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Journal articles on the topic 'Network psychometric'

Author: Grafiati
Published: 10 March 2023

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1

Kan, Kees-Jan, Hannelies de Jonge, Han L. J. van der Maas, Stephen Z. Levine, and Sacha Epskamp. "How to Compare Psychometric Factor and Network Models." Journal of Intelligence 8, no. 4 (October 2, 2020): 35. http://dx.doi.org/10.3390/jintelligence8040035.

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In memory of Dr. Dennis John McFarland, who passed away recently, our objective is to continue his efforts to compare psychometric networks and latent variable models statistically. We do so by providing a commentary on his latest work, which he encouraged us to write, shortly before his death. We first discuss the statistical procedure McFarland used, which involved structural equation modeling (SEM) in standard SEM software. Next, we evaluate the penta-factor model of intelligence. We conclude that (1) standard SEM software is not suitable for the comparison of psychometric networks with latent variable models, and (2) the penta-factor model of intelligence is only of limited value, as it is nonidentified. We conclude with a reanalysis of the Wechlser Adult Intelligence Scale data McFarland discussed and illustrate how network and latent variable models can be compared using the recently developed R package Psychonetrics. Of substantive theoretical interest, the results support a network interpretation of general intelligence. A novel empirical finding is that networks of intelligence replicate over standardization samples.
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2

McNally, Richard J. "Points of contact between network psychometrics and experimental psychopathology." Journal of Experimental Psychopathology 14, no. 1 (January 2023): 204380872311515. http://dx.doi.org/10.1177/20438087231151505.

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Experimental psychology has long embodied the quest to identify the causes of psychopathology. This venerable tradition has been joined in this quest by network theory, a novel approach to conceptualizing episodes of disorder as emerging from complex systems characterized by dynamic interactions of symptoms. Although issuing from the correlational, psychometric tradition rather than the experimental one, it nevertheless offers methods for identifying symptom targets for clinical experimental intervention. The purpose of this article is to sketch the points of contact between network psychometrics and experimental psychopathology.
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3

Christensen, Alexander P., Hudson Golino, and Paul J. Silvia. "A Psychometric Network Perspective on the Validity and Validation of Personality Trait Questionnaires." European Journal of Personality 34, no. 6 (December 2020): 1095–108. http://dx.doi.org/10.1002/per.2265.

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This article reviews the causal implications of latent variable and psychometric network models for the validation of personality trait questionnaires. These models imply different data generating mechanisms that have important consequences for the validity and validation of questionnaires. From this review, we formalize a framework for assessing the evidence for the validity of questionnaires from the psychometric network perspective. We focus specifically on the structural phase of validation, where items are assessed for redundancy, dimensionality, and internal structure. In this discussion, we underline the importance of identifying unique personality components (i.e. an item or set of items that share a unique common cause) and representing the breadth of each trait's domain in personality networks. After, we argue that psychometric network models have measures that are statistically equivalent to factor models but we suggest that their substantive interpretations differ. Finally, we provide a novel measure of structural consistency, which provides complementary information to internal consistency measures. We close with future directions for how external validation can be executed using psychometric network models. © 2020 European Association of Personality Psychology
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Schmank, Christopher J., Sara Anne Goring, Kristof Kovacs, and Andrew R. A. Conway. "Psychometric Network Analysis of the Hungarian WAIS." Journal of Intelligence 7, no. 3 (September 9, 2019): 21. http://dx.doi.org/10.3390/jintelligence7030021.

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The positive manifold—the finding that cognitive ability measures demonstrate positive correlations with one another—has led to models of intelligence that include a general cognitive ability or general intelligence (g). This view has been reinforced using factor analysis and reflective, higher-order latent variable models. However, a new theory of intelligence, Process Overlap Theory (POT), posits that g is not a psychological attribute but an index of cognitive abilities that results from an interconnected network of cognitive processes. These competing theories of intelligence are compared using two different statistical modeling techniques: (a) latent variable modeling and (b) psychometric network analysis. Network models display partial correlations between pairs of observed variables that demonstrate direct relationships among observations. Secondary data analysis was conducted using the Hungarian Wechsler Adult Intelligence Scale Fourth Edition (H-WAIS-IV). The underlying structure of the H-WAIS-IV was first assessed using confirmatory factor analysis assuming a reflective, higher-order model and then reanalyzed using psychometric network analysis. The compatibility (or lack thereof) of these theoretical accounts of intelligence with the data are discussed.
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Yordanova, Juliana, Vasil Kolev, Roumen Kirov, and Aribert Rothenberger. "Comorbidity in the context of neural network properties." Behavioral and Brain Sciences 33, no. 2-3 (June 2010): 176–77. http://dx.doi.org/10.1017/s0140525x1000083x.

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AbstractCramer et al.'s network approach reconceptualizes mental comorbidity on the basis of symptom space originating from psychometric signatures. We argue that the advantages of this approach need to be regarded in the context of the multi-level functional organization of the neural substrate, ranging from neurogenetic to psychometric. Neuroelectric oscillations are proposed as a level-integrating principle.
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Punzi, Clara, Manuela Petti, and Paolo Tieri. "Network-based methods for psychometric data of eating disorders: A systematic review." PLOS ONE 17, no. 10 (October 31, 2022): e0276341. http://dx.doi.org/10.1371/journal.pone.0276341.

