Search

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 three 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 mentalizing/memory areas while making their decisions. Collectively, these findings suggest that older adult learners use social cues differently from non-learners.

To understand psychological data, it is crucial to examine the structure and dimensions of variables. In this study, two Bayesian approaches were developed in network psychometric models to explore the dimensionality structure of the data, termed Bayesian exploratory graph analysis or BEGA. Unlike traditional approaches that provide fixed parameter estimates, BEGA estimates posterior probabilities of graphical structures to assess the conditional dependence relations and then employs the Louvain community detection algorithm to partition and identify groups of nodes, which enables to detect the multidimensional factor structures. Monte Carlo simulations suggested that the two BEGA methods had comparable or better performance when compared with the other network-based method (EGA) and conventional parallel analysis (PA). When estimating the multidimensional factor structure, the analytically based method (i.e., BEGA.A) showed the best balance between accuracy and mean biased/absolute errors, with the second highest accuracy slightly lower than EGA but with the smallest errors. The sampling-based approach (BEGA.S) yielded higher accuracy and smaller errors than PA; lower accuracy but also lower errors than EGA. Both Bayesian techniques had more stable performance than EGA and PA across different data conditions. When estimating the unidimensional structure, the PA technique performed the best, followed closely by BEGA.A, EGA, and then BEGA.S. The study recommends using BEGA.A as an alternative tool for assessing dimensionalities and advocates for the usefulness of BEGA.S as a valuable alternate technique. The study suggested the potential to extend the regularization-based network modeling EGA method to the Bayesian framework.

Enlightened compassion is a morally significant personality trait describing the tendency to show regard for others in an open-minded (vs. rigid or parochial) manner. We examine this trait through a “bottom-up” lens, asking: where is enlightened compassion located within the Big Five (B5) taxonomy? Across three studies comprising seven samples (total N = 2,522), we measure enlightened compassion as an interstitial facet lying between the Compassion aspect of B5 Agreeableness and the Openness aspect of B5 Openness/Intellect. The Enlightened Compassion Scale (EC Scale) has solid structural and content validity, converging strongly with Compassion and Openness (Study 1). Consistent with the bandwidth-fidelity trade-off in hierarchical models of personality traits, enlightened compassion demonstrates incremental validity over-and-above these B5 aspects when predicting theoretically relevant traits (e.g., moral imagination and moral expansiveness; Study 2) and behaviour (expansive charitable donation; Study 3). By locating enlightened compassion and its correlates within the organising framework of the B5, our work serves to deepen and integrate accumulated knowledge on this morally salient feature of personality.

Art is often thought to reflect the culture from which it comes, and viewers’ aesthetic experiences are influenced by sociocultural content depicted in artworks. Previous research suggests that people prefer artworks from their own cultures compared to another. We tested the hypotheses that viewers’ aesthetic experiences of art are modulated by cultural labels as expressed by artist names, and by sociocultural content depicted in the artwork, and predicted an in-group bias in people’s preferences. Across three pre-registered experiments, we explored Northern American and Indian participants’ aesthetic judgements and preferences to abstract and representational artworks. In Experiment 1, when American abstract artworks were assigned with fictional American, Indian, Chinese, or Turkish artist names, we did not find evidence for an in-group bias – aesthetic ratings for artworks were similar across Indian and American participants, irrespective of the artist names to which they were attributed. In Experiment 2 using a two-alternative forced choice paradigm in which participants chose between two artworks attributed to Indian and American artists, we found no differences in Indian and American participant preferences. We found slightly higher shared preferences for Indian artworks among Indian participants compared to American artworks and participants. Our findings from Experiments 1 and 2 that the appreciation of abstract artworks was not affected by a cultural label counter our prediction, based on previous reports of in-group biases in aesthetic judgments. In Experiment 3, we found a small in-group preference for Indian and American/European representational artworks – participants preferred artworks that depicted content from their own culture compared to another. Effects across all experiments persisted when controlling for participants’ age, education, art experience, and openness to experience. The modulation of art perception and appreciation by contextual information and knowledge systems may be flexible, and more influenced by cultural content depicted in artworks than simple cultural framing.

