scholarly journals On top or underneath: where does the general factor of psychopathology fit within a dimensional model of psychopathology?

2020 ◽  
pp. 1-11
Author(s):  
Philip Hyland ◽  
Jamie Murphy ◽  
Mark Shevlin ◽  
Richard P. Bentall ◽  
Thanos Karatzias ◽  
...  
Keyword(s):  
Latent Variable ◽  
Community Sample ◽  
Higher Order ◽  
Model Fit ◽  
Self Report ◽  
General Factor ◽  
Order Model ◽  
First Order ◽  
General Dimension ◽  
Higher Order Models

Abstract Background Dimensional models of psychopathology are increasingly common and there is evidence for the existence of a general dimension of psychopathology (‘p’). The existing literature presents two ways to model p: as a bifactor or as a higher-order dimension. Bifactor models typically fit sample data better than higher-order models, and are often selected as better fitting alternatives but there are reasons to be cautious of such an approach to model selection. In this study the bifactor and higher-order models of p were compared in relation to associations with established risk variables for mental illness. Methods A trauma exposed community sample from the United Kingdom (N = 1051) completed self-report measures of 49 symptoms of psychopathology. Results A higher-order model with four first-order dimensions (Fear, Distress, Externalising and Thought Disorder) and a higher-order p dimension provided satisfactory model fit, and a bifactor representation provided superior model fit. Bifactor p and higher-order p were highly correlated (r = 0.97) indicating that both parametrisations produce near equivalent general dimensions of psychopathology. Latent variable models including predictor variables showed that the risk variables explained more variance in higher-order p than bifactor p. The higher-order model produced more interpretable associations for the first-order/specific dimensions compared to the bifactor model. Conclusions The higher-order representation of p, as described in the Hierarchical Taxonomy of Psychopathology, appears to be a more appropriate way to conceptualise the general dimension of psychopathology than the bifactor approach. The research and clinical implications of these discrepant ways of modelling p are discussed.

scholarly journals Examining the Impostor-Profile—Is There a General Impostor Characteristic?

2021 ◽  
Vol 12 ◽  
Author(s):  
Fabio Ibrahim ◽  
Johann-Christoph Münscher ◽  
Philipp Yorck Herzberg
Keyword(s):  
Convergent Validity ◽  
Factor Model ◽  
Second Order ◽  
Model Fit ◽  
Impostor Phenomenon ◽  
General Factor ◽  
Order Model ◽  
First Order ◽  
Order Factor ◽  
Best Fit

The Impostor-Profile (IPP) is a six-dimensional questionnaire measuring the Impostor Phenomenon facets. This study aims to test (a) the appropriateness of a total score, (b) measurement invariance (MI) between gender, (c) the reliability of the IPP, and (d) the convergent validity of the IPP subscales. The sample consisted of N = 482 individuals (64% female). To identify whether the scales of the IPP form a total score, we compared four models: (1) six correlating subscales, (2) a general factor model, (3) a second-order model with one second-order factor and six first-order factors, and (4) a bifactorial model with six group factors. The bifactorial model obtained the best fit. This supports the assumption of a total impostor score. The inspection of structural validity between gender subgroups showed configural, metric, and partial scalar MI. Factor mean comparisons supported the assumption that females and males differ in latent means of the Impostor Phenomenon expressions. The omega coefficients showed sufficient reliability (≥0.71), except for the subscale Need for Sympathy. Overall, the findings of the bifactor model fit and construct validity support the assumption that the measurement through total expression is meaningful in addition to the theoretically formulated multidimensionality of the Impostor Phenomenon.

