Graphical Exploratory Analysis Research Articles

Multiple pathways to suicide: A network analysis based on three components of psychological pain

BackgroundPsychological pain is a multidimensional structure that has long been recognized as an important risk factor for suicidal ideation and behavior. The roles of interactions among psychological pain subfactors at different stages of suicidality remain unclear. MethodsA relatively large sample of outpatients with major depressive disorder (N = 501) was recruited to complete the Three-Dimensional Psychological Pain Scale (TDPPS). Exploratory graphical analysis (EGA) was conducted to explore the factor structure of TDPPS, thereby ensuring that the psychological pain subfactor was measured accurately. Network analysis included all TDPPS items, depression, passive suicidal ideation (PSI), active suicidal ideation (ASI), and history of suicidal action (SA) to identify key loops of suicidality. ResultsEGA disclosed a three-factor structure of TDPPS comprising cognitive, affective, and motivational factors. Network analysis revealed that items of motivational factors, but none of the items of cognitive and affective factors, were directly linked to PSI, ASI, and SA. Furthermore, three communities were identified by a “walktrap” algorithm. Depression and cognitive factor coalesced into a ‘cognitive’ community, affective factor constituted an ‘affective’ community, and motivational factor, PSI, ASI, and SA comprised a ‘suicidal’ community. LimitationsThis study used a cross-sectional design that cannot provide information on causal relationships among variables in the network. ConclusionsPsychological pain avoidance may be a direct driver of suicidality, and therefore its assessment and intervention in clinical practice is necessary.

Evidence of validity of the Head-Toes-Knees-Shoulders Revised (HTKS-R) for Brazilian children

ABSTRACT This study aims to examine the reliability and validity of the Head-Toes-Knees-Shoulders Revised (HTKS-R) as a measure of self-regulation in Brazilian children aged 3–5. The early childhood period is crucial for laying the foundations for personal, social and cognitive functioning, influencing development throughout life. Self-regulation, a malleable construct, plays a vital role in academic performance and behaviors throughout life. The HTKS-R, a widely used measure, assesses behavioral self-regulation, including cognitive flexibility, working memory and inhibitory control. The study included 379 children between the ages of 3 and 5 from a variety of backgrounds. Data were collected using the HTKS-R, a standardized self-report measure that integrates executive functions into a game to assess self-regulation. Exploratory Graphical Analysis (EGA) and Confirmatory Factor Analysis (CFA) were employed to analyze the data. The results of the EGA and BootEGA indicated a unidimensional structure for the HTKS-R, confirming its internal consistency and stability. The CFA supported the fit of the unidimensional model, demonstrating the test’s construct validity. The HTKS-R showed factorial invariance between genders. The results suggest that the HTKS-R is a reliable and valid measure of self-regulation in Brazilian children aged 3–5. The applicability of the test in different age groups and environments provides valuable information about children’s self-regulation skills. The study contributes to the literature by validating the HTKS-R in the Brazilian context and highlights its potential for assessing self-regulation in diverse populations.

An exploratory graphical analysis of the Montgomery-Åsberg Depression Rating Scale pre- and post-treatment using pooled antidepressant trial secondary data

BackgroundThe 10-item Montgomery-Åsberg Depression Rating Scale (MADRS) is a commonly used measure of depression in antidepressant clinical trials. Numerous studies have adopted classical test theory perspectives to assess the psychometric properties of this scale, finding generally positive results. However, its network configural structure and stability is unexplored across different time-points and treatment groups. AimsTo assess the network structure and stability of the MADRS in clinical settings pre- and post-treatment, and to determine a configurally invariant and stable model across time-points and treatment groups (placebo and intervention). MethodIndividual participant data for 6440 participants from 14 clinical trials of major depressive disorder was obtained from the data repository Vivli.org. Exploratory Graphical Analysis (EGA) was used to identify empirical models pre-treatment (baseline) and post-treatment (8-week outcome). Bootstrapping techniques were applied to obtain optimised configurally invariant models. ResultsEmpirical models presented with performance issues at baseline and for the placebo group at outcome. An abbreviated 8-item single-community model was found to be stable and configurally invariant across time-points and treatment groups. Symptoms such as low mood and lassitude showed most centrality across all models. LimitationsMetric invariance could not be explored due to research environment limitations. ConclusionsAn 8-item one-community variant of the MADRS may provide optimal performance when conducting network analyses of antidepressant clinical trial outcomes. Findings suggest that interventions targeting low mood and lassitude might be most efficacious in treating depression among clinical trial participants. Further considerations of the potential impact on trial design and analysis should be explored.

