And justice for all: Revisiting the Global Belief in a Just World Scale

The Global Belief in a Just World Scale (GBJWS) has been widely used in measuring the Belief in a Just World (BJW) personality trait. Despite its widespread application across the social sciences, the validity of this scale has not been sufficiently tested in the literature. In this research, the authors examine the internal and external validity of the GBJWS using both standard correlational analyses and structural equation modeling (SEM). Specifically, the authors test the concurrent validity, internal consistency, unidimensional structure, convergent validity, and both measurement and latent mean invariance of the scale across gender and culture. The results of a pilot study suggest strong concurrent validity of the GBJWS with other BJW scales, and the findings of the two main studies support the internal and external validity of GBJWS across gender and culture. The authors’ results further show an overall greater level of BJW of Chinese individuals compared to Americans. The present research provides a much needed investigation of the validity of the GBJWS, and answers calls for research examining the scale’s utility across different populations.     

Statistical models for the induction and use of selectional preferences

Selectional preferences have a long history in both generative and computational linguistics. However, since the publication of Resnik's dissertation in 1993, a new approach has surfaced in the computational linguistics community. This new line of research combines knowledge represented in a pre‐defined semantic class hierarchy with statistical tools including information theory, statistical modeling, and Bayesian inference. These tools are used to learn selectional preferences from examples in a corpus. Instead of simple sets of semantic classes, selectional preferences are viewed as probability distributions over various entities. We survey research that extends Resnik's initial work, discuss the strengths and weaknesses of each approach, and show how they together form a cohesive line of research.     

Relationship between resilience,stress and burnout among civil servants in Beijing,China: Mediating and moderating effect analysis

This study aimed to explore the mediating and moderating effect in the relationship between resilience, stress and burnout among civil servants of Beijing, China. A cross-sectional study was conducted among civil servants in Beijing. Totally 541 civil servants completed a self-report questionnaire including three scales measuring civil servants’ resilience, stress and burnout. The data were analyzed with correlation, multiple regression and structural equation modeling. The results revealed that work stress rather than life and health stress could significantly predict burnout. Resilience played a partial mediating role between work stress and burnout, that is, work stress had both a direct and an indirect, via resilience, impact on burnout. Work stress played a partial mediating role between resilience and burnout, thus, resilience could prevent the development of burnout by relieving work stress, in addition to directly relieving it. Moreover, resilience was a moderator between work stress and burnout, and it could serve as a buffer to mitigate the adverse effects of work stress. These results suggest that resilience could be a positive personality trait for alleviating or eliminating work stress and combating burnout of civil servants of Beijing.     

The effects of a short-term mindfulness meditation intervention on coping flexibility

Background and objectives: Mindfulness meditation (MM) training promotes health and well-being. One potential mechanistic link between MM and health may be coping flexibility, (e.g., the ability to monitor and modify coping strategies based on situational needs and strategy effectiveness). We hypothesized that MM training would increase coping flexibility and also explored whether gains in coping flexibility continued to increase after training, or whether they were maintained or lost with time.

Methods and design: One hundred thirteen students (71 female, M_age?=?18.97) were randomly assigned to a waitlist control or MM condition. Participants in the MM condition were trained by a certified MM instructor and given guided recordings for one-week of at-home practice. Participants provided reports of coping flexibility over a three-week span.

Results: Results from multilevel modeling indicated that MM increased coping flexibility among those in the MM condition and among those who spent relatively more time meditating. Results further suggested that the gains in coping flexibility that were evident at post-test were not only maintained but increased in the two weeks after the intervention.

Conclusions: This study provides preliminary support for the assertion that MM increases the ability to monitor and modify coping strategies during times of stress.     

Postselection Inference in Structural Equation Modeling

Abstract

Most statistical inference methods were established under the assumption that the fitted model is known in advance. In practice, however, researchers often obtain their final model by some data-driven selection process. The selection process makes the finally fitted model random, and it also influences the sampling distribution of the estimator. Therefore, implementing naive inference methods may result in wrong conclusions—which is probably a prime source of the reproducibility crisis in psychological science. The present study accommodates three valid state-of-the-art postselection inference methods for structural equation modeling (SEM) from the statistical literature: data splitting (DS), postselection inference (PoSI), and the polyhedral (PH) method. A simulation is conducted to compare the three methods with the commonly used naive procedure under selection events made by L₁-penalized SEM. The results show that the naive method often yields incorrect inference, and that the valid methods control the coverage rate in most cases with their own pros and cons. Real world data examples show the practical use of the valid inference methods.     