The WAIS-III as a Nested Factors Model

2006 ◽  
Vol 27 (2) ◽  
pp. 73-86 ◽  
Author(s):  
Gilles E. Gignac
Keyword(s):  
Digit Span ◽  
Statistical Significance ◽  
Age Groups ◽  
Factor Loading ◽  
Higher Order ◽  
Model Fit ◽  
General Factor ◽  
Weak Evidence ◽  
Order Factor ◽  
Higher Order Models

Past confirmatory factor analytic (CFA) research that has examined the factor structure of the WAIS-III has only investigated the more popular models, such as oblique factors and/or higher-order models. In contrast, CFA modeling based on nested factors modeling has been neglected. Consequently, this study investigated the model fit of various WAIS-III nested factors models, in comparison to the more traditional higher-order models. Based on the WAIS-III standardization sample, the results associated with the nested factors modeling indicated that Digit Span, Arithmetic, and Letter-Number Sequencing did not share any variance with VIQ, independently of “g.” Further, across all age groups, there was only very weak evidence in favor of the plausibility of a Perceptual Organization (PO) factor, independently of a general factor. The results are discussed in light of the distinction between modeling “g” as a higher-order factor and a first-order factor. Researchers are encouraged to model the subtests of intelligence batteries as nested factor models, because of their tendency to be associated with greater model fit, as well the possibility to obtain less ambiguous factor loading estimates and their associated statistical significance.

Effluent carbonaceous biochemical oxygen demand (CBOD) characterization for modern pulp and paper facilities

2010 ◽  
Vol 62 (7) ◽  
pp. 1595-1602
Author(s):  
J. E. Palumbo ◽  
L. C. Brown ◽  
C. V. Maltby ◽  
L. Eppstein
Keyword(s):  
Experimental Design ◽  
Kinetic Models ◽  
Oxygen Demand ◽  
Pulp And Paper ◽  
Model Fit ◽  
Parameter Estimates ◽  
Order Model ◽  
First Order ◽  
Water Quality Models

As receiving water quality models are being used to address dissolved oxygen issues requiring an increased degree of resolution, a more refined characterization of effluent CBOD can become an important aspect of the analysis. The selection and use of kinetic models to identify effluent specific parameters can have a significant impact on this characterization. This study modeled effluents from six pulp and paper facilities in order to reassess the kinetic models, the data, and experimental design used for a typical effluent characterization. The dual first order model fit these effluents with significantly less error than the traditional first order model suggesting a significant fraction of the CBOD is slowly degradable. Because the dual first order model produces a more refined characterization of CBOD kinetics than the first order model, it places an increased demand upon the data used to inform the parameter estimates. Therefore, analysis of the precision of the parameter estimates and methods for improving estimation precision via experimental design are also discussed.

Cognitive Correlates of Complexity of Children's Drawings

1997 ◽  
Vol 85 (3) ◽  
pp. 1079-1089 ◽  
Author(s):  
Barbara J. Bensur ◽  
John Eliot ◽  
Laxmin Hegde
Keyword(s):  
Higher Order ◽  
Second Order ◽  
Developmental Tasks ◽  
General Factor ◽  
First Order ◽  
Cognitive Correlates ◽  
Spatial Intelligence ◽  
Spatial Factor ◽  
Order Factor ◽  
Drawing Tasks

240 children (60 each at ages 4, 6, 8, and 10 years) were administered Dennis' (1987) Five Drawing Tasks and five additional developmental tasks. Three hypotheses were tested: that object recognition and working memory would be related to increasing complexity, that both would load on separate factors, and that higher-order analyses would indicate an underlying second-order spatial factor. Analysis included very strong zero-order correlations with age. When age was partialed out, three first-order factors were obtained. Higher-order analyses yielded one second-order factor which appeared related to a general factor of spatial intelligence.

scholarly journals Support for the higher-order factor structure of the WHODAS 2.0 self-report version in a Dutch outpatient psychiatric setting

2021 ◽  
Author(s):  
Guido L. Williams ◽  
Edwin de Beurs ◽  
Philip Spinhoven ◽  
Gerard Flens ◽  
Muirne C. S. Paap
Keyword(s):  
Factor Structure ◽  
Working Group ◽  
Higher Order ◽  
Second Order ◽  
Self Report ◽  
World Health ◽  
Order Model ◽  
Whodas 2.0 ◽  
Order Factor ◽  
Second Order Model

