Network Psychometrics Research Articles

Unveiling the Context for Implementing Evidence-Based Practice in Spanish Nursing.

This study explores contextual factors affecting evidence-based practice (EBP) adoption among Spanish nurses, examining their interaction with sociodemographic and practice variables to enhance implementation strategies. A national multicenter study employing a cross-sectional online survey design was conducted with an intentional sample of 934 voluntary Spanish practicing nurses. Participants completed the 12-item Barriers/facilitators dimension of the HS-EBP instrument, along with sociodemographic, training and practice information relevant to EBP implementation. Psychometric networks have been utilised as a novel analytical approach. The HS-EBP scores confirm validity and reliability for assessing EBP barriers and facilitators among Spanish nurses. Primary barriers include inadequate EBP discussion spaces, lack of guidance, resistance to change, patient apathy, time constraints and support gaps. Key facilitators are higher academic qualifications, EBP training, professional development participation, educational institution employment and student supervision. Focus is needed on rural nurses and those with heavy clinical duties, facing major EBP barriers. Spanish nurses encounter organisational barriers to EBP implementation, including insufficient incentives and support. Prioritising hands-on training, professional development in teaching and research and robust programs can enhance nurses' ability to advance EBP implementation. The HS-EBP questionnaire is reliable in measuring organisational and contextual factors affecting EBP implementation. Successful implementation of evidence-based practice (EBP) in the Spanish healthcare system depends on developing an appropriate organisational culture and securing support from formal leaders. Additionally, fostering an understanding of the importance of patients and their families among Spanish nurses is crucial for promoting EBP adoption.

A multiple behaviour temporal network analysis for health behaviours during COVID-19.

The aim of this study was to examine the temporal dynamics of multiple health behaviours (physical activity, alcohol consumption, healthy eating, cigarette consumption, recreational drug use, vaping), and pandemic-related health behaviours (e.g., hand washing, physical distancing) using network psychometrics. The International COVID-19 Awareness and Responses Evaluation (iCARE) study is an international multi-wave observational cohort study of public awareness, attitudes, and responses to public health policies implemented to reduce the spread of COVID-19 on people around the world. A sub-sample of longitudinal data from Canadians (n = 254) was analysed across four waves (February-July 2020). We used temporal network models to fit temporal networks, contemporaneous networks, and between-subject networks from items within the iCARE survey. Positive temporal associations were observed between physical activity and healthy eating, and a bidirectional relationship was evident between outdoor mask use and vaping. A contemporaneous network revealed positive associations between consumption behaviours (vaping, cigarette use, alcohol use, and recreational drug use), and negative associations between physical activity and drug use, and healthy eating and cigarette use. Health behaviours are interconnected and can be modelled as networks or behavioural systems. The application of temporal network analysis to the study of multiple health behaviours is well suited to address key research questions in the field such as 'how do multiple health behaviours co-vary with one another over time'. Future research using time series data and measuring affective and cognitive mediators of behaviour, in addition to health behaviours, has the potential to contribute valuable hypothesis-generating insights.

Exploring Estimation Procedures for Reducing Dimensionality in Psychological Network Modeling.

To understand psychological data, it is crucial to examine the structure and dimensions of variables. In this study, we examined alternative estimation algorithms to the conventional GLASSO-based exploratory graph analysis (EGA) in network psychometric models to assess the dimensionality structure of the data. The study applied Bayesian conjugate or Jeffreys' priors to estimate the graphical structure and then used the Louvain community detection algorithm to partition and identify groups of nodes, which allowed the detection of the multi- and unidimensional factor structures. Monte Carlo simulations suggested that the two alternative Bayesian estimation algorithms had comparable or better performance when compared with the GLASSO-based EGA and conventional parallel analysis (PA). When estimating the multidimensional factor structure, the analytically based method (i.e., EGA.analytical) showed the best balance between accuracy and mean biased/absolute errors, with the highest accuracy tied with EGA but with the smallest errors. The sampling-based approach (EGA.sampling) yielded higher accuracy and smaller errors than PA; lower accuracy but also lower errors than EGA. Techniques from the two algorithms had more stable performance than EGA and PA across different data conditions. When estimating the unidimensional structure, the PA technique performed the best, followed closely by EGA, and then EGA.analytical and EGA.sampling. Furthermore, the study explored four full Bayesian techniques to assess dimensionality in network psychometrics. The results demonstrated superior performance when using Bayesian hypothesis testing or deriving posterior samples of graph structures under small sample sizes. The study recommends using the EGA.analytical technique as an alternative tool for assessing dimensionality and advocates for the usefulness of the EGA.sampling method as a valuable alternate technique. The findings also indicated encouraging results for extending the regularization-based network modeling EGA method to the Bayesian framework and discussed future directions in this line of work. The study illustrated the practical application of the techniques to two empirical examples in R.

