Comparing community detection algorithms in psychometric networks: A Monte Carlo simulation.

Identifying the correct number of factors in multivariate data is fundamental to psychological measurement. Factor analysis has a long tradition in the field, but it has been challenged recently by exploratory graph analysis (EGA), an approach based on network psychometrics. EGA first estimates a network and then applies the Walktrap community detection algorithm. Simulation studies have demonstrated that EGA has comparable or better accuracy for recovering the same number of communities as there are factors in the simulated data than factor analytic methods. Despite EGA's effectiveness, there has yet to be an investigation into whether other sparsity induction methods or community detection algorithms could achieve equivalent or better performance. Furthermore, unidimensional structures are fundamental to psychological measurement yet they have been sparsely studied in simulations using community detection algorithms. In the present study, we performed a Monte Carlo simulation using the zero-order correlation matrix, GLASSO, and two variants of a non-regularized partial correlation sparsity induction methods with several community detection algorithms. We examined the performance of these method-algorithm combinations in both continuous and polytomous data across a variety of conditions. The results indicate that the Fast-greedy, Louvain, and Walktrap algorithms paired with the GLASSO method were consistently among the most accurate and least-biased overall.

Dimensionality assessment in the presence of wording effects: A network psychometric and factorial approach.

This study proposes a procedure for substantive dimensionality estimation in the presence of wording effects, the inconsistent response to regular and reversed self-report items. The procedure developed consists of subtracting an approximate estimate of the wording effects variance from the sample correlation matrix and then estimating the substantive dimensionality on the residual correlation matrix. This is achieved by estimating a random intercept factor with unit loadings for all the regular and unrecoded reversed items. The accuracy of the procedure was evaluated through an extensive simulation study that manipulated nine relevant variables and employed the exploratory graph analysis (EGA) and parallel analysis (PA) retention methods. The results indicated that combining the proposed procedure with EGA or PA achieved high accuracy in estimating the substantive latent dimensionality, but that EGA was superior. Additionally, the present findings shed light on the complex ways that wording effects impact the dimensionality estimates when the response bias in the data is ignored. A tutorial on substantive dimensionality estimation with the R package EGAnet is offered, as well as practical guidelines for applied researchers.

The Misinformation Susceptibility Test (MIST): A psychometrically validated measure of news veracity discernment.

Interest in the psychology of misinformation has exploded in recent years. Despite ample research, to date there is no validated framework to measure misinformation susceptibility. Therefore, we introduce Verification done, a nuanced interpretation schema and assessment tool that simultaneously considers Veracity discernment, and its distinct, measurable abilities (real/fake news detection), and biases (distrust/naïvité-negative/positive judgment bias). We then conduct three studies with seven independent samples (Ntotal = 8504) to show how to develop, validate, and apply the Misinformation Susceptibility Test (MIST). In Study 1 (N = 409) we use a neural network language model to generate items, and use three psychometric methods-factor analysis, item response theory, and exploratory graph analysis-to create the MIST-20 (20 items; completion time < 2 minutes), the MIST-16 (16 items; < 2 minutes), and the MIST-8 (8 items; < 1 minute). In Study 2 (N = 7674) we confirm the internal and predictive validity of the MIST in five national quota samples (US, UK), across 2 years, from three different sampling platforms-Respondi, CloudResearch, and Prolific. We also explore the MIST's nomological net and generate age-, region-, and country-specific norm tables. In Study 3 (N = 421) we demonstrate how the MIST-in conjunction with Verification done-can provide novel insights on existing psychological interventions, thereby advancing theory development. Finally, we outline the versatile implementations of the MIST as a screening tool, covariate, and intervention evaluation framework. As all methods are transparently reported and detailed, this work will allow other researchers to create similar scales or adapt them for any population of interest.

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.

Unique Variable Analysis of Redundancy in ADHD Items from the Conners Teacher Rating Scale - Revised: Short.

