scholarly journals Recent Integrations of Latent Variable Network Modeling With Psychometric Models

2021 ◽  
Vol 12 ◽  
Author(s):  
Selena Wang
Keyword(s):  
Graphical Models ◽  
Latent Variable ◽  
Network Modeling ◽  
Network Models ◽  
Latent Variable Models ◽  
Independent Learning ◽  
Psychometric Models ◽  
R Packages

The combination of network modeling and psychometric models has opened up exciting directions of research. However, there has been confusion surrounding differences among network models, graphic models, latent variable models and their applications in psychology. In this paper, I attempt to remedy this gap by briefly introducing latent variable network models and their recent integrations with psychometric models to psychometricians and applied psychologists. Following this introduction, I summarize developments under network psychometrics and show how graphical models under this framework can be distinguished from other network models. Every model is introduced using unified notations, and all methods are accompanied by available R packages inducive to further independent learning.

scholarly journals Interpreting psychometric models

2019 ◽  
Author(s):  
Riet van Bork
Keyword(s):  
Latent Variable ◽  
Latent Trait ◽  
Network Models ◽  
Latent Variable Models ◽  
Causal Mechanism ◽  
Variable Model ◽  
Psychological Construct ◽  
Psychological Constructs ◽  
Common Causes ◽  
Psychometric Models

The field of psychometrics aims to develop theories on how to measure psychological constructs through observable behavior. This dissertation focuses on two psychometric theories that differ in how the psychological construct is related to observable behaviors. Latent trait theory understands psychological constructs as underlying common causes of observed behavior that explain the associations between certain behaviors. Alternatively, in the psychological network theory, behaviors correlate because they mutually reinforce each other and the psychological construct refers to the resulting cluster of associated behaviors. These different theories about how to conceptualize psychological constructs and how to relate these constructs to observable behavior can be formally defined in a set of equations and assumptions that make up a psychometric model. The chapters in this dissertation focus on two types of psychometric models: Latent variable models and network models. Part I of the dissertation focuses on the interpretation of the latent variable model. Part II of the dissertation makes a comparison between latent variable models and network models. While psychometric models can be interpreted as representations of a theory about the data-generating mechanism, this is not necessary. Psychometric models are often viewed as mere descriptions of data. This dissertation shows the importance of thinking through the choice of interpreting psychometric models either as a representation of a causal mechanism or as a description of the data and provides insights in the implications of that choice.

scholarly journals Investigating the Structure of Intelligence Using Latent Variable and Psychometric Network Modeling: A Commentary and Reanalysis

2021 ◽  
Vol 9 (1) ◽  
pp. 8
Author(s):  
Christopher J. Schmank ◽  
Sara Anne Goring ◽  
Kristof Kovacs ◽  
Andrew R. A. Conway
Keyword(s):  
Cognitive Processes ◽  
Latent Variable ◽  
Network Modeling ◽  
Network Models ◽  
Mental Ability ◽  
Variable Model ◽  
General Mental Ability ◽  
Theories Of Intelligence ◽  
Psychological Theories ◽  
Model Preference

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.

Toward scientifically useful quantitative models of psychopathology: The importance of a comparative approach

2010 ◽  
Vol 33 (2-3) ◽  
pp. 163-164 ◽  
Author(s):  
Robert F. Krueger ◽  
Colin G. DeYoung ◽  
Kristian E. Markon
Keyword(s):  
Latent Variable ◽  
Network Models ◽  
Latent Variable Models ◽  
Comparative Approach ◽  
Quantitative Models

AbstractCramer et al. articulate a novel perspective on comorbidity. However, their network models must be compared with more parsimonious latent variable models before conclusions can be drawn about network models as plausible accounts of comorbidity. Latent variable models have proven generative in studying psychopathology and its external correlates, and we doubt network models will prove as useful for psychopathology research.

Latent variable models are network models

2010 ◽  
Vol 33 (2-3) ◽  
pp. 166-166 ◽  
Author(s):  
Peter C. M. Molenaar
Keyword(s):  
Latent Variables ◽  
Latent Variable ◽  
Network Models ◽  
Latent Variable Models ◽  
Variable Theory ◽  
The Relationship

AbstractCramer et al. present an original and interesting network perspective on comorbidity and contrast this perspective with a more traditional interpretation of comorbidity in terms of latent variable theory. My commentary focuses on the relationship between the two perspectives; that is, it aims to qualify the presumed contrast between interpretations in terms of networks and latent variables.

scholarly journals Psychometric Models of Individual Differences in Reading Comprehension: A Reanalysis of Freed, Hamilton, and Long (2017)

2019 ◽  
Author(s):  
Sara Anne Goring ◽  
Christopher J. Schmank ◽  
Michael J. Kane ◽  
Andrew R. A. Conway
Keyword(s):  
Reading Comprehension ◽  
Individual Differences ◽  
Cognitive Abilities ◽  
Latent Variable ◽  
Convergent Validity ◽  
Cognitive Task ◽  
Network Modeling ◽  
Network Models ◽  
Measurement Model ◽  
Common Cause

