Search Results (108)

Examinations are central to higher education, yet students consistently describe them as detrimental to well-being. Drawing on self-determination theory (SDT), we conducted three studies to examine whether multiple-choice examinations could be redesigned to satisfy students’ basic psychological needs (BPNs) and support well-being. In Study 1 (n = 400), we developed and validated the Basic Psychological Need Satisfaction and Frustration Scale for Classroom Assessment (BPNSF-CA). Using bifactor exploratory structural equation modeling (bifactor ESEM), results supported a well-defined single global need fulfillment factor (G-factor) alongside six specific factors (autonomy support/frustration, competence support/frustration, relatedness support/frustration) as well as evidence of validity. In Study 2 (n = 387), we conducted a randomized experiment with three versions of a multiple-choice exam serving as the independent variable (flawed items, high-quality items, and high-quality + need-supportive features). Results showed that high-quality items improved performance, while only the addition of need-supportive features satisfied BPNs with differential patterns for the single G-factor and S-factors. In Study 3 (n = 101), we applied the intervention in a real classroom and tested the mediational role of BPN satisfaction. Results showed that redesigned exams (high-quality + need-supportive features) significantly enhanced perceptions of fairness and success via BPNs. We conclude with a discussion of all three studies, including implications and limitations. Full article

Creeping landslides constitute the predominant form of long-term, slow-moving geohazards in high mountain gorge regions. Under the combined influence of gravity and external triggering factors, these landslides undergo persistent deformation, posing continuous threats to major transportation corridors, hydropower infrastructures, and nearby settlements. Li County is located within the active tectonic belt along the eastern margin of the Tibetan Plateau, characterized by highly variable topography, intensely fractured rock masses, and dense development of creeping landslides. The slip surfaces are typically deeply buried and concealed. Consequently, conventional drilling and profile-based investigations, limited by high costs, sparse sampling points, and poor spatial continuity, are insufficient for identifying the deep-seated structures of such landslides. To address this challenge, this study applies Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) to obtain ascending and descending deformation rate fields for 2022–2024, revealing pronounced spatial heterogeneity and persistent activity across three types of landslides. Based on the principle of mass conservation, the sliding-surface depths of eight typical landslides were inverted, revealing pronounced heterogeneity. The maximum sliding-surface depths range from 32 to 98 m and show strong agreement with borehole and profile data (R² > 0.92; RMSE ±4.96–±16.56 m), confirming the reliability of the inversion method. The GeoDetector model was used to quantitatively evaluate the dominant factors controlling landslide depth. Elevation was identified as the primary control factor, while slope aspect exhibited significant influence in several landslides. All factor combinations showed either “bi-factor enhancement” or “nonlinear enhancement”, indicating that slip-surface depth is governed by synergistic interactions among multiple factors. Boxplot-based statistical analyses further revealed three typical patterns of slip-surface variation with elevation and slope, based on which the landslides were classified into rotational, push-type translational, and traction-type translational categories. By integrating statistical patterns with mechanical models, the study achieves a transition from “form” to “state”, enabling inference of the internal mechanical conditions and evolutionary stages from the observed surface morphology. The results of this study provide an effective technical approach for deep structural detection, identification of controlling factors, and stability evaluation of creeping landslides in high mountain gorge environments. Full article

Background: Gestational guilt is an understudied emotional experience that can affect maternal well-being and prenatal bonding. This study aimed to develop and validate the Pregnancy Guilt Assessment Scale (PGAS) in a sample of Spanish pregnant women, assessing its factorial structure, reliability, and validity. Methods: Four phases were conducted: (1) item generation through focus groups (n = 17) and cognitive interviews (n = 8); (2) expert content validation (n = 3); (3) exploratory factor analysis (EFA) in a pilot sample (n = 85); and (4) confirmatory factor analysis (CFA) and validity testing in an independent sample (n = 171). Additional measures included antenatal depression, prenatal distress, affect, self-esteem, social support, and dispositional guilt. Internal consistency, correlations, and multiple regressions assessed reliability and convergent and incremental validity. Results: The final 16-item PGAS showed a bifactor structure with a general factor and four dimensions: G-LSC (Lack of self-care), G-UEE (Unmet emotional expectations), G-SP (Social pressure), and G-CWR (Conflict with work role). The model demonstrated good fit (χ² = 109.42, df = 88, p = 0.061; CFI = 0.974; TLI = 0.965; RMSEA = 0.069; SRMR = 0.030) and high reliability (α total = 0.96; ω = 0.98; subscales α = 0.90–0.94). PGAS scores correlated positively with dispositional guilt, negative affect, prenatal distress, and antenatal depression. In regressions, G-UEE uniquely predicted depression (β = 0.213, p = 0.002) and G-SP predicted distress (β = 0.303, p < 0.001). Women who had considered pregnancy termination scored higher on guilt (p < 0.001). Conclusions: The PGAS is a reliable and valid instrument for assessing pregnancy-related guilt in Spanish women, with potential relevance for perinatal mental health research and clinical practice, while future studies should evaluate its performance in other cultural settings. Full article