Attention deficit hyperactivity disorder (ADHD) is a neuropsychiatric disorder interfering with the normal development of the child. The disorder can be screened at school with the Conners Teacher Rating Scale Revised Short (CTRS-R:S). This scale goes beyond the disorder itself and covers a wider construct, that of abnormal child behavior. This can be understood as a complex system of mutually influencing entities. We analyzed a data set of 525 children in French-speaking primary schools from Belgium, and estimated a network structure, as well as to determine the local dependence of items through Unique Variable Analysis. A reduced network was computed including 15 non-locally dependent items. The structural consistency of the network was not affected by redundant items and was structurally sound. The reduction of the number of variables in network studies is important to improve the investigation of network structures as well as better interpret results from inference measures.

Entropy Fit Indices: New Fit Measures for Assessing the Structure and Dimensionality of Multiple Latent Variables.

The accurate identification of the content and number of latent factors underlying multivariate data is an important endeavor in many areas of Psychology and related fields. Recently, a new dimensionality assessment technique based on network psychometrics was proposed (Exploratory Graph Analysis, EGA), but a measure to check the fit of the dimensionality structure to the data estimated via EGA is still lacking. Although traditional factor-analytic fit measures are widespread, recent research has identified limitations for their effectiveness in categorical variables. Here, we propose three new fit measures (termed entropy fit indices) that combines information theory, quantum information theory and structural analysis: Entropy Fit Index (EFI), EFI with Von Neumman Entropy (EFI.vn) and Total EFI.vn (TEFI.vn). The first can be estimated in complete datasets using Shannon entropy, while EFI.vn and TEFI.vn can be estimated in correlation matrices using quantum information metrics. We show, through several simulations, that TEFI.vn, EFI.vn and EFI are as accurate or more accurate than traditional fit measures when identifying the number of simulated latent factors. However, in conditions where more factors are extracted than the number of factors simulated, only TEFI.vn presents a very high accuracy. In addition, we provide an applied example that demonstrates how the new fit measures can be used with a real-world dataset, using exploratory graph analysis.

On the equivalency of factor and network loadings.

Recent research has demonstrated that the network measure node strength or sum of a node's connections is roughly equivalent to confirmatory factor analysis (CFA) loadings. A key finding of this research is that node strength represents a combination of different latent causes. In the present research, we sought to circumvent this issue by formulating a network equivalent of factor loadings, which we call network loadings. In two simulations, we evaluated whether these network loadings could effectively (1) separate the effects of multiple latent causes and (2) estimate the simulated factor loading matrix of factor models. Our findings suggest that the network loadings can effectively do both. In addition, we leveraged the second simulation to derive effect size guidelines for network loadings. In a third simulation, we evaluated the similarities and differences between factor and network loadings when the data were generated from random, factor, and network models. We found sufficient differences between the loadings, which allowed us to develop an algorithm to predict the data generating model called the Loadings Comparison Test (LCT). The LCT had high sensitivity and specificity when predicting the data generating model. In sum, our results suggest that network loadings can provide similar information to factor loadings when the data are generated from a factor model and therefore can be used in a similar way (e.g., item selection, measurement invariance, factor scores).

Playing HAVOK on the Chaos Caused by Internet Trolls.

Trump supporting Twitter posting activity from right-wing Russian trolls active during the 2016 United States presidential election was analyzed at multiple timescales using a recently developed procedure for separating linear and nonlinear components of time series. Trump supporting topics were extracted with DynEGA (Dynamic Exploratory Graph Analysis) and analyzed with Hankel Alternative View of Koopman (HAVOK) procedure. HAVOK is an exploratory and predictive technique that extracts a linear model for the time series and a corresponding nonlinear time series that is used as a forcing term for the linear model. Together, this forced linear model can produce surprisingly accurate reconstructions of nonlinear and chaotic dynamics. Using the R package havok, Russian troll data yielded well-fitting models at several timescales, not producing well-fitting models at others, suggesting that only a few timescales were important for representing the dynamics of the troll factory. We identified system features that were timescale-universal versus timescale-specific. Timescale-universal features included cycles inherent to troll factory governance, which identified their work-day and work-week organization, later confirmed from published insider interviews. Cycles were captured by eigen-vector basis components resembling Fourier modes, rather than Legendre polynomials typical for HAVOK. This may be interpreted as the troll factory having intrinsic dynamics that are highly coupled to nearly stationary cycles. Forcing terms were timescale-specific. They represented external events that precipitated major changes in the time series and aligned with major events during the political campaign. HAVOK models specified interactions between the discovered components allowing to reverse-engineer the operation of Russian troll factory. Steps and decision points in the HAVOK analysis are presented and the results are described in detail.