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Background Network science represents a powerful and increasingly promising method for studying complex real-world problems. In the last decade, it has been applied to psychometric data in the attempt to explain psychopathologies as complex systems of causally interconnected symptoms. One category of mental disorders, relevant for their severity, incidence and multifaceted structure, is that of eating disorders (EDs), serious disturbances that negatively affect a person’s eating behavior. Aims We aimed to review the corpus of psychometric network analysis methods by scrutinizing a large sample of network-based studies that exploit psychometric data related to EDs. A particular focus is given to the description of the methodologies for network estimation, network description and network stability analysis providing also a review of the statistical software packages currently used to carry out each phase of the network estimation and analysis workflow. Moreover, we try to highlight aspects with potential clinical impact such as core symptoms, influences of external factors, comorbidities, and related changes in network structure and connectivity across both time and subpopulations. Methods A systematic search was conducted (February 2022) on three different literature databases to identify 57 relevant research articles. The exclusion criteria comprehended studies not based on psychometric data, studies not using network analysis, studies with different aims or not focused on ED, and review articles. Results Almost all the selected 57 papers employed the same analytical procedures implemented in a collection of R packages specifically designed for psychometric network analysis and are mostly based on cross-sectional data retrieved from structured psychometric questionnaires, with just few exemptions of panel data. Most of them used the same techniques for all phases of their analysis. In particular, a pervasive use of the Gaussian Graphical Model with LASSO regularization was registered for in network estimation step. Among the clinically relevant results, we can include the fact that all papers found strong symptom interconnections between specific and nonspecific ED symptoms, suggesting that both types should therefore be addressed by clinical treatment. Conclusions We here presented the largest and most comprehensive review to date about psychometric network analysis methods. Although these methods still need solid validation in the clinical setting, they have already been able to show many strengths and important results, as well as great potentials and perspectives, which have been analyzed here to provide suggestions on their use and their possible improvement.
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Steinley, Douglas, and Michael J. Brusco. "On Fixed Marginal Distributions and Psychometric Network Models." Multivariate Behavioral Research 56, no. 2 (March 4, 2021): 329–35. http://dx.doi.org/10.1080/00273171.2021.1895706.

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8

Lange, Jens, Jonas Dalege, Denny Borsboom, Gerben A. van Kleef, and Agneta H. Fischer. "Toward an Integrative Psychometric Model of Emotions." Perspectives on Psychological Science 15, no. 2 (February 10, 2020): 444–68. http://dx.doi.org/10.1177/1745691619895057.

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Emotions are part and parcel of the human condition, but their nature is debated. Three broad classes of theories about the nature of emotions can be distinguished: affect-program theories, constructionist theories, and appraisal theories. Integrating these broad classes of theories into a unifying theory is challenging. An integrative psychometric model of emotions can inform such a theory because psychometric models are intertwined with theoretical perspectives about constructs. To identify an integrative psychometric model, we delineate properties of emotions stated by emotion theories and investigate whether psychometric models account for these properties. Specifically, an integrative psychometric model of emotions should allow (a) identifying distinct emotions (central in affect-program theories), (b) between- and within-person variations of emotions (central in constructionist theories), and (c) causal relationships between emotion components (central in appraisal theories). Evidence suggests that the popular reflective and formative latent variable models—in which emotions are conceptualized as unobservable causes or consequences of emotion components—cannot account for all properties. Conversely, a psychometric network model—in which emotions are conceptualized as systems of causally interacting emotion components—accounts for all properties. The psychometric network model thus constitutes an integrative psychometric model of emotions, facilitating progress toward a unifying theory.
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Epskamp, Sacha. "Psychometric network models from time-series and panel data." Psychometrika 85, no. 1 (March 2020): 206–31. http://dx.doi.org/10.1007/s11336-020-09697-3.

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Christensen, Alexander P., Yoed N. Kenett, Tomaso Aste, Paul J. Silvia, and Thomas R. Kwapil. "Network structure of the Wisconsin Schizotypy Scales–Short Forms: Examining psychometric network filtering approaches." Behavior Research Methods 50, no. 6 (March 8, 2018): 2531–50. http://dx.doi.org/10.3758/s13428-018-1032-9.

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Bulut, Okan, Damien C. Cormier, Alexandra M. Aquilina, and Hatice C. Bulut. "Age and Sex Invariance of the Woodcock-Johnson IV Tests of Cognitive Abilities: Evidence from Psychometric Network Modeling." Journal of Intelligence 9, no. 3 (July 7, 2021): 35. http://dx.doi.org/10.3390/jintelligence9030035.

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The Woodcock-Johnson IV Tests of Cognitive Abilities (WJ IV COG) is a comprehensive assessment battery designed to assess broad and narrow cognitive abilities, as defined by the Cattell-Horn-Carroll (CHC) theory of intelligence. Previous studies examined the invariance of the WJ assessments across sex and age groups using factor analytic methods. Psychometric network modeling is an alternative methodology that can address both direct and indirect relationships among the observed variables. In this study, we employed psychometric network modeling to examine the invariance of the WJ IV COG across sex and age groups. Using a normative sample (n = 4212 participants) representative of the United States population, we tested the extent to which the factorial structure of the WJ IV COG aligned with CHC theory for the school-aged sample. Next, we used psychometric network modeling as a data-driven method to investigate whether the network structure of the WJ IV COG remains similar across different sex and age (age 6 to 19, inclusively) groups. Our results showed that the WJ IV COG maintained the same network structure across all age and sex groups, although the network structure at younger ages indicated weaker relationships among some subtests. Overall, the results provide construct validity evidence for the WJ IV COG, based on both theoretical and data-driven methods.
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Alves Braga de Oliveira, Melissa, Euclides de Mendonça Filho, Alicia Carissimi, Luciene Lima dos Santos Garay, Marina Scop, Denise Ruschel Bandeira, Felipe Gutiérrez Carvalho, et al. "The Revised Mood Rhythm Instrument: A Large Multicultural Psychometric Study." Journal of Clinical Medicine 10, no. 3 (January 20, 2021): 388. http://dx.doi.org/10.3390/jcm10030388.

