How to Compare Psychometric Factor and Network Models

Kan, Kees-Jan; Jonge, Hannelies de; Maas, Han L. J. van der; Levine, Stephen Z.; Epskamp, Sacha

doi:10.3390/jintelligence8040035

Cited by 61 publications

(51 citation statements)

References 20 publications

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“…So far, network analysis is largely exploratory. Yet, inspired by methods from structural equation modeling, some confirmatory approaches are already available (e.g., Kan et al, 2020).…”

Section: Comparison Of Network Analysis With Other Methodsmentioning

confidence: 99%

Emotions as Overlapping Causal Networks of Emotion Components: Implications and Methodological Approaches

Lange

Zickfeld

2021

Emotion Review

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A widespread perspective describes emotions as distinct categories bridged by fuzzy boundaries, indicating that emotions are distinct and dimensional at the same time. Theoretical and methodological approaches to this perspective still need further development. We conceptualize emotions as overlapping networks of causal relationships between emotion components—networks representing distinct emotions share components with and relate to each other. To investigate this conceptualization, we introduce network analysis to emotion research and apply it to the reanalysis of a data set on multiple positive emotions. Specifically, we describe the estimation of networks from data, and the detection of overlapping communities of nodes in these networks. The network perspective has implications for the understanding of distinct emotions, their co-occurrence, and their measurement.

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“…So far, network analysis is largely exploratory. Yet, inspired by methods from structural equation modeling, some confirmatory approaches are already available (e.g., Kan et al, 2020).…”

Section: Comparison Of Network Analysis With Other Methodsmentioning

confidence: 99%

Emotions as Overlapping Causal Networks of Emotion Components: Implications and Methodological Approaches

Lange

Zickfeld

2021

Emotion Review

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“…In concrete words, cognitive abilities (the nodes) are conceptualized as being more or less directly related to each other (the edges) without postulating any latent factor. Recently, Kan et al ( 2019 ) and Kan et al ( 2020 ) have shown the superior fit of a network model of intelligence compared to a higher-order g model and a bifactor-CFA model. As Network Analysis is not implemented in MPlus and currently not available with binary variables within the reference R package Psychonetrics (Epskamp, 2020a , b ), we did not have the opportunity to compare the ICM-CFA and ESEM models with a Network Analysis model of early numeracy.…”

Section: Discussionmentioning

confidence: 99%

Investigating the Dimensionality of Early Numeracy Using the Bifactor Exploratory Structural Equation Modeling Framework

et al. 2021

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The few studies that have analyzed the factorial structure of early number skills have mainly used confirmatory factor analysis (CFA) and have yielded inconsistent results, since early numeracy is considered to be unidimensional, multidimensional or even underpinned by a general factor. Recently, the bifactor exploratory structural equation modeling (bifactor-ESEM)—which has been proposed as a way to overcome the shortcomings of both the CFA and the exploratory structural equation modeling (ESEM)—proved to be valuable to account for the multidimensionality and the hierarchical nature of several psychological constructs. The present study is the first to investigate the dimensionality of early number skills measurement through the application of the bifactor-ESEM framework. Using data from 644 prekindergarten and kindergarten children (4 to 6 years old), several competing models were contrasted: the one-factor CFA model; the independent cluster model (ICM-CFA); the exploratory structural equation modeling (ESEM); and their bifactor counterpart (bifactor-CFA and bifactor-ESEM, respectively). Results indicated acceptable fit indexes for the one-factor CFA and the ICM-CFA models and excellent fit for the others. Among these, the bifactor-ESEM with one general factor and three specific factors (Counting, Relations, Arithmetic) not only showed the best model fit, but also the best coherent factor loadings structure and full measurement invariance across gender. The bifactor-ESEM appears relevant to help disentangle and account for general and specific factors of early numerical ability. While early numerical ability appears to be mainly underpinned by a general factor whose exact nature still has to be determined, this study highlights that specific latent dimensions with substantive value also exist. Identifying these specific facets is important in order to increase quality of early numerical ability measurement, predictive validity, and for practical implications.

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“…Another context where integration/dissolution ideas recently have been discussed is in the use of network models to model changes in relations between items/symptoms across time (Costantini et al., 2019; Miers et al., 2020). Longitudinal network models were originally developed to study social relations (Niezink et al., 2019) but when network models are applied to capture relations between items/symptoms, the model is an alternative representation of factor models (Kan et al., 2020; van Bork et al., 2019). Especially indices capturing individual differences in changes in the centrality of a network capture the idea of changes in integration/dissolution.…”

Section: Overview Of the Typology And Examplesmentioning

confidence: 99%

What are agile, flexible, or adaptable employees and students? A typology of dynamic individual differences in applied settings

2021

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The applied psychology literature has discussed and used a variety of different definitions of dynamic individual differences. Descriptions like dynamic, agile, adaptive, or flexible can refer to a variety of different types of constructs. The present article contributes to the literature by presenting an organizing typology of dynamic constructs. We also conducted a literature review of four major applied journals over the last 15 years to validate the taxonomy and to use it to map what type of dynamic individual differences constructs are typically studied in the applied psychology literature. The typology includes six basic conceptualizations of dynamic individual differences: Variability constructs (inconsistency across situations), skill acquisition constructs (learning new skills), transition constructs (avoiding “loss” in behavior/skill after unforeseen change), reacquisition constructs (relearning after change), acceleration/deceleration constructs (losing or gaining energy by displaying the behavior), and integration/dissolution constructs (behavior becomes more or less uniform). We provide both verbal and statistical definitions for each of these constructs, and demonstrate how these conceptualizations can be operationalized in assessment and criterion measurement using R code and simulated data. We also show how researchers can test different dynamic explanations using likelihood-based R2 statistics.

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How to Compare Psychometric Factor and Network Models

Cited by 61 publications

References 20 publications

Emotions as Overlapping Causal Networks of Emotion Components: Implications and Methodological Approaches

Emotions as Overlapping Causal Networks of Emotion Components: Implications and Methodological Approaches

Investigating the Dimensionality of Early Numeracy Using the Bifactor Exploratory Structural Equation Modeling Framework

What are agile, flexible, or adaptable employees and students? A typology of dynamic individual differences in applied settings

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