Comorbidity Patterns of Posttraumatic Stress Disorder and Depression Symptoms: Cross-Validation in Two Postearthquake Child and Adolescent Samples

Background. Children and adolescents who have been exposed to a major natural disaster are more likely to suffer from posttraumatic stress disorder (PTSD) and depression. However, only a few studies have examined comorbidity patterns at the symptom level. Furthermore, researchers should validate their findings using multiple samples to address the psychological reproducibility challenge. Methods. The Child PTSD Symptom Scale (CPSS) and Center for Epidemiological Studies Depression Scale for Children (CES-DC) were administered to two postearthquake child and adolescent samples (Wenchuan earthquake, N = 1506 ; Ya’an earthquake, N = 720 ). Each sample was followed up twice. Comorbidity patterns were characterized by cross-lagged panel network analysis (CLPN), and communities were determined by bootstrap exploratory graphical analysis (bootEGA). Results. Except for having difficulty remembering important aspects of the trauma, the remaining dysphoria symptoms could be considered bridge symptoms between PTSD and depression. Most often, intrusive and avoidant symptoms clustered together, whereas dysphoria symptoms tended to cluster with depressive symptoms. The relationship between PTSD and depression was reciprocal; within PTSD, intrusive symptoms often triggered avoidance symptoms. The correlation coefficient between the two networks was 0.70, and the correlation coefficient of node centrality was 0.55. Findings. The association between dysphoria symptoms and depression was strong, and intrusive symptoms constituted the core symptoms of PTSD. Depression and PTSD were causally related, explaining the high comorbidity rates. Two sample networks had similar global characteristics but different local characteristics. The conclusions can be generalized to some extent.

How useful are native language tests for research with advanced second language users?

AbstractWe investigated the extent to which language tests developed for native speakers (L1) can be used with advanced speakers of a second language (L2). We compared the performance of Dutch–English bilinguals with that of native English speakers on a series of English language tests, looking at vocabulary knowledge, crystallized intelligence, reading comprehension, and reading speed. It was found that advanced L2 speakers know fewer L2 words than native speakers and take longer to read texts but perform equally well on text comprehension. Tests optimized for native English-speakers predicted text comprehension less well than tests better adapted to the skill level of the bilinguals (which include the Lextale test). An exploratory graphical analysis suggested that L2 users’ performance on challenging vocabulary tests, along with performance on an English author recognition test, forms a distinct cluster – arguably also measuring interest in English language and culture besides knowledge in general (also called crystallized intelligence).

Web of well-being: re-examining PERMA and subjective well-being through networks

ABSTRACT While positive psychology has been in development for more than 20 years, positive psychologists still face the challenge of finding optimal measurements of well-being. The PERMA framework provides a new understanding of well-being based on years of research. However, whether it adds value in terms of being distinct from the already established measures of Subjective Well-Being (SWB) has recently been under debate. Certain characteristics of PERMA make it unsuited for traditional factor analysis and data analysis techniques. In this paper, we argue that a network approach is more suitable for understanding the interrelated nature of PERMA components. We then show how the components of PERMA and SWB are distinct using exploratory graphical analysis. Our results show that while PERMA predicts SWB, PERMA provides us a more nuanced way to understand the interrelated antecedents of well-being, which we refer to as the web of well-being.

Network Structure of Comorbidity Patterns in U.S. Adults with Depression: A National Study Based on Data from the Behavioral Risk Factor Surveillance System.

