Dimensions of Normal Personality as Networks in Search of Equilibrium: You Can't like Parties if you Don't like People

Network analysis of trait aggression among community youths and juvenile offenders

OBJECTIVE

The mainstream view in trait aggression research has regarded the structure as representing the latent cause of the cognitions, emotions, and behaviors that supposedly reflect its nature. Under network perspective, trait aggression is not a latent cause of its features but a dynamic system of interacting elements. The current study uses network theory to explain the structure of relationships between trait aggression features in juvenile offenders and their peers.

METHODS

Network analysis was applied to investigate the dynamic system of trait aggression operationalized by the Buss-Perry Aggression Questionnaire in a sample of community youths (Mage = 17.46, N = 715) and juvenile offenders (Mage = 18.36, N = 834).

RESULTS

The facet level networks showed that anger is a particularly effective mechanism for activating all other traits. In addition, anger was more strongly associated with physical aggression and the overall network strength was greater in juvenile delinquency networks than in their peers. The item level networks revealed that A4 and A6 exhibited the highest predictability and strength centrality in both samples. Also, the Bayesian network indicated that these two items were positioned at the highest level in the model. There are similarities and differences between juvenile delinquents and community adolescents in trait aggression.

CONCLUSION

Trait aggression was primarily activated by difficulty controlling one's temper and feeling like a powder keg.

Self-other agreement and criterion-related validity of moral pride and hubris

OBJECTIVES

This research investigates the moral implications of trait-level moral pride and hubris, addressing prior limitations by gathering data from multiple sources. We raise two interrelated questions: (1) Do well-acquainted peers agree with their friends on judgments of trait-level moral pride and hubris? (2) Are moral pride and hubris related to divergent (im)moral outcomes, regardless of measurement sources?

METHOD

We collected data from a sample of university students and their friends (N = 173 dyads) in Hong Kong to examine self-other agreement and criterion-related validity of trait-level moral pride and hubris.

RESULTS

Our findings reveal a medium-to-large level of self-other agreement for, as well as a moral divergence of, trait-level moral pride and hubris. Notably, self-reports of moral pride predict prosocial behavior, whereas self-reports of moral hubris predict virtue-signaling behavior, regardless of whether the outcomes are self- or other-reported. Moreover, self-reports trump other-reports in predicting some outcomes, but the reverse is true for other outcomes.

CONCLUSIONS

Our findings suggest that individuals' proneness to experience morally specific pride and hubris constitutes "real" traits, evoking divergent (im)moral outcomes. Furthermore, self- and other-reports each contain some unique trait-relevant information, with their relative predictive power depending on the specific predictor and outcome.

New Psychometric Evidence of the Grief Impairment Scale (GIS) in People Who Have Experienced the Death of a Loved One From a Network Psychometric Approach in Two Latin American Countries

This study aimed to evaluate the psychometric properties of the Grief Impairment Scale (GIS) using a network psychometric model. A total of 1048 individuals from Peru and El Salvador participated. A network psychometric model was used to determine internal structure, reliability, and cross-country invariance. The results indicate that the GIS items were grouped into a single network structure through Exploratory Graph Analysis. Reliability was estimated by structural consistency, and it was found that when replicating the network structure within an empirical dimension, a single network structure was consistently obtained, and all items remained stable. Furthermore, the network structure was invariant, thus functioning similarly across the different country groups. In conclusion, the GIS presented solid psychometric evidence of validity based on its internal structure, reliability, and cross-country invariance. Therefore, the GIS is a psychometrically sound measure of functional impairment symptoms due to grief for Peruvian and Salvadoran individuals.

Comparing the Clique Percolation algorithm to other overlapping community detection algorithms in psychological networks: A Monte Carlo simulation study

In psychological networks, one limitation of the most used community detection algorithms is that they can only assign each node (symptom) to a unique community, without being able to identify overlapping symptoms. The clique percolation (CP) is an algorithm that identifies overlapping symptoms but its performance has not been evaluated in psychological networks. In this study, we compare the CP with model parameters chosen based on fuzzy modularity (CPMod) with two other alternatives, the ratio of the two largest communities (CPRat), and entropy (CPEnt). We evaluate their performance to: (1) identify the correct number of latent factors (i.e., communities); and (2) identify the observed variables with substantive (and equally sized) cross-loadings (i.e., overlapping symptoms). We carried out simulations under 972 conditions (3x2x2x3x3x3x3): (1) data categories (continuous, polytomous and dichotomous); (2) number of factors (two and four); (3) number of observed variables per factor (four and eight); (4) factor correlations (0.0, 0.5, and 0.7); (5) size of primary factor loadings (0.40, 0.55, and 0.70); (6) proportion of observed variables with substantive cross-loadings (0.0%, 12.5%, and 25.0%); and (7) sample size (300, 500, and 1000). Performance was evaluated through the Omega index, Mean Bias Error (MBE), Mean Absolute Error (MAE), sensitivity, specificity, and mean number of isolated nodes. We also evaluated two other methods, Exploratory Factor Analysis and the Walktrap algorithm modified to consider overlap (EFA-Ov and Walk-Ov, respectively). The Walk-Ov displayed the best performance across most conditions and is the recommended option to identify communities with overlapping symptoms in psychological networks.

Psychometric network analysis of the Patient Health Questionnaire-4 (PHQ-4) in Paraguayan general population

BACKGROUND

Depression and anxiety are two of the most prevalent and disabling mental disorders worldwide, both in the general population and in outpatient clinical settings.

OBJECTIVE

This study aimed to analyze the psychometric properties of the Patient Health Questionnaire-4 (PHQ-4) based on network analysis metrics.

METHODS

A total of 911 Paraguayans (23.71% women and 76.29% men; mean age 31.25 years, SD = 10.63), selected by non-probabilistic convenience sampling, participated in the study. Network analysis was used to evaluate the internal structure, reliability, and measurement invariance between men and women.

