scholarly journals A bioenergetic model that links psychopathology and intelligence: Implications for research and clinical practice

2022 ◽  
Author(s):  
Patrick Fissler ◽  
R. Nehir Mavioglu ◽  
Maya Wenzel ◽  
Steffen Stoewer ◽  
Wanja Wolff ◽  
...  
Keyword(s):  
Risk Factors ◽  
Clinical Practice ◽  
Affect Regulation ◽  
Factor Model ◽  
Higher Order ◽  
General Factor ◽  
Cerebral Function ◽  
Bioenergetic Model ◽  
Treatment Research ◽  
P Factor

Decomposing the structure of human cerebral function in its domains, such as affect regulation or cognition, forms the backbone of psychiatric diagnosis and treatment. Research continues to decipher the domains and hierarchical structure of cerebral function. So far, the findings strongly suggest two higher-order latent factors of general psychopathology (p factor) and general intelligence (g factor). Both general factors are functions of the same organ, covary, share risk factors as well as biomarkers, and benefit from the same treatments. However, to our knowledge, a model that connects both components of cerebral function within a higher-order latent factor and describes its potential biological underpinning is lacking. First, we suggest the general factor of cerebral function (c factor) as the shared variance of the measures of g and p in a bi-factor model. Second, we propose and provide evidence that mitochondrial bioenergetics (MB) is one core biological underpinning of c. Third, we describe how this c factor mito-bioenergetics (CMB) model may transform research and clinical practice by advancing knowledge of treatment effects, risk factors, biomarkers and clinical outcomes. Finally, we present a CMB model-based hypothesis stating that fatigue—as a phenotypical correlate of MB—directly loads on c.

scholarly journals What is the p-factor of psychopathology? Some risks of general factor modeling

2017 ◽  
Vol 27 (6) ◽  
pp. 759-773 ◽  
Author(s):  
Riet van Bork ◽  
Sacha Epskamp ◽  
Mijke Rhemtulla ◽  
Denny Borsboom ◽  
Han L. J. van der Maas
Keyword(s):  
Factor Structure ◽  
Factor Model ◽  
Common Factor ◽  
Psychological Disorders ◽  
General Factor ◽  
Personality Research ◽  
Factor Analytic ◽  
History Of ◽  
Factor Modeling ◽  
P Factor

Recent research has suggested that a range of psychological disorders may stem from a single underlying common factor, which has been dubbed the p-factor. This finding may spur a line of research in psychopathology very similar to the history of factor modeling in intelligence and, more recently, personality research, in which similar general factors have been proposed. We point out some of the risks of modeling and interpreting general factors, derived from the fields of intelligence and personality research. We argue that: (a) factor-analytic resolution, i.e., convergence of the literature on a particular factor structure, should not be expected in the presence of multiple highly similar models; and (b) the true underlying model may not be a factor model at all, because alternative explanations can account for the correlational structure of psychopathology.

Higher-Order and Bifactor Models of the Drinking Motives Questionnaire

Assessment ◽  
2016 ◽  
Vol 24 (2) ◽  
pp. 222-231 ◽  
Author(s):  
Andrew Lac ◽  
Candice D. Donaldson
Keyword(s):  
Structural Equation ◽  
Negative Reinforcement ◽  
Factor Model ◽  
Equation Model ◽  
Higher Order ◽  
Bifactor Model ◽  
Drinking Motives ◽  
General Factor ◽  
Fit Indices ◽  
Confirmatory Factor Analyses

The Drinking Motives Questionnaire, previously postulated and documented to exhibit a measurement structure of four correlated factors (social, enhancement, conformity, and coping), is a widely administered assessment of reasons for consuming alcohol. In the current study ( N = 552), confirmatory factor analyses tested the plausibility of several theoretically relevant factor structures. Fit indices corroborated the original four-factor model, and also supported a higher-order factor model involving a superordinate motives factor that explicated four subordinate factors. A bifactor model that permitted items to double load on valence type (positive or negative reinforcement) and source type (external or internal) generated mixed results, suggesting that this 2 × 2 motivation paradigm was not entirely tenable. Optimal fit was obtained for a bifactor model depicting a general factor and four specific factors of motives. Latent factors derived from this structure exhibited criterion validity in predicting frequency and quantity of alcohol usage in a structural equation model. Findings are interpreted in the context of theoretical implications of the instrument, alternative factor structures of drinking motives, and assessment applications.

