Unpacking Constructs: A Network Approach for Studying War Exposure, Daily Stressors and Post-Traumatic Stress Disorder

Central psychological symptoms from a network analysis of patients with anxiety, somatoform or personality disorders before psychotherapy

BACKGROUND

Cross-sectional network analysis examines the relationships between symptoms to explain how they constitute disorders. Up to now, research focuses mostly on depression, posttraumatic stress disorder, and rarely assesses larger networks of various symptoms measured with instruments independent of classifications. Studies on large groups of psychotherapy patients are also rare.

METHODS

Analyzing triangulated maximally filtered graph (TMFG) networks of 62 psychological symptoms reported by 4616 consecutive nonpsychotic adults in 1980-2015.

RESULTS

Case-dropping and nonparametric bootstrap proved the accuracy, stability and reliability of networks in patients' sex-, age-, and time of visit divided subgroups. Feeling that others are prejudiced against the patient was the most central symptom, followed by catastrophic fears, feeling inferior and underestimated. Sadness, panic, and sex-related complaints were less central than we expected. All analysed symptoms were connected, and we found only small sex-related differences between subsamples' networks. No differences were observed for time of visit and age of patients.

LIMITATION

Analyses were cross-sectional and retrospective, not allowing examination of directionality or causality. Further, data are at the between-person level; thus, it is unknown whether the network remains constant for any person over time. One self-report checklist and building binary network method may bias results. Our results indicate how symptoms co-occured before psychotherapy, not longitudinally. Our sample included public university hospital patients, all White-Europeans, predominantly females and university students.

CONCLUSIONS

Hostile projection, catastrophic fears, feeling inferior and underestimated were the most important psychological phenomena reported before psychotherapy. Exploring these symptoms would possibly lead to enhancement of treatments.

A conceptual study on the relationship between daily stressors, stressful life events, and mental health in refugees using network analysis

Introduction

There is growing recognition that daily stressors, such as social and material deficiencies, can be highly detrimental to the mental health of refugees. These stressors are in addition to stressful life events, which have been widely studied in the context of migration and forced displacement. Despite increasing evidence for an ecological model, there is still no consensus regarding the conceptualization of these highly influential factors. In particular, the demarcation of daily stressors from stressful life events and the categorization of daily stressors require further examination in order to develop usable and accurate tools for researchers, design effective interventions for practitioners and assist politicians in designing meaningful policies.

Methods

To address these challenges, we used data from a sample of 392 unaccompanied young refugees from diverse backgrounds and employed network analysis to examine the relationships between daily stressors, stressful life events, and symptoms of depression, anxiety, and post-traumatic stress.

Results

Our findings highlight the significant relationship between daily stressors and mental health, particularly depression. Meaningful clusters of daily stressors include material stressors, social stressors, and social exclusion stressors.

Conclusion

Our results demonstrate the importance of considering daily stressors in the mental health of refugees and suggest that using a network approach offers a viable way to study these complex interrelationships. These findings have implications for researchers, practitioners, and policymakers in understanding and addressing the mental health needs of refugees.

"My brain freezes and I am blocked again": The subjective experience of post-migration living difficulties influenced by complex posttraumatic stress disorder of Afghan asylum seekers and refugees in Austria

BACKGROUND

Potentially traumatic experiences and post-migration living difficulties (PMLDs) undoubtedly leave marks on mental health and psychosocial functioning. While PMLDs are recognised as a risk factor for posttraumatic stress disorder and complex posttraumatic stress disorder (described together here as C/PTSD), recent investigations have found that C/PTSD symptoms might also influence the experience of PMLDs. The subjective experience of and coping with PMLDs in the context of C/PTSD symptoms has not yet been explored.

METHODS

Semi-structured, interpreter-assisted interviews exploring the subjective experience of post-migration living difficulties were conducted with treatment-seeking Afghan refugees and asylum seekers (N = 24) and transcribed verbatim. Participants were screened using the International Trauma Questionnaire (ITQ) and allocated to a C/PTSD group or non-C/PTSD group. We analysed the qualitative interviews using content analysis and then compared the results of the two groups.

RESULTS

Over half of the participants (58.3%) met the criteria for C/PTSD. While the two groups addressed numerous similar themes, the C/PTSD group more frequently mentioned themes associated with C/PTSD symptoms (e.g., intrusions, avoidance, sleep disturbances, affective dysregulation) that influenced their responses to PMLDs. The non-C/PTSD group more often experienced positive emotions such as gratitude and optimism, and showed more active, solution-oriented behaviour as well as positive self-verbalisation.

CONCLUSION

To achieve a deeper understanding of PMLDs, post-traumatic psychopathology should be taken into account, as C/PTSD symptoms influence the experience of and coping with PMLDs. The specifics of individual experiences need to be considered in order to promote adaptive coping with PMLDs and to set individual trauma-focused and transdiagnostic treatment targets. In addition, psychological interventions should incorporate psychoeducation to improve the understanding of the impact of C/PTSD on the current experience of PMLDs.