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Background: Recent studies with the mood rhythm instrument (MRhI) have shown that the presence of recurrent daily peaks in specific mood symptoms are significantly associated with increased risk of psychiatric disorders. Using a large sample collected in Brazil, Spain, and Canada, we aimed to analyze which MRhI items maintained good psychometric properties across cultures. As a secondary aim, we used network analysis to visualize the strength of the association between the MRhI items. Methods: Adults (n = 1275) between 18–60 years old from Spain (n = 458), Brazil (n = 415), and Canada (n = 401) completed the MRhI and the self-reporting questionnaire (SRQ-20). Psychometric analyses followed three steps: Factor analysis, item response theory, and network analysis. Results: The factor analysis indicated the retention of three factors that grouped the MRhI items into cognitive, somatic, and affective domains. The item response theory analysis suggested the exclusion of items that displayed a significant divergence in difficulty measures between countries. Finally, the network analysis revealed a structure where sleepiness plays a central role in connecting the three domains. These psychometric analyses enabled a psychometric-based refinement of the MRhI, where the 11 items with good properties across cultures were kept in a shorter, revised MRhI version (MRhI-r). Limitations: Participants were mainly university students and, as we did not conduct a formal clinical assessment, any potential correlations (beyond the validated SRQ) cannot be ascertained. Conclusions: The MRhI-r is a novel tool to investigate self-perceived rhythmicity of mood-related symptoms and behaviors, with good psychometric properties across multiple cultures.
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Lu, Lu. "Psychometric Model of College Students Based on Time Series Analysis and Its Application in Educational Management." Occupational Therapy International 2022 (May 6, 2022): 1–10. http://dx.doi.org/10.1155/2022/9550463.

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The psychological measurement method of college students is a hot issue in the field of educational management research. Based on the time series analysis theory, this paper constructs a psychological measurement model for college students. This paper analyzes the psychometric behavior data, uses the time series analysis method for behavior prediction, deeply mines the relevant component information of the psychometric data, and solves the problems of weak correlation between the functions of the psychometric platform and low data accuracy of the psychometric model. At the same time, taking the intervention target group and the intervention mode as the basic variables of the intervention classification system, combining these two dimensions, a two-dimensional classification framework for psychometric intervention was proposed, and four types of different psychometric intervention measures were applied. During the simulation process, a psychometric trajectory matrix was constructed, and a two-dimensional data extraction network was used to extract the psychometric pattern data of a certain period of time. The experimental results show that using the student mental state data as a label can obtain a low-coupling training set classification for psychometric effects of college students.
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Ferguson, Cameron. "A network psychometric approach to neurocognition in early Alzheimer's disease." Cortex 137 (April 2021): 61–73. http://dx.doi.org/10.1016/j.cortex.2021.01.002.

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Christensen, Alexander P., and Hudson Golino. "Factor or Network Model? Predictions From Neural Networks." Journal of Behavioral Data Science 1, no. 1 (May 2021): 85–126. http://dx.doi.org/10.35566/jbds/v1n1/p5.

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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.
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Duarte, Vannessa, Paul Leger, Sergio Contreras, and Hiroaki Fukuda. "Using Artificial Neural Network to Detect Fetal Alcohol Spectrum Disorder in Children." Applied Sciences 11, no. 13 (June 26, 2021): 5961. http://dx.doi.org/10.3390/app11135961.

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Fetal alcohol spectrum disorder (FASD) is an umbrella term for children’s conditions due to their mother having consumed alcohol during pregnancy. These conditions can be mild to severe, affecting the subject’s quality of life. An earlier diagnosis of FASD is crucial for an improved quality of life of children by allowing a better inclusion in the educational system. New trends in computer-based diagnosis to detect FASD include using Machine Learning (ML) tools to detect this syndrome. However, most of these studies rely on children’s images that can be invasive and costly. Therefore, this paper presents a study that focuses on evaluating an ANN to classify children with FASD using non-invasive and more accessible data. This data used comes from a battery of tests obtained from children, including psychometric, saccade eye movement, and diffusion tensor imaging (DTI). We study the different configurations of ANN with dense layers being the psychometric data that correctly perform the best with 75% of the outcome. The other models include a feature layer, and we used it to predict FASD using every test individually. Model obtained obtained an accuracy of 88.46% (psychometric, 74.07% (Antisaccadic), 72.24% (Prosaccadic), 88% (Memory guide saccade), and 75% (DTI). These results suggest that the ANN approach is a competitive and efficient methodology to detect FASD. These results are an improvement on Zhang’s 2019 model, which used the same data with less accuracy level.
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RIERA ADROVER, Joan Albert, Albert SESE, and Juan Jose MONTANO. "Mediators’ Skills for Trust Building Inventory. A Psychometric Networks Approach." Revista de Cercetare si Interventie Sociala 77 (June 20, 2022): 106–24. http://dx.doi.org/10.33788/rcis.77.7.

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Trust building depends both on therapeutic alliance construction and the quality of the intervention. Although trust-building has received much attention in the literature, there are hardly any validated tools to assess it. In order to cover this gap, the main goal of this work is to present a psychometric inventory for measuring both the skills for building a therapeutic alliance and the skills for intervention through a mediation process. 170 subjects, mediators and clients, voluntarily participated in the validation study. An advanced approach by means of psychometric networks was used to estimate and test the two-factor hypothesized model: 1) Skills for building a therapeutic alliance, and 2) Skills for the intervention in the mediation process. An Exploratory Graph Analysis (EGA) with the Triangulated Maximally Filtered Graph (TMFG) was implemented for estimating the inventory latent network with all items. Further bootstrapping techniques were used for assessing the latent structure stability. A Confirmatory Factor Analysis (CFA) over the best fitted EGA model was applied with a robust estimator (WLSMV). Reliability analyses for the best-fitted model were also implemented. Results indicated a stable and well-fitted two-factor latent network model, which was also confirmed by CFA: (2scaled = 216.84, df = 169, p = .008, RMSEA = .039, CFI = .986, TLI = .984). All reliability indices for the two-factor model obtained adequate values (above .80) and all items provided adequate psychometric behavior. This new inventory can be useful for developing improvement professional practice training programs, and also for enriching mediation-related higher education curricula.
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Mittinty, Manasi Murthy, Pedro H. R. Santiago, and Lisa Jamieson. "Assessment of Pain-Related Fear in Indigenous Australian Populations Using the Fear of Pain-9 Questionnaire (FPQ-9)." International Journal of Environmental Research and Public Health 19, no. 10 (May 20, 2022): 6256. http://dx.doi.org/10.3390/ijerph19106256.