With the recent proliferation of advanced artificial intelligence (AI) models capable of mimicking human artworks, AI creations might soon replace products of human creativity, although skeptics argue that this outcome is unlikely. One possible reason this may be unlikely is that, independent of the physical properties of art, we place great value on the imbuement of the human experience in art. An interesting question, then, is whether and why people might prefer human- compared to AI-created artworks. To explore these questions, we manipulated the purported creator of pieces of art by randomly assigning a “Human-created” or “AI-created” label to paintings actually created by AI, and then assessed participants’ judgements of the artworks across four rating criteria (Liking, Beauty, Profundity, and Worth). Study 1 found increased positive judgements for human- compared to AI-labeled art across all criteria. Study 2 aimed to replicate and extend Study 1 with additional ratings (Emotion, Story, Meaningful, Effort, and Time to create) intended to elucidate why people more-positively appraise human-labeled artworks. The main findings from Study 1 were replicated, with narrativity (Story) and perceived effort behind artworks (Effort) moderating the label effects (“Human-created” vs. “AI-created”), but only for the sensory-level judgements (Liking, Beauty). Positive personal attitudes towards AI moderated label effects for more-abstract judgements (Profundity, Worth). These studies demonstrate that people tend to be negatively biased against AI-created artworks, which suggests that human engagement in the artistic process contributes positively to our appraisals of art and that, on this basis, AI may not replace human creativity.

Twitter has become a popular platform for sharing and discussing everyday experiences, including creativity. In this study, we explored the public conceptualization of creativity on Twitter by analyzing co-listed hashtags associated with the term #creativity in millions of tweets. Exploratory Graph Analysis was used to identify a network of semantic clusters, and a pre-trained language model yielded the sentiment of the underlying tweets. In addition, we compared the creativity-network to the network of the related concept of #innovation. The semantic clusters of #creativity reflect, on the one hand, well-known everyday creative domains focused on visual and digital arts, storytelling, and handicraft, suggesting an arts bias in the representation of creativity online. On the other hand, other semantic clusters paid tribute to the role of mindsets, teams, and imagination, feature business and innovation, and stress the importance of creativity for mental health. The network of #innovation had a higher prevalence of technological clusters relating to digital transformation in general and in specific areas, such as finance, education, design, and health. Both networks are mostly positive on the level of hashtags and underlying tweets, suggesting that these Twitter spheres highlight potential and success rather than obstacles and failure. Our study demonstrated the feasibility of leveraging online data to provide a bottom-up socio-cultural snapshot of lay conceptualizations of creativity, highlighting commonalities and differences with theorized conceptualizations of creativity.

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.

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.

The nature of associations between variables is important for constructing theory about psychological phenomena. In the last decade, this topic has received renewed interest with the introduction of psychometric network models. In psychology, network models are often contrasted with latent variable (e.g., factor) models. Recent research has shown that differences between the two tend to be more substantive than statistical. One recently developed algorithm called the Loadings Comparison Test (LCT) was developed to predict whether data were generated from a factor or small-world network model. A significant limitation of the current LCT implementation is that it's based on heuristics that were derived from descriptive statistics. In the present study, we used artificial neural networks to replace these heuristics and develop a more robust and generalizable algorithm. We performed a Monte Carlo simulation study that compared neural networks to the original LCT algorithm as well as logistic regression models that were trained on the same data. We found that the neural networks performed as well as or better than both methods for predicting whether data were generated from a factor, small-world network, or random network model. Although the neural networks were trained on small-world networks, we show that they can reliably predict the data-generating model of random networks, demonstrating generalizability beyond the trained data. We echo the call for more formal theories about the relations between variables and discuss the role of the LCT in this process.

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) 2020 APA, all rights reserved).

The investigation of the neural correlates of creative cognition requires researchers to adapt creativity tasks to meet the constraints imposed by cognitive neuroscience research – assessing well-defined cognitive processes, repeated over many trials. We present a systematic review of essential study design parameters in neuroscience research on creativity including trial number, time on task, and when and what responses are collected. The review discloses a substantial heterogeneity of methodological approaches across studies, but also identifies some established common practices. Typical adaptations include the employment of shortened tasks in order to realize more trials per session. Given the sensitivity of neurophysiological assessments to motor artifacts, most studies also take care to ensure a separation between periods of creative thought and the production of responses, enabling them to assess creative performance expressed in various formats including speech, writing, drawing, or playing music. We discuss the pros and cons of the various approaches with respect to the goal to increase reliability of neurophysiological measurements while maintaining valid assessments.