Exploring the structural characteristics of the Adult Social Care Outcomes Toolkit (ASCOT) and ASCOT-Carer

Background

Measurement models inform the approach to assess a measure's validity and also how a measure is understood, applied and interpreted. With preference-based measures (PBMs), it is generally accepted that they are formative; however, if they are applied without preferences, they may be reflective, formative or mixed. In this study, we sought to empirically test whether the reflective, formative or mixed measurement model best describes PBMs of social care-related quality of life (SCRQoL) - specifically, the ASCOT and ASCOT-Carer. We also explored the network approach, as an alternative.

Methods

ASCOT and ASCOT-Carer data were analyzed using confirmatory factor analysis and Multiple Indicators Multiple Causes models to test reflective, formative or mixed measurement models, respectively. Network analysis of partial correlations using the Gaussian graphical model was also conducted.

Results

The results indicated that the reflective measurement model is the worst fit for ASCOT and ASCOT-Carer. The formative or mixed measurement models may apply to ASCOT. The mixed measurement model was the best fit for ASCOT-Carer. The network analysis indicated that the most important or influential items were Occupation and Personal cleanliness and comfort (ASCOT) and Time and space and Self-care (ASCOT-Carer).

Conclusions

The ASCOT and ASCOT-Carer are best described as formative/mixed or mixed measurement models, respectively. These findings may guide the approach to the validation of cross-culturally adapted and translated versions. Specifically, we recommend that EFA be applied to establish structural characteristics, especially if the measure will be applied as a PBM and as a measure of SCRQoL. Network analysis may also provide further useful insights into structural characteristics.

Network analysis of body-related complaints in patients with neurotic or personality disorders referred to psychotherapy

Background

Psychopathology theory and clinical practice require the most complex knowledge about patients' complaints. In patients seeking for psychotherapy, body-related symptoms often complicate treatment.

Aim

This study aimed at examining connections between body-related symptoms, and identification of symptoms which may be responsible for emergency and sustaining of anxiety, somatoform and personality disorders with the use of network analysis.

Methods

In our retrospective research we used data from a sample of 4616 patients of the Department of Psychotherapy, University Hospital in Cracow, diagnosed with anxiety, somatoform or personality disorders. We constructed the Triangulated Maximally Filtered Graph (TMFG) networks of 44 somatoform symptoms endorsed in the symptom checklist "O" (SCL-O) and identified the most central symptoms within the network for all patients and in subgroups of women vs. men, older vs. younger, and diagnosed in 1980-2000 vs. 2000-2015. We used bootstrap to determine the accuracy and stability of five networks' parameters: strength, expected influence, eigenvector, bridge strength and hybrid centrality.

Results

The most central symptoms within the overall network, and in six subnetworks were dyspnea and migratory pains. We identified some gender-related differences, but no differences were observed for the age and time of diagnosis.

Conclusions

Self-reported dyspnea and migratory pains are potential important targets for treatment procedures.

Integrative Resource Model of Workplace Inclusion for Reduced Inequality: Conservation of Resources Perspective

This study provides much-needed empirical study of workplace inclusion of underresourced employees of low socioeconomic status. Based upon a conservation of resources perspective, we have examined the centrality of resources as a key inclusion process for well-being outcomes for employees with insufficient resources. In the context of misuse of institutional power over operative workers within highly segmented and hierarchical work settings, this study validates the importance of economic inclusion for fostering workers' well-being via fair employment practices. This study also offers new knowledge of the integrative resource model of workplace inclusion research by validating workers' personal resources of learning orientation as an internal condition for strengthening the positive effect of economic inclusion on well-being.

Connecting the Dots: Exploring Psychological Network Analysis as a Tool for Analyzing Organizational Survey Data

Organizations allocate considerable resources in surveys aimed at assessing how employees perceive certain job aspects. These perceptions are often modeled as latent constructs (e.g., job satisfaction) measured by multiple indicators. This approach, although useful, has several drawbacks such as a strong reliance on local independence and a lower performance in exploratory contexts with many variables. In this paper, we introduce psychological network analysis (PNA) as a novel method to examine organizational surveys. It is first argued how the network approach allows studying the complex patterns of attitudes, perceptions, and behaviors that make up an organizational survey by modeling them as elements in an interconnected system. Next, two empirical demonstrations are presented showcasing features of this technique using two datasets. The first demonstration relies on original organizational survey data (N = 4270) to construct a network of attitudes and behaviors related to innovative work behavior. In the second demonstration, drawing on archival leadership data from an organization (N = 337), the focus lies on comparing structural properties of leadership attitude networks between subsamples of supervisors and non-supervisors. We conclude this paper by discussing how PNA constitutes a promising avenue for researching organizational phenomena which typically constitute a set of interconnected elements.

Intelligent Palliative Care Based on Patient-Reported Outcome Measures

CONTEXT

The growth of patient reported outcome measures data in palliative care provides an opportunity for machine learning to identify patterns in patient responses signifying different phases of illness.

OBJECTIVES

The study will explore if machine learning and network analysis can identify phases in patient palliative status through symptoms reported on the Integrated Palliative Care Outcome Scale (IPOS).

