Search Results (163)

With the growing accessibility of tools such as online surveys and web scraping, longitudinal social network data are more commonly collected in social science research along with non-network survey data. Such data play a critical role in helping social scientists understand how relationships develop and evolve over time. Existing dynamic network models such as the Stochastic Actor-Oriented Model and the Temporal Exponential Random Graph Model provide frameworks to analyze traits of both the networks and the external non-network covariates. However, research on the dynamic latent space model (DLSM) has focused mainly on factors intrinsic to the networks themselves. Despite some discussion, the role of non-network data such as contextual or behavioral covariates remain a topic to be further explored in the context of DLSMs. In this study, one application of the DLSM to incorporate dynamic non-network covariates collected alongside friendship networks using autoregressive processes is presented. By analyzing two friendship network datasets with different time points and psychological covariates, it is shown how external factors can contribute to a deeper understanding of social interaction dynamics over time. Full article

We investigate whether representation-level, semantic diagnostics expose structure in electroencephalography (EEG) beyond conventional accuracy when contrasting perception and imagination and relating outcomes to self-reported imagery ability. Using a task-independent encoder that preserves scalp topology and temporal dependencies, we learn semantic features from multi-subject, multi-modal EEG (pictorial, orthographic, auditory) and evaluate subject-independent decoding with lightweight heads, achieving state-of-the-art or better accuracy with low variance across subjects. To probe the latent space directly, we introduce threshold-resolved correlation pruning and derive the Semantic Sensitivity Index (SSI) and cross-modal overlap (CMO). While correlations between Vividness of Visual Imagery Questionnaire (VVIQ)/Bucknell Auditory Imagery Scale (BAIS) and leave-one-subject-out (LOSO) accuracy are small and imprecise at n = 12, the semantic diagnostics reveal interpretable geometry: for several subjects, imagination retains a more compact, non-redundant latent subset than perception (positive SSI), and a substantial cross-modal core emerges (CMO ≈ 0.5–0.8). These effects suggest that accuracy alone under-reports cognitive organization in the learned space and that semantic compactness and redundancy patterns capture person-specific phase preferences. Given the small cohort and the subjectivity of questionnaires, the findings argue for semantic, representation-aware evaluation as a necessary complement to accuracy in EEG-based decoding and trait linkage. Full article

Background: An increasing number of university students report feeling lonely, a negative experience arising from a mismatch between perceived and actual social relationships. Loneliness has been linked to poorer mental health. However, the relationship between qualitative (sources of loneliness) and quantitative (high or low) differences in loneliness and mental health is under researched. The aims of this research were to (a) identify profiles of loneliness among university students across three indicators of loneliness, namely, social, family, and romantic indicators, using latent profile analysis (LPA); (b) examine the differences among identified profiles based on dimensions of mental health indicators (depression, anxiety, and stress), social support, and life satisfaction; and (c) assess profile membership based on demographic variables (gender, social isolation, relationship status, and education characteristics) and the Big Five personality traits (extraversion, agreeableness, openness, conscientiousness, and neuroticism). Method: A cross-sectional survey was conducted on 912 university students from five cities in Uttar Pradesh, India. Participants completed questionnaires covering demographic details and validated measures assessing loneliness, depression, stress, anxiety, social support, life satisfaction, and the Big Five personality traits. Data were analyzed using the latent profile module in Jamovi and fit indices, namely, BIC, AIC, and BLRT, and entropy was used to select the best profile. Results: The latent profile analysis identified four profiles for university student loneliness, including Social and emotional lonely (31.4%), Moderate romantic lonely (23.8%), Moderate social lonely (8.2%), and Severe romantic lonely (36.6%). Moreover, the Social and emotional lonely profile scored the highest on depression, anxiety, and stress. The Moderate romantic lonely profile scored the highest on life satisfaction and social support. Being in a relationship decreased the likelihood of being categorized as Severe romantic lonely. In terms of personality, neuroticism was the strongest predictor of profile membership. This study is a step towards identifying at-risk lonely individuals with varying sources of loneliness. Identifying different profiles of lonely individuals will have direct implications for designing interventions that cater to a particular group rather than a one-size-fits-all approach. Full article