People with depression are at increased risk for comorbidities; however, the clustering of comorbidity patterns in these patients is still unclear. The aim of the study was to identify latent comorbidity patterns and explore the comorbidity network structure that included 12 chronic conditions in adults diagnosed with depressive disorder. A cross-sectional study was conducted based on secondary data from the 2017 behavioral risk factor surveillance system (BRFSS) covering all 50 American states. A sample of 89,209 U.S. participants, 29,079 men and 60,063 women aged 18 years or older, was considered using exploratory graphical analysis (EGA), a statistical graphical model that includes algorithms for grouping and factoring variables in a multivariate system of network relationships. The EGA findings show that the network presents 3 latent comorbidity patterns, i.e., that comorbidities are grouped into 3 factors. The first group was composed of 7 comorbidities (obesity, cancer, high blood pressure, high blood cholesterol, arthritis, kidney disease, and diabetes). The second pattern of latent comorbidity included the diagnosis of asthma and respiratory diseases. The last factor grouped 3 conditions (heart attack, coronary heart disease, and stroke). Hypertension reported higher measures of network centrality. Associations between chronic conditions were reported; furthermore, they were grouped into 3 latent dimensions of comorbidity and reported network factor loadings. The implementation of care and treatment guidelines and protocols for patients with depressive symptomatology and multimorbidity is suggested.

Validation of the Post-Traumatic Growth Inventory in Adolescents Who Lost a Family Member in the Past 4 years: Effects of Time After the Death Event and Gender.

Post-traumatic growth is the psychological capacity to perceive positive changes facing high-stress or potentially traumatic events and may be especially useful in lessening their effects in adolescents' lives. Present study aimed to evaluate the psychometric properties of the Post-Traumatic Growth Inventory (PTGI) in 662 Peruvian adolescents who suffered the death of an immediate family member over the last 4years. Firstly, an exploratory graphical analysis (EGA) was conducted to determine the best parsimonious structure of the instrument and confirmed with its respective factor models. The results report adequate fit indices in the EGA Bifactor model. Furthermore, another structural model is included, which reports significant latent effects of the time elapsed since the relative's death and gender (male) on PTGI general factor. Likewise, gender measure was significantly related to items 3, 7, and 11, which is linked to personal growth.

Psychometric Network Analysis of the Problematic Pornography Use Scale (PPUS) in Peruvian Adults.

The aim of this research was to analyze the psychometric properties of the Problematic Pornography Use Scale (PPUS). A sample of 704 Peruvian youth and adults aged 18 to 62 years (M = 26, SD = 6.0) was considered, of which 56% were female and 43% male. The participants were from various cities in Peru: Lima (84%), Trujillo (2.6%), Arequipa (1.8%), and Huancayo (1.6%). The validity of the theoretical structure of the PPUS was carried out by means of two techniques: Confirmatory factor analysis (CFA) and Exploratory Graphical Analysis (EGA), a new effective and efficient dimension evaluation technique, whose measure consists of verifying the fit of the dimension structure. Using the bifactor model, the hypothesis that PPUS has a unifactorial behavior was corroborated. These approximations of unidimensionality are also corroborated by the EGA method, where the centrality parameters and network loadings are found to have acceptable estimates. The results demonstrate the validity of the PPUS, contrasting with the factor model and verifying the unidimensionality of the construct, which provide useful directions for future studies on the instrumentalization of problematic pornography use scale.

Identifying latent comorbidity patterns in adults with perceived cognitive impairment: Network findings from the behavioral risk factor surveillance system.