RESULTS

The results revealed that the PHQ-4 is a unidimensional measure through Exploratory Graph Analysis (EGA). Reliability, through structural consistency, identified that 100% of the time, only a single dimension was obtained, and all items remained stable, as they were always replicated within the empirical dimension. The unidimensional structure has shown evidence of configural invariance; therefore, the network structure functioned equally among the different sex groups.

CONCLUSION

The PHQ-4 presented optimal preliminary evidence of validity based on its internal structure, reliability, and invariance between sexes. Therefore, it may be useful as an accurate and brief measure of anxiety and depressive symptoms in the Paraguayan context.

The Role of Ageist Sexual Stereotypes in the Network of Sexual Difficulties, Sex and Relationship Satisfaction Among Adults Aged 50

As people age, their sexual activity, including sexual and relationship satisfaction, may change due to the onset of health and sexual difficulties and the activation of internalized ageist sexual stereotypes. We studied whether ageist sexual stereotypes were relevant for the importance of sex for people aged 50+ and how sexual and relationship satisfaction are interlinked with health and sexual difficulties when ageist sexual stereotypes are considered. Data for the network analysis were collected from a sample of 897 Czechs and Slovaks aged 50-96 (52% men, mean age = 62.01). Two models were analyzed - with and without the inclusion of ageist sexual stereotypes. The findings indicated that ageist sexual stereotypes play an important role in the sexuality of women and men aged 50 + .The fewer ageist sexual stereotypes people had, the less sexual difficulties they reported and the more they perceived sex as important. The inclusion of stereotypes into the models, however, weakened the negative link between sexual difficulties and the importance of sex for both women and men. These findings show that the effect of ageist sexual stereotypes are likely complex in the sexual lives of people aged 50 +.

Beyond the factor indeterminacy problem using genome-wide association data

Latent factors, such as general intelligence, depression and risk tolerance, are invoked in nearly all social science research where a construct is measured via aggregation of symptoms, question responses or other measurements. Because latent factors cannot be directly observed, they are inferred by fitting a specific model to empirical patterns of correlations among measured variables. A long-standing critique of latent factor theories is that the correlations used to infer latent factors can be produced by alternative data-generating mechanisms that do not include latent factors. This is referred to as the factor indeterminacy problem. Researchers have recently begun to overcome this problem by using information on the associations between individual genetic variants and measured variables. We review historical work on the factor indeterminacy problem and describe recent efforts in genomics to rigorously test the validity of latent factors, advancing the understanding of behavioural science constructs.

Genetic and neural bases of the neuroticism general factor

We applied structural equation modeling to conduct a genome-wide association study (GWAS) of the general factor measured by a neuroticism questionnaire administered to ∼380,000 participants in the UK Biobank. We categorized significant genetic variants as acting either through the neuroticism general factor, through other factors measured by the questionnaire, or through paths independent of any factor. Regardless of this categorization, however, significant variants tended to show concordant associations with all items. Bioinformatic analysis showed that the variants associated with the neuroticism general factor disproportionately lie near or within genes expressed in the brain. Enriched gene sets pointed to an underlying biological basis associated with brain development, synaptic function, and behaviors in mice indicative of fear and anxiety. Psychologists have long asked whether psychometric common factors are merely a convenient summary of correlated variables or reflect coherent causal entities with a partial biological basis, and our results provide some support for the latter interpretation. Further research is needed to determine the extent to which causes resembling common factors operate alongside other mechanisms to generate the correlational structure of personality.

Unique Variable Analysis: A Network Psychometrics Method to Detect Local Dependence

The local independence assumption states that variables are unrelated after conditioning on a latent variable. Common problems that arise from violations of this assumption include model misspecification, biased model parameters, and inaccurate estimates of internal structure. These problems are not limited to latent variable models but also apply to network psychometrics. This paper proposes a novel network psychometric approach to detect locally dependent pairs of variables using network modeling and a graph theory measure called weighted topological overlap (wTO). Using simulation, this approach is compared to contemporary local dependence detection methods such as exploratory structural equation modeling with standardized expected parameter change and a recently developed approach using partial correlations and a resampling procedure. Different approaches to determine local dependence using statistical significance and cutoff values are also compared. Continuous, polytomous (5-point Likert scale), and dichotomous (binary) data were generated with skew across a variety of conditions. Our results indicate that cutoff values work better than significance approaches. Overall, the network psychometrics approaches using wTO with graphical least absolute shrinkage and selector operator with extended Bayesian information criterion and wTO with Bayesian Gaussian graphical model were the best performing local dependence detection methods overall.

Network Analysis of Dark Triad Traits and Emotional Intelligence in Peruvian Adults

Background

The study of Dark Triad traits and emotional variables has been conducted in numerous regions; however, there is a lack of research focusing on Latin American samples and considering variables such as sex and age. Incorporating these variables could enhance our understanding of underlying aversive patterns.

Purpose

This study aimed to analyze the associations between Dark Triad traits and emotional intelligence in Peruvian adults.

Methods

Data were collected in 2021 and 272 adults (M=31.8 years, 68% female) participated through online surveys via non-probability purposive sampling. Dirty Dozen and Trait Meta-Mood Scales were used. Two partial correlation network models were applied between the study variables with and without controlling for gender and age. Bridge-expected influence and predictability analysis were calculated to find interconnected traits and higher connections, respectively.