The concept of cost, defined as the perceived negative consequences of engaging in a task, is an important yet understudied component of the expectancy–value theory of student motivation. In this study, we examined the multidimensional cost structure in mathematics learning, focusing on four facets proposed in recent research: effort, opportunity, emotional, and ego costs. Participants consisted of 1483 ninth-grade students from 24 public schools in Lithuania (56.04% girls; M = 14.88 years). Students completed a questionnaire developed to assess the four cost dimensions, along with measures of their intentions to pursue mathematics, procrastination, and negative classroom emotions; academic achievement data were obtained from school records. Using a split-sample design, we used exploratory and confirmatory factor analyses to test alternative structural representations of cost. A comparison of correlated-factor, higher-order, and bifactor models indicated that the correlated-factor ESEM model best fitted the data, supporting the specificity of the four distinct cost dimensions. Specific associations of each cost dimension with study outcomes further supported this differentiation, with ego cost most notably diverging from the other three cost dimensions. The results confirm the distinct nature of the four cost dimensions and underscore the importance of examining their separate roles in students’ mathematics learning, contributing to the existing evidence from a comparatively understudied cultural context. Full article

In psychology, small sample sizes are a frequent challenge—particularly when studying specific expert populations or using complex and cost-intensive methods like human scoring of creative answers—as they reduce statistical power, bias results, and limit generalizability. They also hinder the use of frequentist confirmatory factor analysis (CFA), which depends on larger samples for reliable estimation. Problems such as non-convergence, inadmissible parameters, and poor model fit are more likely. In contrast, Bayesian methods offer a robust alternative, being less sensitive to sample size and allowing the integration of prior knowledge through parameter priors. In the present study, we introduce small-sample-size structural equation modeling to creativity research by investigating the relationship between creative fluency and nested creative cleverness with right-wing authoritarianism, starting with a sample size of N = 198. We compare the stability of results in frequentist and Bayesian SEM while gradually reducing the sample by n = 25. We find that common frequentist fit indexes degrade below N = 100, while Bayesian multivariate Rhat values indicate stable convergence down to N = 50. Standard errors for fluency loadings inflate 40–50% faster in frequentist SEM compared to Bayesian estimation, and regression coefficients linking RWA to cleverness remain significant across all reductions. Based on these findings, we discuss (1) the critical role of Bayesian priors in stabilizing small-sample SEM, (2) the robustness of the RWA-cleverness relationship despite sample constraints, and (3) practical guidelines for minimum sample sizes in bifactor modeling. Full article

In response to the widespread decline in ecosystem service value (ESV) caused by rapid industrialization and urbanization-driven land-use transitions in Coastal China—characterized by shrinking farmland and expanding built-up land and crystallized in the “core-city sprawl and surrounding-farmland encroachment” pattern—this study integrated land-use and socioeconomic data from 1980 to 2020. Employing the equivalent-factor method and Geodetector model, we quantified the spatiotemporal evolution of ESV and its driving mechanisms across the entire coastal region. The results show that (i) the total ESV experienced a fluctuating increase. (ii) Spatially, the ESV exhibited a “high in the south, low in the north, and higher inland than along the immediate coast” pattern, with mountain–hill belts and estuarine wetlands in the south forming high-value clusters, whereas the Bohai Rim in the north emerged as a low-value zone. (iii) Socioeconomic factors increasingly dominated the driving forces, while NDVI became the most influential natural factor; the interactions between the drivers consistently produced bi-factor enhancement effects. These findings provide a scientific basis for implementing the “Two-Mountains Theory” and optimizing coastal territorial spatial planning. Full article

Background: The general psychopathology factor (p factor) is central to understanding the shared variance across mental disorders, offering a dimensional alternative to traditional diagnostic models. The early identification of this factor in childhood is key for improving prevention and intervention strategies. This study evaluated the Strengths and Difficulties Questionnaire (SDQ) as a brief measure to assess p factor in children. Methods: A community sample of 284 children, ages 6 to 12, was assessed using parent-reported SDQ and the Child Behavior Checklist (CBCL). Confirmatory Factor Analyses compared two models of psychopathology: a higher-order model and a first-order bifactor model. Results: Results showed that the bifactor model provided a better fit for both instruments, with the SDQ showing particularly strong fit indices. Moreover, SDQ-derived p factor scores were strongly correlated with key CBCL scales, particularly attention and externalizing problems, supporting its concurrent validity. Conclusions: These findings suggest that the SDQ, due to its brevity and psychometric robustness, is a valid alternative to the CBCL for assessing general psychopathology in children. Full article