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In this study, we examined the psychometric properties of the Fear of Pain Questionnaire (FPQ-9) in Indigenous Australian people. FPQ-9, a shorter version of the original Fear of Pain Questionnaire-III, was developed to support the demand for more concise scales with faster administration time in the clinical and research setting. The psychometric properties of FPQ-9 in Indigenous Australian participants (n = 735) were evaluated with network psychometrics, such as dimensionality, model fit, internal consistency and reliability, measurement invariance, and criterion validity. Our findings indicated that the original FPQ-9 three-factor structure had a poor fit and did not adequately capture pain-related fear in Indigenous Australian people. On removal of two cross-loading items, an adapted version Indigenous Australian Fear of Pain Questionnaire-7 (IA-FPQ-7) displayed good fit and construct validity and reliability for assessing fear of pain in a sample of Indigenous Australian people. The IA-FPQ-7 scale could be used to better understand the role and impact of fear of pain in Indigenous Australian people living with chronic pain. This could allow for more tailored and timely interventions for managing pain in Indigenous Australian communities.
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Samochernova, E. L., E. A. Sosnovskaya, and A. Sholeh. "Psychometric Analysis of “The Instagram Addiction Scale”." Reflexio 14, no. 2 (September 15, 2022): 61–73. http://dx.doi.org/10.25205/2658-4506-2021-14-2-61-73.

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The urgency of the problem of Internet addiction today is undeniable, the significance of this problem has especially increased during the COVID-19 pandemic. The article presents the results of adaptation of the questionnaire “The Instagram Addiction Scale” (TIAS) for diagnosing the level of dependence on the social network Instagram* in a Russian-speaking sample (n = 416). The psychometric characteristics of the Russian-language version of the questionnaire were assessed: reliability by internal consistency, construct validity and factor structure. According to the results of adaptation, the TIAS questionnaire has good psychometric indicators.
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Schmank, Christopher J., Sara Anne Goring, Kristof Kovacs, and Andrew R. A. Conway. "Investigating the Structure of Intelligence Using Latent Variable and Psychometric Network Modeling: A Commentary and Reanalysis." Journal of Intelligence 9, no. 1 (February 5, 2021): 8. http://dx.doi.org/10.3390/jintelligence9010008.

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In a recent publication in the Journal of Intelligence, Dennis McFarland mischaracterized previous research using latent variable and psychometric network modeling to investigate the structure of intelligence. Misconceptions presented by McFarland are identified and discussed. We reiterate and clarify the goal of our previous research on network models, which is to improve compatibility between psychological theories and statistical models of intelligence. WAIS-IV data provided by McFarland were reanalyzed using latent variable and psychometric network modeling. The results are consistent with our previous study and show that a latent variable model and a network model both provide an adequate fit to the WAIS-IV. We therefore argue that model preference should be determined by theory compatibility. Theories of intelligence that posit a general mental ability (general intelligence) are compatible with latent variable models. More recent approaches, such as mutualism and process overlap theory, reject the notion of general mental ability and are therefore more compatible with network models, which depict the structure of intelligence as an interconnected network of cognitive processes sampled by a battery of tests. We emphasize the importance of compatibility between theories and models in scientific research on intelligence.
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Sofronov, A. G., A. E. Dobrovolskaya, A. V. Trusova, I. A. Getmanenko, and A. N. Gvozdetckii. "A psychodiagnostic tool for the structural assessment of the social network in patients with schizophrenia: validity and psychometric properties." V.M. BEKHTEREV REVIEW OF PSYCHIATRY AND MEDICAL PSYCHOLOGY, no. 1 (April 12, 2021): 77–90. http://dx.doi.org/10.31363/2313-7053-2021-1-77-90.

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The aim of the study was to develop a new valid psychometric diagnostic tool for a multi-factor social network assessment of schizophrenic patients, called «The structural assessment of the social network of schizophrenic patients». The new development is based on the social network analysis model elaborated by S. L. Phillips (1981) and translated into Russian by Gurovich I. Ya. et al. (2007). The authors of this article additionally developed an algorithm for assessing the activity of patients on social networks of the Internet. Reducing non-informative variables and conducting a confirmatory factor analysis in a sample of 265 observations of schizophrenic patients (F20.0) aged from 18 to 55, recruited in four medical organizations (145 patients admitted to the hospital and 55 outpatients), resulted in determining a four-factor structure of the patients’ social network: «Objective parameters», «Internet activity», «Emotional aspect of social support» and«Reciprocal support». The method has demonstrated high internal and external validity, as well as applicability in the clinical practice in schizophrenia due to the low resource consumption and compact applicability. Measurable factor indicators of the patients’ social networks obtained by using the structural assessment of the social network of schizophrenic patients allow to determine the targets for psychocorrectional interventions and to increase the effectiveness of psychosocial rehabilitation. In addition an automated method for calculating final indicators has been developed, as well as manual, practical recommendations and corresponding printed forms.
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boland, hassan. "Development and psychometric properties of the Social Network Sites Engagement Scale (SNSES)." journal of counseling research 19, no. 75 (December 1, 2020): 141–68. http://dx.doi.org/10.29252/jcr.19.75.141.

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Massidda, Davide, Mauro Giovanni Carta, and Gianmarco Altoè. "Integrating Different Factorial Solutions of a Psychometric Tool Via Social Network Analysis." Methodology 12, no. 3 (July 2016): 97–106. http://dx.doi.org/10.1027/1614-2241/a000113.