Results

Subclinical psychopathy primarily showed negative associations with emotional clarity (r=-0.17), followed by emotional attention (r=-0.08) and emotional repair in networks with and without age and gender variables. Machiavellianism consistently displayed a negative association with emotional repair (r=-0.13) in both estimated networks. Conversely, subclinical narcissism exhibited a positive association solely with emotional attention (r=0.15). Furthermore, younger age was linked to higher Machiavellianism (r=-0.23), females showed higher emotional attention (r=0.11), and narcissism connected intrapersonal emotional domains with Dark Traits (bridge expected influence >1.25) while Machiavellianism exhibited greater predictability (r2>0.45) due to reinforcement by other Dark Traits.

Conclusion

This study revealed negative connections between the Dark Triad and emotional intelligence traits, except for narcissism, which was positively associated with emotional attention and functioned as a "bridge" between all traits. However, relying solely on this single positive emotional aspect of narcissism may not be adequate to characterize it as possessing fully adaptive emotional traits. Findings suggest that detailed aspects of emotional intelligence and sociodemographic factors can provide valuable insights into the complex nature of aversive patterns and their broader impact on society.

Network analysis of physical activity and depressive and affective symptoms during COVID-19 home confinement

INTRODUCTION

The aim of this study was to analyze the network structure of physical activity, frequency, depressive, and affective symptoms in people under home isolation due to COVID-19.

METHOD

A longitudinal study was conducted in two phases (beginning (March 19, 2020) and end of home confinement (April 8, 2020)). The sample consisted of 579 participants from Spain (250 men and 329 women) aged 16 to 92 years (overall sample: M = 47.06, SD = 14.52). A network analysis was performed.

RESULTS

Four clusters emerged (PA, depressive symptoms, positive affect, and negative affect). A higher frequency of physical activity was related to better-sustained attention, increased alertness, and enthusiasm. In addition, feelings of guilt and shame were mitigated, and confinement distress and irritability were reduced. Physical activity also mitigated fatigue in women, whereas feelings of unhappiness were reduced in men.

CONCLUSION

Physical activity seems to be an effective option for mitigating the negative effects of the COVID-19 pandemic. Public health policymakers should develop programs to promote physical activity in order to be able to cope with confinement or similar scenarios in the future.

Trait and symptom change in group cognitive behaviour therapy for anxiety and depression

Personality traits underlying both anxiety disorders and depression are more malleable than previously presumed. This study examined associations between changes in personality traits (i.e. negative affectivity and detachment) and alleviation of anxiety and depression symptoms following cognitive behaviour therapy (CBT). We hypothesized that decreases in negative affectivity would predict alleviation of depression and anxiety symptoms and decreases in detachment would predict decreases in depression and, to a lesser degree, anxiety symptoms. Data (N = 156) were collected in a randomized controlled trial comparing transdiagnostic and diagnosis-specific group CBT for patients with major depressive disorder, social anxiety disorder, panic disorder or agoraphobia. We assessed personality traits using the Personality Inventory for DSM-5 (PID-5) and symptoms with the Hopkins Symptom Checklist 25-item scale (SCL). Prediction was based on regression analyses. We found that decreases in negative affectivity predicted lower levels of depression and anxiety symptoms while decreases in detachment only predicted lower levels of depression symptoms. The findings substantiate current efforts to explicate the dynamic interplay between personality traits and symptoms and support the existing focus on targeting negative affectivity and detachment in therapy for anxiety disorders and depression. The trial is registered at clinicaltrials.gov (ID NCT02954731).

Embedding Research on Emotion Duration in a Network Model

Contrary to early theorizing, emotions often last for longer periods of time. Variability in people's emotion duration contributes to psychopathologies. Therefore, emotion theories need to account for this variability. So far, reviews only list predictors of emotion duration without integrating them in a theoretical framework. Mechanisms explaining why these predictors relate to emotion duration remain unknown. I propose to embed research on emotion duration in a network model of emotions and illustrate the central ideas with simulations using a formal network model. In the network model, the components of an emotion have direct causal effects on each other. According to the model, emotions last longer (a) when the components are more strongly connected or (b) when the components have higher thresholds (i.e., they are more easily activated). High connectivity prolongs emotions because components are constantly reactivated. Higher thresholds prolong emotions because components are more easily reactivated even when connectivity is lower. Indirect evidence from research on emotion coherence and research on the relationship of predictors of emotion duration with components outside of emotional episodes supports the usefulness of the network model. I further argue and show in simulations that a common cause model, in which a latent emotion causes changes in emotion components, cannot account for research on emotion duration. Finally, I describe future directions for research on emotion duration and emotion dynamics from a network perspective.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42761-023-00203-3.

How the Complexity of Psychological Processes Reframes the Issue of Reproducibility in Psychological Science

In the past decade, various recommendations have been published to enhance the methodological rigor and publication standards in psychological science. However, adhering to these recommendations may have limited impact on the reproducibility of causal effects as long as psychological phenomena continue to be viewed as decomposable into separate and additive statistical structures of causal relationships. In this article, we show that (a) psychological phenomena are patterns emerging from nondecomposable and nonisolable complex processes that obey idiosyncratic nonlinear dynamics, (b) these processual features jeopardize the chances of standard reproducibility of statistical results, and (c) these features call on researchers to reconsider what can and should be reproduced, that is, the psychological processes per se, and the signatures of their complexity and dynamics. Accordingly, we argue for a greater consideration of process causality of psychological phenomena reflected by key properties of complex dynamical systems (CDSs). This implies developing and testing formal models of psychological dynamics, which can be implemented by computer simulation. The scope of the CDS paradigm and its convergences with other paradigms are discussed regarding the reproducibility issue. Ironically, the CDS approach could account for both reproducibility and nonreproducibility of the statistical effects usually sought in mainstream psychological science.

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.

