Search Results (53)

Technology-based strategies for the prevention and management of suicidal behavior are widely referenced for identifying vulnerable groups and for supporting clinical reasoning, decision-making, and appropriate referrals. In this study, we estimated the interface and content validity evidence of an interactive mobile application developed for managing suicidal behavior. This methodological study employed psychometric parameters to evaluate the content and interface of the mobile application, following five action phases: analysis, design, development, implementation, and evaluation. A total of 27 healthcare professionals participated, selected by convenience sampling, all working within the Psychosocial Care Network across different regions of Brazil. Data were collected using an electronic form, the Delphi technique for evaluation rounds, and a Likert scale to achieve consensus. The validity analysis was based on a Content Validity Index (CVI) equal to or greater than 0.80. The results showed that GerenciaVida, a technology developed for healthcare workers—regardless of their level of care or professional category—can be used to screen for suicide risk in the general population and indicate preventive alternatives. The app demonstrated satisfactory indicators of content validity (0.974) and interface validity (0.963), reflecting clarity (0.925), objectivity (1.00), adequacy (0.925), coherence (0.962), accuracy (0.962), and clinical relevance (1.00). The development path of this mobile application provided scientific, technological, and operational support, establishing it as an innovative care tool. It consolidates valid evidence that supports the identification, risk classification, and prevention of suicidal behavior in various healthcare contexts. Full article

Social desirability is a widely studied phenomenon due to its impact on the validity of self-reported data. It refers to the tendency of individuals to respond to questions in a manner that they believe is socially acceptable or favorable rather than providing truthful or accurate answers. This study evaluated the psychometric properties of the Brief Social Desirability Scale (BSSD-4) in Ecuadorian youth, analyzing its reliability, factorial and convergent validity, and measurement invariance by sex, age group, and experiences of dating violence. An instrumental study was conducted with a non-probabilistic convenience sample of 836 participants (aged 14–26). Reliability was adequate (Ω = 0.75, α = 0.81, CR = 0.759). Confirmatory factor analysis showed good fit indices (CFI = 0.98, TLI = 0.97, RMSEA = 0.056, SRMR = 0.037). Convergent validity was acceptable (AVE = 0.50, VIF < 2.01). A network analysis confirmed the unidimensionality of the scale and structural differences between groups. Measurement invariance by sex and age was verified, but differences in the network structure were found based on victimization and perpetration of violence. The BSSD-4 is a valid and reliable instrument for assessing social desirability in Ecuadorian youth, useful for population studies and intergroup comparisons. Further research is recommended to explore its invariance in populations with a history of violence, as differences in scalar invariance were observed. Full article

Hispanic youth have one of the highest rates of unmet physical and mental health needs. This study aims to examine how child and adolescent healthcare access creates pathways to behavioral/mental health among a national sample of 1711 U.S. Hispanic youth. Using psychometric network analysis, unique pathways in which child healthcare access (i.e., transportation and health service-related factors) and behavioral/mental health were identified. Findings indicate relationships among depression, anxiety, school settings, and friendships. These associations offer a starting point for interventionists and policymakers to ensure that interventions are not targeted individually but from an ecological systems framework. This study may raise awareness of Hispanic youth’s barriers and better equip scientists to plan and implement approaches to address identified barriers. Full article

In recent years, the role of flourishing in mental health has gained growing recognition, making the establishment of psychometrically and culturally validated instruments crucial for advancing theory and practice. The aim of this study is twofold: first, to adapt the Multidimensional Flourishing Scale for use in Turkey and investigate its psychometric properties; second, to explore the interrelationships between indicators of flourishing and psychological distress symptoms using network analysis. A total of 529 undergraduate students, comprising 316 females (59.7%) and 213 males (40.3%), with a mean age of 21.65 years (SD = 1.67), participated in the study. The Multidimensional Flourishing Scale, the Flourishing Scale, and the Kessler Distress Scale (K10) were utilized for data collection. Confirmatory factor analysis, Pearson correlation analysis, and the EBICglasso algorithm for network analyses were performed. The findings revealed that the Turkish version of the Multidimensional Flourishing Scale demonstrated a three-dimensional structure consistent with its original version, with satisfactory psychometric properties, including structural and convergent validity as well as reliability. Domain-level network analysis demonstrated that psychological well-being emerged as the most central node within the network, closely followed by psychological distress. Additionally, psychological distress was negatively associated with all domains of flourishing. At the item level, two symptoms of psychological distress, feeling depressed and feeling restless or fidgety, as well as three indicators of flourishing, feeling positive, feeling happy, and perceiving life as full of meaning, emerged as the most central nodes. These findings provide valuable insights into the central features of flourishing and psychological distress, which could potentially guide clinical practice. Further discussion and implications are elaborated upon in the study. Full article