METHODS

A partly cross-sectional and partially longitudinal observational study was undertaken using the Australasian Karnofsky Performance Scale (AKPS); Integrated Palliative Care Outcome Scale (IPOS); Phase of Illness (POI). Patient palliative records (n = 1507, 65% stable, 20% unstable, 9% deteriorating, 2% terminal) from 804 adult patients enrolled in a New Zealand palliative care service were analysed using a combination of statistical, machine learning and network analysis techniques.

RESULTS

Data from IPOS showed considerable variation with phase. Also, network analysis showed clear associations between items by phase. Six machine learning techniques identified the most important variables for predicting possible transition between phases of illness. Network analysis for all patients showed that Poor Appetite and Loss of Energy were central IPOS items, with Loss of Energy linked to Drowsiness, Shortness of Breath and Lack of Mobility on the one hand, and Poor Appetite linked to Nausea, Vomiting, Constipation and Sore and Dry Mouth on the other.

CONCLUSION

These preliminary results, when coupled with the latest technological developments in mobile apps and wearable technology, could point the way to increased use of digital therapeutics in continuous palliative care monitoring.

The boundaries between complex posttraumatic stress disorder symptom clusters and post-migration living difficulties in traumatised Afghan refugees: a network analysis

BACKGROUND

Psychological distress due to the ongoing war, violence, and persecution is particularly common among Afghan asylum seekers and refugees. In addition, individuals face a variety of post-migration living difficulties (PMLDs). Complex posttraumatic stress symptoms are among the most common mental health problems in this population, and were associated with the overall burden of PMLDs. The complex interplay of posttraumatic symptoms has been investigated from a network perspective in previous studies. However, individuals are embedded in and constantly react to the environment, which makes it important to include external factors in network models to better understand the etiology and maintaining factors of posttraumatic mental health problems. PMLDs are a major risk factor for posttraumatic distress and considering their impact in interventions might improve response rates. However, the interaction of these external factors with posttraumatic psychopathological distress is not yet fully understood. Thus, we aimed to illuminate the complex interaction between PMLDs and CPTSD symptom clusters.

OBJECTIVE

The main objective is the exploration of the network structure and the complex interplay of ICD-11 CPTSD symptom clusters and distinct forms of PMLDs.

METHOD

The symptom clusters of CPTSD and PMLDs were collected within a randomised controlled trial among 93 treatment-seeking Afghan asylum seekers and refugees via a fully structured face-to-face and interpreter assisted interview. Using a network analytical approach, we explored the complex associations and network centrality of the CPTSD symptom clusters and the PMLD factors: discrimination & socio-economical living conditions, language acquisition & barriers, family concerns, and residence insecurity.

RESULTS

The results suggest direct links within and between the constructs (CPTSD, PMLD). Almost all PMLD factors were interrelated and associated to CPTSD, family concerns was the only isolated variable. The CPTSD symptom cluster re-experiencing and the PMLD factor language acquisition & barriers connected the two constructs. Affective dysregulation had the highest and avoidance the lowest centrality.

CONCLUSIONS

Re-experiencing and affective dysregulation have the strongest ties to PMLDs. Thus, these domains might explain the strong association of posttraumatic psychopathology with PLMDs and, consequently, prioritization of these domains in treatment approaches might both facilitate treatment response and reduce burden caused by PMLDs.

A network analysis of postmigration living difficulties in refugees and asylum seekers

BACKGROUND

The prevalence of mental disorders among asylum seekers and refugees is elevated compared to the general population. The importance of post-migration living difficulties (PMLDs), stressors faced after displacement, has recently been recognized due to research demonstrating their moderating role of on mental health outcomes. Traditionally, PMLDs were investigated as count variables or latent variables, disregarding plausible interrelationships among them.

OBJECTIVES

To use network analysis to investigate the associations among PMLDs.

METHODS

Based on a cross-sectional measurement of seventeen PMLDs in a clinical sample of traumatized asylum seekers and refugees (N = 151), a partial correlation network was estimated, and its characteristics assessed.

RESULTS

The network consisted of 71 of the 120 possible edges. The strongest edge was found between 'Communication difficulties' and 'Discrimination'. 'Loneliness, boredom, or isolation' had highest predictability.

CONCLUSION

Our finding of an association between communication difficulties and discrimination has been documented before and is of importance given the known negative impact of discrimination on mental and physical health outcomes. The high predictability of isolation is indicative of multiple associations with other PMLDs and highlights its importance among the investigated population. Our results are limited by the cross-sectional nature of our study and the relatively modest sample size.

Illusory-Correlation Effects on Implicit and Explicit Evaluation

Research suggests that people sometimes perceive a relationship between stimuli when no such relationship exists (i.e., illusory correlation). Illusory-correlation effects are thought to play a central role in the formation of stereotypes and evaluations of minority versus majority groups, often leading to less favorable impressions of minorities. Extant theories differ in terms of whether they attribute illusory-correlation effects to processes operating during learning (belief formation) or measurement (belief expression), and whether different evaluation measures should be differentially sensitive to illusory-correlation effects. Past research found mixed evidence for dissociative effects of illusory-correlation manipulations on measures of implicit (i.e., automatic) and explicit (i.e., controlled) evaluation. Four high-powered studies obtained illusory-correlation effects on explicit evaluations, but not implicit evaluations probed with an Implicit Association Test, Evaluative Priming Task, and Affect Misattribution Procedure. The results are consistent with theories that attribute illusory-correlation effects to processes during belief expression.