Using network analysis to detect associations between suspected painful health conditions and behaviour in dogs

Pain associated with chronic health conditions in non-human animals is an important animal welfare issue. To identify animals in pain and develop an understanding of the mechanisms by which pain affects behaviour, it is therefore important to establish the direct behavioural effects of painful health conditions. We reanalyse data from a cross-sectional survey that considered the presence or absence of a painful condition in dogs and quantified their affective predispositions using the Positive and Negative Activation Scale (PANAS). By applying ideas from network theory, we conceptualise pain as a stressor that exerts direct effects on a network of interacting behavioural variables, and subsequently estimated a network model of conditional dependence relations. Painful health conditions were positively conditionally associated with age (posterior mean partial correlation, ρ = 0.34; standard deviation [SD]=0.05), and negatively conditionally associated with the item 'your dog is full of energy' (ρ = -0.14; SD=0.06). In turn, the energy item was conditionally associated with other PANAS items which were marginally associated with pain, such as items representing ease of excitability and persistence in play. This suggests these marginal effects might be indirectly mediated via the energy item. Further, utilising the posterior predictive distribution we estimated that the median conditional probability (95% credible interval) of a painful health condition given an answer of 'strongly agree' on the energy item was 0.08 (0.05, 0.11), which increased to 0.32 (0.09, 0.58), given a response of 'strongly disagree'. This provides a potentially clinically useful interpretation of the conditional dependencies detected in the network.

A Comparison of Confirmatory Factor Analysis and Network Models for Measurement Invariance Assessment When Indicator Residuals are Correlated

Social science research is heavily dependent on the use of standardized assessments of a variety of phenomena, such as mood, executive functioning, and cognitive ability. An important assumption when using these instruments is that they perform similarly for all members of the population. When this assumption is violated, the validity evidence of the scores is called into question. The standard approach for assessing the factorial invariance of the measures across subgroups within the population involves multiple groups confirmatory factor analysis (MGCFA). CFA models typically, but not always, assume that once the latent structure of the model is accounted for, the residual terms for the observed indicators are uncorrelated (local independence). Commonly, correlated residuals are introduced after a baseline model shows inadequate fit and inspection of modification indices ensues to remedy fit. An alternative procedure for fitting latent variable models that may be useful when local independence does not hold is based on network models. In particular, the residual network model (RNM) offers promise with respect to fitting latent variable models in the absence of local independence via an alternative search procedure. This simulation study compared the performances of MGCFA and RNM for measurement invariance assessment when local independence is violated, and residual covariances are themselves not invariant. Results revealed that RNM had better Type I error control and higher power compared to MGCFA when local independence was absent. Implications of the results for statistical practice are discussed.

Auditing the research practices and statistical analyses of the group-level temporal network approach to psychological constructs: A systematic scoping review

Network analyses have become increasingly common within the field of psychology, and temporal network analyses in particular are quickly gaining traction, with many of the initial articles earning substantial interest. However, substantial heterogeneity exists within the study designs and methodology, rendering it difficult to form a comprehensive view of its application in psychology research. Since the field is quickly growing and since there have been many study-to-study variations in terms of choices made by researchers when collecting, processing, and analyzing data, we saw the need to audit this field and formulate a comprehensive view of current temporal network analyses. To systematically chart researchers' practices when conducting temporal network analyses, we reviewed articles conducting temporal network analyses on psychological variables (published until March 2021) in the framework of a scoping review. We identified 43 articles and present the detailed results of how researchers are currently conducting temporal network analyses. A commonality across results concerns the wide variety of data collection and analytical practices, along with a lack of consistency between articles about what is reported. We use these results, along with relevant literature from the fields of ecological momentary assessment and network analysis, to formulate recommendations on what type of data is suited for temporal network analyses as well as optimal methods to preprocess and analyze data. As the field is new, we also discuss key future steps to help usher the field's progress forward and offer a reporting checklist to help researchers navigate conducting and reporting temporal network analyses.

Within-Person Temporal Associations Among Self-Reported Physical Activity, Sleep, and Well-Being in College Students

OBJECTIVE

A holistic understanding of the naturalistic dynamics among physical activity, sleep, emotions, and purpose in life as part of a system reflecting wellness is key to promoting well-being. The main aim of this study was to examine the day-to-day dynamics within this wellness system.

METHODS

Using self-reported emotions (happiness, sadness, anger, anxiousness) and physical activity periods collected twice per day, and daily reports of sleep and purpose in life via smartphone experience sampling, more than 28 days as college students ( n = 226 young adults; mean [standard deviation] = 20.2 [1.7] years) went about their daily lives, we examined day-to-day temporal and contemporaneous dynamics using multilevel vector autoregressive models that consider the network of wellness together.

RESULTS

Network analyses revealed that higher physical activity on a given day predicted an increase of happiness the next day. Higher sleep quality on a given night predicted a decrease in negative emotions the next day, and higher purpose in life predicted decreased negative emotions up to 2 days later. Nodes with the highest centrality were sadness, anxiety, and happiness in the temporal network and purpose in life, anxiety, and anger in the contemporaneous network.

CONCLUSIONS

Although the effects of sleep and physical activity on emotions and purpose in life may be shorter term, a sense of purpose in life is a critical component of wellness that can have slightly longer effects, bleeding into the next few days. High-arousal emotions and purpose in life are central to motivating people into action, which can lead to behavior change.