Genetic and Environmental Architecture of Five Factor Model and Super-Factors: An Italian Twin Study

2021 ◽  
Vol 25 ◽  
Author(s):  
Antonella Gigantesco ◽  
Corrado Fagnani ◽  
Guido Alessandri ◽  
Enrica Carluccio ◽  
Maria Antonietta Stazi ◽  
...  
Keyword(s):  
Big Five ◽  
Twin Study ◽  
Factor Model ◽  
Five Factor Model ◽  
Genetic Correlations ◽  
Higher Order ◽  
General Factor ◽  
Twin Design ◽  
Five Dimensions ◽  
Genes And Environment

Abstract No previous research explored the genetic and environmental structure of Big Five dimensions of personality and higher-order factors in a single twin study, except, in part, for just one study. We used the twin design to estimate the effects of genes and environment on both Five Factor model and related second- and third-order factors (i.e., Alpha [stability], Beta [plasticity], and GFP [general factor of personality]). We analyzed data from 314 adult twins (157 pairs: 83 monozygotic, 74 dizygotic; mean age: 52 years) enrolled in the Italian Twin Register. Participants underwent clinical and instrumental evaluations, and completed a 25-adjective list drawn from the Short Adjectives Checklist to Measure Big Five (SACBIF). We applied quantitative genetic models to unravel the sources of variation and covariation for the Big Five and higher-order factors. We found a similar etiological architecture across the different levels of analysis, with moderate to substantial non-additive genetic and unique environmental influences on all the personality traits, and no shared environmental contribution for any of them. We also detected significant genetic correlations for the Big Five dimensions and the Alpha and Beta super-factors. With some limitations, our results suggest that the etiological architecture of personality may be invariant to the factor level of analysis.

scholarly journals Comparing Job Applicants to Non–Applicants Using An Item–Level Bifactor Model on the Hexaco Personality Inventory

2017 ◽  
Vol 31 (6) ◽  
pp. 669-684 ◽  
Author(s):  
Jeromy Anglim ◽  
Gavin Morse ◽  
Reinout E. De Vries ◽  
Carolyn MacCann ◽  
Andrew Marty ◽  
...  
Keyword(s):  
Social Desirability ◽  
Factor Model ◽  
Higher Order ◽  
Personality Inventory ◽  
Bifactor Model ◽  
General Factor ◽  
Personality Psychology ◽  
Job Applicant ◽  
Item Level ◽  
Improved Model

The present study evaluated the ability of item–level bifactor models (a) to provide an alternative explanation to current theories of higher order factors of personality and (b) to explain socially desirable responding in both job applicant and non–applicant contexts. Participants (46% male; mean age = 42 years, SD = 11) completed the 200–item HEXACO Personality Inventory–Revised either as part of a job application ( n = 1613) or as part of low–stakes research ( n = 1613). A comprehensive set of invariance tests were performed. Applicants scored higher than non–applicants on honesty–humility ( d = 0.86), extraversion ( d = 0.73), agreeableness ( d = 1.06), and conscientiousness ( d = 0.77). The bifactor model provided improved model fit relative to a standard correlated factor model, and loadings on the evaluative factor of the bifactor model were highly correlated with other indicators of item social desirability. The bifactor model explained approximately two–thirds of the differences between applicants and non–applicants. Results suggest that rather than being a higher order construct, the general factor of personality may be caused by an item–level evaluative process. Results highlight the importance of modelling data at the item–level. Implications for conceptualizing social desirability, higher order structures in personality, test development, and job applicant faking are discussed. Copyright © 2017 European Association of Personality Psychology

scholarly journals Three recommendations based on a comparison of the reliability and validity of the predominant models used in research on the empirical structure of psychopathology

2020 ◽  
Author(s):  
Miriam K. Forbes ◽  
Ashley Lauren Greene ◽  
Holly Levin-Aspenson ◽  
Ashley L. Watts ◽  
Michael Hallquist ◽  
...  
Keyword(s):  
Factor Model ◽  
Reliability And Validity ◽  
The United States ◽  
Higher Order ◽  
Bifactor Model ◽  
Model Fit ◽  
General Factor ◽  
Single Factor ◽  
Over Time ◽  
Single Factor Model