Background/Objectives: The execution of clinical trials is increasingly constrained by operational complexity, regulatory requirements, and variability in site performance. These challenges have direct implications for the reliability of trial outcomes. However, standardized methods to evaluate site-level performance remain underdeveloped. This study introduces the Clinical Trial Site Performance Measure (CT-SPM), a novel framework designed to systematically capture site-level operational quality and to provide a scalable short form for routine monitoring. Methods: We conducted a multicenter study across six Italian academic hospitals (January–June 2025). Candidate performance indicators were identified through a systematic review and expert consultation, followed by validation and reduction using advanced statistical approaches, including factor modeling, ROC curve analysis, and nonparametric scaling methods. The CT-SPM was assessed for structural validity, discriminative capacity, and feasibility for use in real-world settings. Results: From 126 potential indicators, 18 were retained and organized into four domains: Participant Retention and Consent, Data Completeness and Timeliness, Adverse Event Reporting, and Protocol Compliance. A bifactor model revealed two higher-order dimensions (participant-facing and data-facing performance), highlighting the multidimensional nature of site operations. A short form comprising four items demonstrated good scalability and sufficient accuracy to identify underperforming sites. Conclusions: The CT-SPM represents an innovative, evidence-based instrument for monitoring trial execution at the site level. By linking methodological rigor with real-world applicability, it offers a practical solution for benchmarking, resource allocation, and regulatory compliance. This approach contributes to advancing clinical research by providing a standardized, data-driven method to evaluate and improve performance across networks. Full article

Nomophobia is a multifaceted phenomenon characterized by fear and anxiety when individuals feel disconnected from their technological environment. Its assessment remains difficult due to limited tools and lack of empirically supported cut-off points. This study aimed to contribute to the Italian validation of the Nomophobia Questionnaire (NMP-Q), testing a four-factor structure and establishing normative data by age and gender. Data were collected from 1447 participants. Exploratory Structural Equation Modeling (ESEM) assessed different factorial configurations. A bifactor ESEM (B-ESEM) with a four-factor solution showed the best fit (CFI = 0.95; TLI = 0.94; RMSEA = 0.06), offering a more accurate representation than the three-factor model. Scores were computed for the 1st and the 99th percentile and for each ventile; the 80th and 95th percentiles indicate risk and presence of nomophobia, respectively. Females scored highest across age groups, while older adults reported the lowest levels. These findings support the NMP-Q’s reliability and use in the Italian context. Full article

An accurate assessment of alcohol consumption is essential for identifying at-risk individuals and informing prevention and intervention strategies. The present study aimed to validate the Italian version of the Alcohol Use Questionnaire (AUQ), a self-report instrument designed to assess both general alcohol intake and binge drinking patterns. A sample of 378 Italian participants (54.5% female; M_age = 26.76 years, SD = 8.44) completed the AUQ along with additional measures assessing binge eating and psychological vulnerabilities related to addiction. Confirmatory factor analysis supported a bifactor model reflecting two distinct but related dimensions: general intake and binge drinking. Network analysis highlighted the central role of perceived frequency of intoxication within the structure of alcohol-related behaviors. Both AUQ indices showed good internal consistency and significant associations with external variables, particularly impulsivity, dissociation, and affect dysregulation, supporting construct validity. The Italian AUQ emerges as a valid and reliable tool for assessing alcohol use patterns and may be useful in both research and clinical practice. Full article

Despite the high similarity of reasoning ability items, research indicates that individuals apply different strategies when solving them. The two distinct strategies are response elimination and constructive matching. The latter, frequently showing a positive correlation with reasoning ability, entails the individual systematically investigating the presented problem matrix of an item before scanning the response alternatives. To further understand the sources of individual differences in strategy use during test taking, three different eye-tracking metrics were investigated in participants (N = 210) solving the Raven’s Advanced Progressive Matrices (APM). Relying on the fixed-links modeling approach, bifactor models were fit to the data. The latent model approach revealed, in line with other research, a positive correlation between reasoning ability and constructive matching. The results further indicated that a change in strategy use was correlated with the item-position effect and not reasoning ability. The former exhibited a different direction of effect, depending on the eye-tracking metric analyzed. When investigating the toggle rate, the participants used more constructive matching towards the end of the APM. The proportional time to first fixation on response alternatives indicated less constructive matching as the test progressed, and the proportional time on the problem matrix exhibited no distinct pattern regarding a change in strategy use. These diverging results point towards the possibility of a more nuanced problem-solving behavior than previously assumed. By including the item-position effect in the analyses, the increasing individuals differences in problem-solving behavior can be taken into account, which could be a necessary step in attaining a more comprehensive understanding of problem-solving behavior. Full article