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Abstract. Evaluating the factorial structure of a psychometric test is crucial to capture the complexity of a psychological phenomenon. Indeed, for the same test, several studies may find different factorial solutions which, in turn, may be explained by within and/or between sample variability. In this paper we introduce a novel quantitative approach to combine different factorial solutions of the same test. We propose to use a method based on Social Network Analysis to create and statistically evaluate an integrated factorial structure based on the information provided by previous researches. We present an application to the Mood Disorder Questionnaire by considering different factorial structures reported in the literature. The integrated factorial solution indicates the presence of three factors supporting the multidimensionality of the test. The role of single items in the composition of factors is also evaluated and discussed in terms of differences and similarities between the five original studies and the new integrated model. From an applied perspective, our approach may be useful to assist researchers in summarizing different factorial solutions for the same test efficiently. Furthermore, the resulting integrated factor solution could serve as baseline model to validate the structure of the test by applying confirmatory factor techniques to new data.
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MacLeod, Jeffrey W., Michael A. Lawrence, Meghan M. McConnell, Gail A. Eskes, Raymond M. Klein, and David I. Shore. "Appraising the ANT: Psychometric and theoretical considerations of the Attention Network Test." Neuropsychology 24, no. 5 (2010): 637–51. http://dx.doi.org/10.1037/a0019803.

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Kan, Kees-Jan, Han L. J. van der Maas, and Stephen Z. Levine. "Extending psychometric network analysis: Empirical evidence against g in favor of mutualism?" Intelligence 73 (March 2019): 52–62. http://dx.doi.org/10.1016/j.intell.2018.12.004.

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Nikolaev, Konstantin Sergeevich, Fail Mubarakovich Gafarov, and Pavel Nikolaevich Ustin. "Metrics for Personal Profiles of Social Network Users." SHS Web of Conferences 79 (2020): 01012. http://dx.doi.org/10.1051/shsconf/20207901012.

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This paper discusses the technical details of obtaining and processing data to determine a set of characteristics of texts from social networks, genre preferences in movies and music genres for students of Kazan Federal University who have different academic performance (successful, average, not-successful). The selection of such characteristics is carried out using machine learning methods (Word2Vec, tSNE). The data obtained is used in the development of a functional psychometric model of cognitive behavioral predictors of an individual’s activity within the framework of their educational activities. We also developed a web application for visualizing the obtained data using the Flask engine.
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Christensen, Alexander P., and Hudson Golino. "Estimating the Stability of Psychological Dimensions via Bootstrap Exploratory Graph Analysis: A Monte Carlo Simulation and Tutorial." Psych 3, no. 3 (August 27, 2021): 479–500. http://dx.doi.org/10.3390/psych3030032.

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Exploratory Graph Analysis (EGA) has emerged as a popular approach for estimating the dimensionality of multivariate data using psychometric networks. Sampling variability, however, has made reproducibility and generalizability a key issue in network psychometrics. To address this issue, we have developed a novel bootstrap approach called Bootstrap Exploratory Graph Analysis (bootEGA). bootEGA generates a sampling distribution of EGA results where several statistics can be computed. Descriptive statistics (median, standard error, and dimension frequency) provide researchers with a general sense of the stability of their empirical EGA dimensions. Structural consistency estimates how often dimensions are replicated exactly across the bootstrap replicates. Item stability statistics provide information about whether dimensions are unstable due to misallocation (e.g., item placed in the wrong dimension), multidimensionality (e.g., item belonging to more than one dimension), and item redundancy (e.g., similar semantic content). Using a Monte Carlo simulation, we determine guidelines for acceptable item stability. After, we provide an empirical example that demonstrates how bootEGA can be used to identify structural consistency issues (including a fully reproducible R tutorial). In sum, we demonstrate that bootEGA is a robust approach for identifying the stability and robustness of dimensionality in multivariate data.
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Ebisch, Sjoerd J. H., Andrea Scalabrini, Georg Northoff, Clara Mucci, Maria Rita Sergi, Aristide Saggino, Antonio Aquino, et al. "Intrinsic Shapes of Empathy: Functional Brain Network Topology Encodes Intersubjective Experience and Awareness Traits." Brain Sciences 12, no. 4 (April 5, 2022): 477. http://dx.doi.org/10.3390/brainsci12040477.

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Trait empathy is an essential personality feature in the intricacy of typical social inclinations of individuals. Empathy is likely supported by multilevel neuronal network functioning, whereas local topological properties determine network integrity. In the present functional MRI study (N = 116), we aimed to trace empathic traits to the intrinsic brain network architecture. Empathy was conceived as composed of two dimensions within the concept of pre-reflective, intersubjective understanding. Vicarious experience consists of the tendency to resonate with the feelings of other individuals, whereas intuitive understanding refers to a natural awareness of others’ emotional states. Analyses of graph theoretical measures of centrality showed a relationship between the fronto-parietal network and psychometric measures of vicarious experience, whereas intuitive understanding was associated with sensorimotor and subcortical networks. Salience network regions could constitute hubs for information processing underlying both dimensions. The network properties related to empathy dimensions mainly concern inter-network information flow. Moreover, interaction effects implied several sex differences in the relationship between functional network organization and trait empathy. These results reveal that distinct intrinsic topological network features explain individual differences in separate dimensions of intersubjective understanding. The findings could help understand the impact of brain damage or stimulation through alterations of empathy-related network integrity.
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Cramer, Angélique O. J., Lourens J. Waldorp, Han L. J. van der Maas, and Denny Borsboom. "Comorbidity: A network perspective." Behavioral and Brain Sciences 33, no. 2-3 (June 2010): 137–50. http://dx.doi.org/10.1017/s0140525x09991567.

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AbstractThe pivotal problem of comorbidity research lies in the psychometric foundation it rests on, that is, latent variable theory, in which a mental disorder is viewed as a latent variable that causes a constellation of symptoms. From this perspective, comorbidity is a (bi)directional relationship between multiple latent variables. We argue that such a latent variable perspective encounters serious problems in the study of comorbidity, and offer a radically different conceptualization in terms of a network approach, where comorbidity is hypothesized to arise from direct relations between symptoms of multiple disorders. We propose a method to visualize comorbidity networks and, based on an empirical network for major depression and generalized anxiety, we argue that this approach generates realistic hypotheses about pathways to comorbidity, overlapping symptoms, and diagnostic boundaries, that are not naturally accommodated by latent variable models: Some pathways to comorbidity through the symptom space are more likely than others; those pathways generally have the same direction (i.e., from symptoms of one disorder to symptoms of the other); overlapping symptoms play an important role in comorbidity; and boundaries between diagnostic categories are necessarily fuzzy.
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Pretorius, T. Brian. "Willingness to use Social Support: Use of the Network Orientation Scale with Black South African Students." Psychological Reports 73, no. 3_part_1 (December 1993): 1011–17. http://dx.doi.org/10.1177/00332941930733pt144.

