Psychological Network Analysis Research Articles

Risk and protective factors for suicidal ideation among Saudi adolescents: A network analysis.

Suicide is among the leading cause of death in adolescence and suicidal ideation is its most critical precursor. Suicidal ideation being a complex phenomenon involving multiple factors, this study aimed to use network analysis to depict the interplay of risk and protective factors for suicidal ideation. An online survey was conducted with 614 Saudi adolescents (mean age = 16.16, SD = 1.88, range = 12-18, 61% female). Participants completed instruments to assess suicidal ideation, bullying victimization and perpetration, psychache, family cohesion and adaptability, positive parenting, and multidimensional social support. A psychological network analysis was estimated to determine the most central risk and protective factors for suicidal ideation. As per strength and expected influence centrality, the risk factors that were central were psychache and bullying victimization, and the central protective factors were family cohesion and support from significant other. The network analysis showed that other factors directly connected with suicidal ideation were bullying perpetration, family adaptability, positive parenting, support from family, and support from friends. This network study highlights the importance of addressing both risk and protective factors in an attempt to deal with suicidal ideation. The findings suggest that interventions targeting psychological pain, bullying, and family bonds may be crucial in the treatment and prevention of suicidal ideation among adolescents.

Basic mathematical skills and fraction understanding predict percentage understanding: Evidence from an intelligent tutoring system

Research on fostering learning about percentages within intelligent tutoring systems (ITSs) is limited. Additionally, there is a lack of data‐driven approaches for improving the design of ITS to facilitate learning about percentages. To address these gaps, we first investigated whether students' understanding of basic mathematical skills (eg, arithmetic, measurement units and geometry) and fractions within an ITS predicts their understanding of percentages. We then applied a psychological network analysis to evaluate interdependencies within the data on 44 subtopics of basic mathematical concepts, fractions and percentages. We leveraged a large‐scale dataset consisting of 2798 students using the ITS bettermarks and working on approximately 4.1 million mathematical problems. We found that advanced arithmetic, measurement units, geometry and fraction understanding significantly predicted percentage understanding. Closer inspection indicated that percentage understanding was best predicted by problems sharing similar features, such as fraction word problems and fraction/natural number multiplication/division problems. Our findings suggest that practitioners and software developers may consider revising specific subtopics which share features with percentage problems for students struggling with percentages. More broadly, our study demonstrates how evaluating interdependencies between subtopics covered within an ITS as a data‐driven approach can provide practical insights for improving the design of ITSs. Practitioner notesWhat is already known about this topic Longitudinal studies showed that basic mathematical skills predict fraction understanding. There is only limited evidence on whether similar predictions can be observed for percentage understanding—in general and within intelligent tutoring systems. Process data from such intelligent tutoring systems can be leveraged to pursue both educational research questions and optimizing digital learning software. Problems involving percentages typically are word problems requiring multiplications and/or divisions. What this paper adds Similar to the case of fractions, students' performance on advanced arithmetic, measurement units and geometry significantly predicted performance with percentages. Students' performance with fractions also predicted performance with percentages significantly. A psychological network analysis was applied to evaluate specific interdependencies between a range of subtopics (eg, Multiplying and dividing fractions, Adding and subtracting fractions and Calculating with percentages). Fraction word problems and fraction problems involving multiplication/division turned out to be the best predictors of understanding percentages. Implications for practice and/or policy When facing difficulties with percentages, revision of previous mathematical concepts sharing similar features (eg, fraction word problems, fraction/natural number multiplication/division problems) may be advised. Software developers may consider implementing such data‐driven revision recommendations for students facing difficulties within intelligent tutor systems. Psychological network analysis can be utilized as a learning analytics method for easy‐to‐access visualizations illustrating relationships between a large range of different subtopics.