Association Rule Learning Is an Easy and Efficient Method for Identifying Profiles of Traumas and Stressors that Predict Psychopathology in Disaster Survivors: The Example of Sri Lanka

Research indicates that psychopathology in disaster survivors is a function of both experienced trauma and stressful life events. However, such studies are of limited utility to practitioners who are about to go into a new post-disaster setting as (1) most of them do not indicate which specific traumas and stressors are especially likely to lead to psychopathology; and (2) each disaster is characterized by its own unique traumas and stressors, which means that practitioners have to first collect their own data on common traumas, stressors and symptoms of psychopathology prior to planning any interventions. An easy-to-use and easy-to-interpret data analytical method that allows one to identify profiles of trauma and stressors that predict psychopathology would be of great utility to practitioners working in post-disaster contexts. We propose that association rule learning (ARL), a big data mining technique, is such a method. We demonstrate the technique by applying it to data from 337 survivors of the Sri Lankan civil war who completed the Penn/RESIST/Peradeniya War Problems Questionnaire (PRPWPQ), a comprehensive, culturally-valid measure of experienced trauma, stressful life events, anxiety and depression. ARL analysis revealed five profiles of traumas and stressors that predicted the presence of some anxiety, three profiles that predicted the presence of severe anxiety, four profiles that predicted the presence of some depression and five profiles that predicted the presence of severe depression. ARL allows one to identify context-specific associations between specific traumas, stressors and psychological distress, and can be of great utility to practitioners who wish to efficiently analyze data that they have collected, understand the output of that analysis, and use it to provide psychosocial aid to those who most need it in post-disaster settings.

A Network Approach to Studying the Associations Between Posttraumatic Stress Disorder Symptoms and Dissociative Experiences

In recent years, there has been a growing recognition of a dissociative subtype of posttraumatic stress disorder (D-PTSD), characterized by experiences of depersonalization (DP) and derealization (DR), among individuals with PTSD. Little is known, however, about how experiences of DP and/or DR are associated with the experience of other PTSD symptoms. The central aim of the present paper was to explore the associations among DP, DR, and other PTSD symptoms by means of a network analysis of cross-sectional data for 557 participants whose overall self-reported PTSD symptom severity warranted a probable PTSD diagnosis. Three notable findings emerged: (a) a strong association between DP and DR, (b) the identification of DP as the most central symptom in the network, and (c) the discovery that clusters of symptoms in the network were roughly consistent with DSM-5 PTSD criteria. We discuss these findings in light of some considerations, including the nature of our sample and the limits of interpreting cross-sectional network models.

The network approach to posttraumatic stress disorder: a systematic review

Background: The empirical literature of network analysis studies of posttraumatic stress symptoms (PTSS) has grown rapidly over the last years. Objective: We aimed to assess the characteristics of these studies, and if possible, the most and least central symptoms and the strongest edges in the networks of PTSS. Method: The present systematic review, conducted in PsycInfo, Medline, and Web of Science, synthesizes findings from 20 cross-sectional PTSS network studies that were accepted for publication between January 2010 and November 2018 (PROSPERO ID: CRD42018112825). Results: Results indicated that the network studies investigated a broad range of samples and that most studies used similar analytic approaches including stability analysis. Only strength centrality was generally adequately stable. Amnesia was consistently reported to have lowest strength, while there was substantial heterogeneity regarding which nodes had highest strength centrality. The strongest edge weights were typically within each DSM-IV/DSM-5 PTSD symptom cluster. Conclusions: Hypothesis-driven studies are needed to determine whether the heterogeneity in networks resulted from differences in samples or whether they are the product of underlying methodological reasons.

Understanding the Contribution of HRM Bundles for Employee Outcomes Across the Life-Span

Using the Job Demands-Resources model literature and the life-span theory as scholarly frameworks, we examined the effects of job demands and job resources as mediators in the relationship between bundles of used HRM practices and employee outcomes. In addition, we tested for age differences in our research model. Findings confirmed the hypothesized original 2-factor structure representing maintenance and development HRM practices. Structural Equation Modeling analyses showed that the maintenance HRM bundle related directly and negatively to employee outcomes, without moderating effects of age. However, job resources appeared to mediate this relationship in a positive way as it also did for the development HRM bundle. Whereas this study showed the 'driving power' of the actual use of HRM bundles through job resources, regardless of the employee's age, this study also suggests a 'dark side' of HRM. In particular, we found that development HRM bundles may also increase job demands, which, in turn, may result in lower levels of beneficial employee outcomes. These empirical outcomes demonstrate the strength of the driving power eliciting from job resources preceded by any HRM bundle. Moreover, this effect appears to apply to employees of all ages. Our moderated-mediation model appeared robust for several control variables. Overall, this study provides an extension of the well-known Job Demands-Resources model by including maintenance and development bundles of HRM practices used by employees that have a differential effect on job demands and job resources which in turn have an impact on employee outcomes.