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This study focused on the psychometric properties of the Network Orientation Scale as used with South African students. This scale presumably measures an individual's willingness to use personal social resources. 450 black South African students completed the Network Orientation Scale, a biographical questionnaire, and a number of social support questionnaires. The instrument showed good internal consistency and scores correlated significantly (- .09 to - .53) with all the social support scales. In addition, correlations of the Network Orientation Scale scores with a number of demographic variables are reported.
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Mendoza, Norman B., and Zi Yan. "Validation of a Subject-Specific Student Self-Assessment Practice Scale (SaPS) Among Secondary School Students in the Philippines." Journal of Psychoeducational Assessment 39, no. 4 (February 18, 2021): 481–93. http://dx.doi.org/10.1177/0734282921994374.

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Self-assessment is fundamental to self-regulated learning; however, instruments to measure self-assessment practices are limited to a few developed educational systems. This study examined the psychometric properties of the Self-assessment Practice Scale (SaPS) in the English language subject using data from 778 secondary school students from the Philippines. We used confirmatory factor analysis (CFA) and Rasch analysis to test the SaPS’ within-network validity, then bivariate correlations and structural equation modeling (SEM) for between-network validity. The CFA supported the scale’s four-factor structure, and the Rasch analysis supported the scale’s dimensionality, rating scale effectiveness, and item fit statistics. The four SaPS subscales were positively correlated to agentic, cognitive, and metacognitive engagement. SEM results show that all SaPS factors (except self-monitoring) had significant associations to the engagement outcomes. This study highlights the sound psychometric properties of SaPS in a new educational context and its applicability as a subject-specific measure of assessment-as-learning strategies.
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Soltani, Nadia, Shahram Vahedi, Mansour Bayrami, and Mohammad Hossein Zarghami. "Investigating the psychometric properties of english vocabulary size test using network data analysis." Journal Of Psychological Science 20, no. 103 (October 1, 2021): 1131–46. http://dx.doi.org/10.52547/jps.20.103.1131.

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Christensen, Alexander,P. "NetworkToolbox: Methods and Measures for Brain, Cognitive, and Psychometric Network Analysis in R." R Journal 10, no. 2 (2019): 422. http://dx.doi.org/10.32614/rj-2018-065.

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Wang, Jennifer M. "Psychometric Properties of the Network Relationship Inventory-Social Provision Version in Chinese Youth." Child Psychiatry & Human Development 45, no. 6 (January 31, 2014): 695–702. http://dx.doi.org/10.1007/s10578-014-0438-6.

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Christodoulou, Andria, Michalis Michaelides, and Maria Karekla. "Network analysis: A new psychometric approach to examine the underlying ACT model components." Journal of Contextual Behavioral Science 12 (April 2019): 285–89. http://dx.doi.org/10.1016/j.jcbs.2018.10.002.

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Haghdadi, Mina, and Mohammad Fatemi. "Artificial neural network prediction of the psychometric activities of phenylalkylamines using DFT-calculated molecular descriptors." Journal of the Serbian Chemical Society 75, no. 10 (2010): 1391–404. http://dx.doi.org/10.2298/jsc100408116h.

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In the present work, a quantitative structure-activity relationship (QSAR) method was used to predict the psychometric activity values (as mescaline unit, log MU) of 48 phenylalkylamine derivatives from their density functional theory (DFT) calculated molecular descriptors and an artificial neural network (ANN). In the first step, the molecular descriptors were obtained by DFT calculation at the 6-311G
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Shin, Na Young. "Psychometric Properties of the Bergen Social Media Addiction Scale in Korean Young Adults." Psychiatry Investigation 19, no. 5 (May 25, 2022): 356–61. http://dx.doi.org/10.30773/pi.2021.0294.

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Objective The aim of this study was to examine the psychometric properties of the Bergen Social Media Addiction Scale (BSMAS) in Korean young adults.Methods A total of 401 Korean university students (mean age±SD, 21.9±1.8 years; female, 72.8%) participated in the study. They completed the Korean version of BSMAS, the Social Network Site Addiction Proneness Scale (SAPS), and the Social Network Site Use Motives Scale. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were conducted in half-split samples to assess the construct validity of the BSMAS. Internal consistency, test-retest reliability, and convergent validity were examined.Results Both EFA and CFA supported a unidimensional model of the BSMAS. Indices of test-retest reliability at a 3-week interval (r=0.75) and internal consistency (Cronbach’s alpha=0.86) of the BSMAS were acceptable and convergent validity between the BSMAS and the SAPS (r=0.86) was satisfactory.Conclusion The results of this study suggest that the BSMAS is an effective tool for screening social network site (SNS) addiction in Korean young adults and is a psychometrically sound measure that can be used in future Korean research on SNS addiction.
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W.Wiggins, Mark, Jaime Auton, and Daniel Sturman. "Evaluating Situation Assessment in Distributed Network Electricity Control." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 64, no. 1 (December 2020): 263–67. http://dx.doi.org/10.1177/1071181320641062.

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Situation assessment is a critical skill amongst electricity distribution controllers but can be difficult to assess in practice. In the present research, we adapted a psychometric approach based on the principle that expert situation assessment engages cue-based associations from memory. Using consortium norming, we acquired performance data from 320 network control operators for five network control-related tasks that comprised the electricity network service provider edition of the EXPERT Intensive Skills Evaluation (EXPERTise 2.0) software. Operators were distributed across 11 Distributed Network Service Providers (DNSP), with data collected over three phases. Data were examined across the three phases of data collection and suggest that DNSPs can monitor the situation assessment of network controllers at an individual and at an organisational level, affording the opportunity to identify changes in performance and implement remedial strategies as appropriate.
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Northcott, Sarah, and Katerina Hilari. "Stroke Social Network Scale: development and psychometric evaluation of a new patient-reported measure." Clinical Rehabilitation 27, no. 9 (April 10, 2013): 823–33. http://dx.doi.org/10.1177/0269215513479388.