Background/Objectives: Behavioral inhibition (BI) has been extensively studied as an early-appearing risk factor for adverse developmental outcomes. One pathway through which BI may confer risk is via reduced competence to interact effectively with peers. Research demonstrating concurrent relations between BI and social competence supports this pathway, yet not all inhibited children experience social difficulties. This study adopted a person-centered approach to examine heterogeneity of temperament traits within a highly inhibited preschool sample and to identify how broader temperament traits contribute to variability in social functioning. Methods: Parents of preschoolers (N = 254) who met criteria for BI (≥85th percentile on the Behavioral Inhibition Questionnaire) completed measures of their child’s temperament (Children’s Behavior Questionnaire) and social competence (Social Skills Improvement System). Latent Profile Analysis was conducted using six temperament traits reflecting regulation and reactivity (anger, attentional focusing, inhibitory control, high-intensity pleasure, perceptual sensitivity, and approach). Profile differences in social competence were examined using multivariate analyses controlling for age and gender. Results: A three-profile solution emerged: Regulated, Unregulated and Angry, and Typical BI. Profile membership accounted for almost 37% of the variance in social skills scores. The Regulated group, marked by high attentional and inhibitory control and low anger, demonstrated the strongest social skills and lowest internalizing and externalizing problems. The Unregulated and Angry group, characterized by high anger and poor regulation, exhibited the greatest social difficulties. BI level itself did not significantly differentiate profiles or predict social competence. Conclusions: Findings underscore that BI is not a uniform risk factor but joins with other temperamental traits to shape social outcomes. Level of BI did not differentiate profiles or relate to social functioning, highlighting the importance of considering co-occurring regulatory and reactive traits to explain variability in outcomes among inhibited children. Identifying specific temperamental constellations may enhance early identification and inform targeted interventions for socially at-risk inhibited children. Full article

Relational aggression (RA) is characterised by social manipulation and covert harm, often involving fluid and overlapping experiences of both perpetration and victimisation. We used latent profile analysis (LPA) to identify subgroups of young Australian adults based on their self-reported experiences of RA and explore whether these RA typologies are associated with broader aggressive traits and behaviours. We used a community sample of Australian adults aged 18–25 (N = 206, Mean age = 21.8, SD = 2.24, 77% female). Three distinct profiles emerged: predominantly victimised, combined victims–perpetrators (enmeshed), and the uninvolved. We observed strong indications that the experience of RA, even when predominantly as victimisation, was associated with increased odds of experiencing and perpetrating any aggression or violent behaviour compared to the uninvolved (OR = 5.17, [1.42–18.87] and OR = 3.21 [1.09–9.63] for the enmeshed and victimised classes, respectively, perpetrating any violent act). Conclusion: These results suggest the bidirectional nature of RA extends into young adulthood, and that distinct RA profiles exhibit differing patterns of broader aggressive behaviour. This study highlights that any approaches to further investigating or intervening with RA require consideration of the bidirectional nature of RA between perpetration and victimisation. Full article

Identifying item–trait relationships is a core task in multidimensional item response theory (MIRT). Common empirical approaches include exploratory item factor analysis (EIFA) with rotations, the expectation maximization-based $L_1$ regularization (EML1) algorithm, and the expectation model selection (EMS) algorithm. While these methods typically assume multivariate normality of latent traits, empirical data often deviate from this assumption. This study evaluates the robustness of EIFA, EML1, and EMS, when latent traits violate normality assumptions. Using the independent generator transform, we generate latent variables under varying levels of skewness, excess kurtosis, numbers of non-normal dimensions, and inter-factor correlations. We then assess the performance of each method in terms of the F1-score for identifying item–trait relationships and mean squared error (MSE) of parameter estimations. The results indicate that non-normality leads to a reduction in F1-score and an increase in MSE generally. For F1-score, EMS performs best with small samples (e.g., $N=500$), whereas EIFA with rotations yields the highest F1-score in larger samples. In terms of estimation accuracy, EMS and EML1 generally yield lower MSEs than EIFA. The effects of non-normality are also demonstrated by applying these methods to a real data set from the Depression, Anxiety, and Stress Scale. Full article

