scholarly journals A Psychometric Network Perspective on the Validity and Validation of Personality Trait Questionnaires

2019 ◽  
Author(s):  
Alexander P. Christensen ◽  
Hudson Golino ◽  
Paul Silvia
Keyword(s):  
Personality Trait ◽  
Latent Variable ◽  
Structural Phase ◽  
External Validation ◽  
Network Models ◽  
Factor Models ◽  
Complementary Information ◽  
Future Directions ◽  
Common Cause ◽  
Structural Consistency

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 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.

scholarly journals A Psychometric Network Perspective on the Validity and Validation of Personality Trait Questionnaires

2020 ◽  
Vol 34 (6) ◽  
pp. 1095-1108 ◽  
Author(s):  
Alexander P. Christensen ◽  
Hudson Golino ◽  
Paul J. Silvia
Keyword(s):  
Personality Trait ◽  
Latent Variable ◽  
European Association ◽  
Structural Phase ◽  
External Validation ◽  
Network Models ◽  
Complementary Information ◽  
Future Directions ◽  
Personality Psychology ◽  
Structural Consistency

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

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.

Conclusions and Future Directions for the Study of Accurate Personality Judgment

2020 ◽  
Author(s):  
Tera D. Letzring
Keyword(s):  
Standardized Tests ◽  
Personality Trait ◽  
Training Programs ◽  
Future Research ◽  
Research Topics ◽  
Theoretical Frameworks ◽  
Future Directions ◽  
Personality Judgment ◽  
Exciting Time ◽  
And Training

This chapter identifies several well-established findings and overarching themes within personality trait accuracy research, and highlights especially promising directions for future research. Topics include (1) theoretical frameworks for accuracy, (2) moderators of accuracy and the context or situation in which judgments are made, (3) the important consequences of accuracy, (4) interventions and training programs to increase judgmental ability and judgability, (5) the generalizability of previous findings, and (6) standardized tests of the accuracy of judging personality traits. The chapter ends by stating that it is an exciting time to be a researcher studying the accuracy of personality trait judgments.

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.

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.

Brain disorders? Not really: Why network structures block reductionism in psychopathology research

2018 ◽  
Vol 42 ◽  
Author(s):  
Denny Borsboom ◽  
Angélique O. J. Cramer ◽  
Annemarie Kalis
Keyword(s):  
Mental Disorders ◽  
Clinical Psychology ◽  
Network Models ◽  
Mental States ◽  
Modern Medicine ◽  
Research Strategy ◽  
Network Structures ◽  
Common Cause ◽  
Biological Basis ◽  
Holistic Research

AbstractIn the past decades, reductionism has dominated both research directions and funding policies in clinical psychology and psychiatry. The intense search for the biological basis of mental disorders, however, has not resulted in conclusive reductionist explanations of psychopathology. Recently, network models have been proposed as an alternative framework for the analysis of mental disorders, in which mental disorders arise from the causal interplay between symptoms. In this target article, we show that this conceptualization can help explain why reductionist approaches in psychiatry and clinical psychology are on the wrong track. First, symptom networks preclude the identification of a common cause of symptomatology with a neurobiological condition; in symptom networks, there is no such common cause. Second, symptom network relations depend on the content of mental states and, as such, feature intentionality. Third, the strength of network relations is highly likely to depend partially on cultural and historical contexts as well as external mechanisms in the environment. Taken together, these properties suggest that, if mental disorders are indeed networks of causally related symptoms, reductionist accounts cannot achieve the level of success associated with reductionist disease models in modern medicine. As an alternative strategy, we propose to interpret network structures in terms of D. C. Dennett's (1987) notion ofreal patterns, and suggest that, instead of being reducible to a biological basis, mental disorders feature biological and psychological factors that are deeply intertwined in feedback loops. This suggests that neither psychological nor biological levels can claim causal or explanatory priority, and that a holistic research strategy is necessary for progress in the study of mental disorders.

scholarly journals Strategic Cognitive Sequencing: A Computational Cognitive Neuroscience Approach

2013 ◽  
Vol 2013 ◽  
pp. 1-18 ◽  
Author(s):  
Seth A. Herd ◽  
Kai A. Krueger ◽  
Trenton E. Kriete ◽  
Tsung-Ren Huang ◽  
Thomas E. Hazy ◽  
...  
Keyword(s):  
Cognitive Process ◽  
Network Models ◽  
Brain Regions ◽  
Human Cognition ◽  
Neural Network Models ◽  
Future Directions ◽  
Error Learning ◽  
Cognitive Actions ◽  
Self Instruction ◽  
The Brain

We address strategic cognitive sequencing, the “outer loop” of human cognition: how the brain decides what cognitive process to apply at a given moment to solve complex, multistep cognitive tasks. We argue that this topic has been neglected relative to its importance for systematic reasons but that recent work on how individual brain systems accomplish their computations has set the stage for productively addressing how brain regions coordinate over time to accomplish our most impressive thinking. We present four preliminary neural network models. The first addresses how the prefrontal cortex (PFC) and basal ganglia (BG) cooperate to perform trial-and-error learning of short sequences; the next, how several areas of PFC learn to make predictions of likely reward, and how this contributes to the BG making decisions at the level of strategies. The third models address how PFC, BG, parietal cortex, and hippocampus can work together to memorize sequences of cognitive actions from instruction (or “self-instruction”). The last shows how a constraint satisfaction process can find useful plans. The PFC maintains current and goal states and associates from both of these to find a “bridging” state, an abstract plan. We discuss how these processes could work together to produce strategic cognitive sequencing and discuss future directions in this area.

scholarly journals Personality and Incident Alzheimer’s Disease: Theory, Evidence, and Future Directions

2018 ◽  
Vol 75 (3) ◽  
pp. 513-521 ◽  
Author(s):  
Suzanne C Segerstrom
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Health Behavior ◽  
Personality Change ◽  
Present Review ◽  
Future Directions ◽  
Common Cause ◽  
Future Studies ◽  
Disease Spectrum ◽  
Noninvasive Assessment

Abstract Personality, especially the dimensions of neuroticism and conscientiousness, has prospectively predicted the risk of incident Alzheimer’s disease (AD). Such a relationship could be explained by personality and AD risk having a common cause such as a gene; by personality creating a predisposition for AD through health behavior or inflammation; by personality exerting a pathoplastic effect on the cognitive consequences of neuropathology; or by AD and personality change existing on a disease spectrum that begins up to decades before diagnosis. Using the 5-dimensional taxonomy of personality, the present review describes how these models might arise, the evidence for each, and how they might be distinguished from one another empirically. At present, the evidence is sparse but tends to suggest predisposition and/or pathoplastic relationships. Future studies using noninvasive assessment of neuropathology are needed to distinguish these 2 possibilities.

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