Network-based validation of the psychometric questionnaire EDI-3 for the assessment of eating disorders

Assessing the validity of a psychometric test is fundamental to ensure a reliable interpretation of its outcomes. Few attempts have been made recently to complement classical approaches (e.g., factor models) with a novel technique based on network analysis. The objective of the current study is to carry out a network-based validation of the Eating Disorder Inventory 3 (EDI-3), a questionnaire designed for the assessment of eating disorders. Exploiting a reliable, open source sample of 1206 patients diagnosed with an eating disorder, we set up a robust validation process encompassing detection and handling of redundant EDI-3 items, estimation of the cross-sample psychometric network, resampling bootstrap procedure and computation of the median network of the replica samples. We then employed a community detection algorithm to identify the topological clusters, evaluated their coherence with the EDI-3 subscales and replicated the full validation analysis on the subpopulations corresponding to patients diagnosed with either anorexia nervosa or bulimia nervosa. Results of the network-based analysis, and particularly the topological community structures, provided support for almost all the composite scores of the EDI-3 and for 2 single subscales: Bulimia and Maturity Fear. A moderate instability of some dimensions led to the identification of a few multidimensional items that should be better located in the intersection of multiple psychological scales. We also found that, besides symptoms typically attributed to eating disorders, such as drive for thinness, also non-specific symptoms like low self-esteem and interoceptive deficits play a central role in both the cross-sample and the diagnosis-specific networks. Our work adds insights into the complex and multidimensional structure of EDI-3 by providing support to its network-based validity on both mixed and diagnosis-specific samples. Moreover, we replicated previous results that reinforce the transdiagnostic theory of eating disorders.

Idiographic Ising and Divide and Color Models: Encompassing Networks for Heterogeneous Binary Data

The Ising model is a graphical model that has played an essential role in network psychometrics. It has been used as a theoretical model to conceptualize psychological concepts and as a statistical model to analyze psychological data. Using graphical models such as the Ising model to analyze psychological data has been heavily critiqued since these data often come from cross-sectional applications. An often voiced concern is the inability of the Ising model to express heterogeneity in the population. The idiographic approach has been posed as an alternative and aims to infer individual network structures. While idiographic networks overcome population heterogeneity, it is unclear how they aggregate into established cross-sectional phenomena. This paper establishes a formal bridge between idiographic and cross-sectional network approaches of the Ising model. We ascertain unique topological structures that characterize individuals and aggregate into an Ising model cross-sectionally. This new formulation supports population heterogeneity while being consistent with cross-sectional phenomena. The proposed theory also establishes a new statistical framework for analyzing populations of idiographic networks for binary variables. The Ising model and the divide and color model are special cases of this new framework. We introduce a Gibbs sampling algorithm to estimate models from this new framework.

Characterizing altruistic motivation in potential volunteers for SARS-CoV-2 challenge trials

In human challenge trials (HCTs), volunteers are deliberately infected with an infectious agent. Such trials can be used to accelerate vaccine development and answer important scientific questions. Starting early in the COVID-19 pandemic, ethical concerns were raised about using HCTs to accelerate development and approval of a vaccine. Some of those concerns pertained to potential exploitation of and/or lack of truly informed consent from volunteers. Specific areas of concern arose around individuals who may be unusually risk-seeking or too economically vulnerable to refuse the payments these trials provide, as opposed to being motivated primarily by altruistic goals. This pre-registered study is the first large-scale survey to characterize people who, early in the pandemic, expressed interest and intention to volunteer to participate in COVID-19 HCTs. We found that individuals expressing interest in SARS-CoV-2 HCTs exhibit consistently altruistic motivations without any special indication of poor risk perception or economic vulnerability. In finding that, early in the pandemic, COVID-19 HCTs were able to attract volunteers whose values align with the nature of these trials, and who are not unusually vulnerable to exploitation, this study may allay some ethical concerns about the volunteers interested in participating in such trials.

Neuropsychological networks in cognitively healthy older adults and dementia patients

Neuropsychological tests have commonly been used to determine the organization of cognitive functions by identifying latent variables. In contrast, an approach which has seldom been employed is network analysis. We characterize the network structure of a set of representative neuropsychological test scores in cognitively healthy older adults and MCI and dementia patients using network analysis. We employed the neuropsychological battery from the National Alzheimer's Coordinating Center which included healthy controls (n = 7623), mild cognitive impairment patients (n = 5981) and dementia patients (n = 2040), defined according to the Clinical Dementia Rating. The results showed that, according to several network analysis measures, the most central cognitive function is executive function followed by attention, language, and memory. At the test level, the most central test was the Trail Making Test B, which measures cognitive flexibility. Importantly, these results and most other network measures, such as the community organization and graph representation, were similar across the three diagnostic groups. Therefore, network analysis can help to establish a ranking of cognitive functions and tests based on network centrality and suggests that this organization is preserved in dementia. Central nodes might be particularly relevant both from a theoretical and clinical point of view, as they are more associated with other nodes, and their disruption is likely to have a larger effect on the overall network than peripheral nodes. The present analysis may provide a proof of principle for the application of network analysis to cognitive data.

Shared and unique interoceptive deficits in high alexithymia and neuroticism

Interoception is the perception of internal bodily signals. It is considered fundamental to developing emotional awareness. For this reason, interoceptive deficits are often associated with alexithymia, a condition characterized by difficulty identifying feelings (DIF), difficulty describing feelings (DDF), and an externally-oriented style of thinking (EOT). Yet, the atypical interoception found in alexithymia might be of a similar type and/or more serious than those found in other partially overlapping constructs that entail emotional difficulties and behavioural patterns associated with specific emotional styles. Our study explores this issue by examining the relationship between the interoceptive deficits associated with alexithymia and the Big Five personality traits. A non-clinical sample (N = 504) completed the Toronto Alexithymia Scale, the Big Five Inventory and the Multidimensional Assessment of Interoceptive Awareness. Data were analysed using a network analytic approach that conceives psychological traits as networks of interacting symptoms. The estimated network highlighted that EOT is the alexithymia component least associated with interoception and most associated with lower Openness to Experience. Conversely, DIF and Neuroticism are, respectively, the dimensions of alexithymia and the Big Five most highly associated with interoception. We also compared interoceptive abilities in the four groups of participants whose scores were a) high for both alexithymia and neuroticism, b) high only for alexithymia c), high only for neuroticism, and d) low for both. High alexithymia was especially associated with the tendency to ignore sensations of pain or discomfort, while neuroticism was more indicative of the tendency to worry about these sensations. These results suggest that while high alexithymia and neuroticism share some interoceptive deficits, others are unique to alexithymia and contribute to overall lower interoceptive ability in this condition. Our findings suggest that interventions to enhance awareness of bodily sensations can be beneficial especially for profiles who present high neuroticism and alexithymia.