Dimensionality assessment in bifactor structures with multiple general factors: A network psychometrics approach.

The accuracy of factor retention methods for structures with one or more general factors, like the ones typically encountered in fields like intelligence, personality, and psychopathology, has often been overlooked in dimensionality research. To address this issue, we compared the performance of several factor retention methods in this context, including a network psychometrics approach developed in this study. For estimating the number of group factors, these methods were the Kaiser criterion, empirical Kaiser criterion, parallel analysis with principal components (PAPCA) or principal axis, and exploratory graph analysis with Louvain clustering (EGALV). We then estimated the number of general factors using the factor scores of the first-order solution suggested by the best two methods, yielding a "second-order" version of PAPCA (PAPCA-FS) and EGALV (EGALV-FS). Additionally, we examined the direct multilevel solution provided by EGALV. All the methods were evaluated in an extensive simulation manipulating nine variables of interest, including population error. The results indicated that EGALV and PAPCA displayed the best overall performance in retrieving the true number of group factors, the former being more sensitive to high cross-loadings, and the latter to weak group factors and small samples. Regarding the estimation of the number of general factors, both PAPCA-FS and EGALV-FS showed a close to perfect accuracy across all the conditions, while EGALV was inaccurate. The methods based on EGA were robust to the conditions most likely to be encountered in practice. Therefore, we highlight the particular usefulness of EGALV (group factors) and EGALV-FS (general factors) for assessing bifactor structures with multiple general factors. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

3D electron microscopy and volume-based bouton sorting reveal the selectivity of inputs onto geniculate relay cell and interneuron dendrite segments.

Introduction: The visual signals evoked at the retinal ganglion cells are modified and modulated by various synaptic inputs that impinge on lateral geniculate nucleus cells before they are sent to the cortex. The selectivity of geniculate inputs for clustering or forming microcircuits on discrete dendritic segments of geniculate cell types may provide the structural basis for network properties of the geniculate circuitry and differential signal processing through the parallel pathways of vision. In our study, we aimed to reveal the patterns of input selectivity on morphologically discernable relay cell types and interneurons in the mouse lateral geniculate nucleus. Methods: We used two sets of Scanning Blockface Electron Microscopy (SBEM) image stacks and Reconstruct software to manually reconstruct of terminal boutons and dendrite segments. First, using an unbiased terminal sampling (UTS) approach and statistical modeling, we identified the criteria for volume-based sorting of geniculate boutons into their putative origins. Geniculate terminal boutons that were sorted in retinal and non-retinal categories based on previously described mitochondrial morphology, could further be sorted into multiple subpopulations based on their bouton volume distributions. Terminals deemed non-retinal based on the morphological criteria consisted of five distinct subpopulations, including small-sized putative corticothalamic and cholinergic boutons, two medium-sized putative GABAergic inputs, and a large-sized bouton type that contains dark mitochondria. Retinal terminals also consisted of four distinct subpopulations. The cutoff criteria for these subpopulations were then applied to datasets of terminals that synapse on reconstructed dendrite segments of relay cells or interneurons. Results: Using a network analysis approach, we found an almost complete segregation of retinal and cortical terminals on putative X-type cell dendrite segments characterized by grape-like appendages and triads. On these cells, interneuron appendages intermingle with retinal and other medium size terminals to form triads within glomeruli. In contrast, a second, presumed Y-type cell displayed dendrodendritic puncta adherentia and received all terminal types without a selectivity for synapse location; these were not engaged in triads. Furthermore, the contribution of retinal and cortical synapses received by X-, Y- and interneuron dendrites differed such that over 60% of inputs to interneuron dendrites were from the retina, as opposed to 20% and 7% to X- and Y-type cells, respectively. Conclusion: The results underlie differences in network properties of synaptic inputs from distinct origins on geniculate cell types.

Cognitive and personality predictors of school performance from preschool to secondary school: An overarching model.