BackgroundPeople with cognitive impairment may be exposed to an increased risk of comorbidities; however, the clustering of comorbidity patterns in these patients is unclear.ObjectiveTo explore the network structure of chronic comorbidity in a U.S. national sample spanning all 50 U.S. states with more than 170,000 participants reporting perceived cognitive impairment.MethodsThis is a cross-sectional study conducted using Behavioral Risk Factor Surveillance System (BRFSS) secondary data collected in 2019 and covering 49 U.S. states, the District of Columbia, Guam, and the Commonwealth of Puerto Rico. A total of 15,621 non-institutionalized U.S. adult participants who reported “yes” to the subjective cognitive impairment question were considered, of whom 7,045 were men and 8,576 were women. All participants were aged 45 years or older. A statistical graphical model was used that included clustering algorithms and factorization of variables in a multivariate network relationship system [exploratory graphical analysis (EGA)].ResultsThe results of the EGA show associations between the comorbid conditions evaluated. These associations favored the clustering of various comorbidity patterns. In fact, three patterns of comorbidities have been identified: (1) arthritis, asthma, respiratory diseases, and depression, (2) obesity, diabetes, blood pressure high, and blood cholesterol high, and (3) heart attack, coronary heart disease, stroke, and kidney disease.ConclusionThese results suggest the development of interdisciplinary treatment strategies in patients with perceived cognitive impairment, which could help to design an integrated prevention and management of the disease and other related health problems, such as Alzheimer's disease and related dementias.

Family Aggression Screening Tool (FAST): Factor structure and psychometric properties of subscales

BackgroundThe Family Aggression Screening Tool (FAST) is an instrument to screen for experiences of maltreatment using primarily pictorial representations, including direct victimisation and exposure to intimate partner violence. The initial psychometric properties of the FAST were reported in the original research in the United Kingdom. ObjectiveThis research aimed to replicate and to extend the evaluation of the psychometric properties of FAST in Brazil. Participants and settingData consisted of 648 youth aged 11 to 17 years from public schools in Brazil. MethodWe employed confirmatory factor analysis (CFA), exploratory factor analysis (EFA) and exploratory graphical analysis (EGA). Concurrent validity was supported by strong correlations between the FAST's emotional and physical victimisation subscales and Childhood Trauma Questionnaire (CTQ) emotional and physical abuse subscales. ResultsCFA resulted in a solution including second and first order factors, resembling the original structure. FAST's reliability was assessed both through internal consistency and test-retest, showing favorable coefficients. ConclusionOur results suggest that FAST has good psychometric properties for the Brazilian population with respect to both its validity and reliability.

Measuring the Influence of Environment on Behaviour: A Multimethod Multisample Validation of the Situational Strength at Work (SSW) Scale in Spanish-Speaking Samples

Environment and personality interact determining the manner a subject behaves, but research on how to conceptualise and measure the environment is still scarce. This article addresses this issue presenting strong evidence on the reliability and validity of the Situational Strength at Work (SSW) Scale (Meyer et al., 2014) in Spanish-speaking work contexts. Through three studies, we found sound evidence for the four-factor structure of the situational strength’s construct, comprising clarity, consistency, constraints, and consequences, with adequate reliability for each factor. Results of studies also found that the instrument is invariant according to sex, country (Spain, Ecuador, Mexico), and occupation (salespeople, teachers, office workers). Convergent and discriminant validity were successfully tested, and exploratory graphical network analysis depicted satisfactory results. Theoretical and practical implications are pointed out, and it is concluded that the SSW scale is a suitable instrument for investigating situational strength at work cross-nationally in Spanish-speaking contexts.

The Use of Exploratory Graph Analysis to Validate Trust in Relationships Scale

Today, various methods have been developed with a purpose to determine the number of factors underlying a construct. However, there is no definitive agreement on which techniques to be preferred to extract the underlying dimensions. To this end, Exploratory Graphical Analysis (EGA), a recently proposed method, has been compared with traditional methods and the results have revealed that the EGA is less affected from conditions like sample size and inter-dimensional correlation. Besides, it provides more stable results across different conditions. Considering the attractive opportunities it offers, this method has taken its place in the literature as a remarkable alternative to traditional methods. The EGA provides unique outputs compared to other factor extraction techniques. Considering this, interpreting the results obtained within this new and promising framework is assumed to contribute to validation studies. Based on this reality, this study aims to apply the EGA method to Trust in Relations Scale (TRS) and therefore to contribute to its validity. The investigation of TRS’s reliability and validity has already been documented, presenting research opportunities to researchers in the field of positive psychology. The results revealed that, the EGA produces dimensionality structures identical to confirmatory factor analysis (CFA) and exploratory factor analysis (EFA). In addition, further psychometrical indicators within the framework of network analysis are provided. The findings of the study are believed to contribute to the validity of the already existing Trust in Relationships Scale.