Understanding a learner’s resources as a system of interacting components, the success of a learning process is determined by the effectiveness of their interactions. Theoretical assumptions and empirical findings clearly show the importance of resource availability in learning systems but do not sufficiently consider the individuality or the temporal and situational aspects of resource regulation. Therefore, the current study addresses the complex interplay between learning resources (educational and learning capitals) in an individual learner (N = 1) by utilizing multivariate time series data of a 50-day vocabulary learning process with daily assessments of learning resource availability, performance, learning duration, and stress. We draw on methods of psychometric network analysis, modeling all variables in simultaneous interaction and allowing predictions between all variables from measuring point to measuring point (temporal dynamics). Specifically, using a Graphical Vector Autoregressive (graphicalVAR) model, yielding a contemporaneous and a temporal dynamics network model, we identified pivotal resources in regulating the student’s learning processes and outcomes, including resources with strong connections to other variables, intermediary resources, and resources maintaining the system’s homeostasis. This innovative approach has possible applications as a diagnostic tool that lays the foundation for tailored interventions. Full article

Dealing with mathematics can induce significant anxiety, affecting academic performance: this phenomenon is known as Math Anxiety (MA). While math anxiety scales were mostly developed in English, some have been translated and validated for Italian populations (e.g., the Abbreviated Math Anxiety Scale). This study translated the 3-factor MAS-UK scale into Italian, producing a new tool, MAS-IT, which was validated in a sample of 324 Italian psychology undergraduates. Confirmatory Factor Analysis (CFA) tested the original MAS-UK 3-factor model and revealed that it did not fit the MAS-IT data. A subsequent Exploratory Graph Analysis (EGA) identified four distinct factors of math anxiety in MAS-IT. The “Passive Observation MA” factor remained stable across the analyses, whereas the “Evaluation MA” and “Everyday/Social MA” items showed poor stability. These quantitative findings suggest potential cultural or contextual differences in the expression of math anxiety among today’s psychology undergraduates, highlighting the need for more appropriate assessment tools tailored to this population. Full article

Psychometric network analysis (PNA) has been gaining great popularity over the past decade. As a promising dimensionality assessment method, existing research has shown that PNA is able to outperform traditional methods such as exploratory factor analysis in examining the internal structure of a latent construct, and various R packages have been developed to carry out PNA. Yet, PNA has not been widely used in various fields due to researchers’ lack of familiarization with this method and the available R packages. Therefore, this study aims to briefly review the PNA method, compare different R packages, and provide step-by-step guidance on how to use these R packages to conduct PNA using a personality dataset. Full article

This study proposes a multimodal deep learning model for automated scoring of image-based divergent thinking tests, integrating visual and semantic features to improve assessment objectivity and efficiency. Utilizing 708 Chinese high school students’ responses from validated tests, we developed a system combining pretrained ResNet50 (image features) and GloVe (text embeddings), fused through a fully connected neural network with MSE loss and Adam optimization. The training set (603 images, triple-rated consensus scores) showed strong alignment with human scores (Pearson r = 0.810). Validation on 100 images demonstrated generalization capacity (r = 0.561), while participant-level analysis achieved 0.602 correlation with total human scores. Results indicate multimodal integration effectively captures divergent thinking dimensions, enabling simultaneous evaluation of novelty, fluency, and flexibility. This approach reduces manual scoring subjectivity, streamlines assessment processes, and maintains cost-effectiveness while preserving psychometric rigor. The findings advance automated cognitive evaluation methodologies by demonstrating the complementary value of visual-textual feature fusion in creativity assessment. Full article

