scholarly journals The Effects of Using Partial or Uncorrected Correlation Matrices When Comparing Network and Latent Variable Models

2020 ◽  
Vol 8 (1) ◽  
pp. 7 ◽  
Author(s):  
Dennis McFarland
Keyword(s):  
Network Model ◽  
Test Scores ◽  
Latent Variable ◽  
Partial Correlation ◽  
Network Models ◽  
Latent Variable Models ◽  
Correlation Matrices ◽  
Partial Correlations ◽  
Shared Variance

Network models of the WAIS-IV based on regularized partial correlation matrices have been reported to outperform latent variable models based on uncorrected correlation matrices. The present study sought to compare network and latent variable models using both partial and uncorrected correlation matrices with both types of models. The results show that a network model provided better fit to matrices of partial correlations but latent variable models provided better fit to matrices of full correlations. This result is due to the fact that the use of partial correlations removes most of the covariance common to WAIS-IV tests. Modeling should be based on uncorrected correlations since these represent the majority of shared variance between WAIS-IV test scores.

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.

scholarly journals Factor or Network Model? Predictions From Neural Networks

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Alexander P. Christensen ◽  
Keyword(s):  
Neural Networks ◽  
Network Model ◽  
Latent Variable ◽  
Random Network ◽  
Network Models ◽  
Small World ◽  
Small World Network ◽  
Monte Carlo Simulation Study ◽  
Psychological Phenomena ◽  
The Neural Networks

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.

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 DYNAMIC ASSET CORRELATIONS BASED ON VINES

2018 ◽  
Vol 35 (1) ◽  
pp. 167-197 ◽  
Author(s):  
Benjamin Poignard ◽  
Jean-David Fermanian
Keyword(s):  
Maximum Likelihood ◽  
Partial Correlation ◽  
Asset Returns ◽  
Multivariate Garch ◽  
Conditional Correlation ◽  
Correlation Matrices ◽  
New Family ◽  
Partial Correlations ◽  
Garch Processes ◽  
Quasi Maximum Likelihood

We develop a new method for generating dynamics of conditional correlation matrices of asset returns. These correlation matrices are parameterized by a subset of their partial correlations, whose structure is described by a set of connected trees called “vine”. Partial correlation processes can be specified separately and arbitrarily, providing a new family of very flexible multivariate GARCH processes, called “vine-GARCH” processes. We estimate such models by quasi-maximum likelihood. We compare our models with DCC and GAS-type specifications through simulated experiments and we evaluate their empirical performances.

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.

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.

Latent variable models for harmonization of test scores: A case study on memory

2019 ◽  
Vol 62 (1) ◽  
pp. 34-52 ◽  
Author(s):  
Edwin R. Heuvel ◽  
Lauren E. Griffith ◽  
Nazmul Sohel ◽  
Isabel Fortier ◽  
Graciela Muniz‐Terrera ◽  
...  
Keyword(s):  
Test Scores ◽  
Latent Variable ◽  
Latent Variable Models

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 RMT Assessments of the Market Latent Information Embedded in the Stocks' Raw, Normalized, and Partial Correlations

2009 ◽  
Vol 2009 ◽  
pp. 1-13 ◽  
Author(s):  
Dror Y. Kenett ◽  
Yoash Shapira ◽  
Eshel Ben-Jacob
Keyword(s):  
New York ◽  
Random Matrix ◽  
Partial Correlation ◽  
Stock Exchange ◽  
Market Information ◽  
Correlation Matrices ◽  
Tel Aviv ◽  
Time Period ◽  
Partial Correlations ◽  
Tel Aviv Stock Exchange

We present here assessment of the latent market information embedded in the raw, affinity (normalized), and partial correlations. We compared the Zipf plot, spectrum, and distribution of the eigenvalues for each matrix with the results of the corresponding random matrix. The analysis was performed on stocks belonging to the New York and Tel Aviv Stock Exchange, for the time period of January 2000 to March 2009. Our results show that in comparison to the raw correlations, the affinity matrices highlight the dominant factors of the system, and the partial correlation matrices contain more information. We propose that significant stock market information, which cannot be captured by the raw correlations, is embedded in the affinity and partial correlations. Our results further demonstrate the differences between NY and TA markets.

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