Post-traumatic stress disorder and depression among Syrian refugees residing in the Kurdistan region of Iraq

Background

Since the Syrian civil war began in March 2011, more than half of the Syrian population was forced to escape from their homes, and more than 5 million of them fled their country. The aim of the present study is to estimate the psychological consequences of this conflict among the refugee population who fled to Iraq.

Method

In 2017, a team of locally trained psychologists and social workers interviewed 494 married couples (988 individuals) who were Syrian Kurdish refugees in the Kurdistan Region of Iraq. Validated Kurdish Kurmanji and Arabic versions of post-traumatic stress disorder (PTSD) Checklist for DSM-5 and depression section of Hopkins Symptom Checklist-25 were used for assessing PTSD and depression symptoms.

Results

Almost all of the participants (98.5%) had experienced at least one traumatic event and 86.3% of them experienced three or more traumatic event types. The prevalence of probable PTSD was about 60%. Gender, length of time in the camp, area in which participants were grown up, and the number of traumatic event types were significant predictors for the presence of PTSD symptoms. Approximately the same rate of participants (59.4%) experienced probable depression, which was associated with gender, age, time spent in the camp, and the number of traumatic event types.

Conclusion

PTSD and depression are prevalent among refugees exposed to traumatic events, and various variables play important roles. The pattern of risk factors in this population is consistent with findings from war-affected populations in other regions and should be considered for intervention within this population and more broadly.

The Predictors of Consumer Behavior in Relation to Organic Food in the Context of Food Safety Incidents: Advancing Hyper Attention Theory Within an Stimulus-Organism-Response Model

Despite the rapid development of China's organic food industry in recent years, the market size of this industry remains relatively small. Since the organic food market started late in China, consumer groups are mainly concentrated in large cities at present. It is, therefore, urgent to take effective measures to promote the development of China's organic food market. The current study focuses on the direct and indirect relationships between food safety incidents and organic food purchases by considering the Chinese context, using stimulus-organism-response model and information use environment theory, and introducing a hyper attention cognitive model. The results show that external stimulus (food safety incidents) and internal stimulus (consumer environment orientation) can significantly affect consumers' response (namely, consumer organic cognition), and the enhancement of consumer organic cognition can promote consumer organic purchase. In addition, consumers' information environment (the information relating to food safety incidents and environment) can significantly affect their organic food purchase. Moreover, food safety incidents can attract consumers' hyper attention and thus has a positive impact on consumers' cognition of organic food. These findings have important implications for research and practice.

Mapping Ethnic Stereotypes and Their Antecedents in Russia: The Stereotype Content Model

The stereotype content model (SCM), originating in the United States and generalized across nearly 50 countries, has yet to address ethnic relations in one of the world's most influential nations. Russia and the United States are somewhat alike (large, powerful, immigrant-receiving), but differ in other ways relevant to intergroup images (culture, religions, ideology, and history). Russian ethnic stereotypes are understudied, but significant for theoretical breadth and practical politics. This research tested the SCM on ethnic stereotypes in a Russian sample (N = 1115). Study 1 (N = 438) produced an SCM map of the sixty most numerous domestic ethnic groups (both ethnic minorities and immigrants). Four clusters occupied the SCM warmth-by-competence space. Study 2 (N = 677) compared approaches to ethnic stereotypes in terms of status and competition, cultural distance, perceived region, and four intergroup threats. Using the same Study 1 groups, the Russian SCM map showed correlated warmth and competence, with few ambivalent stereotypes. As the SCM predicts, status predicted competence, and competition negatively predicted warmth. Beyond the SCM, status and property threat both were robust antecedents for both competence and warmth for all groups. Besides competition, cultural distance also negatively predicted warmth for all groups. The role of the other antecedents, as expected, varied from group to group. To examine relative impact, a network analysis demonstrated that status, competition, and property threat centrally influence many other variables in the networks. The SCM, along with antecedents from other models, describes Russian ethnic-group images. This research contributes: (1) a comparison of established approaches to ethnic stereotypes (from acculturation and intergroup relations) showing the stability of the main SCM predictions; (2) network structures of the multivariate dependencies of the considered variables; (3) systematically cataloged images of ethnic groups in Russia for further comparisons, illuminating the Russian historical, societal, and interethnic context.

Formative psychosocial evaluation using dynamic networks: trauma, stressors, and distress among Darfur refugees living in Chad

BACKGROUND

Research on the impact of various types of stressors on refugee wellbeing may not readily inform those designing interventions about the supports that will be most helpful in particular settings. Composite variables used in psychosocial research that represent overarching types of stressors provide only vague information about intervention targets. Dynamic networks model individual phenomena separately (i.e., specific stressors and symptoms of distress) to inform how phenomena interact with each other in ways that may be useful for individuals planning interventions in humanitarian aid settings.