In recent years, network psychometrics has emerged as an alternative to the reflective latent variable model. This model conceptualizes traits as complex systems of behaviors mutually interacting with each other. Although this model offers important advantages compared to the reflective model, questions remain regarding the necessary sample size and the influence of factors such as the number of nodes and edges. This study aims to evaluate the psychometric network model performance under different conditions of sample size, number of nodes, and number of edges. The methodology involved a simulation with 1000 replicates for each combination of sample size, number of nodes, and the value of gamma parameter, which is used to determine the magnitude of the edges considered significant. The effect of these conditions on the accuracy of edge estimations and centrality indices (strength and expected influence) was assessed using sensitivity, specificity, and bias indicators. Results suggest that sample size and network complexity have a more significant impact than γ, methodological guidelines being proposed to support decision-making in applied research. In summary, this study provides empirically grounded recommendations that can guide applied researchers in designing robust psychometric network analyses and ensuring reliable estimation of model parameters. Full article

Background: Endocrine autoimmune diseases (AIDs) exhibit special polygenic characteristics in human leucocyte antigen (HLA) region. Current understanding of their association with lipid metabolism remains constrained by imprecise polygenic risk score (PRS) modeling. Advanced analytical approaches are needed to elucidate the association between genetic susceptibility and lipid metabolic dysregulation. Methods: We proposed a genetic distance-based clumping gPRS to account for linkage disequilibrium in the HLA region. gPRS and pathway gPRS were constructed for individuals diagnosed with type I diabetes (T1D), Graves’ disease (GD), Hashimoto thyroiditis (HT) and Addison’s disease (AD) in the UK Biobank, with sex considered as a stratification factor. Latent correlations between gPRS and phenotypes were explored using Kendall’s tau test, two-trait LD score regression (LDSC) and gene annotation. Results: Lipid metabolism served an important function through immune and inflammatory biomarkers across multiple traits. Males with low genetic risk tended to have lower high-density lipoprotein cholesterol level, while the correlation presented the opposite pattern in females. Increased genetic susceptibility to AIDs was associated with elevated levels of low-density lipoprotein cholesterol, triglycerides in low-density lipoprotein (LDL) and very-low-density lipoprotein (VLDL) across all traits. Moreover, levels of polyunsaturated fatty acids, including omega-3 and omega-6, decreased with higher PRS in males and females, while those of monounsaturated fatty acids exhibited an increasing trend. Conclusion: Our study constructed more precise polygenic risk scores of AIDs, highlighting inflammation-mediated lipid metabolism as a potential pathogenic mechanism in endocrine AIDs, offering valuable insights into shared etiology for future comprehensive investigations. Full article

Androgyny, characterized by high levels of both masculinity and femininity traits, is linked to adaptive psychological outcomes. However, existing research has typically examined these traits at the latent variable level, obscuring the specific trait facets that are central to androgynous development. Using network analysis, this study investigated the androgynous structure network at the level of trait facets to identify the most influential facets and explore sex-specific structures. A convenience sample of 1270 Chinese adolescents (M_age = 15.41, SD = 0.88; 611 females) completed the validated Chinese Sex-Role Inventory, which measures 32 facets of masculinity and femininity traits. In the full sample, “calm” exhibited the highest expected influence (EI = 1.11). Crucially, the masculinity facet “magnanimous” was the most powerful bridge to the femininity network (bridge EI = 1.56), particularly for males (bridge EI = 1.18); the femininity facet “thoughtful” (bridge EI = 0.97) was the most powerful bridge to the masculinity network, especially for females (bridge EI = 0.86). Significant sex differences were observed in global EI, with females showing greater global network activation (p = 0.008). The sex difference was additionally evident in “thoughtful” (male < female, p = 0.022) and “magnanimous” (male > female, p = 0.043). Such findings highlight the pivotal roles of “magnanimous” for males and “thoughtful” for females in fostering androgyny. The study advances the understanding of androgyny by delineating its facet-level structure and underscores the value of sex-specific strategies in fostering balanced gender-typed trait development. The convenience sample may limit the generalizability of these findings. Full article