Development and Validation of a New Measure of Work Annoyance Using a Psychometric Network Approach

Existing measures of the impact of job characteristics on workers’ well-being do not directly assess the extent to which such characteristics (e.g., opportunity to learn new skills) are perceived as positive or negative. We developed a measure, the Work Annoyance Scale (WAS), of the level of annoyance that workers feel about certain aspects of the job and evaluated its psychometric properties. Using archival data from two cohorts (n = 2226 and 655) of workers that had undergone an annual medical examination for occupational hazard, we show the usefulness of the network psychometric approach to scale validation and its similarities and differences from a traditional factor analytic approach. The results revealed a two-dimensional structure (working conditions and cognitive demands) that was replicable across cohorts and bootstrapped samples. The two dimensions had adequate structural consistency and discriminant validity with respect to other questionnaires commonly used in organizational assessment, and showed a consistent pattern of association with relevant background variables. Despite the need for more extensive tests of its content and construct validity in light of the organizational changes due to the COVID-19 pandemic and of an evaluation of the generalizability of the results to cultural contexts different from the Italian one, the WAS appears as a psychometrically sound tool for assessment and research in organizational contexts.

Patterned person-situation fit in daily life: Examining magnitudes, stabilities, and correlates of trait-situation and state-situation fit

Person-situation fit can be operationalized as within-person associations between profiles of personality traits and situation characteristics ( trait-situation fit) as well as personality states and situation characteristics ( state-situation fit). We provide an initial examination of basic properties (magnitudes, individual differences, reliabilities, intercorrelations), short-term stability (across weeks), and nomological correlates of overall and distinctive profile-level person-situation fits. In a real-life, multi-method multi-occasion design ( N = 204–209), we obtained data on participants’ traits (self- and informant-reported) as well as, at four time-points from their everyday lives, on situation characteristics (self- and coder-reported) and states (self-reported). Profile scores ( q-correlations) were computed across 35 cognate items between the CAQ (traits), RSQ (situations), and RBQ (states). Our descriptive and exploratory findings indicated that trait-situation and state-situation fits were sizable (overall more so than distinctive forms), and that there were substantial individual differences, which were only modestly stable during a short period and had some plausible nomological correlates (i.e., lower depression and neuroticism, but higher psychological well-being and happiness) that were driven mainly by normativity. Most findings replicated across measurement sources (self- vs. other-reports). Person-situation fit concepts, once further corroborated, could further personality-psychological research.

A network approach can improve eating disorder conceptualization and treatment

Eating disorders are severe mental illnesses with the second highest mortality rate of all psychiatric illnesses. Eating disorders are exceedingly deadly because of their complexity. Specifically, eating disorders are highly comorbid with other psychiatric illnesses (up to 95% of individuals with an eating disorder have at least one additional psychiatric illness), have extremely heterogeneous presentations, and individuals often migrate from one specific eating disorder diagnosis to another. In this Perspective, we propose that understanding eating disorder comorbidity and heterogeneity via a network theory approach offers substantial benefits for both conceptualization and treatment. Such a conceptualization, strongly based on theory, can identify specific pathways that maintain psychiatric comorbidity, how diagnoses vary across individuals, and how specific symptoms and comorbidities maintain illness for one individual, thereby paving the way for personalized treatment.

This is the way: Network perspective on targets for spatial ability development programmes

BACKGROUND

Spatial ability (SA) was shown to be important for success in different fields, including STEM. Recent research suggested that SA is a unitary construct, rather than a set of related skills. However, it is not clear how individual differences in different facets of SA emerge, and how they relate to variance in general cognitive ability.

AIMS

The aim of the present study was threefold: 1) to examine the structure of SA testing nine theoretical models; 2) to explore the relation between 16 different facets of SA with general cognitive ability; and 3) to identify central facet(s) within the network of SA - with most links and/or strongest links to other facets.

SAMPLE

The study participants were 958 university students from Russia.

METHODS

The study used a comprehensive battery of 16 SA tests and a verbal ability measure.

RESULTS

Results supported previous research, suggesting moderate overlap between all SA facets. Factor analysis suggested several potential structures, with similar fit indices for five different theoretically driven models, including split into small- and large scale; partially independent manipulation, visualization and navigation facets. Confirmatory factor analysis, mediation and network analyses showed spatial ability being largely independent from verbal ability. In addition, network analysis showed that navigation according to directions is in the centre of the network, potentially linking all SA facets.

CONCLUSION

The results have potential implications for identifying the best targets for SA interventions. The next step in research is conducting experimental studies to evaluate effectiveness of interventions targeting navigation in comparison with other facets of SA.

Taking Stock and Moving Forward: A Personalized Perspective on Mixed Emotions

Research on mixed emotions is flourishing but fractured. Several psychological subfields are working in parallel and separately from other disciplines also studying mixed emotions, which has led to a disorganized literature. In this article, we provide an overview of the literature on mixed emotions and discuss factors contributing to the lack of integration within and between fields. We present an organizing framework for the literature of mixed emotions on the basis of two distinct goals: solving the bipolar-bivariate debate and understanding the subjective experience of mixed emotions. We also present a personalized perspective that can be used when studying the subjective experience of mixed emotions. We emphasize the importance of assessing both state and trait emotions (e.g., momentary emotions, general levels of affect) alongside state and trait context (e.g., physical location, culture). We discuss three methodological approaches that we believe will be valuable in building a new mixed-emotions literature-inductive research methods, idiographic models of emotional experiences, and empirical assessment of emotion-eliciting contexts. We include recommendations throughout on applying these methods to research on mixed emotions, and we conclude with avenues for future interdisciplinary research. We hope that this perspective will foster research that results in the organized accumulation of knowledge about mixed emotions.