This study addresses the persistent issue of gender bias in project management by developing and validating a practical survey tool for monitoring gender-related perceptions within project implementation frameworks. Using a Knowledge, Attitudes, and Practices (KAP) approach, a survey instrument was designed to assess awareness of gender equity policies, perceptions of inclusivity, and experiences related to sexual harassment (SASH) within project teams. The tool was piloted in a Horizon Europe project (Healthy Sailing), with responses collected from 66 participants (academics, maritime professionals, researchers, and government stakeholders). Exploratory Factor Analysis (EFA) revealed a five-factor structure explaining 72.29% of total variance, with the two dominant factors—Perceived Gender Bias and Organizational Safety—demonstrating excellent internal consistency (Cronbach’s alpha > 0.90). Confirmatory Factor Analysis (CFA) and bifactor modeling indicated areas for further refinement, with RMSEA values exceeding optimal thresholds. The results underscore the potential of the KAP-based tool to support gender-sensitive quality management practices in project-based environments, while highlighting the need for ongoing psychometric validation. The study contributes a novel, empirically grounded instrument for promoting inclusivity and equity in project management. Full article

The Entrapment Scale was developed to measure the feeling of being trapped by external situations or internal experiences (such as thoughts and emotions) without the possibility of escape. This perception, especially when combined with feelings of defeat, is central to integrated motivational–volitional (IMV) model of suicidal behavior. This study adapts the Entrapment Scale for Spanish adolescents and emerging adults, focusing on its internal structure, reliability, and criterion validity. We assessed 849 participants (48.1% male) aged 12 to 22 and compared three models: a correlated two-factor model, a second-order factor model, and a bifactor model. The bifactor model showed the best fit, indicating that a general entrapment factor influenced all items, while specific internal and external factors captured unique aspects. Importantly, distinguishing between internal and external entrapment can guide the development of more personalized and effective therapeutic strategies, as the relevance of each dimension may vary depending on the behaviors or symptoms present. This approach allows clinicians to target interventions more precisely to the individual’s needs. Theoretical and practical implications for understanding and addressing suicidal behavior are discussed. Full article

Urban morphology’s effects on carbon dioxide reduction and sustainable development have drawn more attention. The county scale is crucial in influencing urban development and is the central element of China’s recent urbanization. To achieve scientific urban planning and fully explore its potential in carbon emission reduction, local governments need to investigate the impact of urban morphology on carbon emissions (CE). However, previous studies have predominantly focused on provincial capitals and urban clusters. To address this gap, this study quantified four aspects of urban form, combined energy consumption, and nighttime light data to estimate CE in Chinese counties from 2000 to 2020 and analyzed the effects of these factors on CE using multiscale geographically weighted Regression(MGWR) models and geographic detectors. The following are the main findings: (1) Total CE at the county scale in China has consistently increased from 2000 to 2020. (2) The largest patch index (LPI) is the most influential urban morphological factor on CE, while the impact of Class Area (CA) has been increasing. (3) Bi-factor enhancement and nonlinear enhancement are the two primary interaction types of urban morphological factors; the most important interaction is between LSI and CA. (4) The urban morphological factors exhibit varying degrees of spatial heterogeneity, with the influencing factors ranked as CA > LPI > path density (PD) > edge density (ED) > patch cohesion index (COHESION), where LPI and CA consistently show a positive effect on CE. This study’s findings establish a scientific foundation for land spatial planning and tailored emission reduction methods at the county scale in China. Full article

Background. Healthy eating habits are essential for preventing chronic diseases and improving quality of life. However, there is a lack of brief and culturally adapted instruments for accurate assessment. Therefore, the aim of this study was to develop and validate the Brief Healthy Eating Habits Scale (BHEHS-6B, Version 1.0), based on Harvard’s Healthy Eating Plate, in a sample of young, middle-aged, and older Peruvian adults. Methods. The study followed a psychometric design. A non-probabilistic sample of 223 participants (both sexes; mean age = 41.6, SD = 15.8) was drawn from Metropolitan Lima, Peru. The BHEHS-6B (Version 1.0) was administered. Results. The bifactor model confirmed the unidimensional structural validity of the BHEHS-6B, showing acceptable global fit indices (CFI = 0.987, TLI = 0.937, SRMR = 0.025, RMSEA = 0.081) and an adequate hierarchical omega for the general factor (G = 0.638), supporting the use of a single total score. Finally, internal consistency was adequate for the total scale (α = 0.769, ω = 0.780). Conclusions. The BHEHS-6B is a valid and reliable instrument for assessing healthy eating habits, demonstrating evidence of strong content validity, internal consistency, and an adequate factor structure. Moreover, as a brief instrument, it is particularly useful for studies aiming to evaluate multiple variables and for the implementation of public health policies focused on improving community health. Full article