METHODS

Using archival data from Darfur refugees, we used a dynamic networks approach to model relationships between locally-validated measures of traumatic events, displacement stressors, impairment, and distress.

RESULTS

Findings aligned with previous research on the centrality of basic needs in refugee populations. Further, specific stressors were highlighted as particularly impactful for this population, and sleep and physical difficulties emerged as key aspects of distress and impairment, suggesting areas for targeted intervention. Conclusions: Dynamic network approaches may be fruitful for identifying setting-specific intervention targets and maximizing the impact of limited resources in humanitarian aid settings.

Network structure of the Wisconsin Schizotypy Scales-Short Forms: Examining psychometric network filtering approaches

Schizotypy is a multidimensional construct that provides a useful framework for understanding the etiology, development, and risk for schizophrenia-spectrum disorders. Past research has applied traditional methods, such as factor analysis, to uncovering common dimensions of schizotypy. In the present study, we aimed to advance the construct of schizotypy, measured by the Wisconsin Schizotypy Scales-Short Forms (WSS-SF), beyond this general scope by applying two different psychometric network filtering approaches-the state-of-the-art approach (lasso), which has been employed in previous studies, and an alternative approach (information-filtering networks; IFNs). First, we applied both filtering approaches to two large, independent samples of WSS-SF data (ns = 5,831 and 2,171) and assessed each approach's representation of the WSS-SF's schizotypy construct. Both filtering approaches produced results similar to those from traditional methods, with the IFN approach producing results more consistent with previous theoretical interpretations of schizotypy. Then we evaluated how well both filtering approaches reproduced the global and local network characteristics of the two samples. We found that the IFN approach produced more consistent results for both global and local network characteristics. Finally, we sought to evaluate the predictability of the network centrality measures for each filtering approach, by determining the core, intermediate, and peripheral items on the WSS-SF and using them to predict interview reports of schizophrenia-spectrum symptoms. We found some similarities and differences in their effectiveness, with the IFN approach's network structure providing better overall predictive distinctions. We discuss the implications of our findings for schizotypy and for psychometric network analysis more generally.

Network models can help focus research on the role of culture and context in psychopathology, but don't discount latent variable models

Borsboom et al. correctly note that the use of latent variable models in cross-cultural research has resulted in a futile search for universal, biological causes of psychopathology; however, this is not an inevitable outcome of such models. While network analytic approaches require further development, network models have the potential to better elucidate the role of cultural and contextual variables related to psychopathology.

The impact of valenced verbal information on implicit and explicit evaluation: the role of information diagnosticity, primacy, and memory cueing

Previous research has shown that the presentation of valenced information about a target stimulus sometimes has different effects on implicit and explicit stimulus evaluations. Importantly, however, research examining the moderators of implicit-explicit dissociations has often failed to account for differences in the properties of the instruments used to measure implicit and explicit evaluations, preventing a clear interpretation of the results. In an effort to overcome these limitations, we conducted a study that probed the impact of valenced information on implicit and explicit evaluations as measured with procedures that were matched on methodological factors. Participants first read positive and negative information about a person named Bob and then completed measures of implicit and explicit evaluations of Bob. We examined the moderating effect of three characteristics: information diagnosticity, primacy, and whether information retrieval was cued during evaluation. Results of two high-powered experiments showed an effect of diagnosticity on implicit and explicit evaluations, replicating previous work, and extending it to new evaluation measures. We observed primacy effects on explicit evaluations in Experiment 1 and on implicit evaluations in Experiment 2. However, we did not observe memory cueing effects or any interactions. We discuss practical implications as well as implications for cognitive evaluation theories.

Network analysis: a brief overview and tutorial

Objective : The present paper presents a brief overview on network analysis as a statistical approach for health psychology researchers. Networks comprise graphical representations of the relationships (edges) between variables (nodes). Network analysis provides the capacity to estimate complex patterns of relationships and the network structure can be analysed to reveal core features of the network. This paper provides an overview of networks, how they can be visualised and analysed, and presents a simple example of how to conduct network analysis in R using data on the Theory Planned Behaviour (TPB). Method: Participants (n = 200) completed a TPB survey on regular exercise. The survey comprised items on attitudes, normative beliefs, perceived behavioural control, and intentions. Data were analysed to examine the network structure of the variables. The EBICglasso was applied to the partial correlation matrix. Results: The network structure reveals the variation in relationships between the items. The network split into three distinct communities of items. The affective attitude item was the central node in the network. However, replication of the network in larger samples to produce more stable and robust estimates of network indices is required. Conclusions: The reported network reveals that the affective attitudinal variable was the most important node in the network and therefore interventions could prioritise targeting changing the emotional responses to exercise. Network analysis offers the potential for insight into structural relations among core psychological processes to inform the health psychology science and practice.