Unidimensional community detection: A monte carlo simulation, grid search, and comparison.

Unidimensionality is fundamental to psychometrics. Despite the recent focus on dimensionality assessment in network psychometrics, unidimensionality assessment remains a challenge. Community detection algorithms are the most common approach to estimate dimensionality in networks. Many community detection algorithms maximize an objective criterion called modularity. A limitation of modularity is that it penalizes unidimensional structures in networks, favoring two or more communities (dimensions). In this study, this penalization is discussed and a solution is offered. Then, a Monte Carlo simulation using one- and two-factor models is performed. Key to the simulation was the condition of model error or the misfit of the population factor model to the generated data. Based on previous simulation studies, several community detection algorithms that have performed well with unidimensional structures (Leading Eigenvalue, Leiden, Louvain, and Walktrap) were compared. A grid search was performed on the tunable parameters of these algorithms to determine the optimal trade-off between unidimensional and bidimensional recovery. The best-performing parameters for each algorithm were then compared against each other as well as maximum likelihood factor analysis and parallel analysis (PA) with mean and 95th percentile eigenvalues. Overall, the Leiden and Louvain algorithms and PA methods were the most accurate methods to recover unidimensional and bidimensional structures and were the most robust to model error. More nuanced method recommendations for specific unidimensional and bidimensional conditions are provided. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Longitudinal Associations Between Cyberbullying Victimization and Cognitive and Affective Components of Subjective Well-Being in Adolescents: A Network Analysis

In recent years, there has been a notable increase in studies aimed at investigating the relationships between cyberbullying and subjective well-being (SWB) among adolescent populations. Nonetheless, the existing literature on this topic remains relatively sparse. Consequently, this study aims to explore the prospective associations of these constructs through the application of psychometric network analysis. Two cross-lagged network analyses were conducted (Wave 1 and Wave 2), alongside a longitudinal network analysis, encompassing 888 students aged 12 to 16 years (M = 12.61; SD = 1.79) from five secondary educational institutions. The results from both cross-lagged networks at both time points revealed significant associations among overall life satisfaction (OLS) and the various domains of life satisfaction, thereby reinforcing the central role of OLS within the network architecture of adolescent well-being. Regarding the affective component of SWB, “happy” emerged as the affect demonstrating the highest level of influence and impact at both time points. Additionally, a robust temporal consistency of the network structure was observed. In the longitudinal network analysis, the trajectories of the variables across time indicated that forms of cyberbullying, specifically “exclusion” and “harassment,” were prospectively and negatively related to “satisfaction with friends” and “satisfaction with body image.” The results of these findings are discussed in terms of their practical implications for the prevention and intervention of cyberbullying in adolescent population.

A Good check on the Bayes factor

Bayes factor hypothesis testing provides a powerful framework for assessing the evidence in favor of competing hypotheses. To obtain Bayes factors, statisticians often require advanced, non-standard tools, making it important to confirm that the methodology is computationally sound. This paper seeks to validate Bayes factor calculations by applying two theorems attributed to Alan Turing and Jack Good. The procedure entails simulating data sets under two hypotheses, calculating Bayes factors, and assessing whether their expected values align with theoretical expectations. We illustrate this method with an ANOVA example and a network psychometrics application, demonstrating its efficacy in detecting calculation errors and confirming the computational correctness of the Bayes factor results. This structured validation approach aims to provide researchers with a tool to enhance the credibility of Bayes factor hypothesis testing, fostering more robust and trustworthy scientific inferences.

Psychometric network analysis in rehabilitation research: A methodological demonstration in depression symptoms of veterans and service members at 1 and 2 years after traumatic brain injury.