Buoyancy and engagement in online English learning: The mediating roles and complex interactions of anxiety, enjoyment, and boredom

Academic buoyancy is a positive trait that has recently received extensive attention. Given the positive psychology turn in SLA, exploring interactions among academic buoyancy, emotions, and online English learning engagement (OELE) is crucial for students’ growth. However, their relationships remain under-investigated. Therefore, we combined structural equation modeling (SEM) with psychological network analysis (PNA) to elaborate the relationships among these variables within 442 Chinese EFL learners. SEM indicated that buoyancy significantly positively predicted enjoyment and OELE, and negatively predicted anxiety and boredom. Enjoyment significantly positively predicted OELE, whereas anxiety and boredom negatively predicted OELE. Furthermore, anxiety, enjoyment, and boredom significantly mediated the relationship between academic buoyancy and OELE. PNA showed that engagement occupies the central position in the network. Academic buoyancy serves as a bridge, connecting emotions and engagement. The current study is committed to providing novel insights into research methods and online instructional design for foreign language teaching and learning.

Psikopatoloji Araştırmalarında Yeni Bir Yöntem: Ağ Analizi

Though network analysis has a long history in both natural and social sciences it has emerged as a new method in psychology in recent years. Unlike medical disorders, mental disorders are not observable in laboratory. However, we can identify them by the way of observable symptoms. According to the network perspective, a disorder occurs when an external event triggers a psychological symptom. Activated symptom also interacts with other symptoms and forms a pattern of symptoms. Network approach criticizes traditional categorical diagnostic approach and focuses on symptom organization. Probably, treating the most effective symptom will accelerate recovery process and provide more effective treatment. Network analysis can be used in both cross-sectional and longitudinal studies. Psychological networks provide opportunities to investigate direction of the relationship among symptoms, comorbidity, external triggers of psychological symptoms, effectiveness of treatment, comparison of symptom pattern according to sample characteristics. Despite the utility of psychological networks, accuracy of them has been questioned and certain methods to prove accuracy of networks proposed as response. Technological progress in recent years enabled network analysis to be more eligible in psychology. R Statistics software is very useful in network analysis which is totally free and open sourced and supported by many additional packages. This review article aims is to provide information about usage of network analysis in psychology, especially in clinical research. In the first part historical and theoretical background of network analysis was introduced and in the following parts structure, validity of psychological networks and R Statistics Software which is used for conducting network analysis were explained briefly.

A psychological network analysis of the relationship among component importance measures

Importance measures (IMs) in networks are indices that allow the analysis and evaluation of the network components that are most critical to the performance of the network. Such information is useful for a decision-maker as it enables taking actions to prevent or improve the performance of the network in the face of changing operational events (e.g., the identification of important links that should be hardened or made redundant). This paper presents an approach to analyze the relationships between the IMs through the use of so-called psychological networks, which estimate the characteristics of a new kind of network wherein the “nodes” correspond to IMs and the connecting links and their capacities are derived statistically using the IMs calculated. Such estimation does not use any a priori information of relationships among IMs. The approach proposed in this work defines an equivalence paradigm not described previously in the literature between the approach used in psychology and the IMs used to measure networks. As a result, the main characteristics of the relationships among IMs are derived, such as magnitude, sign, and robustness of the selected IMs. An example related to a transportation network and a set of flow-based IMs illustrates the contribution of psychological networks for understanding how the IMs interact.

Evaluating the content structure of intelligent tutor systems—A psychological network analysis

The adoption of intelligent tutoring systems (ITSs) worldwide has led to a considerable accumulation of process data as students interact with different learning topics within these systems. Typically, these learning topics are structured within ITSs (e.g., the fraction topic includes subtopics such as a fraction number line subtopic). However, there is a lack of methods that offer quick, data-driven insights into the content structure of ITSs, particularly through easily accessible visualizations. Here, we applied psychological network analysis to process data (230,241 students; 5,365,932 problem sets) from an ITS for learning mathematics to explore performance interdependencies between 40 different subtopics. We argue that the visualization of these content interdependencies allows for a quick empirical evaluation of the validity of the existing structuring of the respective learning content. These insights allow for deriving recommendations concerning potential changes in the ITS structure and are thus highly valuable for ITS developers. Our results are also relevant for researchers as the interdependencies illustrated through psychological network analysis can contribute towards a better understanding of the interplay between mathematical skills. Together, our results indicate that psychological network analysis represents a valuable data-driven method to evaluate and optimize ITSs.