Illustration of Step-Wise Latent Class Modeling With Covariates and Taxometric Analysis in Research Probing Children's Mental Models in Learning Sciences

This paper illustrates two psychometric methods, latent class analysis (LCA) and taxometric analysis (TA) using empirical data from research probing children's mental representation in science learning. LCA is used to obtain a typology based on observed variables and to further investigate how the encountered classes might be related to external variables, where the effectiveness of classification process and the unbiased estimations of parameters become the main concern. In the step-wise LCA, the class membership is assigned and subsequently its relationship with covariates is established. This leading-edge modeling approach suffers from severe downward-biased estimations. The illustration of LCA is focused on alternative bias correction approaches and demonstrates the effect of modal and proportional class-membership assignment along with BCH and ML correction procedures. The illustration of LCA is presented with three covariates, which are psychometric variables operationalizing formal reasoning, divergent thinking and field dependence-independence, respectively. Moreover, taxometric analysis, a method designed to detect the type of the latent structural model, categorical or dimensional, is introduced, along with the relevant basic concepts and tools. TA was applied complementarily in the same data sets to answer the fundamental hypothesis about children's naïve knowledge on the matters under study and it comprises an additional asset in building theory which is fundamental for educational practices. Taxometric analysis provided results that were ambiguous as far as the type of the latent structure. This finding initiates further discussion and sets a problematization within this framework rethinking fundamental assumptions and epistemological issues.

Conflict and well-being: a comparative study of health-related quality of life, stress, and insecurity of university students in the West Bank and Jordan

PURPOSE

A significant body of research indicates that the conflict environment is detrimental to the quality of life and well-being of civilians. This study assesses the health-related quality of life, stress, and insecurity of the West Bank, which has been engaged in conflict for seven decades, in comparison to a demographically and culturally similar population in Jordan, a neighboring nation with no conflict. We expect the Jordanian sample to report better functioning.

METHODS

We collected 793 surveys from university students (mean age = 20.2) in Nablus, West Bank (398 [50.2%]) and Irbid, Jordan (395 [49.8%]). The survey instrument consisted of the SF-36 to measure HRQoL, the PSS-4 to measure stress, and an insecurity scale, along with demographic characteristics.

RESULTS

Our findings indicate that outcomes in the West Bank were not significantly worse than in Jordan, and in some cases represented better functioning, especially in the SF-36 measures.

CONCLUSIONS

Our counterintuitive results suggest that health and well-being outcomes are dependent on many factors in addition to conflict. For one, it may be that the better perceived health and well-being of the Palestinians is because they have developed a culture of resilience. Additionally, Jordanians are undergoing a period of instability due to internal struggles and surrounding conflicts.

Evidence that psychopathology symptom networks have limited replicability

Network analysis is quickly gaining popularity in psychopathology research as a method that aims to reveal causal relationships among individual symptoms. To date, 4 main types of psychopathology networks have been proposed: (a) association networks, (b) regularized concentration networks, (c) relative importance networks, and (d) directed acyclic graphs. The authors examined the replicability of these analyses based on symptoms of major depression and generalized anxiety between and within 2 highly similar epidemiological samples (i.e., the National Comorbidity Survey-Replication [n = 9282] and the National Survey of Mental Health and Wellbeing [n = 8841]). Although association networks were stable, the 3 other types of network analysis (i.e., the conditional independence networks) had poor replicability between and within methods and samples. The detailed aspects of the models-such as the estimation of specific edges and the centrality of individual nodes-were particularly unstable. For example, 44% of the symptoms were estimated as the "most influential" on at least 1 centrality index across the 6 conditional independence networks in the full samples, and only 13-21% of the edges were consistently estimated across these networks. One of the likely reasons for the instability of the networks is the predominance of measurement error in the assessment of individual symptoms. The authors discuss the implications of these findings for the growing field of psychopathology network research, and conclude that novel results originating from psychopathology networks should be held to higher standards of evidence before they are ready for dissemination or implementation in the field. (PsycINFO Database Record

Post-conflict struggles as networks of problems: A network analysis of trauma, daily stressors and psychological distress among Sri Lankan war survivors

A growing body of literature indicates that the mental distress experienced by survivors of war is a function of both experienced trauma and stressful life events. However, the majority of these studies are limited in that they 1) employ models of psychological distress that emphasize underlying latent constructs and do not allow researchers to examine the unique associations between particular symptoms and various stressors; and 2) use one or more measures that were not developed for that particular context and thus may exclude key traumas, stressful life events and symptoms of psychopathology. The current study addresses both these limitations by 1) using a novel conceptual model, network analysis, which assumes that symptoms covary with each other not because they stem from a latent construct, but rather because they represent meaningful relationships between the symptoms; and 2) employing a locally developed measure of experienced trauma, stressful life problems and symptoms of psychopathology. Over the course of 2009-2011, 337 survivors of the Sri Lankan civil war were administered the Penn-RESIST-Peradeniya War Problems Questionnaire (PRPWPQ). Network analysis revealed that symptoms of psychopathology, problems pertaining to lack of basic needs, and social problems were central to the network relative to experienced trauma and other types of problems. After controlling for shared associations, social problems in particular were the most central, significantly more so than traumatic events and family problems. Several particular traumatic events, stressful life events and symptoms of psychopathology that were central to the network were also identified. Discussion emphasizes the utility of such network models to researchers and practitioners determining how to spend limited resources in the most impactful way possible.

