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

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.

New multiplicities of dreaming and REMing

2000 ◽  
Vol 23 (6) ◽  
pp. 953-955 ◽  
Author(s):  
Harry T. Hunt
Keyword(s):  
Factor Models ◽  
Single Factor ◽  
Multiple Dimensions ◽  
Orientation Response

The five authors vary in the degree to which the recent neuroscience of the REM state leads them towards multiple dimensions and forms of dreaming consciousness (Hobson et al.; Nielsen; Solms) or toward all-explanatory single factor models (Vertes & Eastman, Revonsuo). The view of the REM state as a prolongation of the orientation response to novelty fits best with the former pluralisms but not the latter monisms.[Hobson et al.; Nielsen; Revonsuo; Solms; Vertes & Eastman]

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

How Data Analysis Can Dominate Interpretations of Dominant General Factors

2015 ◽  
Vol 8 (3) ◽  
pp. 438-445 ◽  
Author(s):  
Brenton M. Wiernik ◽  
Michael P. Wilmot ◽  
Jack W. Kostal
Keyword(s):  
Data Analysis ◽  
Substantial Proportion ◽  
Specific Factor ◽  
General Factor ◽  
Data Sets ◽  
Single Factor ◽  
Traditional Methods ◽  
External Variables ◽  
Specific Factors ◽  
Data Analytic

A dominant general factor (DGF) is present when a single factor accounts for the majority of reliable variance across a set of measures (Ree, Carretta, & Teachout, 2015). In the presence of a DGF, dimension scores necessarily reflect a blend of both general and specific factors. For some constructs, specific factors contain little unique reliable variance after controlling for the general factor (Reise, 2012), whereas for others, specific factors contribute a more substantial proportion of variance (e.g., Kinicki, McKee-Ryan, Schriesheim, & Carson, 2002). We agree with Ree et al. that the presence of a DGF has implications for interpreting scores. However, we argue that the conflation of general and specific factor variances has the strongest implications for understanding how constructs relate to external variables. When dimension scales contain substantial general and specific factor variance, traditional methods of data analysis will produce ambiguous or even misleading results. In this commentary, we show how several common data analytic methods, when used with data sets containing a DGF, will substantively alter conclusions.

scholarly journals Factor Structure of the “Top Ten” Positive Emotions of Barbara Fredrickson

2021 ◽  
Vol 12 ◽  
Author(s):  
Leopold Helmut Otto Roth ◽  
Anton-Rupert Laireiter
Keyword(s):  
Positive Psychology ◽  
Factor Structure ◽  
Positive Emotions ◽  
Factor Model ◽  
Factor Models ◽  
Cluster Solution ◽  
Special Role ◽  
Single Factor ◽  
Theoretical Significance

In order to contribute to the consolidation in the field of Positive Psychology, we reinvestigated the factor structure of top 10 positive emotions of Barbara Fredrickson. Former research in experimental settings resulted in a three-cluster solution, which we tested with exploratory and confirmatory methodology against different factor models. Within our non-experimental data (N = 312), statistical evidence is presented, advocating for a single factor model of the 10 positive emotions. Different possible reasons for the deviating results are discussed, as well as the theoretical significance to various subfields in Positive Psychology (e.g., therapeutical interventions). Furthermore, the special role of awe within the study and its implications for further research in the field are discussed.

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.

scholarly journals Perceived Stress Scale: confirmatory factor analysis of the PSS14 and PSS10 versions in two samples of pregnant women from the BRISA cohort

2017 ◽  
Vol 33 (12) ◽  
Author(s):  
Ana Valéria Carvalho Pires Yokokura ◽  
Antônio Augusto Moura da Silva ◽  
Juliana de Kássia Braga Fernandes ◽  
Cristina Marta Del-Ben ◽  
Felipe Pinheiro de Figueiredo ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Pregnant Women ◽  
Perceived Stress ◽  
Discriminant Validity ◽  
Factor Models ◽  
Single Factor ◽  
Perceived Stress Scale ◽  
Stress Scale ◽  
Confirmatory Factor

This study aimed to assess the dimensional structure, reliability, convergent validity, discriminant validity, and scalability of the Perceived Stress Scale (PSS). The sample consisted of 1,447 pregnant women in São Luís (Maranhão State) and 1,400 in Ribeirão Preto (São Paulo State), Brazil. The 14 and 10-item versions of the scale were assessed using confirmatory factor analysis, using weighted least squares means and variance (WLSMV). In both cities, the two-factor models (positive factors, measuring resilience to stressful situations, and negative factors, measuring stressful situations) showed better fit than the single-factor models. The two-factor models for the complete (PSS14) and reduced scale (PSS10) showed good internal consistency (Cronbach’s alpha ≥ 0.70). All the factor loadings were ≥ 0.50, except for items 8 and 12 of the negative dimension and item 13 of the positive dimension. The correlations between both dimensions of stress and psychological violence showed the expected magnitude (0.46-0.59), providing evidence of an adequate convergent construct validity. The correlations between the scales’ positive and negative dimensions were around 0.74-0.78, less than 0.85, which suggests adequate discriminant validity. Extracted mean variance and scalability were slightly higher for PSS10 than for PSS14. The results were consistent in both cities. In conclusion, the single-factor solution is not recommended for assessing stress in pregnant women. The reduced, 10-item two-factor scale appears to be more appropriate for measuring perceived stress in pregnant women.

Sign in / Sign up

Export Citation Format

Share Document