In the last half of the 20th century, psychology and neuroscience have experienced a renewed interest in intraindividual variation. To date, there are few quantitative methods to evaluate whether a population structure (between-person) is likely to hold for individual people. We present a network information theoretic approach to evaluate the extent to which a system possesses the ergodic property. We introduce a new metric, the ergodicity information index (EII), that can inform whether a set of multivariate time series (or a set of intensive longitudinal measures) should be represented as multiple individual structures or as a single population structure. The EII index quantifies the amount of information lost by representing all individuals with a single population structure. If the individuals don't have a similar structure, representing them with a single population network leads to a loss of information. The EII index value will be higher than in cases where all individuals have a similar structure. A Monte-Carlo simulation is implemented to test the EII index, and the results show that the new index has a 94% accuracy in differentiating data in which all individuals have a similar structure vs. data in which the individuals don't have a similar structure. The paper also presents two new techniques designed to help applied researchers to analyze data when the ergodicity property does not hold. The EII bootstrap test obtains a sampling distribution of EII values as if all participants in the data have the population network structure and compare this null distribution to the empirical EII value. Significant differences indicate that the empirical data cannot be expected to be generated from an ergodic process, and the population structure is not sufficient to describe all individuals. The Domenico clustering method estimates the Von Neumann entropy of two networks and computes their Jensen-Shannon Distance (JSD). Then, a complete-linkage agglomerative hierarchical clustering method is applied to the JSD, and the clustering partitioning is obtained via modularization maximization. The Domenico clustering method allows the discovery of groups of individuals with a similar structure. Finally, two empirical examples are shown, one using data from an intensive longitudinal experience sampling study examining Big Five personality measured by the Big Five Inventory-2, and the other using resting state neuroimaging data taken from a study examining creativity which used the 268-node Shen brain atlas. Starting with personality, the bootstrap EII test was significant, suggesting that the BFI-2 data were nonergodic. Following up on the bootstrap EII test, the information clustering was applied, and the single cluster test was performed. The single cluster test suggested that the empirical networks had significantly greater JSD values than the random networks meaning that the single cluster detected was not meaningful, and each individual in the sample is unique. The brain networks had a significant bootstrap EII test and significantly larger JSD values than random networks. These results, in line with the personality data, suggest that (resting state) brain networks are not ergodic, and no meaningful groups can be formed (i.e., each individual is unique). In sum, the personality and (resting state) brain networks do not possess ergodicity and therefore lose information when data are aggregated into a single population.

Increasing research efforts are focused on explaining the cognitive bases of creativity. However, it remains unclear when and how cognitive factors such as intelligence and executive function uniqu...

The study of trait curiosity — individual differences in curious thoughts, feelings, and actions — sorts into two approaches. One looks downward by unpacking and differentiating trait curiosity, with an emphasis on curiosity’s facets, kinds, and parts. Another looks upward by locating trait curiosity within the larger structure of global personality traits. This article reviews research that looks upward by locating curiosity within modern models of Openness to Experience. Our review indicates (1) that most — but not all — models of Openness to Experience explicitly include curiosity-related facets, and (2) that they tend to favor curiosity’s academic and intellectual forms. In accordance with recent network-psychometric analyses, we propose that a broader sense of curiosity — captured by intellectual curiosity, intellectual interests, and variety seeking — emerges from a large pool of Openness to Experience inventories, and that these curiosity facets are central to the global trait. The literature that looks upward at curiosity would benefit from connecting to the diverse and expansive literature that looks downward at it, and network science models offer a fruitful strategy for building a hierarchical model of curiosity across these levels of abstraction.

Establishing measurement invariance (MI) is vital when using any psychological measurement to ensure applicability and comparability across groups (or time points). If MI is violated, mean differences among groups could be due to the measurement rather than true differences in the latent variable. SEM is one of the most common methods for testing MI, however existing methods are subject to many sources of reduced power due to model misspecification (e.g., noninvariant referent indicators, reliance on data-driven methods). Research has shown that many studies testing MI report inaccurate or inadequately described models, where errors in MI modeling are primarily predicted by software choice. Additionally, unequal group sample sizes may impact goodness of fit measures used in testing MI. In network psychometrics, the available methods for comparing network structures is not conceptually analogous to MI and relies on the testing of partial correlations. We propose a more conceptually consistent method for testing MI within the Exploratory Graph Analysis (EGA) framework using network loadings, which are comparable to factor loadings. We calculate the difference in network loadings between groups and, using permutation testing, compare each original network loading difference to a permutated null distribution to determine significance. We conducted a simulation study following commonly found data structures in psychological research, including unequal group sample sizes, and found that compared to SEM methods for testing partial metric invariance, power is comparable using the proposed method and even improved in conditions such as smaller or unequal sample sizes with lower noninvariance effect size.