Mindfulness networks: Analyzing associations with self‐compassion, other‐compassion, need fulfillment, and satisfaction in midlife married Canadians

AbstractInterest in mindfulness in the field of romantic relationships is growing. Drawing from a Self‐Determination Theory (SDT) perspective—which proposes that the basic psychological needs for autonomy, competence, and relatedness are foundational for well‐being—we attempted to map out the complex associations between mindfulness, self‐compassion, other‐compassion, basic need fulfillment in relationships, and increased relationship and sexual satisfaction. A sample of 640 midlife (40–59‐year‐old) married Canadians was recruited from a national panel. To test the associations at a systems‐level, we utilized psychological network analysis based on the premise that the relational and sexual effects of mindfulness are understood as part of a dynamic and multivariate network of associations with other variables. Need fulfillment in relationships (particularly relatedness needs) occupied a central position in the model, connecting mindfulness and self‐compassion with relationship satisfaction and sexual satisfaction. The findings underscored the major importance of SDT in relationships, and the overall structure of the network was consistent with growing theories of mindfulness in relationships. Future research employing longitudinal network models will aid in elucidating this system's operation over time.

Understanding the relations between personality traits, bullying perpetration, and victimization among Chinese adolescents: a psychological network analysis

Understanding the relations between personality traits, bullying perpetration, and victimization among Chinese adolescents: a psychological network analysis

Putting the Vicious Cycle to the Test: Evidence for the Cognitive Behavioral Model of Persistent Somatic Symptoms From an Online Study.

In clinical practice, persistent somatic symptoms are regularly explained using a cognitive-behavioral model (CBM). In the CBM, predisposing, perpetuating, and precipitating factors are assumed to interact and to cause the onset and endurance of somatic symptoms. However, these models are rarely investigated in their entirety. We conducted an online survey during the COVID-19 pandemic. A total of 2114 participants from the general German population completed questionnaires that measured different factors of the CBM. We used state negative affectivity and neuroticism as predisposing factors, fear of a COVID-19 infection as the precipitating factor, and somatic symptoms, misinterpretation of bodily symptoms, attention allocation to bodily symptoms, and health anxiety as perpetuating factors. Moreover, we added safety and avoidance behavior as end points to the model. We conducted a psychological network analysis to exploratively study the relationships between the model's different factors and tested the assumptions of the CBM by evaluating a structural equation model (SEM) that incorporated all factors of the model. Network analyses revealed clustering in our data: Health anxiety and different cognitive factors are closely related, whereas somatic symptoms and state negative affectivity are strongly associated. Our SEM showed adequate fit. Our findings from an exploratory and a confirmatory approach give empirical support for the CBM, suggesting it as a suitable model to explain bodily symptoms in the general population and to possibly guide clinical practice. The network model additionally indicates the necessity to apply an individualized CBM for patients, depending on a preponderance of either persistent somatic symptoms or health concerns.

Differential associations between insight and quality-of-life dimensions among individuals with schizophrenia.