A Network Comparison of Motives behind Online Sexual Activities and Problematic Pornography Use during the COVID-19 Outbreak and the Post-Pandemic Period

Many researchers have considered whether online sexual activities (OSAs) increased over the course of the COVID-19 pandemic and whether these have led to an increase in problematic pornography use (PPU). This study investigated the impact of COVID-19 on PPU through pornography use motivations (PUMs) and OSAs to develop a better understanding of the mechanism and changes affecting PPU. Two groups of Chinese adults were recruited during the initial months of the pandemic (April 2020, n₁ = 496) and the post-pandemic period (October 2021, n₂ = 504). A network analysis was conducted to compare the structures of PPU symptoms among the two groups. The results showed that PUMs and OSAs were stronger predictors of PPU during the pandemic than post-pandemic (R²_pandemic = 57.6% vs. R²_{post-pandemic} = 28.7%). The motives of fantasy, sexual pleasure, stress reduction, and self-exploration were the prominent motivations during these two periods, but we found distinct PPU-related communities. PPU, sexual pleasure, and viewing sexually explicit materials (a type of OSAs) constituted a community during the pandemic but not in the post-pandemic’s network. The present study indicated that the pandemic may not have been the only factor impacting the higher rate of PPU. Instead, the higher frequency of OSAs during the pandemic may have been a strategy to cope with stress and to safely satisfy sexual desire.

Interindividual age differences in personality structure

Most investigations in the structure of personality traits do not adequately address age; instead, they presuppose a constant structure across the lifespan. Further, few studies look at the structure of personality traits a-theoretically, often neglecting to examine the relationship among indicators within a trait (convergence) and across traits (divergence). Using a network approach, the present study examines (1) age differences in divergence and convergence, (2) the similarity between the Big Five and network structures, and (3) the consistency of network structure across age groups in a large, cross-sectional sample. Results indicate that convergence shows early gains in adolescence with few differences across the lifespan, while divergence mostly weakens across adulthood. The result of these age-related differences is that Big Five indicators only parallel the Big Five structure among young but not older adults. The structure of young adults tends to be quite similar while the network structures of older adults appear to greatly differ from one another. These results suggest that older adults have a different structure of personality than younger adults and suggest that future research should not assume consistency in personality structure across the lifespan.

Decent work in Italy: A network analysis

The present study investigated the Italian version of the Decent Work Scale (DWS) with a sample of 645 workers (females = 65.1%; mean age = 43.9 years; SD = 10.9) according to a network perspective. We compared factorial and network models and estimated the regularized partial correlations for the five DWS domains: physically and interpersonally safe working conditions (SC), access to healthcare (AH), adequate compensation (AC), hours that allow for free time and rest (FT) and organizational values complement family and social values (CV). The results highlighted that the network model showed the best fit to the data. Among the most central domains in the network, the high centrality of CV suggests that this domain could represent an effective target for actions addressed to fostering decent work in Italy. In contrast, the low centrality of AC and FT underlines the urgent need to advocate for more decent remunerations and working hours in Italy.

Coexisting service-related factors preceding suicide: a network analysis

OBJECTIVES

The overall objective was to analyse service-related factors involved in the complex processes that precede suicide in order to identify potential targets for intervention.

DESIGN AND SETTING

Explorative network analysis study of post-suicide root cause analysis data from Swedish primary and secondary healthcare.

PARTICIPANTS

217 suicide cases reported to the Swedish national root cause analysis database between 2012 and 2017.

PRIMARY AND SECONDARY OUTCOME MEASURES

A total of 961 reported incidents were included. Demographic data and frequencies of reported deficiencies were registered. Topology, centrality indices and communities were explored for three networks. All networks have been tested for robustness and accuracy.

RESULTS

Lack of follow-up, evaluations and insufficient documentation issues emerged as central in the network of major themes, as did the contributing factors representing organisational problems, failing procedures and miscommunication. When analysing the subthemes of deficiencies more closely, disrupted treatments and staffing issues emerged as prominent features. The network covering the subthemes of contributing factors also highlighted discontinuity, fragile work structures, inadequate routines, and lack of resources and relevant competence as potential triggers. However, as the correlation stability coefficients for this network were low, the results need further investigation. Four communities were detected covering nodes for follow-up, evaluation, cooperation, and procedures; communication, documentation and organisation; assessments of suicide risk and psychiatric status; and staffing, missed appointments and declined treatment.

CONCLUSION

The results of this study suggest that healthcare providers may improve patient safety in suicide preventive pathways by taking active measures to provide regular follow-ups to patients with elevated suicide risk. In some cases, declined or cancelled appointments could be a warning sign. Tentative results show organisational instability, in terms of work structure, resources and staffing, as a potential target for intervention, although this must be more extensively explored in the future.