The present study compared the primary models used in research on the structure of psychopathology (i.e., correlated factor, higher-order, and bifactor models) in terms of structural validity (model fit and factor reliability), longitudinal measurement invariance, concurrent and prospective predictive validity in relation to important outcomes, and longitudinal consistency in individuals’ factor score profiles. Two simpler operationalizations of a general factor of psychopathology were also examined—a single-factor model and a count of diagnoses. Models were estimated based on structured clinical interview diagnoses in two longitudinal waves of nationally representative data from the United States (n = 43,093 and n = 34,653). Models that included narrower factors (fear, distress, and externalizing) were needed to capture the observed multidimensionality of the data. In the correlated factor and higher-order models these narrower factors were reliable, largely invariant over time, had consistent associations with indicators of adaptive functioning, and had moderate stability within individuals over time. By contrast, the fear and distress specific factors in the bifactor model did not show good reliability or validity throughout the analyses. Notably, the general factor of psychopathology (p-factor) performed similarly well across tests of reliability and validity regardless of whether the higher-order or bifactor model was used; the simplest (single-factor) model was also comparable across most tests, with the exception of model fit. Given the limitations of categorical diagnoses, it will be important to repeat these analyses using dimensional measures. We conclude that when aiming to understand the structure and correlates of psychopathology it is important to: 1) look beyond model fit indices to choose between different models; 2) examine the reliability of latent variables directly; and 3) be cautious when isolating and interpreting the unique effects of specific psychopathology factors, regardless of which model is used.

scholarly journals Comparing Job Applicants to Non-Applicants Using an Item-Level Bifactor Model on the HEXACO Personality Inventory

2017 ◽  
Author(s):  
Jeromy Anglim ◽  
Gavin Morse ◽  
Reinout E. de Vries ◽  
Carolyn MacCann ◽  
Andrew Marty
Keyword(s):  
Social Desirability ◽  
Factor Model ◽  
Higher Order ◽  
Personality Inventory ◽  
Bifactor Model ◽  
Order Structure ◽  
General Factor ◽  
Job Applicant ◽  
Item Level ◽  
Improved Model

The present study evaluated the ability of item-level bifactor models (a) to provide an alternative explanation to current theories of higher-order factors of personality, and (b) to explain socially desirable responding in both job applicant and non-applicant contexts. Participants (46% male; mean age=42 years, SD=11) completed the 200- item HEXACO Personality Inventory-Revised (HEXACO PI-R) either as part of a job application (n = 1613) or as part of low-stakes research (n = 1613). A comprehensive set of invariance test were performed. Applicants scored higher than non-applicants on honesty- humility (d = 0.86), extraversion (d = 0.73), agreeableness (d = 1.06), and conscientiousness (d = 0.77). The bifactor model provided improved model fit relative to a standard correlated factor model, and loadings on the evaluative factor of the bifactor model were highly correlated with other indicators of item social desirability. The bifactor model explained approximately two-thirds of the differences between applicants and non- applicants. Results suggest that rather than being a higher-order construct, the general factor of personality may be caused by an item- level evaluative process. Results highlight the importance of modelling data at the item-level. Implications for conceptualizing social desirability, higher-order structure in personality, test development, and job applicant faking are discussed.

Challenging the Multidimensional Conception of Perceived Person-Environment Fit

2020 ◽  
pp. 1-9
Author(s):  
Julian M. Etzel ◽  
Gabriel Nagy
Keyword(s):  
Higher Education ◽  
University Students ◽  
Educational Outcomes ◽  
Factor Model ◽  
Higher Order ◽  
Substantial Proportion ◽  
Unique Variance ◽  
First Order ◽  
Person Environment Fit ◽  
Order Factor

Abstract. In the current study, we examined the viability of a multidimensional conception of perceived person-environment (P-E) fit in higher education. We introduce an optimized 12-item measure that distinguishes between four content dimensions of perceived P-E fit: interest-contents (I-C) fit, needs-supplies (N-S) fit, demands-abilities (D-A) fit, and values-culture (V-C) fit. The central aim of our study was to examine whether the relationships between different P-E fit dimensions and educational outcomes can be accounted for by a higher-order factor that captures the shared features of the four fit dimensions. Relying on a large sample of university students in Germany, we found that students distinguish between the proposed fit dimensions. The respective first-order factors shared a substantial proportion of variance and conformed to a higher-order factor model. Using a newly developed factor extension procedure, we found that the relationships between the first-order factors and most outcomes were not fully accounted for by the higher-order factor. Rather, with the exception of V-C fit, all specific P-E fit factors that represent the first-order factors’ unique variance showed reliable and theoretically plausible relationships with different outcomes. These findings support the viability of a multidimensional conceptualization of P-E fit and the validity of our adapted instrument.