In schizophrenia, insight, the recognition that one has a medical illness that requires treatment, has long been related to deteriorated quality of life. Yet, insight and quality of life are broad constructs that encompass several dimensions. Here, we investigated differential associations between insight and quality-of-life dimensions using a psychological network approach. We extracted data from the French network of rehabilitation centers REHABase (January 2016 to December 2022, N = 1,056). Our psychological network analysis modeled insight and quality of life as a network of interacting dimensions: three insight dimensions (awareness of illness, reattribution of symptoms to the disease, and recognition of treatment need) and eight quality-of-life dimensions (autonomy, physical and psychosocial well-being, relationships with family, friends and romantic partners, resilience, and self-esteem). Insight was negatively associated with quality of life. Our psychological network analysis revealed a strong negative association between awareness of disease and self-esteem. Both dimensions were the strongest nodes in the overall network. Our network analysis also revealed a significant but positive connection between recognition of treatment needs and resilience. While insight and quality of life are overall negatively associated, we found both negative and positive connections between insight and quality-of-life dimensions. The negative relationship between insight and quality of life may reflect the deleterious effects of diagnostic labeling on a patient's self-esteem. Yet, acknowledgment of treatment needs may have positive effects on quality of life and may promote recovery, perhaps because it emphasizes the need for support rather than labels and abnormalities. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The interplay of depressive symptoms and self-efficacy in adolescents: a network analysis approach.

Self-efficacy, a critical psychological construct representing an individual's belief in their ability to control their motivation, behavior, and social environment. In adolescents, self-efficacy plays a crucial role in mental health, particularly concerning depressive symptoms. Despite substantial research, the complex interplay between self-efficacy and depressive symptoms in adolescents remains incompletely understood. The aim of this study is to investigate the complex interrelationships between self-efficacy and depressive symptoms in adolescents using psychological network analysis. The cross-sectional study involved 3,654 adolescents. Self-efficacy was assessed using the General Self-Efficacy Scale (GSES), and depressive symptoms were measured with the Patient Health Questionnaire-9 (PHQ-9). Network analysis, incorporating the least absolute shrinkage and selection operator (LASSO) technique and centrality analysis, constructed and compared self-efficacy networks between depressive symptoms and healthy control groups. Of the 3,654 participants, 560 (15.32%) met criteria for moderate to severe depressive symptoms (PHQ-9 scores ≥10). Among those with depressive symptoms, 373 (66.61%) had moderate, 126 (22.50%) had moderate-severe, and 61 (10.89%) had severe symptoms. Bivariate correlation analyses revealed a significant negative correlation between depressive symptoms and self-efficacy (r = -0.41, p < 0.001). The results of the network analysis showed significant differences in self-efficacy networks between adolescents with and without depressive symptoms (global strength: S = 0.25, p < 0.05). Depressed participants showed a network with reduced global strength, suggesting diminished interconnectedness among self-efficacy items. Specific connections within the self-efficacy network were altered in the presence of depressive symptoms. Bridge analysis revealed that effort-based problem-solving (bridge strengths = 0.13) and suicidal ideation (bridge strengths = 0.09) were the key bridge nodes. Adolescent depressive symptoms significantly impacts the self-efficacy network, resulting in diminished integration of self-efficacy and highlighting the complex interplay between self-efficacy and depressive symptoms. These findings challenge the traditional unidimensional view of self-efficacy and emphasize the need for tailored interventions focusing on unique self-efficacy profiles in adolescents with depressive symptoms.

Understanding public perceptions toward sustainable healthcare through psychological network analysis of material preference and attitudes toward plastic medical devices

Recent and potential future health-care users (i.e., the public) are important stakeholders in the transition toward environmentally sustainable healthcare. However, it remains unclear whether, according to the public, there is room for sustainable innovations in materials for plastic medical devices (PMD). This study explores preferences regarding conventional or bio-based PMD, and psychological mechanisms underlying these preferences. We administered two surveys among Dutch adults from a research panel. Results from the first survey (i.e., open-text survey on attitude elements; NStudy1 = 66) served as input for the second survey (i.e., Likert-scale survey on beliefs, emotions, perceived control, social norms, trust, related to current and bio-based PMD, and health and age; NStudy2 = 1001; Mage = 47.35; 54.4% female). The second survey was completed by 501 participants who, in the last two years, received care in which PMD were used, and 500 participants who did not. Cross-sectional psychological networks were estimated with data from the second study using the EBICglasso method. Results showed that participants preferred bio-based over conventional PMD, and this applied regardless of whether devices are used inside or outside of the body. Results also showed emotions play an important role, with emotions regarding bio-based PMD being strongly related to preference. Furthermore, comparing recent and potential future receivers of PMD revealed differences in preference but comparable relations between preference and other psychological variables. This study shows that receivers’ perspectives should not be seen as potential barriers, but as additional motivation for transitioning toward sustainable healthcare. Recommendations for implementation are discussed.