Locating the Dark Triad in a Multidimensional Personality Space

The Dark Triad traits of Psychopathy, Machiavellianism, and Narcissism should be clearly recognizable within a multidimensional personality space. Two such personality spaces were investigated in this study: HEXACO (Honesty-Humility, Emotionality, Extraversion, Agreeableness, Conscientiousness, and Openness to Experience); and the Zuckerman-Kuhlman-Aluja Personality Questionnaire (ZKA-PQ) space (Extraversion, Neuroticism, Activity, Sensation Seeking, and Aggressiveness). Our sample comprised 289 participants (137 males, 145 females, 7 unspecified) who completed these three questionnaires: HEXACO-60, ZKA-PQ/SF, and the SD3, assessing the Dark Triad. We reduced the dimensionality of each space to that of a 2D representation using Smallest Space Analysis (SSA). Three research questions guided the data analysis: (a) Do the HEXACO and ZKA-PQ SSA spaces conform to the structure of a radex? (b) Will these spaces remain invariant following the entry of the Dark Triad traits into the analyses? (c) Where will the Dark Triad traits be located in each SSA space? For ZKA-PQ space, the structure was clearly indicative of a radex, both prior to entering the Dark Triad traits into the analysis, and subsequent to this. Psychopathy and Machiavellianism were in close proximity in the Aggressiveness region; Narcissism was positioned at the common origin. In contrast, HEXACO space did not conform to a radex; furthermore, the presence of the Dark Triad traits distorts this 2D SSA space.

ConNEcT: A Novel Network Approach for Investigating the Co-occurrence of Binary Psychopathological Symptoms Over Time

Network analysis is an increasingly popular approach to study mental disorders in all their complexity. Multiple methods have been developed to extract networks from cross-sectional data, with these data being either continuous or binary. However, when it comes to time series data, most efforts have focused on continuous data. We therefore propose ConNEcT, a network approach for binary symptom data across time. ConNEcT allows to visualize and study the prevalence of different symptoms as well as their co-occurrence, measured by means of a contingency measure in one single network picture. ConNEcT can be complemented with a significance test that accounts for the serial dependence in the data. To illustrate the usefulness of ConNEcT, we re-analyze data from a study in which patients diagnosed with major depressive disorder weekly reported the absence or presence of eight depression symptoms. We first extract ConNEcTs for all patients that provided data during at least 104 weeks, revealing strong inter-individual differences in which symptom pairs co-occur significantly. Second, to gain insight into these differences, we apply Hierarchical Classes Analysis on the co-occurrence patterns of all patients, showing that they can be grouped into meaningful clusters. Core depression symptoms (i.e., depressed mood and/or diminished interest), cognitive problems and loss of energy seem to co-occur universally, but preoccupation with death, psychomotor problems or eating problems only co-occur with other symptoms for specific patient subgroups.

Objective Bayesian Edge Screening and Structure Selection for Ising Networks

The Ising model is one of the most widely analyzed graphical models in network psychometrics. However, popular approaches to parameter estimation and structure selection for the Ising model cannot naturally express uncertainty about the estimated parameters or selected structures. To address this issue, this paper offers an objective Bayesian approach to parameter estimation and structure selection for the Ising model. Our methods build on a continuous spike-and-slab approach. We show that our methods consistently select the correct structure and provide a new objective method to set the spike-and-slab hyperparameters. To circumvent the exploration of the complete structure space, which is too large in practical situations, we propose a novel approach that first screens for promising edges and then only explore the space instantiated by these edges. We apply our proposed methods to estimate the network of depression and alcohol use disorder symptoms from symptom scores of over 26,000 subjects.

A New Perspective on "Drinking" Self: A Network Approach to Characterizing Drinking-Related Changes in Personality

BACKGROUND

Previous research suggests that self-knowledge, particularly perceptions of oneself as a "drinker," may influence the development and progression of alcohol use and related problems, such as binge drinking. While existing studies have provided empirical evidence for the importance of assessing drinking self-perception within the five-factor model of personality framework, further examination with novel analytic methods, such as network analyses, could improve understanding of the drinker self-concept. Methods: Five factor traits of openness to experience, conscientiousness, extraversion, agreeableness, and emotional stability were assessed among a large sample of young adults (n = 423) across "general" and "drinking" contexts. Features of the personality networks were investigated, including topology, network centrality, stability of the network across "general" and "drinking" assessments, as well as differences in the network across the two assessments. Results: Individual personality items were more related to other items within the same trait than to other traits. There was no most central item in the networks, but item strength was uncorrelated to mean-level of the item. The network structure was stable across both assessments, although the overall strength of item relationships significantly increased in the drinking personality network. Conclusions: Examining drinking personality using a network analytic framework provided novel insights into drinker self-concept and the role drinking contexts might play in self-perception of personality in those contexts.

Supplemental data for this article is available online at https://doi.org/10.1080/10826084.2022.2034874 .

Why are you (un)conscientious? The dynamic interplay of goals, states, and traits in everyday life

Personality involves both trait and state components, personal goals serving a crucial regulatory function for the expression of personality states. The present study investigates the dynamic interplay between conscientiousness-related goals, conscientious personality states, and trait conscientiousness. A sample of 244 community participants responded to a baseline survey (T1), a 5-times-a-day Ecological Momentary Assessment (EMA) for 15 days, and a post-EMA survey (T2). Pre-registered multilevel analyses indicated significant contemporaneous positive and negative associations between momentary conscientious and unconscientious goals and state conscientiousness, respectively. Cross-lagged associations also emerged, with goals predicting future states of conscientiousness. A latent growth model was fitted on a subsample of participants (N = 159). Results indicated that change in trait conscientiousness from T1 to T2 was explained by growth in conscientiousness-related goals during the EMA phase, with a mediating effect of growth in state conscientiousness. Overall, the results corroborate the importance of goals for modeling contemporaneous and cross-lagged personality dynamics, both in short and longer timeframes.

Recent Integrations of Latent Variable Network Modeling With Psychometric Models

The combination of network modeling and psychometric models has opened up exciting directions of research. However, there has been confusion surrounding differences among network models, graphic models, latent variable models and their applications in psychology. In this paper, I attempt to remedy this gap by briefly introducing latent variable network models and their recent integrations with psychometric models to psychometricians and applied psychologists. Following this introduction, I summarize developments under network psychometrics and show how graphical models under this framework can be distinguished from other network models. Every model is introduced using unified notations, and all methods are accompanied by available R packages inducive to further independent learning.