Latent variables play a crucial role in psychometric research, yet traditional models often struggle to address context-dependent effects, ambivalent states, and non-commutative measurement processes. This study proposes a quantum-inspired framework for latent variable modeling that employs Hilbert space representations, allowing questionnaire items to be treated as pure or mixed quantum states. By integrating concepts such as superposition, interference, and non-commutative probabilities, the framework captures cognitive and behavioral phenomena that extend beyond the capabilities of classical methods. To illustrate its potential, we introduce quantum-specific metrics—fidelity, overlap, and von Neumann entropy—as complements to correlation-based measures. We also outline a machine-learning pipeline using complex and real-valued neural networks to handle amplitude and phase information. Results highlight the capacity of quantum-inspired models to reveal order effects, ambivalent responses, and multimodal distributions that remain elusive in standard psychometric approaches. This framework broadens the multivariate analysis theoretical and methodological toolkit, offering a dynamic and context-sensitive perspective on latent constructs while inviting further empirical validation in diverse research settings. Full article

Adolescence is a period marked by challenges, including problems that appear in the adolescent’s context. To manage these, adolescents use a series of emotional regulation skills that can be more or less adaptive. Less adaptive regulation is related to problem behaviors such as alcohol abuse, substance addiction, problematic internet use, and/or suicidal behavior. This study employs psychometric networks to analyze the association between these problem behaviors, the existence of contextual problems, and the use of cognitive emotional regulation strategies. We performed this analysis for the total sample: the male sample and the female sample. The total sample consists of 758 participants; 424 females (55.4%) and 341 males (44.6%) between the ages of 12 and 21 years (M age = 15.85; SD = 2311). The results show that less adaptive regulation strategies are the most central node, exhibiting a positive relationship with problem behaviors and contextual problems. In contrast, adaptive regulation strategies are a less influential node. Finally, problem behaviors are related to each other. Differences emerged between the male sample and the female sample. These findings contribute to improving our understanding of the phenomenon as well as to the construction of preventive interventions. Full article

The objective of the present study was to analyze the psychometric properties of a life satisfaction scale in older Peruvian adults using an exploratory graph analysis (EGA) approach. A total of 407 older adults aged between 60 and 95 years (M = 69.5; SD = 6.7) from three comprehensive elderly care centers (CIAMs) in Lima, Peru, were recruited. A non-probabilistic convenience sampling was used. The Satisfaction with Life Scale (SWLS) was analyzed using EGA with the Gaussian GLASSO model to assess its dimensionality and structural consistency. The relationship with other variables was analyzed using scales such as the GAD-7 and PHQ-9. The network structure of the SWLS indicates a single dimension. Additionally, network loadings (nodes) were examined, showing high values (>0.35) for most items except item 1, which had a moderate loading (>0.25). Structural reliability showed that a single dimension was identified 100% of the time. The post hoc CFA considering the unidimensional network structure obtained through EGA showed satisfactory fit (χ2/df = 3.48, CFI = 0.96, TLI = 0.92, SRMR = 0.02, RMSEA = 0.07 [90% CI 0.05, 0.08]). Finally, internal consistency reliability was acceptable (ω = 0.92). The SWLS measure is robust and consistent. These findings are a valuable reference for advancing research on aging in Peru, as they provide a practical, valid, and reliable measure. Full article