Psychometric network analysis (PNA) is an application of dynamic systems theory that can inform measurement of complex rehabilitation phenomena such as depressive symptom patterns in veterans and service members (V/SMs) after traumatic brain injury (TBI). This study applied PNA to the Patient Health Questionnaire-9 (PHQ-9), a common measure of depressive symptoms, in a sample of V/SMs with TBI at Years 1 and 2 (Y1-2) postinjury. A sample of 808 V/SMs with TBI participated, 594 contributing PHQ-9 data at Y1 and 585 at Y2. Participants were recruited while or after receiving inpatient postacute rehabilitation from one of five Veterans Affairs Polytrauma Rehabilitation Centers. The networks were stable and invariant over time. At both times, network structure revealed the cardinal depressive symptom "feeling down, depressed, or hopeless," as evidenced by its strength centrality. In the Y1 network, the suicidal ideation node was connected exclusively to the network through the guilt node, and in the Y2 network, the suicidal ideation node formed a second connection through the low mood node. The guilt node was the second most influential node at Y1 but was replaced by anhedonia node at Y2. This study demonstrated the potential of PNA in rehabilitation research and identified the primacy of feeling down, depressed, and hopeless after TBI at both Y1 and Y2, with guilt being the second most influential symptom at Y1, but replaced by anhedonia at Y2, providing supportive evidence that the relationships among depressive symptoms after TBI are dynamic over time. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Supplemental Material for Psychometric Network Analysis in Rehabilitation Research: A Methodological Demonstration in Depression Symptoms of Veterans and Service Members at 1 and 2 Years After Traumatic Brain Injury

Supplemental Material for Psychometric Network Analysis in Rehabilitation Research: A Methodological Demonstration in Depression Symptoms of Veterans and Service Members at 1 and 2 Years After Traumatic Brain Injury

Emotional Regulation, Coping, and Resilience in Informal Caregivers: A Network Analysis Approach.

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.

Workaholism, work engagement, and burnout among academics in Montenegro: A psychometric network approach.

The academic environment is known for its high demands in research, teaching, and administration, that along with increasing publish or perish culture can lead to reduced psychological well-being and mental health issues. This study aimed to investigate the associations between workaholism, work engagement, and burnout among academics in Montenegro. A cross-sectional design was used to develop anonymous online survey. Data was collected from 131 participants employed as teaching and research staff at public and private universities. To measure the variables of interest we used: ultra-short Utrecht Work Engagement Scale (UWES-3), the work-related burnout subscale from the Copenhagen Burnout Inventory (CBI-7) and the Dutch Work Addiction Scale (DUWAS-10). Psychometric network analysis was employed to examine the relationships among variables. The findings revealed two distinct clusters: the first containing the dimensions of work engagement and the second containing burnout and the dimensions of workaholism. The two clusters were connected with the dimensions of dedication - burnout having the strongest edge (-0.25 and -0.40). In the cross-sample network the strongest connection was burnout -working excessively (.35). No significant differences in network density (0.80 (12/15 edges)) and global strength (p = 0.159) in the networks of public and private universities were found. Results of the network centrality and the edge strength analyses suggest that the interventions focused at increasing dedication while not fostering a work environment that encourages working excessively might be the key to preventing and reducing burnout in academia across contexts of public and private universities.

Connecting the dots: using a network approach to study the wellbeing spectrum

In positive psychology, defining the wellbeing construct has been a challenge. We used the psychometric network approach to study the structure of wellbeing. The sample consisted of Dutch adults registered with the Netherlands Twin Register. The variables were measured through standardized surveys. The network was estimated using the Mixed Graphical Models method and least absolute shrinkage and selection operator (LASSO) regularization to limit the number of spurious edges. We estimated a network in a trimming sample (N=1343, 63% females, Mage = 53.18, SDage = 9.45) and in an estimation sample (N=726, 75% females, Mage = 45.27, SDage = 11.12) to examine its performance and accuracy. Our final network consists of a positive cluster including satisfaction with life, subjective happiness, and flourishing items, and a negative cluster including depressive symptoms, loneliness, and neuroticism items. We identified the four most central nodes: one satisfaction with life item, one neuroticism item, and two depression items. This suggests that to get a general sense of the wellbeing construct, these items would serve as most informative. The network approach clearly demonstrates the different, yet connected positive and negative clusters of wellbeing and therefore re-affirms the complex interconnectivity of wellbeing phenotypes. In addition, the network results reject the view of strictly delineated wellbeing domains. Having identified the most central nodes in the network, these can be used in futures studies with limited resources, as they are likely to be the most representative of the wellbeing spectrum.