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Picco, Soledad, Maria E. Pedreira, and Rodrigo S. Fernández. "Psychometric validation of the survey of autobiographical memory: confirmatory factor analysis and network analysis." Memory 28, no. 8 (September 1, 2020): 1037–50. http://dx.doi.org/10.1080/09658211.2020.1812662.

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Datu, Jesus Alfonso D., and Weipeng Yang. "Psychometric Validity and Gender Invariance of the Academic Buoyancy Scale in the Philippines: A Construct Validation Approach." Journal of Psychoeducational Assessment 36, no. 3 (October 17, 2016): 278–83. http://dx.doi.org/10.1177/0734282916674423.

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The current study examined the psychometric validity and gender invariance of the Academic Buoyancy Scale in the Philippines through a construct validation approach. In terms of within-network construct validity, our results demonstrated that the unidimensional model of academic buoyancy significantly fit the current sample and was invariant across gender. Male students scored significantly higher than female students on academic buoyancy. Regarding between-network construct validity, our results revealed that academic buoyancy was positively associated with behavioral and emotional engagement. Implications of the findings of the study are discussed.
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Manas Kumar Yogi and Yamuna Mundru. "Detecting Insider Threat through Psychometric Scores and Work Environment." Journal of Soft Computing Paradigm 4, no. 3 (October 12, 2022): 200–212. http://dx.doi.org/10.36548/jscp.2022.3.008.

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Nowadays the cyber-threat is looming large from disgruntled employees rather than external attackers. With the advent of modern threat models which help in development of adversary attack scenarios, the security designers can get an idea of attack mitigation strategies. The analysis of threat model helps in knowing the unforeseen circumstances which can lead to a cyber-security risk. If the weakness in the network or in any other element of a cyber-ecosystem is identified during the design of security models, then eventually it will lead to stronger security applications. This paper formulates a framework of identification of insider threat which may be a result of various factors in an organization. The proposed technique considers psychometric condition of an employee along with other factors which may give rise as a threat to the organization. The dataset has been used to train the model and the experimental results have found an effective way to detect the insider threat in specific cases.
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Magnavita, Nicola, and Carlo Chiorri. "Development and Validation of a New Measure of Work Annoyance Using a Psychometric Network Approach." International Journal of Environmental Research and Public Health 19, no. 15 (July 30, 2022): 9376. http://dx.doi.org/10.3390/ijerph19159376.

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Existing measures of the impact of job characteristics on workers’ well-being do not directly assess the extent to which such characteristics (e.g., opportunity to learn new skills) are perceived as positive or negative. We developed a measure, the Work Annoyance Scale (WAS), of the level of annoyance that workers feel about certain aspects of the job and evaluated its psychometric properties. Using archival data from two cohorts (n = 2226 and 655) of workers that had undergone an annual medical examination for occupational hazard, we show the usefulness of the network psychometric approach to scale validation and its similarities and differences from a traditional factor analytic approach. The results revealed a two-dimensional structure (working conditions and cognitive demands) that was replicable across cohorts and bootstrapped samples. The two dimensions had adequate structural consistency and discriminant validity with respect to other questionnaires commonly used in organizational assessment, and showed a consistent pattern of association with relevant background variables. Despite the need for more extensive tests of its content and construct validity in light of the organizational changes due to the COVID-19 pandemic and of an evaluation of the generalizability of the results to cultural contexts different from the Italian one, the WAS appears as a psychometrically sound tool for assessment and research in organizational contexts.
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Valdez, Jana Patricia M., and Samuel Kai Wah Chu. "Examining the Psychometric Validity of the Five-Item Gratitude Questionnaire: An Item Response Theory Approach." Journal of Psychoeducational Assessment 38, no. 4 (December 17, 2018): 529–36. http://dx.doi.org/10.1177/0734282918816542.

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The present investigation explored the psychometric validity of the five-item Gratitude Questionnaire (GQ-5) using a construct validation approach. Concerning within-network construct validity, results of item response theory (IRT) analysis via graded response model (GRM) showed that this scale could not discriminate individuals who score high in gratitude and the 7-scale response options could be modified to a 6-scale response choice. As regards to between-network construct validity, findings demonstrated that gratitude was positively correlated to both controlled and autonomous motivation. Implications are discussed to refine the assessment of gratitude in the school contexts.
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Wandschneider, Britta, Jane Burdett, Lucy Townsend, Andrea Hill, Pamela J. Thompson, John S. Duncan, and Matthias J. Koepp. "Effect of topiramate and zonisamide on fMRI cognitive networks." Neurology 88, no. 12 (February 17, 2017): 1165–71. http://dx.doi.org/10.1212/wnl.0000000000003736.

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Objective:To investigate the effects of topiramate (TPM), zonisamide (ZNS), and levetiracetam (LEV) on cognitive network activations in patients with focal epilepsy using an fMRI language task.Methods:In a retrospective, cross-sectional study, we identified patients from our clinical database of verbal fluency fMRI studies who were treated with either TPM (n = 32) or ZNS (n = 51). We matched 62 patients for clinical measures who took LEV but not TPM or ZNS. We entered antiepileptic comedications as nuisance variables and compared out-of-scanner psychometric measures for verbal fluency and working memory between groups.Results:Out-of-scanner psychometric data showed overall poorer performance for TPM compared to ZNS and LEV and poorer working memory performance in ZNS-treated patients compared to LEV-treated patients. We found common fMRI effects in patients taking ZNS and TPM, with decreased activations in cognitive frontal and parietal lobe networks compared to those taking LEV. Impaired deactivation was seen only with TPM.Conclusions:Our findings suggest that TPM and ZNS are associated with similar dysfunctions of frontal and parietal cognitive networks, which are associated with impaired performance. TPM is also associated with impaired attenuation of language-associated deactivation. These studies imply medication-specific effects on the functional neuroanatomy of language and working memory networks.Classification of evidence:This study provides Class III evidence that in patients with focal epilepsy, TPM and ZNS compared to LEV lead to disruption of language and working memory networks.
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Furman, Wyndol, and Duane Buhrmester. "Methods and Measures: The Network of Relationships Inventory: Behavioral Systems Version." International Journal of Behavioral Development 33, no. 5 (August 14, 2009): 470–78. http://dx.doi.org/10.1177/0165025409342634.