Representation of exploratory graphical analysis: a contribution to clinical instrumental research.

Representation of exploratory graphical analysis: a contribution to clinical instrumental research.

Population Pharmacokinetic Analysis of Emapalumab, a Fully Human, Anti-Interferon Gamma Monoclonal Antibody, in Children with Primary Hemophagocytic Lymphohistiocytosis

Introduction: Primary hemophagocytic lymphohistiocytosis (HLH) is a rare syndrome characterized by pathologic immune activation and hyperinflammation. It typically manifests during infancy and is invariably fatal if untreated. Interferon gamma (IFNy) is considered a key contributor to the hyperinflammation of HLH. Thus, neutralization of IFNy could help control the disease until haematopoietic stem cell transplantation, the only curative treatment. Emapalumab, a fully human, anti-IFNy monoclonal antibody, binds to and neutralizes IFNy. Emapalumab is the first and only approved (FDA) treatment for primary HLH. It is indicated for the treatment of adult and pediatric patients with HLH with refractory, recurrent, or progressive disease, or intolerance to conventional HLH therapy. Objective: To develop a population pharmacokinetic (PK) model to describe the PK profile of emapalumab. Methods: PK data were obtained from patients with primary HLH administered emapalumab intravenously during an open-label, single-group, phase 2/3 clinical trial (NCT01818492) and as part of a compassionate use program. A population PK analysis was performed using nonlinear mixed effects modeling (NONMEM® version 7.3.0). Predictive performance of the model was assessed using a visual predictive check. Results: The PK of emapalumab was adequately described by a two-compartment model. All model parameters were estimated with good precision. Central and peripheral volumes of distribution were 0.059 and 0.079 L/kg, respectively. Exploratory graphical analysis showed that (i) IFNy production varied significantly between and within patients as a function of time; (ii) the higher the IFNy production, the faster the elimination of emapalumab due to target-mediated drug disposition; and (iii) the higher the IFNy production, the higher the dose of emapalumab required to reach the neutralizing concentration of IFNy (evidenced by a higher target-mediated clearance of emapalumab). Of the parameters examined, only body weight and total IFNy (free and bound) levels were found to significantly influence the PK of emapalumab. The allometric exponents of body weight for the volume of distribution and clearance were 1 (fixed) and 0.886 (95% confidence interval 0.68, 1.09), respectively. These values support the bodyweight-based dosing of emapalumab (i.e. mg/kg). At values of total IFNy from 103 to 106 pg/mL, the total clearance (linear + target mediated) of emapalumab ranged from 0.0012 to 0.0140 L/h for a bodyweight of 5 kg, with corresponding terminal half-lives from 17.5 to 2.3 days. This wide variance in clearances and half-lives partly explains the emapalumab dose adaptations that are required for treating primary HLH patients. Conclusion: The population PK model reliably predicted serum concentrations of emapalumab in patients with primary HLH. The central and peripheral volumes of distribution were low, clearance was slow, and the terminal half-life long. The expected dynamic of the biology of IFNy was confirmed and included in the population PK model. Simulations using this model supported the proposed dosing scheme of emapalumab in patients with primary HLH. Disclosures Laveille: Sobi: Consultancy. Jacqmin:Sobi: Consultancy. de Min:Sobi: Consultancy.

