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.

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.

Aesthetic emotions are affected by context: a psychometric network analysis.

Aesthetic emotions are defined as emotions arising when a person evaluates a stimulus for its aesthetic appeal. Whether these emotions are unique to aesthetic activities is debated. We address this debate by examining if recollections of different types of engaging activities entail different emotional profiles. A large sample of participants were asked to recall engaging aesthetic (N = 167), non-aesthetic (N = 160), or consumer (N = 172) activities. They rated the extent to which 75 candidate aesthetic emotions were evoked by these activities. We applied a computational psychometric network approach to represent and compare the space of these emotions across the three conditions. At the behavioral level, recalled aesthetic activities were rated as the least vivid but most intense compared to the two other conditions. At the network level, we found several quantitative differences across the three conditions, related to the typology, community (clusters) and core nodes (emotions) of these networks. Our results suggest that aesthetic and non-aesthetic activities evoke emotional spaces differently. Thus, we propose that aesthetic emotions are distributed differently in a multidimensional aesthetic space than for other engaging activities. Our results highlight the context-specificity of aesthetic emotions.

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).

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.

Updating the Aesthetic Fluency Scale: Revised long and short forms for research in the psychology of the arts.

People's knowledge about the arts shapes how they experience and engage with art. Since its introduction, the 10-item Aesthetic Fluency Scale has been widely used to measure self-reported art knowledge. Drawing from findings and researchers' experience since then, the present work develops and evaluates a Revised Aesthetic Fluency Scale using item response theory to broaden its scope (36 items) and refine its response scale. In a large sample (n = 2,089 English-speaking adults), Study 1 found strong evidence for unidimensionality, good item fit, and a difficulty level suitable for its targeted population; Study 2 (n = 392) provided initial evidence for score validity via relationships with art engagement, Openness to Experience, and aesthetic responsiveness; and Study 3 derived a brief, 10-item form for time-constrained projects. Taken together, the revised scales build upon lessons learned from the original and appear promising for the next generation of research.

Age-related differences in the social associative learning of trust information.

Trust is a key component of social interaction. Older adults, however, often exhibit excessive trust relative to younger adults. One explanation is that older adults may learn to trust differently than younger adults. Here, we examine how younger (N = 33) and older adults (N = 30) learn to trust over time. Participants completed a classic iterative trust game with 3 partners. Younger and older adults shared similar amounts but differed in how they shared money. Compared to younger adults, older adults invested more with untrustworthy partners and less with trustworthy partners. As a group, older adults displayed less learning than younger adults. However, computational modeling suggests that this is not because older adults learn differently from positive and negative feedback than younger adults. Model-based fMRI analyses revealed several age- and learning-related differences in neural processing. Specifically, we found that older learners (N = 19), relative to older non-learners (N = 11), had greater reputation-related activity in metalizing/memory areas while making their decisions. Collectively, these findings suggest that older adult learners use social cues differently from non-learners.

The facets of psychopathology in patients with cancer: Cross-sectional and longitudinal network analyses.

OBJECTIVE: Cancer patients display heterogeneous psychopathology, comprising depressive, anxiety, hostility, and somatic symptoms. Often, clinical pictures evolve over time deteriorating the individual functioning and prognosis. Network models can reveal the relationships between symptoms, thus providing clinical insights. METHOD: This study examined data of the Brief Symptom Inventory and the Distress Thermometer, from 1108 cancer outpatients. Gaussian Graphical Models were estimated using regularized and non-regularized Bayesian methods. In addition, we used community detection methods to identify the most relevant symptom groupings, and longitudinal network analyses on 515 participants to examine the connections between symptoms over three months. RESULTS: The network models derived from baseline data suggested symptoms clustered into three main complexes (depression/anxiety, hostility, and somatic symptoms). Symptoms related to depression and hostility were highly connected with suicidal and death thoughts. Faintness, weakness, chest pain, and dyspnoea, among somatic symptoms, were more strongly connected with psychopathological features. Longitudinal analyses revealed that sadness, irritability, nervousness, and tension predicted each other. Panic and death thoughts predicted fearfulness and faintness. CONCLUSIONS: Somatic symptoms, sadness, irritability, chronic and acute anxiety interact between each other, shaping the heterogeneous clinical picture of distress in cancer. This study, strengthened by robust methods, is the first to employ longitudinal network analyses in cancer patients. Further studies should evaluate whether targeting specific symptoms might prevent the onset of chronic distress and improve clinical outcomes in cancer patients.