Response biases related to wording effects, the inconsistent responding to positively and negatively worded self-report items, are pervasive in the behavioral and health sciences. Although several factor modeling strategies have been proposed to mitigate their adverse effects, there is limited systematic research assessing their performance with exploratory structural equation models (ESEM). The present study evaluated the impact of different types of wording effects (random and straight-line carelessness, acquiescence, item difficulty, mixed) on ESEM models incorporating two popular method modeling strategies, the correlated traits-correlated methods minus one (CTC[M-1]) model and random intercept item factor analysis (RIIFA), as well as the “do nothing” approach. Five variables were manipulated using Monte Carlo methods: the type and magnitude of wording effects, factor loadings, factor correlations, and sample size. Overall, the results showed that ignoring wording effects can lead to poor model fit and serious distortions of the ESEM estimates. The RIIFA approach generally performed best in countering these adverse impacts and recovering the unbiased factor structures. A straightforward guide is offered to applied researchers who wish to use ESEM with mixed-worded scales.

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. Although extensive empirical research had shown that wording effects negatively impact latent dimensionality estimates, there was scarce systematic research assessing the problem, and no validated solutions had been offered. The procedure developed consisted in subtracting the wording effects variance from the sample correlation matrix and then estimating the substantive dimensionality on the residual correlation matrix. This was 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 a broad simulation study that manipulated six 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 and practical guidelines for applied researchers.

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 three 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 mentalizing/memory areas while making their decisions. Collectively, these findings suggest that older adult learners use social cues differently from non-learners.

The current paper presents the network psychometric framework for dimensionality and item analysis termed exploratory graph analysis (EGA). It starts by briefly contextualizing the field of network psychometrics and the early work from the 50’s and 60’s. Then it provides a brief overview of exploratory graph analysis and other recent developments, such as the network loadings (a metric akin to factor loadings), total entropy fit index (verification of dimensionality fit), dynamic EGA, bootstrap EGA for dimensionality and item stability, random intercepts EGA (handling wording effects), and hierarchical EGA to estimate high-order structures (e.g., generalized bifactor models). The goal of the paper is to present the reader with a list of contextualized references.

Inner music—hearing music inside your head that isn’t playing in the environment—is a common experience that takes many forms. Research on inner music, however, has primarily emphasized instances of involuntary, aversive musical imagery, such as “earworms.” The present research develops a new conceptual framework, consisting of five fundamental dimensions, that can advance our understanding of inner music. In an experience-sampling study, a sample of musicians and people from the general university community (N = 132) was recruited to examine inner music as it occurs in-the-moment in everyday life. Over the course of a week, participants were contacted throughout the day and asked about their experiences with inner music, with an emphasis on the five dimensions: Affective Valence, Repetitiveness, Vividness, Mental Control, and Length. The results showed that there is variability at both the within-person and between-person levels on each of the proposed dimensions—people have a variety of musical imagery experiences, not just a few different types. Additionally, these dimensions were related to three different individual difference factors: personality, musical expertise, and auditory imagery ability. Openness to experience and extraversion, musical training, and the ability to form vivid auditory images were the primary predictors of the qualities of inner music. Additionally, the present research has implications for how musical imagery is measured—retrospective and in-the-moment reports differed considerably, suggesting people cannot accurately recall their inner music experiences. Overall, the findings show considerable variability in musical imagery and that focusing on specific types of inner music (e.g., earworms) ignores many other experiences. Looking at the qualities of inner music will be fruitful for future work and broaden the scope of research.

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.