Cognitive diagnosis models (CDMs) assess the proficiency of examinees in specific skills. Online education has increased the amount of data that is available on the response behaviour of examinees. Traditional CDMs determine the state of skills by modelling information on item response results and ignoring vital response time information. In this study, a CDM, named RT-CDM, which models the condition dependence between response time and response accuracy on the speed-accuracy exchange criterion, is proposed. The model’s continuous latent trait function and response time function, used for more precise cognitive analyses, makes it a tractable, interpretable skill diagnosis model. The Markov chain Monte Carlo algorithm is used to estimate the parameters of the RT-CDM. We evaluate RT-CDM through controlled simulations and three real datasets—PISA 2015 computer-based mathematics, EdNet-KT1, and MATH—against multiple baselines, including classical CDMs (e.g., DINA/IRT), RT-extended IRT and joint models (e.g., 4P-IRT, JRT-DINA), and neural CDMs (e.g., NCD, ICD, MFNCD). Across datasets, RT-CDM consistently achieves superior predictive performance, demonstrates stable parameter recovery in simulations, and delivers stronger diagnostic interpretability by leveraging RT alongside RA. Full article

Plant functional traits (PFTs) serve as key predictors of plant survival and adaptation to environmental gradients. Studies on intraspecific variation in PFTs are crucial for evaluating species’ adaptation to projected climate change and developing long-term conservation strategies. This study systematically investigated PFT responses in Polyspora chrysandra (Theaceae, Yunnan, China) through an integrated multivariate analysis of 20 leaf functional traits (LFTs) and 33 environmental factors categorized into geographical conditions (GCs), climate factors (CFs), soil properties (SPs), and ultraviolet radiation factors (UVRFs). To disentangle complex environmental–trait relationships, we employed redundancy analysis (RDA), hierarchical partitioning (HP), and partial least squares structural equation modeling (PLS-SEM) to assess direct, indirect, and latent relationships. Results showed that the intraspecific coefficient of variation (CV) ranged from 7.071% to 25.650%. Leaf tissue density (LTD), specific leaf area (SLA), leaf fresh weight (LFW), leaf dry weight (LDW), and leaf area (LA) exhibited moderate intraspecific trait variation (ITV), while all other traits demonstrated low ITV. Reference Bulk density (RBD) and Silt emerged as significant factors driving the variation. Latitude (Lat), altitude (Alt), and mean warmest month temperature (MWMT) were also identified as key influences. HP analysis revealed Silt as the most important predictor (p < 0.05). Latent variable analysis indicated descending contribution rates: SPs (31.51%) > GCs (11.52%) > CFs (11.04%) > UVRFs (10.29%). Co-effect analysis highlighted significant coupling effects involving RBD and cation exchange capacity of clay (CECC), as well as organic carbon content (OCC) and UV-B seasonality (UVB2). Path analysis showed SPs as having the strongest influence on leaf thickness (LT), followed by GCs and UVRFs. These findings provide empirical insights into the biogeographical patterns of ITV in P. chrysandra, enhance the understanding of plant environmental adaptation mechanisms, and offer a theoretical foundation for studying community assembly and ecosystem function maintenance. Full article

Fine-grained modeling of driving styles is critical for decision making in autonomous driving. However, existing methods are constrained by the high cost of manual labeling and a lack of interpretability. This study proposes an unsupervised disentanglement framework based on a variational autoencoder (VAE), which, for the first time, enables the automatic extraction of interpretable driving style representations from car-following trajectories. The key innovations of this work are threefold: (1) a dual-decoder VAE architecture is designed, leveraging driver identity as a proxy label to guide the learning of the latent space; (2) self-dynamics and interaction dynamics features are decoupled, with an attention mechanism employed to quantify the influence of the lead vehicle; (3) a bidirectional interpretability verification framework is established between latent variables and trajectory behaviors. Evaluated on a car-following dataset comprising 25 drivers, the model achieves a Driver Identification accuracy of 98.88%. Mutual information analysis reveals the physical semantics encoded in major latent dimensions. For instance, latent dimension z₂₂ is strongly correlated with the minimum following distance and car-following efficiency. One-dimensional latent traversal further confirms that individual dimensions modulate specific behavioral traits: increasing z₂₂ improves safety margins by 18% but reduces efficiency by 23%, demonstrating that it encodes a trade-off between safety and efficiency. This work provides a controllable representation framework for driving style transfer in autonomous systems and offers a more granular approach for analyzing driver behavior in car-following scenarios, with potential for extension to broader driving contexts. Full article