In this article, existing research investigating how school performance relates to cognitive, self-awareness, language, and personality processes is reviewed. We outline the architecture of the mind, involving a general factor, g, that underlies distinct mental processes (i.e., executive, reasoning, language, cognizance, and personality processes). From preschool to adolescence, g shifts from executive to reasoning and cognizance processes; personality also changes, consolidating in adolescence. There are three major trends in the existing literature: (a) All processes are highly predictive of school achievement if measured alone, each accounting for ∼20% of its variance; (b) when measured together, cognitive processes (executive functions and representational awareness in preschool and fluid intelligence after late primary school) dominate as predictors (over ∼50%), drastically absorbing self-concepts and personality dispositions that drop to ∼3%-5%; and (c) predictive power changes according to the processes forming g at successive levels: attention control and representational awareness in preschool (∼85%); fluid intelligence, language, and working memory in primary school (∼53%); fluid intelligence, language, self-evaluation, and school-specific self-concepts in secondary school (∼70%). Stability and plasticity of personality emerge as predictors in secondary school. A theory of educational priorities is proposed, arguing that (a) executive and awareness processes; (b) information management; and (c) reasoning, self-evaluation, and flexibility in knowledge building must dominate in preschool, primary, and secondary school, respectively. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Modeling Latent Topics in Social Media using Dynamic Exploratory Graph Analysis: The Case of the Right-wing and Left-wing Trolls in the 2016 US Elections.

The past few years were marked by increased online offensive strategies perpetrated by state and non-state actors to promote their political agenda, sow discord, and question the legitimacy of democratic institutions in the US and Western Europe. In 2016, the US congress identified a list of Russian state-sponsored Twitter accounts that were used to try to divide voters on a wide range of issues. Previous research used latent Dirichlet allocation (LDA) to estimate latent topics in data extracted from these accounts. However, LDA has characteristics that may limit the effectiveness of its use on data from social media: The number of latent topics must be specified by the user, interpretability of the topics can be difficult to achieve, and it does not model short-term temporal dynamics. In the current paper, we propose a new method to estimate latent topics in texts from social media termed Dynamic Exploratory Graph Analysis (DynEGA). In a Monte Carlo simulation, we compared the ability of DynEGA and LDA to estimate the number of simulated latent topics. The results show that DynEGA is substantially more accurate than several different LDA algorithms when estimating the number of simulated topics. In an applied example, we performed DynEGA on a large dataset with Twitter posts from state-sponsored right- and left-wing trolls during the 2016 US presidential election. DynEGA revealed topics that were pertinent to several consequential events in the election cycle, demonstrating the coordinated effort of trolls capitalizing on current events in the USA. This example demonstrates the potential power of our approach for revealing temporally relevant information from qualitative text data.

Longitudinal impact and effects of booster sessions in a cognitive training program for healthy older adults.

This paper reports the results from a 3-year follow-up study to measure the long-term efficacy of a cognitive training for healthy older adults and investigates the effects of booster sessions using an entropy-based metric. DESIGN: semi-randomized quasi-experimental controlled design. PARTICIPANTS: 50 older adults, (M = 73.3, SD = 7.77) assigned into experimental (N = 25; Mean age = 73.9; SD = 8.62) and control groups (N = 25; mean age = 72.9; SD = 6.97). INSTRUMENTS: six subtests of WAIS and two episodic memory tasks. PROCEDURES: the participants were assessed on four occasions: after the end of the original intervention, pre-booster sessions (three years after the original intervention), immediately after the booster sessions and three months after the booster sessions. RESULTS: the repeated measures ANOVA showed that two of the cognitive gains reported in the original intervention were also identified in the follow-up: Coding (F(1, 44) = 11.79, MSE = 0.77, p = .001, eta squared = 0.084) and Picture Completion (F(1, 47) = 10.01, MSE = 0.73, p = .003, eta squared = 0.060). After the booster sessions, all variables presented a significant interaction between group and time favorable to the experimental group (moderate to high effect sizes). To compare the level of cohesion of the cognitive variables between the groups, an entropy-based metric was used. The experimental group presented a lower level of cohesion on three of the four measurement occasions, suggesting a differential impact of the intervention with immediate and short-term effects, but without long-term effects.