Abstract Purpose Previous studies of the World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) interview version suggested a second-order model, with a general disability factor and six factors on a lower level. The goal of this study is to investigate if we can find support for a similar higher-order factor structure of the 36-item self-report version of the WHODAS 2.0 in a Dutch psychiatric outpatient sample. We aim to give special attention to the differences between the non-working group sample and the working group sample. Additionally, we intend to provide preliminary norms for clinical interpretation of the WHODAS 2.0 scores in psychiatric settings. Methods Patients seeking specialized ambulatory treatment, primarily for depressive or anxiety symptoms, completed the WHODAS 2.0 as part of the initial interview. The total sample consisted of 770 patients with a mean age of 37.5 years (SD = 13.3) of whom 280 were males and 490 were females. Several factorial compositions (i.e., one unidimensional model and two second-order models) were modeled using confirmatory factor analysis (CFA). Descriptive statistics, model-fit statistics, reliability of the (sub)scales, and preliminary norms for interpreting test scores are reported. Results For the non-working group, the second-order model with a general disability factor and six factors on a lower level, provided an adequate fit. Whereas, for the working group, the second-order model with a general disability factor and seven factors on a lower level seemed more appropriate. The WHODAS 2.0 36-item self-report form showed adequate levels of reliability. Percentile ranks and normalized T-scores are provided to aid clinical evaluations. Conclusion Our results lend support for a factorial structure of the WHODAS 2.0 36-item self-report version that is comparable to the interview version. While we conjecture that a seven-factor solution might give a better reflection of item content and item variance, further research is needed to assess the clinical relevance of such a model. At this point, we recommend using the second-order structure with six factors that matches past findings of the interview form.

scholarly journals The Physiometrics of Inflammatory Proteins and Implications for Medical and Psychiatric Research: Investigation of Empirically-informed Inflammatory Composites

2021 ◽  
Author(s):  
Daniel P Moriarity ◽  
Lauren M Ellman ◽  
Christopher L Coe ◽  
Lyn Abramson ◽  
Lauren B Alloy
Keyword(s):  
Factor Structure ◽  
Latent Variable ◽  
A Priori ◽  
Model Fit ◽  
Depression Symptoms ◽  
First Order ◽  
Empirically Supported ◽  
Inflammatory Proteins ◽  
The Individual ◽  
Inflammation Factor

Much inflammation research examines individual proteins; however, some studies have used summed score composites of all available inflammatory markers without first investigating dimensionality. Using three different samples (MIDUS-2: N = 1,255 adults, MIDUS-R: N =863 adults, and ACE: N = 315 adolescents), this study investigates the dimensionality of eight inflammatory proteins (C-reactive protein (CRP), interleukin (IL)-6, IL-8, IL-10, tumor necrosis factor-a; (TNF-a;), fibrinogen, E-selectin, and intercellular adhesion molecule (ICAM)-1) and compares the resulting factor structure to a) an a priori factor structure in which all inflammatory proteins equally load onto a single dimension (a technique that has been used previously) and b) proteins modeled individually (i.e., no latent variable) in terms of model fit, replicability, reliability, temporal stability, and their associations with medical history and depression symptoms. A hierarchical factor structure with two first-order factors (Factor 1A: CRP, IL-6, fibrinogen; Factor 2A: TNFa;, IL-8, IL-10, ICAM-1, IL-6) and a second-order general inflammation factor was identified in MIDUS-2 and replicated in MIDUS-R and partially replicated in ACE (which unfortunately only had CRP, IL-6, IL-8, IL-10, and TNFa; but, unlike the other two, has longitudinal data). Both the empirically-identified structure and modeling proteins individually fit the data better compared to the one-dimensional a priori structure. Results did not clearly indicate whether the empirically-identified factor structure or the individual proteins modeled without a latent variable had superior model fit. Modeling the empirically-identified factors and individual proteins (without a latent factor) as outcomes of medical diagnoses resulted in comparable conclusions, but modeling empirically-identified factors resulted in fewer results lost to correction for multiple comparisons. Importantly, when the factor scores were recreated in a longitudinal dataset, none of the individual proteins, the a priori factor, or the empirically-identified general inflammation factor significantly predicted concurrent depression symptoms in multilevel models. However, both empirically-identified first-order factors were significantly associated with depression, in opposite directions. Measurement properties are reported for the different aggregates and individual proteins as appropriate, which can be used in the design and interpretation of future studies. These results indicate that modeling inflammation as a unidimensional construct equally associated with all available proteins does not fit the data well. Instead, empirically-supported aggregates of inflammation, or individual inflammatory markers, should be used in accordance with theory. Further, the aggregation of shared variance achieved by constructing empirically-supported aggregates might increase predictive validity compared to other modeling choices, maximizing statistical power.

