Editorial: Putting Practicality Into “p”: Leveraging General Factor Models of Psychopathology in Clinical Intervention

2019 ◽  
Vol 58 (8) ◽  
pp. 751-753
Author(s):  
John D. Haltigan
Keyword(s):  
Clinical Intervention ◽  
Factor Models ◽  
General Factor

General Factor Models and the Structure of Security Returns

1983 ◽  
Vol 18 (1) ◽  
pp. 31 ◽  
Author(s):  
Lawrence Kryzanowski ◽  
Minh Chau To
Keyword(s):  
Factor Models ◽  
General Factor ◽  
Security Returns

scholarly journals Quantifying the strength of general factors in psychopathology: A comparison of CFA with maximum likelihood estimation, BSEM and ESEM/EFA bi-factor approaches

2018 ◽  
Author(s):  
Aja Louise Murray ◽  
Tom Booth ◽  
Manuel Eisner ◽  
Ingrid Obsuth ◽  
Denis Ribeaud
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Likelihood Estimation ◽  
Factor Models ◽  
General Factor ◽  
Normative Sample ◽  
Ml Estimation ◽  
Co Morbidity ◽  
Simulation Results ◽  
P Factor

Whether or not importance should be placed on an all-encompassing general factor of psychopathology (or p-factor) in classifying, researching, diagnosing and treating psychiatric disorders depends (amongst other issues) on the extent to which co-morbidity is symptom-general rather than staying largely within the confines of narrower trans-diagnostic factors such as internalising and externalising. In this study we compared three methods of estimating p-factor strength. We compared omega hierarchical and ECV calculated from CFA bi-factor models with maximum likelihood (ML) estimation, from ESEM/EFA models with a bifactor rotation, and from BSEM bi-factor models. Our simulation results suggested that BSEM with small variance priors on secondary loadings may be the preferred option. However, CFA with ML also performed well provided secondary loadings were modelled We provide two empirical examples of applying the three methodologies using a normative sample of youth (z-proso, n=1286) and University counselling sample (n= 359).

scholarly journals Factor Structure and Measurement Invariance Across Gender of the Beck Depression Inventory-II in Adolescent Psychiatric Patients

2020 ◽  
Vol 11 ◽  
Author(s):  
Ferdinand Keller ◽  
Inken Kirschbaum-Lesch ◽  
Joana Straub
Keyword(s):  
Measurement Invariance ◽  
Beck Depression Inventory ◽  
Factor Structure ◽  
Goodness Of Fit ◽  
Clinical Sample ◽  
Factor Models ◽  
Bifactor Model ◽  
General Factor ◽  
Factor Analyses ◽  
Specific Factors

The revised version of the Beck Depression Inventory (BDI-II) is one of the most frequently applied questionnaires not only in adults, but also in adolescents. To date, attempts to identify a replicable factor structure of the BDI-II have mainly been undertaken in adult populations. Moreover, most of the studies which included minors and were split by gender lacked confirmatory factor analyses and were generally conducted in healthy adolescents. The present study therefore aimed to determine the goodness of fit of various factor models proposed in the literature in an adolescent clinical sample, to evaluate alternative solutions for the factor structure and to explore potential gender differences in factor loadings. The focus was on testing bifactor models and subsequently on calculating bifactor statistical indices to help clarify whether a uni- or a multidimensional construct is more appropriate, and on testing the best-fitting factor model for measurement invariance according to gender. The sample comprised 835 adolescent girls and boys aged 13–18 years in out- and inpatient setting. Several factor models proposed in the literature provided a good fit when applied to the adolescent clinical sample, and differences in goodness of fit were small. Exploratory factor analyses were used to develop and test a bifactor model that consisted of a general factor and two specific factors, termed cognitive and somatic. The bifactor model confirmed the existence of a strong general factor on which all items load, and the bifactor statistical indices suggest that the BDI-II should be seen as a unidimensional scale. Concerning measurement invariance across gender, there were differences in loadings on item 21 (Loss of interest in sex) on the general factor and on items 1 (Sadness), 4 (Loss of pleasure), and 9 (Suicidal Thoughts) on the specific factors. Thus, partial measurement invariance can be assumed and differences are negligible. It can be concluded that the total score of the BDI-II can be used to measure depression severity in adolescent clinical samples.

A Bifactor Model of School Engagement

2016 ◽  
Vol 40 (5) ◽  
pp. 471-480 ◽  
Author(s):  
Kristjan K. Stefansson ◽  
Steinunn Gestsdottir ◽  
G. John Geldhof ◽  
Sigurgrimur Skulason ◽  
Richard M. Lerner
Keyword(s):  
Academic Achievement ◽  
School Engagement ◽  
Predictive Power ◽  
Factor Models ◽  
Bifactor Model ◽  
Outcome Variable ◽  
Behavioral Engagement ◽  
General Factor ◽  
Multidimensional Models ◽  
Three Factor

School engagement involves cognitive, emotional, and behavioral components that overlap conceptually. This conceptual ambiguity has led to measures that have either consisted of one general factor or separate correlated factors. However, neither approach can sufficiently account for both the uniqueness and the overlap of the subcomponents. The bifactor model has been recommended to determine the degree to which a measure is unidimensional versus multidimensional. In this study, we examined the validity of a multidimensional measure of school engagement in adolescence, the Behavioral-Emotional-Cognitive School Engagement Scale (BEC-SES; Li & Lerner, 2013), by comparing the model fit and predictive power of the widely-used one- and three-factor models with a bifactor model. Using data from 561 youth in Iceland (46% girls, Mage at Wave 1 = 14.3 years, SD = 0.3), only the multidimensional models (i.e., the three-factor and bifactor models) gave a good fit to the data. We then assessed the predictive power of the multidimensional models for academic achievement. The addition of academic achievement as an outcome variable to the bifactor model revealed that general school engagement, as well as specific behavioral engagement, predicted achievement. These findings are distinct from previous results using three-factor models, which indicated that behavioral engagement alone predicted later achievement. The results of the current study support the use of a bifactor model when using measures of school engagement.