Genetic, epigenetic, and environmental factors controlling oxytocin receptor gene expression.

BACKGROUND: The neuropeptide oxytocin regulates mammalian social behavior. Disruptions in oxytocin signaling are a feature of many psychopathologies. One commonly studied biomarker for oxytocin involvement in psychiatric diseases is DNA methylation at the oxytocin receptor gene (OXTR). Such studies focus on DNA methylation in two regions of OXTR, exon 3 and a region termed MT2 which overlaps exon 1 and intron 1. However, the relative contribution of exon 3 and MT2 in regulating OXTR gene expression in the brain is currently unknown. RESULTS: Here, we use the prairie vole as a translational animal model to investigate genetic, epigenetic, and environmental factors affecting Oxtr gene expression in a region of the brain that has been shown to drive Oxtr related behavior in the vole, the nucleus accumbens. We show that the genetic structure of Oxtr in prairie voles resembles human OXTR. We then studied the effects of early life experience on DNA methylation in two regions of a CpG island surrounding the Oxtr promoter: MT2 and exon 3. We show that early nurture in the form of parental care results in DNA hypomethylation of Oxtr in both MT2 and exon 3, but only DNA methylation in MT2 is associated with Oxtr gene expression. Network analyses indicate that CpG sites in the 3' portion of MT2 are most highly associated with Oxtr gene expression. We also identify two novel SNPs in exon 3 of Oxtr in prairie voles and a novel alternative transcript originating from the third intron of the gene. Expression of the novel alternative transcript is associated with genotype at SNP KLW2. CONCLUSIONS: These results identify putative regulatory features of Oxtr in prairie voles which inform future studies examining OXTR in human social behaviors and disorders. These studies indicate that in prairie voles, DNA methylation in MT2, particularly in the 3' portion, is more predictive of Oxtr gene expression than DNA methylation in exon 3. Similarly, in human temporal cortex, we find that DNA methylation in the 3' portion of MT2 is associated with OXTR expression. Together, these results suggest that among the CpG sites studied, DNA methylation of MT2 may be the most reliable indicator of OXTR gene expression. We also identify novel features of prairie vole Oxtr, including SNPs and an alternative transcript, which further develop the prairie vole as a translational model for studies of OXTR.

Eye-tracking reveals agency in assisted autistic communication.

About one-third of autistic people have limited ability to use speech. Some have learned to communicate by pointing to letters of the alphabet. But this method is controversial because it requires the assistance of another person-someone who holds a letterboard in front of users and so could theoretically cue them to point to particular letters. Indeed, some scientists have dismissed the possibility that any nonspeaking autistic person who communicates with assistance could be conveying their own thoughts. In the study reported here, we used head-mounted eye-tracking to investigate communicative agency in a sample of nine nonspeaking autistic letterboard users. We measured the speed and accuracy with which they looked at and pointed to letters as they responded to novel questions. Participants pointed to about one letter per second, rarely made spelling errors, and visually fixated most letters about half a second before pointing to them. Additionally, their response times reflected planning and production processes characteristic of fluent spelling in non-autistic typists. These findings render a cueing account of participants' performance unlikely: The speed, accuracy, timing, and visual fixation patterns suggest that participants pointed to letters they selected themselves, not letters they were directed to by the assistant. The blanket dismissal of assisted autistic communication is therefore unwarranted.

Investigating the performance of exploratory graph analysis and traditional techniques to identify the number of latent factors: A simulation and tutorial.

Exploratory graph analysis (EGA) is a new technique that was recently proposed within the framework of network psychometrics to estimate the number of factors underlying multivariate data. Unlike other methods, EGA produces a visual guide-network plot-that not only indicates the number of dimensions to retain, but also which items cluster together and their level of association. Although previous studies have found EGA to be superior to traditional methods, they are limited in the conditions considered. These issues are addressed through an extensive simulation study that incorporates a wide range of plausible structures that may be found in practice, including continuous and dichotomous data, and unidimensional and multidimensional structures. Additionally, two new EGA techniques are presented: one that extends EGA to also deal with unidimensional structures, and the other based on the triangulated maximally filtered graph approach (EGAtmfg). Both EGA techniques are compared with 5 widely used factor analytic techniques. Overall, EGA and EGAtmfg are found to perform as well as the most accurate traditional method, parallel analysis, and to produce the best large-sample properties of all the methods evaluated. To facilitate the use and application of EGA, we present a straightforward R tutorial on how to apply and interpret EGA, using scores from a well-known psychological instrument: the Marlowe-Crowne Social Desirability Scale. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Is Small Still Beautiful for the Strengths and Difficulties Questionnaire? Novel Findings Using Exploratory Structural Equation Modeling.