Identifying physical and psychological risk factors for musculoskeletal pain in student musicians to tailor the curriculum: a cross-sectional study protocol

IntroductionInsufficient identification and understanding of risk factors make musicians engaging in professional practice particularly vulnerable to musculoskeletal pain. To support positive music learning and good mental, physical, and social health,...

Network structure of the links between extracurricular time-use and delinquent behaviors: Moving forward and beyond linear relations.

Using psychological network analysis, this study explored the heterogeneity of the network structure between extracurricular time-use and delinquency using a nationally representative longitudinal survey of at-school students in China (N = 10,279, 47.3% female, average age 13.6, 91.2% Han ethnicity). The results are threefold: First, time stimulation of activities occurs on weekdays, while time displacement and stimulation occur on weekends. Second, delinquent behaviors are positively correlated, forming a problem behavior syndrome. Smoking or drinking is the central delinquent behavior. Third, negative consequences of specific time-use behaviors are more likely to occur on weekends than on weekdays, and time-use behavior may function differently on weekdays versus weekends. Among them, going to coffeenets or game-centers serves as the bridge with the highest potential of triggering delinquency.

The network structure of dysfunctional metacognitions, CAS strategies, and symptoms

In the metacognitive model of psychological disorders, metacognitive strategies and corresponding underlying metacognitive beliefs intensify and maintain emotional distress symptoms. In the current study, our three objectives were to evaluate and replicate the network structure of dysfunctional metacognitions as assessed with the MCQ-30, to examine its stability when adding relevant covariates in the form of metacognitive strategies (worry and rumination) and symptoms (anxiety and depression), and to evaluate how different sets of dysfunctional metacognitions are more or less strongly linked differently to metacognitive strategies and symptoms. A cross-sectional university sample with a mean age of 26 years (N = 440; Males = 156, Females = 283) completed the Metacognitions Questionnaire–30, Penn State Worry Questionnaire, Ruminative Response Scale, and Hopkins Symptom Checklist. Data were analysed using psychological network analysis in R-studio statistical software. The network structure of dysfunctional metacognitions replicated well with item clusters that correspond to clinically meaningful substructures in the metacognitive model. Negative metacognitive beliefs and beliefs about uncontrollability might have more functional significance in the mutual connections between dysfunctional meta-domains as well as the connections with metacognitive strategies and symptoms. For worry and anxiety, negative beliefs about uncontrollability and corresponding danger of worry were more prominently connected in the network structure. For rumination, cognitive self-consciousness was more prominent, whereas for depression, need for control was more prominently connected. Support was found for mutual interdependence between different sets of dysfunctional metacognitive beliefs, that metacognitive beliefs are linked to but separate from metacognitive strategies, and that these may function together in affecting emotional distress symptoms

Relationships between prescribed anti-osteoporotic treatment and necessity-concerns assessment: a psychological network analysis

Relationships between prescribed anti-osteoporotic treatment and necessity-concerns assessment: a psychological network analysis

The predictors of L2 grit and their complex interactions in online foreign language learning: motivation, self-directed learning, autonomy, curiosity, and language mindsets