The dependability of the general factor of intelligence: Why small, single-factor models do not adequately represent g

Intelligence ◽  
2011 ◽  
Vol 39 (5) ◽  
pp. 418-433 ◽  
Author(s):  
Jason T. Major ◽  
Wendy Johnson ◽  
Thomas J. Bouchard
Keyword(s):  
Factor Models ◽  
General Factor ◽  
Single Factor

scholarly journals Test of the bifactor model of job-related affective well-being

2019 ◽  
Vol 15 (2) ◽  
pp. 342-357
Author(s):  
Mariola Laguna ◽  
Emilia Mielniczuk ◽  
Wiktor Razmus
Keyword(s):  
Well Being ◽  
Factor Models ◽  
Bifactor Model ◽  
General Factor ◽  
Good Representation ◽  
Factorial Structure ◽  
Work Context ◽  
Multidimensional Measure ◽  
Global Aspect ◽  
Correlated Factors

The multidimensional measure of the job-related affective well-being developed by Warr (1990) is a frequently used tool estimating affect in the work context. Alternative factorial models of this measure were tested in previous studies. Recently a bifactor model has been proposed as alternative factorial structure recommended for multifaceted constructs. It allows capturing the global aspect of the construct along with the specificity of its subdimensions. We conducted two studies to test a bifactor model on Warr’s measure and to compare it to factor models proposed in earlier studies. This bifactor model identified one general factor in addition to four unique factors. Two studies were conducted among employees (Study 1; N = 869) and entrepreneurs (Study 2; N = 204). Results of both studies corroborate a four correlated factors model as superior to the bifactor model. The model with four unique but correlated factors representing anxiety, comfort, depression, and enthusiasm is a good representation of job-related affective well-being measured by Warr’s instrument, both in a sample of employees and entrepreneurs.

scholarly journals Implicit realism impedes progress in psychology: Comment on Fried (2020)

2020 ◽  
Author(s):  
Tal Yarkoni
Keyword(s):  
Real World ◽  
Network Modeling ◽  
Theory Development ◽  
Factor Models ◽  
General Factor ◽  
Alternative Possibility ◽  
Psychological Constructs ◽  
Central Argument ◽  
Aesthetic Preferences

Fried (in press) argues that progress in the factor and network modeling literatures is currently impeded by inadequate theory development. Here I take issue with this conclusion, focusing on two broad concerns. First, I argue that much of Fried's criticism of previous work (e.g., of general factor models) reflects a particular set of aesthetic preferences and priorities that other researchers are under no obligation to share. Second, I argue that Fried’s central argument tacitly assumes a strong realism about psychological constructs that is difficult to defend, and has deleterious practical consequences. When stripped of its realist commitments, Fried’s paper provides the reader with little reason to suppose that improved theory development would do much to facilitate progress in psychology. I suggest that applied psychologists may want to consider an alternative possibility---namely, that models constructed at a psychological level of description are simply not very conducive to the production of effective real-world predictions or interventions.

When a Truly Positive Correlation Turns Negative: How Different Approaches to Model Hierarchically Structured Constructs Affect Estimated Correlations to Covariates

2021 ◽  
pp. 089020702110501 ◽  
Author(s):  
Morten Moshagen
Keyword(s):  
Measurement Model ◽  
Factor Models ◽  
Higher Order ◽  
General Factor ◽  
Single Factor ◽  
Personality Psychology ◽  
Order Factor ◽  
The Mean ◽  
Biased Estimates ◽  
True Correlation

Many constructs in personality psychology assume a hierarchical structure positing a general factor along with several narrower subdimensions or facets. Different approaches are commonly used to model such a structure, including higher-order factor models, bifactor models, single-factor models based on the responses on the observed items, and single-factor models based on parcels computed from the mean observed scores on the subdimensions. The present article investigates the consequences of adopting a certain approach for the validity of conclusions derived from the thereby obtained correlation of the most general factor to a covariate. Any of the considered approaches may closely approximate the true correlation when its underlying assumptions are met or when model misspecifications only pertain to the measurement model of the hierarchical construct. However, when misspecifications involve nonmodeled covariances between parts of the hierarchically structured construct and the covariate, higher-order models, single-factor representations, and facet-parcel approaches can yield severely biased estimates sometimes grossly misrepresenting the true correlation and even incurring sign changes. In contrast, a bifactor approach proved to be most robust and to provide rather unbiased results under all conditions. The implications are discussed and recommendations are provided.

Sign in / Sign up

Export Citation Format

Share Document