Maladaptive daydreaming and problematic online behaviors: A network analysis approach

Previous evidence showed that Maladaptive Daydreaming (MD) is positively associated with different Problematic Online Behaviors (POBs). This study aimed to investigate the mutual relationship between MD and several POBs. Data were collected from 1209 participants, aged 18–73 (M = 30.59, SD = 12.26), through self-report questionnaires assessing MD and POBs, including problematic online gambling, problematic online gaming, problematic cybersex, Problematic Social Media Use (PSMU), problematic online shopping, and cyberchondria. Two psychometric network analyses were performed to examine the associations between all POBs at item levels, and between MD and POBs at construct levels. Results revealed six distinct communities, supporting the specificity of each POB. Furthermore, MD was found to be more strongly associated with PSMU and cyberchondria, and weakly associated with problematic online gaming, problematic cybersex, and problematic online shopping. The mutual relationships between MD and POBs might have relevant implications for the assessment and treatment of these clinical conditions.

Which self-stigma of seeking help (SSOSH) version has adequate psychometric properties? An analysis of the SEM approach and network psychometrics in the Peruvian sample

Which self-stigma of seeking help (SSOSH) version has adequate psychometric properties? An analysis of the SEM approach and network psychometrics in the Peruvian sample

A Psychometric Network Analysis Approach for Detecting Item Wording Effects in Self-report Measures across Subgroups

In this study, we explored psychometric network analysis (PNA) as an alternative method for identifying item wording effects in self-report instruments. We examined the functioning of negatively worded items in the network structures of two math-related scales from the 2019 Trends in International Mathematics and Science Study (TIMSS); Students Like Learning in Mathematics (SLLM); and Students Confident in Mathematics (SCM). We also explored how the negatively worded items functioned in network structures across demographic subgroups. Data were drawn from eight countries that represented diverse levels of math performance and cultural attitudes toward school ( n = 75,972). We found that negatively worded items were distinct from the positively worded items in the SLLM and SCM item networks, and that this effect was consistent across all age- and country-level subgroups. Based on these findings, we recommend PNA as a data-driven approach for detecting wording effects effectively.

A psychometric evaluation of Professional Quality of Life Scale Version 5 (ProQOL 5) in a UK-based sample of allied mental health professionals

The Professional Quality of Life Scale Version 5 (ProQOL 5; Stamm, 2010) is often used to assess burnout, secondary traumatic stress, and compassion satisfaction in allied mental health professionals in the UK. However, vital empirical evidence assessing psychometric properties of this instrument for this occupational group or in this national context does not exist. Therefore, the aim of the present study was to conduct a confirmatory factor analysis to validate ProQOL 5 in a sample of 366 UK-based clinical psychologists, counsellors, and psychotherapists recruited via online purposive sampling. The findings indicated that in alignment with existing research, the original three-factor structure demonstrated poor fit to data. Thus, the structure was investigated further with a novel technique in network psychometrics called bootstrapped exploratory graph analysis (bootEGA). The results indicated that 21 items from the original 30-item ProQOL 5 demonstrated satisfactory levels of item stability, i.e., all items were replicated in more than 80% of bootstrapped samples. The present study is the first to evaluate the internal structure of ProQOL 5 in a sample of mental health professionals based in the UK.

Developmental structure of digital maturity

The last decades have seen growing attention for the positive and negative effects of digital technology on adolescent's wellbeing. Large individual variability in these effects is likely caused by individual differences in the way people interact with digital devices. Digital maturity aims to capture the extent to which young individuals use digital technology in a healthy and adaptive way. The recently developed Digital Maturity Inventory (DIMI) uses self-report to measure ten domains, from digital literacy to emotion regulation, that constitute digital maturity of adolescents. To better understand the relative contribution and interplay of the different domains in the development of digital maturity, we employed a moderated psychometric network model. We measured digital maturity scores of 378 participants, aged 9–43 years. The results revealed that support-seeking and the regulation of aggressive impulses are central domains within the network, indicating important starting points for intervention studies. Although age did not moderate the connections within the network, we did find that older participants were more digitally literate and asked less often for support regarding digital issues. These results suggest that digital maturity is a relatively robust concept across adolescence and into adulthood and provide important footholds for interventions.

Measurement targets for network constructs in psychopathology

In psychology, regulation of measurement practices relies on the use of psychometric models that relate the targeted psychological construct to a set of observations in empirical research. Recent work has argued for a new conceptualization, in which psychological constructs are viewed as networks. However, in contrast to existing models, network psychometrics has not articulated normative guidelines for measurement. To develop normative criteria for measurement in the network framework, a clearer picture is needed about possible measurement targets in a network. Using the example of depression, we explore five such potential measurement targets in the network framework: the structure of the network, the current state of the network, and three properties of the dynamical landscape, the expected state, vulnerability, and stable state. We discuss strategies for measuring these targets as well as challenges that arise in doing so.

Depressive symptomatology from a network perspective: Differences in the experience of symptoms involved in the self-recognition of depression and the diagnosis process by social position.