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This paper describes an alternative version of the Network of Relationships Inventory, which was designed to assess how frequently different relationships were used to fulfill the functions of three behavioral systems: attachment, caregiving, and affiliation. Psychometric and validational evidence is presented including: (a) high internal consistency for all scales and composites; (b) a second order factor structure of support and negative interactions for each relationship; (c) moderately high stability over a one year period; (d) moderate convergence among different reporters; (e) theoretically meaningful differences among different relationships; (f) moderate associations among different relationships; (g) associations with the original Network of Relationships Inventory; and (h) relations with observed interactions with mothers and friends.
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Santiago, Pedro Henrique Ribeiro, Marko Milosevic, Xiangqun Ju, Wendy Cheung, Dandara Haag, and Lisa Jamieson. "A network psychometric validation of the Children Oral Health-Related Quality of Life (COHQoL) questionnaire among Aboriginal and/or Torres Strait Islander children." PLOS ONE 17, no. 8 (August 18, 2022): e0273373. http://dx.doi.org/10.1371/journal.pone.0273373.

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In Australia, research evidence has shown that Aboriginal and/or Torres Strait Islander children experience a higher burden of oral health diseases compared to other non-Indigenous children. The impact of oral health diseases on children’s functional and psychosocial outcomes led to the development of several instruments to evaluate child oral health-related quality of life (COHQoL), such as the Parental-Caregiver Perception Questionnaire (P-CPQ) and the Family Impact Scale (FIS). However, the psychometric properties of these instruments have been evaluated only in Western cultures and have not been investigated for Aboriginal children in Australia. The current study aimed to examine the psychometric properties of the short-forms P-CPQ and FIS for Aboriginal and/or Torres Strait Islander children aged 2–3 years. Data were collected from the South Australian Aboriginal Birth Cohort (SAABC), including 270 Aboriginal children aged 2–3 years. Network psychometric models were used to investigate dimensionality, item redundancy, structural consistency and item stability, model fit, internal consistency reliability and criterion validity. We propose an instrument named Aboriginal Children’s Oral Health-Related Quality of Life Questionnaire (A-COHQoL). Our findings indicated that, after the exclusion of four problematic items, the A-COHQoL showed a three-dimensional structure (“Parent/Family Activities”, “COHQoL” and “Family Conflict”) with good model fit and reliability. The A-COHQoL is a psychometrically robust and sensitive instrument that is readily available for Aboriginal and/or Torres Strait Islander children aged 2–3 years in Australia and can be adapted in the future for Indigenous child groups in other countries.
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Kim, Junghoon, Drew Parker, John Whyte, Tessa Hart, John Pluta, Madhura Ingalhalikar, H. B. Coslett, and Ragini Verma. "Disrupted Structural Connectome Is Associated with Both Psychometric and Real-World Neuropsychological Impairment in Diffuse Traumatic Brain Injury." Journal of the International Neuropsychological Society 20, no. 9 (October 2014): 887–96. http://dx.doi.org/10.1017/s1355617714000812.

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AbstractTraumatic brain injury (TBI) is likely to disrupt structural network properties due to diffuse white matter pathology. The present study aimed to detect alterations in structural network topology in TBI and relate them to cognitive and real-world behavioral impairment. Twenty-two people with moderate to severe TBI with mostly diffuse pathology and 18 demographically matched healthy controls were included in the final analysis. Graph theoretical network analysis was applied to diffusion tensor imaging (DTI) data to characterize structural connectivity in both groups. Neuropsychological functions were assessed by a battery of psychometric tests and the Frontal Systems Behavior Scale (FrSBe). Local connection-wise analysis demonstrated reduced structural connectivity in TBI arising from subcortical areas including thalamus, caudate, and hippocampus. Global network metrics revealed that shortest path length in participants with TBI was longer compared to controls, and that this reduced network efficiency was associated with worse performance in executive function and verbal learning. The shortest path length measure was also correlated with family-reported FrSBe scores. These findings support the notion that the diffuse form of neuropathology caused by TBI results in alterations in structural connectivity that contribute to cognitive and real-world behavioral impairment. (JINS, 2014, 20, 1–10)
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Denissen, Jaap J. A., Rinie Geenen, Maarten Selfhout, and Marcel A. G. van Aken. "Single‐item big five ratings in a social network design." European Journal of Personality 22, no. 1 (February 2008): 37–54. http://dx.doi.org/10.1002/per.662.

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To develop and validate an ultra‐short measure to assess the Big Five in social network designs, the unipolar items of the Ten‐Item Personality Inventory were adapted to create a bipolar single‐item scale (TIPI‐r), including a new Openness item. Reliability was examined in terms of the internal consistency and test–retest stability of self‐ratings and peer‐rating composites (trait reputations). Validity was examined by means of convergence between TIPI‐r and Big Five Inventory (BFI) scores, self‐peer agreement and projection (intra‐ individual correlation between self‐ and peer‐ratings). The psychometric quality of the TIPI‐r differed somewhat between scales and the different reliability and validity criteria. The high reliability of the peer‐rating composites motivates to use the TIPI‐r in future studies employing social network designs. Copyright © 2007 John Wiley & Sons, Ltd.
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Sigerson, Leif, and Cecilia Cheng. "Scales for measuring user engagement with social network sites: A systematic review of psychometric properties." Computers in Human Behavior 83 (June 2018): 87–105. http://dx.doi.org/10.1016/j.chb.2018.01.023.

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