An individual differences perspective on pragmatic abilities in the preschool years

Pragmatic abilities are fundamental to successful language use and learning. Individual differences studies contribute to understanding the psychological processes involved in pragmatic reasoning. Small sample sizes, insufficient measurement tools, and a lack of theoretical precision have hindered progress, however. Three studies addressed these challenges in three- to 5-year-old German-speaking children (N = 228, 121 female). Studies 1 and 2 assessed the psychometric properties of six pragmatics tasks. Study 3 investigated relations among pragmatics tasks and between pragmatics and other cognitive abilities. The tasks were found to measure stable variation between individuals. Via a computational cognitive model, individual differences were traced back to a latent pragmatics construct. This presents the basis for understanding the relations between pragmatics and other cognitive abilities.

Research Highlights

• Individual differences in pragmatic abilities are important to understanding variation in language development.
• Research in this domain lacks a precise theoretical framework and psychometrically high-quality measures.
• We present six tasks capturing a wide range of pragmatic abilities with excellent re-test reliability.
• We use a computational cognitive model to provide a substantive theory of individual differences in pragmatic abilities.

     

A Computational and Empirical Investigation of Graphemes in Reading

It is often assumed that graphemes are a crucial level of orthographic representation above letters. Current connectionist models of reading, however, do not address how the mapping from letters to graphemes is learned. One major challenge for computational modeling is therefore developing a model that learns this mapping and can assign the graphemes to linguistically meaningful categories such as the onset, vowel, and coda of a syllable. Here, we present a model that learns to do this in English for strings of any letter length and any number of syllables. The model is evaluated on error rates and further validated on the results of a behavioral experiment designed to examine ambiguities in the processing of graphemes. The results show that the model (a) chooses graphemes from letter strings with a high level of accuracy, even when trained on only a small portion of the English lexicon; (b) chooses a similar set of graphemes as people do in situations where different graphemes can potentially be selected; (c) predicts orthographic effects on segmentation which are found in human data; and (d) can be readily integrated into a full‐blown model of multi‐syllabic reading aloud such as CDP++ (Perry, Ziegler, & Zorzi, 2010). Altogether, these results suggest that the model provides a plausible hypothesis for the kind of computations that underlie the use of graphemes in skilled reading.     

Factors affecting perceived safety and enjoyment based on driver experience

A positive driving experience, especially for older drivers, increases the attention to the emotional dimensions of driving, such as the driver’s perceived safety. Therefore, this study empirically presents factors affecting driver experience and compares them between older and younger drivers. Consequently, we conducted a face-to-face survey on elderly and young drivers and analyzed the data of 246 drivers using structural equation modeling. The analysis presented measurements and structural model evaluations. Considering the analysis, it was found that driving-related information and car-exterior context affect the perceived safety and enjoyment of a driver’s experience. Additionally, car-exterior context exerts a greater influence on the perceived safety and enjoyment of the elderly drivers’ group than the young drivers’ group. The results of this study will empirically contribute to the satisfaction of driver experience and perceived safety improvement in the future. It also provides a basis for the development of driving interfaces to improve the quality of the driving experience of the elderly.     

Estimating Age-Based Developmental Trajectories Using Latent Change Score Models Based on Measurement Occasion

Abstract

Accelerated longitudinal designs (ALDs) are designs in which participants from different cohorts provide repeated measures covering a fraction of the time range of the study. ALDs allow researchers to study developmental processes spanning long periods within a relatively shorter time framework. The common trajectory is studied by aggregating the information provided by the different cohorts. Latent change score (LCS) models provide a powerful analytical framework to analyze data from ALDs. With developmental data, LCS models can be specified using measurement occasion as the time metric. This provides a number of benefits, but has an important limitation: It makes it not possible to characterize the longitudinal changes as a function of a developmental process such as age or biological maturation. To overcome this limitation, we propose an extension of an occasion-based LCS model that includes age differences at the first measurement occasion. We conducted a Monte Carlo study and compared the results of including different transformations of the age variable. Our results indicate that some of the proposed transformations resulted in accurate expectations for the studied process across all the ages in the study, and excellent model fit. We discuss these results and provide the R code for our analysis.