Individual differences in reading comprehension have often been explored using latent variable modeling (LVM), to assess the relative contribution of domain-general and domain-specific cognitive abilities. However, LVM is based on the assumption that the observed covariance among indicators of a construct is due to a common cause (i.e., a latent variable; Pearl, 2000). This is a questionable assumption when the indicator variables are measures of performance on complex cognitive tasks. According to Process Overlap Theory (POT; Kovacs & Conway, 2016), multiple processes are involved in cognitive task performance and the covariance among tasks is due to the overlap of processes across tasks. Instead of a single latent common cause, there are thought to be multiple dynamic manifest causes, consistent with an emerging view in psychometrics called network theory (Barabási, 2012; Borsboom & Cramer, 2013). In the current study, we reanalyzed data from Freed et al. (2017) and compared two modeling approaches: LVM (Study 1) and psychometric network modeling (Study 2). In Study 1, two exploratory LVMs demonstrated problems with the original measurement model proposed by Freed et al. Specifically, the model failed to achieve discriminant and convergent validity with respect to reading comprehension, language experience, and reasoning. In Study 2, two network models confirmed the problems found in Study 1, and also served as an example of how network modeling techniques can be used to study individual differences. In conclusion, more research, and a more informed approach to psychometric modeling, is needed to better understand individual differences in reading comprehension.

A network psychometric approach to neurocognitive functioning in Alzheimer's disease

2020 ◽  
Author(s):  
Cameron Ferguson
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Network Analysis ◽  
Psychometric Properties ◽  
Graphical Models ◽  
Network Structure ◽  
Latent Variable ◽  
Network Models ◽  
Information Criterion ◽  
Graphical Lasso

Introduction: descriptions of the typical pattern of neurocognitive impairment in Alzheimer’s disease (AD) refer to relationships between neurocognitive domains as well as deficits within domains. However, the former of these relationships have not been statistically modelled. Accordingly, this study aimed to model the unique variance between neurocognitive variables in AD, amnestic mild cognitive impairment (aMCI), and cognitive normality (CN) using network analysis. Methods: Gaussian Graphical Models with Extended Bayesian Information Criterion model selection and graphical lasso regularisation were used to estimate network models of neurocognitive variables in AD (n = 229), aMCI (n = 397) and CN (n = 193) groups. The psychometric properties of the models were investigated using simulation and bootstrapping procedures. Exploratory analyses of network structure invariance across groups were conducted. Results: neurocognitive network models were estimated for each group and found to have good psychometric properties. Exploratory investigations suggested that network structure was not invariant across CN and aMCI (p = 0.03), CN and AD (p < 0.01), and aMCI and AD neurocognitive networks (p < 0.01).Conclusions: network analysis can be used to robustly model the relationships between neurocognitive variables in AD, aMCI and CN. Network structure was not invariant, suggesting that relationships between neurocognitive variables differ across groups along the AD spectrum. Points of convergence and contrast with latent-variable models are explored.

Network models can help focus research on the role of culture and context in psychopathology, but don't discount latent variable models

2019 ◽  
Vol 42 ◽  
Author(s):  
Nuwan Jayawickreme ◽  
Andrew Rasmussen ◽  
Alison Karasz ◽  
Jay Verkuilen ◽  
Eranda Jayawickreme
Keyword(s):  
Latent Variable ◽  
Network Models ◽  
Latent Variable Models ◽  
Cross Cultural ◽  
Contextual Variables ◽  
Cultural Research ◽  
Cross Cultural Research ◽  
Development Network ◽  
Further Development

AbstractBorsboom et al. correctly note that the use of latent variable models in cross-cultural research has resulted in a futile search for universal, biological causes of psychopathology; however, this is not an inevitable outcome of such models. While network analytic approaches require further development, network models have the potential to better elucidate the role of cultural and contextual variables related to psychopathology.

scholarly journals How to Compare Psychometric Factor and Network Models

2020 ◽  
Vol 8 (4) ◽  
pp. 35
Author(s):  
Kees-Jan Kan ◽  
Hannelies de Jonge ◽  
Han L. J. van der Maas ◽  
Stephen Z. Levine ◽  
Sacha Epskamp
Keyword(s):  
Latent Variable ◽  
Structural Equation ◽  
Factor Model ◽  
Empirical Finding ◽  
Network Models ◽  
Latent Variable Models ◽  
R Package ◽  
Equation Modeling ◽  
Adult Intelligence Scale ◽  
Intelligence Scale

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.

scholarly journals Toward an Integrative Psychometric Model of Emotions

2019 ◽  
Author(s):  
Jens Lange ◽  
Jonas Dalege ◽  
Denny Borsboom ◽  
Gerben van Kleef ◽  
Agneta Fischer
Keyword(s):  
Network Model ◽  
Latent Variable ◽  
Latent Variable Models ◽  
Human Condition ◽  
Causal Relationships ◽  
Theoretical Perspectives ◽  
Psychometric Models ◽  
Psychometric Model ◽  
The Human Condition

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 them in 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 (a) delineate properties of emotions stated by emotion theories, and (b) investigate whether psychometric models account for these properties. Specifically, an integrative psychometric model of emotions should allow identifying distinct emotions (central in affect program theories), should allow between and within person variation of emotions (central in constructionist theories), and should allow 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.

scholarly journals Toward an Integrative Psychometric Model of Emotions

2020 ◽  
Vol 15 (2) ◽  
pp. 444-468 ◽  
Author(s):  
Jens Lange ◽  
Jonas Dalege ◽  
Denny Borsboom ◽  
Gerben A. van Kleef ◽  
Agneta H. Fischer
Keyword(s):  
Network Model ◽  
Latent Variable ◽  
Latent Variable Models ◽  
Human Condition ◽  
Causal Relationships ◽  
Theoretical Perspectives ◽  
Psychometric Models ◽  
Psychometric Model ◽  
The Human Condition

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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