Semantic network analysis (SemNA): A tutorial on preprocessing, estimating, and analyzing semantic networks.

To date, the application of semantic network methodologies to study cognitive processes in psychological phenomena has been limited in scope. One barrier to broader application is the lack of resources for researchers unfamiliar with the approach. Another barrier, for both the unfamiliar and knowledgeable researcher, is the tedious and laborious preprocessing of semantic data. We aim to minimize these barriers by offering a comprehensive semantic network analysis pipeline (preprocessing, estimating, and analyzing networks), and an associated R tutorial that uses a suite of R packages to accommodate the pipeline. Two of these packages, SemNetDictionaries and SemNetCleaner, promote an efficient, reproducible, and transparent approach to preprocessing linguistic data. The third package, SemNeT, provides methods and measures for estimating and statistically comparing semantic networks via a point-and-click graphical user interface. Using real-world data, we present a start-to-finish pipeline from raw data to semantic network analysis results. This article aims to provide resources for researchers, both the unfamiliar and knowledgeable, that reduce some of the barriers for conducting semantic network analysis. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

What kind of impacts can artwork have on viewers? Establishing a taxonomy for aesthetic impacts.

What kinds of impacts can visual art have on a viewer? To identify potential art impacts, we recruited five aesthetics experts from different academic disciplines: art history, neuroscience, philosophy, psychology and theology. Together, the group curated a set of terms that corresponded to descriptive features (124 terms) and cognitive-affective impacts (69 terms) of artworks. Using these terms as prompts, participants (n = 899) were given one minute to generate words for each term related to how an artwork looked (descriptive features) or made them think or feel (cognitive-affective impacts). Using network psychometric approaches, we identified terms that were semantically similar based on participants' responses and applied hierarchical exploratory graph analysis to map the relationships between the terms. Our analyses identified 17 descriptive dimensions, which could be further reduced to 5, and 11 impact dimensions, which could be further reduced to 4. The resulting taxonomy demonstrated overlap between the descriptive and impact networks as well as consistency with empirical evidence. This taxonomy could serve as the foundation to empirically evaluate art's impacts on viewers.

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.

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.

Intrinsic functional connectivity in the default mode network predicts mnemonic discrimination: A connectome-based modeling approach.

The ability to distinguish existing memories from similar perceptual experiences is a core feature of episodic memory. This ability is often examined using the mnemonic similarity task in which people discriminate memories of studied objects from perceptually similar lures. Studies of the neural basis of such mnemonic discrimination have mostly focused on hippocampal function and connectivity. However, default mode network (DMN) connectivity may also support such discrimination, given that the DMN includes the hippocampus, and its connectivity supports many aspects of episodic memory. Here, we used connectome-based predictive modeling to identify associations between intrinsic DMN connectivity and mnemonic discrimination. We leveraged a wide range of abilities across healthy younger and older adults to facilitate this predictive approach. Resting-state functional connectivity in the DMN predicted mnemonic discrimination outside the MRI scanner, especially among prefrontal and temporal regions and including several hippocampal regions. This predictive relationship was stronger for younger than older adults, primarily for temporal-prefrontal connectivity. The novel associations established here are consistent with mounting evidence that broader cortical networks including the hippocampus support mnemonic discrimination. They also suggest that age-related network disruptions undermine the extent that the DMN supports this ability. This study provides the first indication of how intrinsic functional properties of the DMN support mnemonic discrimination.

Education shapes the structure of semantic memory and impacts creative thinking.

Education is central to the acquisition of knowledge, such as when children learn new concepts. It is unknown, however, whether educational differences impact not only what concepts children learn, but how those concepts come to be represented in semantic memory-a system that supports higher cognitive functions, such as creative thinking. Here we leverage computational network science tools to study hidden knowledge structures of 67 Swiss schoolchildren from two distinct educational backgrounds-Montessori and traditional, matched on socioeconomic factors and nonverbal intelligence-to examine how educational experience shape semantic memory and creative thinking. We find that children experiencing Montessori education show a more flexible semantic network structure (high connectivity/short paths between concepts, less modularity) alongside higher scores on creative thinking tests. The findings indicate that education impacts how children represent concepts in semantic memory and suggest that different educational experiences can affect higher cognitive functions, including creative thinking.

