The Impact of Psychological Distance on Judging Satisfaction with Nuclear Energy Policy via Knowledge Calibration and an Integrated Causal Path Model

This study investigated how, through knowledge calibration and a causal path model, psychological distance can explain the level of satisfaction with nuclear energy policy. The investigation used multiple regression analysis and path analysis to explore relationships among variables. Data from 1056 adults revealed that more knowledge-calibrated individuals have more positive attitudes toward nuclear energy policy. In addition, the psychological distance influences policy satisfaction by mediation of perceived risk of nuclear energy. This study aimed to increase the understanding of the dynamic of satisfaction with and acceptance of nuclear energy policy among stakeholders. Thus, based on the construal level theory, the study addressed the importance of knowledge and psychological distance in explaining variation in satisfaction and acceptance about nuclear policy.

Key factors influencing earthquake-induced liquefaction and their direct and mediation effects

Many factors impact earthquake-induced liquefaction, and there are complex interactions between them. Therefore, rationally identifying the key factors and clarifying their direct and indirect effects on liquefaction help to reduce the complexity of the predictive model and improve its predictive performance. This information can also help researchers understand the liquefaction phenomenon more clearly. In this paper, based on a shear wave velocity (Vs) database, 12 key factors are quantitatively identified using a correlation analysis and the maximum information coefficient (MIC) method. Subsequently, the regression method combined with the MIC method is used to construct a multiple causal path model without any assumptions based on the key factors for clarifying their direct and mediation effects on liquefaction. The results show that earthquake parameters produce more important influences on the occurrence of liquefaction than soil properties and site conditions, whereas deposit type, soil type, and deposit age produce relatively small impacts on liquefaction. In the multiple causal path model, the influence path of each factor on liquefaction becomes very clear. Among the key factors, in addition to the duration of the earthquake and Vs, other factors possess multiple mediation paths that affect liquefaction; the thickness of the critical layer and thickness of the unsaturated zone between the groundwater table and capping layer are two indirect-only mediators, and the fines content and thickness of the impermeable capping layer induce suppressive effects on liquefaction. In addition, the constructed causal model can provide a logistic regression model and a structure of the Bayesian network for predicting liquefaction. Five-fold cross-validation is used to compare and verify their predictive performances.

The Impact of Corporate Social Responsibility on Organisational Citizenship Behaviour, Work Engagement, and Job Embeddedness

This study examines the impact of corporate social responsibility on organisational citizenship behaviour, work engagement, and job embeddedness. Structural equation modeling tests were conducted on 522 responses gathered from telecommunications companies in the Kurdistan Region of Iraq. The results depicted that corporate social responsibility improvements have positive effects on organisational citizenship behaviour, work engagement, and job embeddedness. Further observations depicted an insignificant positive partial causal path between corporate social responsibility, work engagement, and organizational citizenship behaviour. This study's novelty elements are inherent in its potency to examine the causal path between corporate social responsibility, work engagement, and organizational citizenship behavior. This study contributes to the literature by further expanding job embeddedness theory and proposing a comprehensive job embeddedness framework that researchers and practitioners can adopt in future research.

A Comparative Study of Variables Related to Students’ Success in the 2015 International TIMSS Test

Objective: This study aimed to investigate the factors affecting students' academic achievement based on Thames international test data. Methods: The method of this study is quantitative comparative. The statistical sample of this study consists of eighth-grade students from 57 countries who participated in the 2015 Thames International Test. The data set was analyzed using the fuzzy logic approach. Results: The necessary and sufficient individual conditions showed that the conditions of the relationship between home and school, school social atmosphere, students' attitudes, and educational activities in the classroom are each a necessary condition, and family background is a sufficient condition for achievement (academic achievement). Conclusion: In the causal and combined causes, among the many causal paths, only one causal path based on theoretical and experimental adequacy criteria (coverage and adaptation coefficient) was of theoretical and experimental importance was necessary. In this causal path, family background and the relationship between home and school in combination provided a sufficient turning point in the occurrence of the desired result (academic success).

Statistical Control Requires Causal Justification

It is common practice in psychological research to use statistical control to remove the effect of third variables in correlational or quasi-experimental studies. Controlling for relevant confounders can improve estimates of the causal path from a predictor to the outcome, but this only works under ideal circumstances. When the selected control variables are inappropriate, controlling can result in regression coefficient estimates that are further from the causal path than uncontrolled estimates. Despite the ubiquity of control variables in published regression analyses and the consequences of controlling for inappropriate third variables, the selection of control variables is rarely well justified. We argue that to carefully select appropriate control variables researchers must propose and defend a causal structure that includes the outcome, predictors, and plausible confounders. Throughout, we underscore the importance of causality when selecting control variables by demonstrating via simulation the consequence of controlling for inappropriate variables. Finally, we provide practical recommendations for applied researchers who wish to use statistical control.

Issues and Opportunities in Objective Numeracy Research

This chapter, “Issues and Opportunities in Objective Numeracy Research,” discusses three cross-cutting questions in objective numeracy research. The first two issues concern the correlational nature of most objective numeracy research. Alternative explanations exist for the effects of numeracy on decisions and life outcomes. In particular, this chapter questions whether general intelligence can explain objective numeracy effects and whether the reverse causal path may offer a better explanation. Objective numeracy generally emerges as the better explanation, but alternative explanations remain of some effects (e.g., worse health sometimes may produce lower numeracy). The third issue concerns researchers’ experimental design decisions and what these results might teach us about how to improve numeric reasoning in decisions.