*Exploratory graph analysis* (EGA) is used to estimate the structural organization of variables, uncovering latent dimensions as clusters of nodes. EGA first estimates a weighted network then uses the Walktrap algorithm to detect clusters of nodes. The Walktrap algorithm uses random walks to estimate the topography of a graph. The number of random walks taken (*t*) is typically set statically. However, the impact of *t* and the properties determining its optimization have yet to be fully researched. The present study proposes and tests a new approach optimizing *t* by iteratively varying *t* and employ *total entropy fit index* as a fit index to identify the number of steps that best fit the data using a Monte-Carlo simulation varying data structure characteristics. Results indicate that the proposed method is most effective for a higher number of variables per factor and when variables are polytomous. Varying *t* is important as spurious connections are introduced between communities. An empirical example using the Developmental Coordination Disorder Questionnaire is shown demonstrating improved measure interpretation by optimizing the Walktrap algorithm. The paper finishes with a discussion about the relevance of the findings and future directions for research.

The Aesthetic Fluency Scale is a commonly used measure of people’s art knowledge. This scale was initially developed for museum visitors, but its usage has expanded to other populations, including non-arts students. The present research used an Item Response Theory approach to better understand the scale’s functioning in two samples—artistically engaged individuals (i.e., museum visitors and art students) and non-arts students—and any differences in scale properties between the samples (i.e., differential item functioning). Overall, terms related to art styles were easiest, the non-arts students had lower scores than the artistically engaged, and most items showed marked differences between the two samples. These results suggest that using this scale to draw comparisons between these populations is inappropriate. Our results also identify avenues for future development of the scale, including expanding the pool of terms used and revisiting the number of response options.

This document is a verbatim copy of a guest blog post I wrote for the European Journal of Personality: https://www.ejp-blog.com/blog/2021/3/2/personality-structure-after-the-big-five. This document includes formal citations but informal writing. Please visit the European Journal of Personality blog for this post and other great personality content. Cheers

Jones, Ma, and McNally (2019) introduce a novel network measure called bridge centrality, which aims to quantify psychopathological symptoms that may contribute to comorbidity. Centrality measures in psychometric networks have come under scrutiny recently with several researchers arguing for more explicit interpretations of what they purport to measure. One useful strategy has been to connect centrality measures to more traditional psychometric measures such as factor loadings. In this commentary, we aim to do the same by connecting bridge centrality to a recently developed measure called network loadings, which are roughly equivalent with factor loadings. By re-analyzing a recent simulation study, we demonstrate that bridge centrality (specifically bridge strength and bridge expected influence) are nothing more than the (absolute) sum of network cross-loadings. We provide an empirical example to demonstrate this equivalency as well as to show some potential shortcomings of bridge centrality when there are more than two disorders in a network. We raise concerns over bridge centrality’s utility and recommend researchers use network loadings instead.

The current paper compared the empirical structure of 280 variables from the 2016 wave of the Health and Retirement Study (N = 16,327) estimated using exploratory graph analysis with a theoretical structure based on 20 broad domains of intrinsic capacity, functional ability and environment, identified in the International Classification of Functioning, Disability and Health compendium. The results showed that a structure with 21 first-order factors had the best fit to the data (i.e., lowest total entropy fit value) for both the training and validation sample. A second-order exploratory graph analysis was applied on the interfactor correlation matrix and identified five second-order factors. The five-factor structure presented a better fit than the theoretical three-factor structure (approximately) representing intrinsic capacity, functional ability and environment. A close inspection of the network structure generated by analyzing the rotated network loadings of the 21 first-order factors revealed an interplay between cognition, mobility, need for help with daily activities, walking capacity, physical capacity, liver functioning, positive affect and perceived mastery, low perceived control, and depression/negative mood. Combined, our results can help guide future research by providing a framework for estimating the structure of multi-domain aging research as well as generating questions that can be addressed in future research.

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).

In seven studies (n = 1133), adults tried to create funny ideas and then rated the funniness of their responses, which were also independently rated by judges. In contrast to the common “funnier than average” effect found for global self-ratings, people were relatively modest and self-critical about their specific ideas. Extraversion (r = 0.12 [0.07, 0.18], k = 7) and openness to experience (r = 0.09 [0.03, 0.15], k = 7) predicted rating one’s responses as funnier; women rated their responses as less funny (d = −0.28 [−0.37, −0.19], k = 7). The within-person correlation between self and judge ratings was small but significant (r = 0.13 [0.07, 0.19], k = 7), so people had some insight into their ideas’ funniness.

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's 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.