Statistical Methods in Human Resource Research: Dearth or Diversity? Evidence from Published Literature

This article attempts to find out if there is breadth in application of quantitative techniques in published literature within the field of human resource management (HRM). In addition, it investigates the holistic use of specific categories of statistics, and if there are categories that are neglected. The study utilises a combination of research questions and hypotheses. The broad categories of statistics that this study focussed on include descriptive, data science statistics, exploratory graphical, advanced statistics such as structural equation modelling, Bayesian statistics and inferential statistics. It goes further to study application of machine learning statistics in HRM research. Using archival methodology, the article utilises a sample of 120 journal papers to answer formulated research questions and hypotheses. Descriptive statistics, exploratory graphical analysis and inferential statistics are used in the analysis. The findings indicate that there are neglected statistics in HRM research. Overall, most statistical categories are underutilised. HRM journal editors, researchers and practitioners must stock HRM methodological toolbox.

Association of High Body Mass Index with Response Outcomes in Patients with CML-CP Treated with Dasatinib Versus Imatinib in the First Line: Exploratory Post Hoc Analysis of the Phase 3 DASISION Trial

Association of High Body Mass Index with Response Outcomes in Patients with CML-CP Treated with Dasatinib Versus Imatinib in the First Line: Exploratory Post Hoc Analysis of the Phase 3 DASISION Trial

Natural resources and human development: Evidence from mineral-dependent African countries using exploratory graphical analysis

In this study, we propose a new approach for the visual inspection of interactions between human development and economic growth and a set of variables that reflect the dependence of mineral resources. We focus on the ten African countries with the highest share of mineral exports compared to total exports in the period from 2007 to 2016. First, we provide a descriptive analysis and we rank the countries according to their average annual growth in relation to a set of proxies of resource dependence and economic indicators during the sample period. Second, we cluster the different states by means of a dimensionality-reduction technique that enabled synthesising the information in the rankings into two factors: (a) economic growth and human development, and (b) growth in mineral resource dependence. Finally, we project all countries into a perceptual map and observe four clusters that roughly correspond to the main African regions, and whose distribution shows a slightly negative slope (indicative of a weak inverse relationship between mineral dependence and development). Regarding the interactions among variables, we observe a tenuous negative association between average growth in human development and the relative weight of mineral rents. These results, coupled with the fact that the average growth in resource rents does not materialise in higher economic growth, indicate that corruption may be impeding economic development, especially if it is understood under a more inclusive perspective that incorporates health and education. This finding underscores the need to promote institutional quality and to develop a mining strategy to help revert the effects of the resource curse in mineral-dependent African countries. Accordingly, we propose a series of policy measures based on four main objectives: competitiveness, transparency, sustainability and inclusiveness.

Factorial Structure of the Frugality Scale: Exploratory Evidence

Abstract This article aims to promote an investigation of the psychometric properties of Frugality Scale adapted to Brazilian Portuguese. Frugality is characterized by using and consuming goods in a resourceful way, fully exploring its durability as well. To this end, 626 people responded to an online form that contained the frugality scale. Procedures such as non-graphical solutions to the scree plot, exploratory graphical analysis and a Schmid-Leiman factor solution point to evidence that the scale’s structure is not one-dimensional but two-dimensional. Finally, modeling strategies also indicate that a bifactor solution can be applied.

UNCEMENTED ARTHROPLASTY AFTER HIP METASTATIC DISEASE AND MULTIPLE MYELOMA.

ABSTRACTObjective: To describe a case series using a combination of narrative, graphical exploratory analysis and Bayesian Network modeling. Methods: Case series with 34 patients undergoing uncemented and hybrid arthroplasty procedures secondary to hip pain or fracture secondary to metastatic disease or multiple myeloma. Results:The most common tumors included gastrointestinal, multiple myeloma and breast cancer. Most devices were total arthroplasty (n = 16, 84.2%) rather than partial and uncemented arthroplasty (n = 12, 63.2%) rather than hybrid. The average time between surgery and deambulation was 20 days, the average length of hospital stay was 13 days, and the average patient survival was 589 days. Only one infection was reported. Uncemented and hybrid arthroplasty devices did not differ regarding time to walk, as well as the length of hospital stay in this sample. Conclusion: Our model may be used as a prior for the addition of subsequent patient samples, personalizing, thus, its recommendations to other patient populations. Level of Evidence IV, Case series.