While social disparities in depression are well-documented, the symptom experience across social positions remains less studied. This study examines the connections between depressive symptoms and self-recognizing a depressive episode, on the one hand, and clinical diagnosis, on the other hand, by three social position indicators. We analyzed baseline data from a population-based cohort of adults living in France, grouping participants by three indicators: education, financial difficulties, and occupation, and stratifying by sex. Utilizing a psychometric network approach, we estimated 24 networks. Nodes corresponded to the 20 CES-D items and 1 external variable, either 'Limitations due to depression' or 'Clinical depression'. Comparisons between socially disadvantaged and advantaged groups across the three social indicators were made in terms of network structures, global strength, and edge weights involving symptoms and both external nodes. The study included data from 201,952 participants. Individuals in lower social positions exhibited higher rates of depressive-related variables. Four depressive symptoms emerged as crucial, being linked both to 'Clinical depression' and 'Limitations' across all social positions. Socially disadvantaged groups had denser networks. Some of the tests comparing network structures according to social position were significant, suggesting differences in the symptom activation chains. Connections between each external node and 'Felt depressed' and 'Could not get going' were non-invariant in educational and financial-based networks. Findings highlight four depressive symptoms, likely to play a key role in the experience of depression across all social positions. Other insights from specific symptoms could be used for improving depression care among disadvantaged populations.

Exploring networks of complex developmental trauma symptomatology among children and adolescents involved in child welfare.

Clinical presentations of child and adolescent psychopathology can vary systematically for boys and girls. While network analysis is increasingly being applied to explore psychopathology in adults, there is a dearth of network studies considering differences in symptoms for boys and girls, particularly in developmental trauma-related symptomatology. This study involves rural children (n=375, 39.47% girls) and adolescents (n=291, 51.20% girls) involved with child protection services in Ontario, Canada. Caregivers completed the Assessment Checklist for Children or Adolescents within the first 6months of care. Psychometric network analyses were conducted using subscales for boys and girls. Differences were examined via network comparison permutation tests, moderated network models, and independent t-tests. Attachment-related interpersonal difficulties were the most central nodes in the child and adolescent networks for both boys and girls. Emotional dysregulation also had high strength centrality for adolescents. While network comparison tests found the overall network structures and global network strength to be invariant between boys and girls for children and adolescents, moderated network models and independent t-tests revealed several differences with regards to the expression of specific symptoms. Among children, girls exhibited more indiscriminate and pseudomature interpersonal behaviors, whereas boys expressed significantly more non-reciprocal interpersonal behaviors and self-injury. Adolescent girls exhibited more behavioral dysregulation and suicide discourse in the moderated network model; t-tests also indicated higher levels of emotional dysregulation, negative self-image, and other items considered clinically important complex trauma symptoms (e.g., distrust of adults, confused belonging). This study supports evidence of differences in the expression of complex trauma symptomatology for boys and girls. Additionally, girls exhibit more symptoms, in general. Consistent with the transdiagnostic conceptualization of the consequences of developmental trauma, findings demonstrate the primacy of attachment-specific difficulties and emotion dysregulation.

Measuring the teachers’ talent management

PurposeA crucial issue in educational management refers to helping teachers reach their full potential and manage their talents. Although managing talent is advised as an essential resource for organizational transformation to maximize performance and to promote a school’s knowledge capital increase, Teachers’ talent management (TTM) is not an agreed-upon concept in the literature. The aim of this study is threefold: (1) to propose a theoretical and operational TTM definition, (2) to develop a test to measure TTM and (3) to discuss the value of TTM implementation in educational settings.Design/methodology/approachA psychometric validation design according to international standards for test development is implemented. A cross-sectional intentional sampling with 200 schools is used for obtaining reliability and validity evidence. We operationalized TTM using 9 factors (10 items per factor, 6-point Likert scale): attraction, selection, development, retention, succession, climate, culture, evaluation and knowledge management.FindingsResults show adequate evidence of reliability and validity, focusing on items' content and latent structure. The combined use of structural equation models (SEM) and psychometric networks (PN) detects a six-factor model with a test reduced version including 61 items, getting 64.28% of the total explained variance and adequate reliability indices and factor loadings.Originality/valueThe Teachers' Talent Management Assessment Test (TTMAT) can contribute educational institutions to create a culture of excellence and deliver the best possible education to their students. Educational transformation will only happen if teachers are professionalized, trained, motivated and supported to inspire and to guide their learners to reach their objectives and well-being.