Speeded Paper-Pencil Sustained Attention and Mental Speed Tests

2008 ◽  
Vol 29 (4) ◽  
pp. 205-216 ◽  
Author(s):  
Stefan Krumm ◽  
Lothar Schmidt-Atzert ◽  
Kurt Michalczyk ◽  
Vanessa Danthiir
Keyword(s):  
Sustained Attention ◽  
Time Pressure ◽  
Factor Model ◽  
Higher Order ◽  
Future Research ◽  
Perceptual Speed ◽  
Speed Factor ◽  
Close Relationship ◽  
Task Processing ◽  
Order Factor

Mental speed (MS) and sustained attention (SA) are theoretically distinct constructs. However, tests of MS are very similar to SA tests that use time pressure as an impeding condition. The performance in such tasks largely relies on the participants’ speed of task processing (i.e., how quickly and correctly one can perform the simple cognitive tasks). The present study examined whether SA and MS are empirically the same or different constructs. To this end, 24 paper-pencil and computerized tests were administered to 199 students. SA turned out to be highly related to MS task classes: substitution and perceptual speed. Furthermore, SA showed a very close relationship with the paper-pencil MS factor. The correlation between SA and computerized speed was considerably lower but still high. In a higher-order general speed factor model, SA had the highest loading on the higher-order factor; the higher-order factor explained 88% of SA variance. It is argued that SA (as operationalized with tests using time pressure as an impeding condition) and MS cannot be differentiated, at the level of broad constructs. Implications for neuropsychological assessment and future research are discussed.

Controlling Hypertension and Stroke Prevention – From Guideline to Clinical Practice

2011 ◽  
Vol 3 (1) ◽  
pp. 30
Author(s):  
Anding Xu ◽  
Zefeng Tan ◽  
◽  
Keyword(s):  
Risk Factors ◽  
Blood Pressure ◽  
Clinical Trials ◽  
Clinical Practice ◽  
Stroke Prevention ◽  
Modifiable Risk Factors ◽  
Cerebrovascular Events

Hypertension is the most important of the prevalent and modifiable risk factors for stroke. Based on evidence, blood pressure (BP) lowering is recommended in guidelines for the prevention of stroke. However, there are still some uncertainties in the guidelines for controlling BP and preventing stroke in patients with previous cerebrovascular events, such as the goal BP, who to treat and which class of BP-lowering drugs to use. This article discusses these questions by reviewing guidelines and corresponding clinical trials, with the aim of reducing the gap between guidelines and clinical practice.

Has the time come to integrate genetic risk scores into clinical practice?

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
F.V Moniz Mendonca ◽  
M.I Mendonca ◽  
A Pereira ◽  
J Monteiro ◽  
J Sousa ◽  
...  
Keyword(s):  
Risk Factors ◽  
Clinical Practice ◽  
Genetic Variants ◽  
Genetic Risk ◽  
Odds Ratio ◽  
Risk Scores ◽  
Cumulative Number ◽  
Genetic Risk Scores ◽  
Risk Alleles ◽  
Cad Risk

Abstract Background The risk for Coronary Artery Disease (CAD) is determined by both genetic and environmental factors, as well as by the interaction between them. It is estimated that genetic factors could account for 40% to 55% of the existing variability among the population (inheritability). Therefore, some authors have advised that it is time we integrated genetic risk scores into clinical practice. Aim The aim of this study was to evaluate the magnitude of the association between an additive genetic risk score (aGRS) and CAD based on the cumulative number of risk alleles in these variants, and to estimate whether their use is valuable in clinical practice. Methods A case-control study was performed in a Portuguese population. We enrolled 3120 participants, of whom 1687 were CAD patients and 1433 were normal controls. Controls were paired to cases with respect to gender and age. 33 genetic variants known to be associated with CAD were selected, and an aGRS was calculated for each individual. The aGRS was further subdivided into deciles groups, in order to estimate the CAD risk in each decile, defined by the number of risk alleles. The magnitude of the risk (odds ratio) was calculated for each group by multiple logistic regression using the 5th decile as the reference group (median). In order to evaluate the ability of the aGRS to discriminate susceptibility to CAD, two genetic models were performed, the first with traditional risk factors (TRF) and second with TRF plus aGRS. The AUC of the two ROC curves was calculated. Results A higher prevalence of cases over controls became apparent from the 6th decile of the aGRS, reflecting the higher number of risk alleles present (see figure). The difference in CAD risk was only significant from the 6th decile, increasing gradually until the 10th decile. The odds ratio (OR) for the last decile related to 5th decile (median) was 1.87 (95% CI:1.36–2.56; p<0.0001). The first model yielded an AUC=0.738 (95% CI:0.720–0.755) and the second model was slightly more discriminative for CAD risk (AUC=0.748; 95% CI:0.730–0.765). The DeLong test was significant (p=0.0002). Conclusion Adding an aGRS to the non-genetic risk factors resulted in a modest improvement in the ability to discriminate the risk of CAD. Such improvement, even if statistically significant, does not appear to be of real value in clinical practice yet. We anticipate that with the development of further knowledge about different SNPs and their complex interactions, and with the inclusion of rare genetic variants, genetic risk scores will be better suited for use in a clinical setting. Funding Acknowledgement Type of funding source: None

Sign in / Sign up

Export Citation Format

Share Document