysed the potential moderation of sex and age in the relationship between work–family conflict, cognitive appraisal, and burnout due to inconsistent findings on how these personal variables can interfere on these relations. In this study, we used structural equation modelling (SEM) to test the mediating of cognitive appraisal in the relationship between work–family conflicts and burnout. A survey with measures of work–family conflicts, cognitive appraisal, and burnout was administered to the participants consisting of 438 Portuguese teachers from kindergarten through high school, aged between 28 and 67 years (M = 46.85; SD = 7.88), 304 of whom were females (69.41%). The results confirmed that cognitive appraisal partially mediated the relationship between work–family conflict and burnout. The mediation effect of cognitive appraisal on the relationship between work–family conflict and burnout was invariant regardless of teachers’ sex or age. In sum, cognitive appraisal should be considered in order to understand teachers’ adaptation to work.     

Examining the Effects of Controlling for Shared Variance among the Dark Triad Using Meta‐analytic Structural Equation Modelling

Multivariate procedures (e.g. structural equation modelling) are essential to personality psychology, but interpretive difficulties can arise when examining the relations between residualized variables (i.e. the residual content of a variable after its overlap with other variables has been statistically controlled for) and outcomes of interest. These issues have been the focus of recent debate within the research literature on the Dark Triad, which is a collection of interrelated but theoretically distinct personality constructs made up of narcissism, Machiavellianism and psychopathy. The present paper highlights previous work on the issue of partialling and also makes use of recent developments surrounding meta‐analytic structural equation modelling to reliably assess the impact of partialling on the empirical profiles of the Dark Triad components. The results show that numerous interpretive difficulties arise after partialling the overlap among the Dark Triad components, most notably for narcissism and Machiavellianism. The results are discussed in the context of contemporary Dark Triad research in addition to discussing the implications for structural equation modelling methods in personality psychology more generally. Recommendations are made for how future research can mitigate the interpretive difficulties that may arise from partialling. Copyright © 2018 European Association of Personality Psychology     

A model‐based process to improve robustness in Multicriteria Decision Aiding interventions

A model‐based process is a methodological approach that facilitates interventions in new and ill or not structured problem situations. It is oriented towards improving the robustness of the intervention and its results in an incremental definition of both the problem formulation and the model, in order to reduce the uncertainty that is not only associated with the model parameters but also with the model structure, and sometimes also with the problem formulation. The nature and results of this approach, which consider the interaction between an analyst and the actors of the decision system as the decision aiding core, are proposed in relation to some actual interventions.