During the present decade a large body of research has employed confirmatory factor analysis (CFA) to evaluate the factor structure of the Strengths and Difficulties Questionnaire (SDQ) across multiple languages and cultures. However, because CFA can produce strongly biased estimations when the population cross-loadings differ meaningfully from zero, it may not be the most appropriate framework to model the SDQ responses. With this in mind, the current study sought to assess the factorial structure of the SDQ using the more flexible exploratory structural equation modeling approach. Using a large-scale Spanish sample composed of 67,253 youths aged between 10 and 18 years (M = 14.16, SD = 1.07), the results showed that CFA provided a severely biased and overly optimistic assessment of the underlying structure of the SDQ. In contrast, exploratory structural equation modeling revealed a generally weak factorial structure, including questionable indicators with large cross-loadings, multiple error correlations, and significant wording variance. A subsequent Monte Carlo study showed that sample sizes greater than 4,000 would be needed to adequately recover the SDQ loading structure. The findings from this study prevent recommending the SDQ as a screening tool and suggest caution when interpreting previous results in the literature based on CFA modeling.

Mining concepts of health responsibility using text mining and exploratory graph analysis.

BACKGROUND: Occupational therapists need to know about people's beliefs about personal responsibility for health to help them pursue everyday activities. AIM: The study aims to employ state-of-the-art quantitative approaches to understand people's views of health and responsibility at different ages. METHODS: A mixed method approach was adopted, using text mining to extract information from 233 interviews with participants aged 5 to 96 years, and then exploratory graph analysis to estimate the number of latent variables. The fit of the structure estimated via the exploratory graph analysis was verified using confirmatory factor analysis. RESULTS: Exploratory graph analysis estimated three dimensions of health responsibility: (1) creating good health habits and feeling good; (2) thinking about one's own health and wanting to improve it; and 3) adopting explicitly normative attitudes to take care of one's health. The comparison between the three dimensions among age groups showed, in general, that children and adolescents, as well as the old elderly (>73 years old) expressed ideas about personal responsibility for health less than young adults, adults and young elderly. CONCLUSIONS: Occupational therapists' knowledge of the concepts of health responsibility is of value when working with a patient's health, but an identified challenge is how to engage children and older persons.

Exploratory Graph Analysis of the Multidimensional Schizotypy Scale.

The present study examined the dimensional structure underlying the Multidimensional Schizotypy Scale (MSS) and its brief version (MSS-B). We used Exploratory Graph Analysis (EGA) to evaluate their dimensional structure in two large, independent samples (n = 6265 and n = 1000). We then used Confirmatory Factor Analysis (CFA) to compare the fit of the theoretical dimensions with the EGA dimensions. For the MSS, EGA identified four dimensions: positive schizotypy, two dimensions of negative schizotypy (affective and social anhedonia), and disorganized schizotypy. For the MSS-B, EGA identified three dimensions, which corresponded to the theorized positive, negative, and disorganized dimensions. Based on the MSS's EGA dimensions, we also estimated a four-factor model for the MSS-B. The CFA comparison found that the four-factor model fit significantly better than the theoretical three-factor model for both the MSS and MSS-B, providing support for the theoretical model and offering a more nuanced interpretation of the negative schizotypy factor. In addition, EGA also revealed that the positive and negative schizotypy dimensions of the MSS and MSS-B might be mediated by the disorganized dimension. Our findings offer new implications for future research on the MSS and MSS-B dimensions that may provide differential associations with interview and questionnaire measures.

gP for What is Common between Developing Intelligence and Personality: Response to the Commentators.

Three are the main postulates of our article under discussion: First, both human intelligence and personality are hierarchically organized, with a general factor at the apex of each hierarchy, i. [...].