Aesthetic Preference for Glossy Materials: An Attempted Replication and Extension.

The psychology of art and aesthetics has a long-standing interest in how low-level features, such as symmetry, curvature, and color, affect people's aesthetic experience. Recent research in this tradition suggests that people find glossy, shiny objects and materials more attractive than flat, matte ones. The present experiment sought to replicate and extend research on the attractiveness of images printed on glossy and flat paper. To control for several possible confounding factors, glossiness was manipulated between-person and varied with methods that held constant factors like weight, color quality, and resolution. To extend past work, we explored art expertise and Openness to Experience as potential moderators. A sample of 100 adults viewed landscape photographs on either high-gloss photo paper or on identical paper in which a flat, matte spray finish had been applied. Ratings of attractiveness showed weak evidence for replication. People rated the glossy pictures as more attractive than the matte ones, but the effect size was small (d = -0.23 [-0.62, 0.16]) and not statistically significant. Attractiveness ratings were significantly moderated, however, by individual differences in the aesthetic appreciation facet of Openness to Experience. When aesthetic appreciation was high, people found the images attractive regardless of condition; when it was low, people strongly preferred the glossy images over the matte ones, thus showing the classic glossiness effect. We conclude with some methodological caveats for future research.

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).

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.

Intelligence and creativity share a common cognitive and neural basis.

Are intelligence and creativity distinct abilities, or do they rely on the same cognitive and neural systems? We sought to quantify the extent to which intelligence and creative cognition overlap in brain and behavior by combining machine learning of fMRI data and latent variable modeling of cognitive ability data in a sample of young adults (N = 186) who completed a battery of intelligence and creative thinking tasks. The study had 3 analytic goals: (a) to assess contributions of specific facets of intelligence (e.g., fluid and crystallized intelligence) and general intelligence to creative ability (i.e., divergent thinking originality), (b) to model whole-brain functional connectivity networks that predict intelligence facets and creative ability, and (c) to quantify the degree to which these predictive networks overlap in the brain. Using structural equation modeling, we found moderate to large correlations between intelligence facets and creative ability, as well as a large correlation between general intelligence and creative ability (r = .63). Using connectome-based predictive modeling, we found that functional brain networks that predict intelligence facets overlap to varying degrees with a network that predicts creative ability, particularly within the prefrontal cortex of the executive control network. Notably, a network that predicted general intelligence shared 46% of its functional connections with a network that predicted creative ability-including connections linking executive control and salience/ventral attention networks-suggesting that intelligence and creative thinking rely on similar neural and cognitive systems. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Associations between symptoms of problematic smartphone, Facebook, WhatsApp, and Instagram use: An item-level exploratory graph analysis perspective.

BACKGROUND AND AIMS: Studies have demonstrated associations between both problematic smartphone and social networks use with everyday life adversities. However, examination of associations between problematic smartphone use (PSU) and problematic use of specific social networking platforms, especially on item-level data, has received relatively little attention. Therefore, the aim of the current study was to explore how items of problematic smartphone, Facebook, WhatsApp, and Instagram use are associated. METHODS: 949 German-speaking adults participated in a web survey study. The participants were queried about their socio-demographics as well as levels of problematic smartphone, Facebook, WhatsApp, and Instagram use. In addition to bivariate correlation analysis, exploratory graph analysis (EGA), a type of network analysis, was conducted. RESULTS: The results showed that while problematic Facebook and Instagram use seem to be distinct phenomena, problematic smartphone and WhatsApp use were heavily intertwined. Furthermore, the only cross-platform symptom observed was the extent of reported pain in wrists and neck due to digital technology use. The EGA network models showed very good stability in bootstrap analyses. DISCUSSION AND CONCLUSIONS: In general, the results of this study suggest that while Instagram and Facebook use may potentially constitute distinct problematic behaviors, problematic smartphone/WhatsApp use scales may be measuring highly similar or even the same construct.

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).