Public health emergencies such as the COVID-19 pandemic can further strain the mental health of informal caregivers who provide unpaid assistance to family members or friends who need support due to illness, disability, or aging. However, there is a lack of research exploring the resources and adaptive strategies that promote resilience in informal caregivers. This cross-sectional study used psychometric network analysis to model the interplay between coping strategies, emotion regulation, trait resilience, and anxiety and depression symptoms in 351 Italian informal caregivers. The results showed that coping through a positive attitude, emotional reappraisal, and trait resilience were the most central and interconnected nodes in the network. These adaptive strategies buffered against the negative impact of anxiety and depression symptoms, providing valuable insights into the mechanisms underlying resilience and well-being in informal caregivers. Clinically, it is crucial to assess and foster these resilience-promoting factors (positive attitude coping, cognitive reappraisal, and trait resilience) to help mitigate the mental health challenges faced by informal caregivers, especially in the context of public health crises such as the COVID-19 pandemic. Full article

Latent variables analysis is an important part of psychometric research. In this context, factor analysis and other related techniques have been widely applied for the investigation of the internal structure of psychometric tests. However, these methods perform a linear dimensionality reduction under a series of assumptions that could not always be verified in psychological data. Predictive techniques, such as artificial neural networks, could complement and improve the exploration of latent space, overcoming the limits of traditional methods. In this study, we explore the latent space generated by a particular artificial neural network: the variational autoencoder. This autoencoder could perform a nonlinear dimensionality reduction and encourage the latent features to follow a predefined distribution (usually a normal distribution) by learning the most important relationships hidden in data. In this study, we investigate the capacity of autoencoders to model item–factor relationships in simulated data, which encompasses linear and nonlinear associations. We also extend our investigation to a real dataset. Results on simulated data show that the variational autoencoder performs similarly to factor analysis when the relationships among observed and latent variables are linear, and it is able to reproduce the factor scores. Moreover, results on nonlinear data show that, differently than factor analysis, it can also learn to reproduce nonlinear relationships among observed variables and factors. The factor score estimates are also more accurate with respect to factor analysis. The real case results confirm the potential of the autoencoder in reducing dimensionality with mild assumptions on input data and in recognizing the function that links observed and latent variables. Full article

Interest in psychosocial predictors of doping has been increasing as a way of finding new approaches to reduce the use of performance-enhancing drugs. This investigation aimed to test the psychometric properties of an instrument to assess doping attitudes in Brazilian athletes. The PEAS was validated in Brazilian sports through a process of translation, back-translation and content validity assessment, presenting satisfactory evidence based on its content (CVC > 0.80). Then, 994 athletes from different sexes, types of sports and competitive levels answered the Brazilian version of the PEAS. The results showed satisfactory evidence of validity based on its response process, internal structure (X²/df = 2.04; RMSEA = 0.032 (0.026–0.038); CFI = 0.96; TLI = 0.95) and reliability (Cronbach’s α, McDonald’s ω and CR > 0.70). Network analysis was also used to further explore the PEAS’s internal structure. Overall, the results provide support for the adoption of the PEAS for Brazilian athletes and possibly other Portuguese-speaking countries. Full article

The Flourishing Scale (FS) is one of the most well-known tools for assessing psychological flourishing. However, its psychometric properties have been little analyzed in the case of teachers. This study aimed to examine the validity of the scale in the case of a sample of Romanian teachers and to analyze the latter’s level of flourishing. In this regard, 323 Romanian teachers from the pre-university education system were recruited. Confirmatory factor analysis (CFA) was used to assess the construct validity of the scale, and Cronbach’s α and McDonald’s ω indices were used to assess internal consistency. The convergent validity was assessed by associating the FS with other instruments related to well-being: the Mental Health Continuum-Short Form and the Scale of Positive and Negative Experience. Network analysis was performed to examine the items that are particularly influential in the scale. As a result of the CFA, the one-factor structure of the scale was certified (χ²/df = 1.39; CFI = 0.99; RMSEA = 0.035). The internal consistency is excellent (both α and ω = 0.89). The FS correlates with both of the scales which operationalize components of well-being. The teachers’ flourishing level is above average. The network approach showed that the items related to self-acceptance, optimism, and respect had the highest indicators of centrality, and the item related to supportive social relationships was the least informative in the network. For the male subsample, flourishing means optimism about the future and respect for others, and for the female respondents, it is related to self-acceptance and respect. The results provide support for using the scale in assessing flourishing among teachers. Full article