Learning a foreign language is a long-term process requiring persistence and a willingness to engage in activities that will help develop communicative competence. An important role on the way to achieving linguistic proficiency is played by L2 grit. However, foreign language learners differ in the intensity of this trait. We identify the most important predictors of L2 grit in the online language learning context. Basing on a literature review, we first identify possible predictors in the generic process of L2 learning, including language mindsets as well as two general psychological dispositions: a sense of autonomy and curiosity, and subsequently one predictor specifically related to the virtual learning context, i.e., readiness for online learning. A multiple linear regression model built using questionnaire data collected from 615 learners from 69 countries enrolled in language courses taught in a remote or hybrid mode reveals that L2 grit is most determined by two dimensions of readiness for online learning, and to a lesser extent by learners’ autonomy and curiosity, and significantly and positively correlates with these factors. The regression model explains 48% of variance in the dependent variable. A subsequent psychological network analysis, one of the first applications of this method in the field of SLA, permitted estimating the relative importance of these factors in the complex network of associations, indicating equally strong direct connections between L2 grit and both dimensions of readiness for online learning, and a much weaker edge linking L2 grit with autonomy. The findings have practical implications for language teachers in remote and hybrid settings.

Assessing perceptions of resilience: The understanding from network analysis.

Previous studies have yet to reach a consensus on the construct of resilience perception, and how to enhance the effect of resilience intervention remains an urgent issue. In this consideration, this study examines the fundamental construct of resilience. It provides insight into the critical prevention goal for resilience intervention by utilizing the latest methods of psychological network analysis. The sample is the graduate students enrolled in September 2021. Participants completed (1) the Connor-Davidson Resilience Scale, (2) the University of Washington Resilience Scale-8 Item, (3) the Brief Resilience Scale, and (4) the Resilience Scale for Adults, each representing different orientations of resilience. The network analysis grants greater clarity to the resilience perception as a dynamic system that interacts between an individual's tendency to intrinsic capacity and response to external resources. This study has shown that a positive perception of external social resources is the most important for individuals' resilience cognition; the effect of resilience intervention can be achieved more quickly by changing the individual's sense of hope. Based on the results, a psychometric instrument that integrates different orientations of resilience concepts and is based on time-varying needs to be developed.

The use of psychological network analysis in informal dementia care: an empirical illustration

Objective: Theoretical models in informal dementia care have been developed to understand how risk and protective factors interact to cause caregiver’s distress. The development of psychological network analysis provides a rich complement to our current models, as explores how different variables (or nodes) are associated using graph theories. Methods: The present study explored the use of network analysis using data from 125 informal caregivers of their partner with dementia (PwD). The included variables were recipient’s dependency, self-efficacy, conflict within the family, dyadic adjustment, and caregiver’s distress. Results: The analysis suggests a complex network of interacting variables. The core variable was not the caregiver’s distress but rather their dyadic adjustment with their PwD. Variables were associated with caregiver distress through a large array of direct and indirect pathways and were associated with each other in the form of an asymmetric spider’s web.Conclusion: The results show the complex interplay of variables in a psychological network. The central role of distress suggests a complex and dynamic role, notably through a bidirectional influence with quality of interactions. In the same way, quality of interactions appeared as one of the strongest nodes, its connectivity suggesting a crucial role to consider in our models and interventions.

Psychological network analysis of the relations between beliefs about smoking for smokers with and without a recent quit attempt

Objective Psychological network analysis was used to evaluate the relations between beliefs about cigarette smoking in current smokers with and without a recent quit attempt and determine if these networks differed in global strength (how strongly beliefs are related) or global structure (which beliefs are related). Design Using two publicly available datasets, the California Smokers’ Cohort (CSC; N = 933) and Wave 1 of the Population Assessment of Tobacco and Health (PATH; N = 7855), we evaluated differences in global strength and global structure of the beliefs held by current smokers with and without a recent quit attempt. Main Outcome Measures Strength and structure of networks generated for current smokers with and without a recent quit attempt. Results In the CSC dataset there were differences between smokers with and without a recent quit attempt in global structure and marginal differences in global strength; the PATH dataset suggested small differences in global strength and structure. Conclusion The present study suggests that consideration of relations among smoking beliefs may be a valuable contribution to characterizing smoking beliefs when assessing smoking quit attempts.