Moving Forward: Challenges and Directions for Psychopathological Network Theory and Methodology

Since the introduction of mental disorders as networks of causally interacting symptoms, this novel framework has received considerable attention. The past years have resulted in over 40 scientific publications and numerous conference symposia and workshops. Now is an excellent moment to take stock of the network approach: What are its most fundamental challenges, and what are potential ways forward in addressing them? After a brief conceptual introduction, we first discuss challenges to network theory: (1) What is the validity of the network approach beyond some commonly investigated disorders such as major depression? (2) How do we best define psychopathological networks and their constituent elements? And (3) how can we gain a better understanding of the causal nature and real-life underpinnings of associations among symptoms? Next, after a short technical introduction to network modeling, we discuss challenges to network methodology: (4) heterogeneity of samples studied with network analytic models, and (5) a lurking replicability crisis in this strongly data-driven and exploratory field. Addressing these challenges may propel the network approach from its adolescence into adulthood and promises advances in understanding psychopathology both at the nomothetic and idiographic level.

Modeling Psychological Attributes in Psychology - An Epistemological Discussion: Network Analysis vs. Latent Variables

Network Analysis is considered as a new method that challenges Latent Variable models in inferring psychological attributes. With Network Analysis, psychological attributes are derived from a complex system of components without the need to call on any latent variables. But the ontological status of psychological attributes is not adequately defined with Network Analysis, because a psychological attribute is both a complex system and a property emerging from this complex system. The aim of this article is to reappraise the legitimacy of latent variable models by engaging in an ontological and epistemological discussion on psychological attributes. Psychological attributes relate to the mental equilibrium of individuals embedded in their social interactions, as robust attractors within complex dynamic processes with emergent properties, distinct from physical entities located in precise areas of the brain. Latent variables thus possess legitimacy, because the emergent properties can be conceptualized and analyzed on the sole basis of their manifestations, without exploring the upstream complex system. However, in opposition with the usual Latent Variable models, this article is in favor of the integration of a dynamic system of manifestations. Latent Variables models and Network Analysis thus appear as complementary approaches. New approaches combining Latent Network Models and Network Residuals are certainly a promising new way to infer psychological attributes, placing psychological attributes in an inter-subjective dynamic approach. Pragmatism-realism appears as the epistemological framework required if we are to use latent variables as representations of psychological attributes.

A network approach to modeling comorbid internalizing and alcohol use disorders

Internalizing disorders co-occur with alcohol use disorder (AUD) at a rate that exceeds chance and compromise conventional AUD treatment. The "vicious cycle" model of comorbidity specifies drinking to cope (DTC) as a link between these disorders that, when not directly addressed, undermines the effectiveness of conventional treatments. Interventions based on this model have proven successful but there is no direct evidence for how and to what extent DTC contributes to the maintenance of comorbidity. In the present study, we used network analysis to depict associations between syndrome-specific groupings of internalizing symptoms, alcohol craving, and drinking behavior, as well as DTC and other extradiagnostic variables specified in the vicious cycle model (e.g., perceived stress and coping self-efficacy). Network analyses of 362 individuals with comorbid anxiety and AUD assessed at the beginning of residential AUD treatment indicated that while internalizing conditions and drinking elements had only weak direct associations, they were strongly connected with DTC and perceived stress. Consistent with this, centrality indices showed that DTC ranked as the most central/important element in the network in terms of its "connectedness" to all other network elements. A series of model simulations-in which individual elements were statistically controlled for-demonstrated that DTC accounted for all the relationships between the drinking-related elements and internalizing elements in the network; no other variable had this effect. Taken together, our findings suggest that DTC may serve as a "keystone" process in maintaining comorbidity between internalizing disorders and AUD. (PsycINFO Database Record

The interplay between cognitive risk and resilience factors in remitted depression: A network analysis

BACKGROUND

Individuals in remission from depression are at increased risk for developing future depressive episodes. Several cognitive risk- and resilience factors have been suggested to account for this vulnerability. In the current study we explored how risk- and protective factors such as cognitive control, adaptive and maladaptive emotion regulation, residual symptomatology, and resilience relate to one another in a remitted depressed (RMD) sample.

METHODS

We examined the relationships between these constructs in a cross-sectional dataset of 69 RMD patients using network analyses in order to obtain a comprehensive, data-driven view on the interplay between these constructs. We subsequently present an association network, a concentration network, and a relative importance network.

RESULTS

In all three networks resilience formed the central hub, connecting perceived cognitive control (i.e., working memory complaints), emotion regulation, and residual symptomatology. The contribution of the behavioral measure for cognitive control in the network was negligible. Moreover, the directed relative importance network indicates bidirectional influences between these constructs, with all indicators of centrality suggesting a key role of resilience in remission from depression.

LIMITATIONS

The presented findings are cross-sectional and networks are limited to a fixed set of key constructs in the literature pertaining cognitive vulnerability for depression.

CONCLUSIONS

These findings indicate the importance of resilience to successfully cope with stressors following remission from depression. Further in-depth studies will be essential to identify the specific underlying resilience mechanisms that may be key to successful remission from depression.