First order or higher order general factor?

1997 ◽  
Vol 4 (3) ◽  
pp. 193-211 ◽  
Author(s):  
Stanley A. Mulaik ◽  
Douglas A. Quartetti
Keyword(s):  
Higher Order ◽  
General Factor ◽  
First Order

Effects of Relaxation and Anticipation on Riemann Solutions of Payne-Whitham Model

2000 ◽  
Vol 1710 (1) ◽  
pp. 131-135 ◽  
Author(s):  
H. M. Zhang ◽  
T. Kim
Keyword(s):  
Experimental Validation ◽  
Initial Density ◽  
Higher Order ◽  
Splitting Scheme ◽  
Order Model ◽  
Riemann Solutions ◽  
Flux Splitting ◽  
Lwr Model ◽  
Strong Anticipation ◽  
Higher Order Models

The solutions of Riemann problems of a particular higher-order model—the Payne-Whitham (PW) model—are studied using Roe’s flux splitting scheme as presented by Leo and Pretty. Despite numerous works on higher-order models, little is known about Riemann solutions of these models and how relaxation and anticipation affect these solutions. Riemann solutions of the PW model are computed and compared with those of the Lighthill-Whitham-Richards (LWR) model having the same initial (density) data. It was found that faster relaxation forces the PW model to behave much like the LWR model, that strong anticipation has a stabilizing effect on traffic, and that shock waves travel at different speeds in the PW model than they do in the LWR model. These findings provide a basic checklist for experimental validation of PW-like higher models.

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 General Factors of Psychopathology, Personality, and Personality Disorder: Across Domain Comparisons

2018 ◽  
Vol 6 (4) ◽  
pp. 581-589 ◽  
Author(s):  
Joshua R. Oltmanns ◽  
Gregory T. Smith ◽  
Thomas F. Oltmanns ◽  
Thomas A. Widiger
Keyword(s):  
Older Adults ◽  
Personality Disorder ◽  
Community Sample ◽  
Self Report ◽  
General Factor ◽  
Combined Model ◽  
General Factor Of Personality ◽  
Report Data ◽  
Self Report Data ◽  
P Factor

Three separate and distinct literatures exist investigating general factors of psychopathology (p factor), personality (general factor of personality, GFP), and personality disorder (g-PD). Surprisingly, there has been little to no investigation regarding the convergence of these three distinct general factors. In the present investigation, two studies were conducted examining the convergence of the p factor, GFP, and g-PD. In Study 1, a combined model extracting all three factors from self-report data simultaneously found high convergence. The findings for the g-PD and GFP were replicated in Study 2 using multimethod data, wherein the GFP and the g-PD were extracted from a community sample of 1,630 older adults and correlated with an index of maladaptivity. The present findings support the position that general factors of psychopathology, personality disorder, and personality are likely to entail a common individual differences continuum, which may impact on how these general factors are to be understood.

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