Machiavellianism and borderline personality are known for influencing interpersonal dynamics through manipulative behaviors. Machiavellianism is characterized by calculated, egotistic, and callous manipulation, while borderline personality involves emotionally driven impulsive manipulation due to instability and fear of abandonment. In this study, we explored the relationships of the two constructs with respect to broader personality constructs. Adult participants (N = 1011; Mage = 49.08 years, SD = 17.15) completed two measures each for Machiavellianism and borderline personality and a single inventory measuring the Big Five personality traits. Latent Profile Analysis (LPA) was used to investigate subgroups within the data. Machiavellianism was more strongly negatively associated with agreeableness and conscientiousness, while borderline personality traits were more strongly linked to neuroticism (more positively), agreeableness, and conscientiousness (both more negatively). Two distinct latent profiles emerged. Based on these findings, we suggest that Machiavellianism can align with either adaptive or maladaptive functioning, whereas a combination of Machiavellianism and borderline personality traits underscores a tendency towards manipulative behaviors with emotional instability. We suggest that future research build upon our findings by investigating concrete manipulative acts predicted by borderline personality and Machiavellianism. Full article

Background: This study explores post-traumatic growth (PTG) among parents of childhood cancer survivors (CCSs), a group often underrepresented in research. Method: A convergent parallel mixed-methods design integrating Bayesian Multilevel Latent Class Analysis and Thematic Analysis was utilized in a longitudinal study involving 58 caregivers (50 mothers, 8 fathers) from the Children’s Clinical University Hospital in Riga. Quantitative data were collected at diagnosis using the Psychosocial Assessment Tool (PAT) and Big Five Inventory-10 (BFI-10). Follow-up assessments post-treatment included the Responses to Stress Questionnaire (RSQ), Impact of Event Scale-Revised (IES-R), and the Post-traumatic Growth Inventory (PTGI). Qualitative data were collected through structured interviews. Results: A 2-class model distinguished parents with low PTG from those with moderate to high PTG. Change in values, detachment from trivial stressors, and acceptance of life emerged as key indicators of growth. PTG was not significantly correlated with overall post-traumatic stress symptoms, but engagement coping strategies showed a positive association with PTG and personality traits like extraversion and openness. Conclusions: The mixed methods approach revealed sample-specific PTG elements not reflected in standardized tools. Initial perceptions of the cancer diagnosis shaped psychological outcomes, with PTG facilitated by adaptive coping, self-reflection, support, emotional disclosure, and psychological struggle. This study offers the first insights into PTG among Latvian parents of CCSs, a previously unexplored area. Full article

Background/Objectives: Executive function (EF) difficulties are increasingly recognized as closely linked to ADHD, particularly when assessed via rating scales. Methods: The present study investigated the nature of these associations, using the Conners 3 Rating Scales to assess ADHD symptoms and the Executive Function Questionnaire (EFQU) to assess EF impairments, in a sample of 1068 children (40.8% males, 38.8% females) aged 7–14 years (M = 10.7, SD = 1.74). Results: Both parent and teacher ratings revealed strong correlations, particularly between inattentive symptoms and EF difficulties, across multiple executive domains. To examine whether these associations stemmed from construct or phrasing overlap, exploratory and confirmatory factor analyses were conducted. The results demonstrate that the Conners 3 and the EFQU capture distinct latent dimensions of functioning, with virtually no overlap in item content. Conclusions: The strength and consistency of the associations between these latent factors support the interpretation that, although conceptually distinct, ADHD symptoms and EF impairments are empirically intertwined in everyday functioning, as consistently reported by both parents and teachers. Interestingly, teachers provided more integrated views of behavior, while parents tended to distinguish ADHD and EF traits more clearly. These findings underscore the importance of multi-informant assessment and contextual variability in understanding children’s functioning. Full article