Confirmatory factor analysis models of factorial invariance: A multifaceted approach

1994 ◽  
Vol 1 (1) ◽  
pp. 5-34 ◽  
Author(s):  
Herbert W. Marsh
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Factorial Invariance ◽  
Multifaceted Approach ◽  
Confirmatory Factor ◽  
Analysis Models

Giving G a Meaning: An Application of the Bifactor-(S-1) Approach to Realize a More Symptom-Oriented Modeling of the Beck Depression Inventory–II

Assessment ◽  
2018 ◽  
Vol 27 (7) ◽  
pp. 1429-1447 ◽  
Author(s):  
Manuel Heinrich ◽  
Pavle Zagorscak ◽  
Michael Eid ◽  
Christine Knaevelsrud
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Beck Depression Inventory ◽  
Factor Structure ◽  
Clinical Sample ◽  
First Order ◽  
Confirmatory Factor ◽  
Bifactor Models ◽  
Current Article ◽  
Analysis Models

The Beck Depression Inventory–II is one of the most frequently used scales to assess depressive burden. Despite many psychometric evaluations, its factor structure is still a topic of debate. An increasing number of articles using fully symmetrical bifactor models have been published recently. However, they all produce anomalous results, which lead to psychometric and interpretational difficulties. To avoid anomalous results, the bifactor-(S-1) approach has recently been proposed as alternative for fitting bifactor structures. The current article compares the applicability of fully symmetrical bifactor models and symptom-oriented bifactor-(S-1) and first-order confirmatory factor analysis models in a large clinical sample ( N = 3,279) of adults. The results suggest that bifactor-(S-1) models are preferable when bifactor structures are of interest, since they reduce problematic results observed in fully symmetrical bifactor models and give the G factor an unambiguous meaning. Otherwise, symptom-oriented first-order confirmatory factor analysis models present a reasonable alternative.

scholarly journals Translation and psychometrical properties of Coronavirus Anxiety Scale (CAS) in Peruvian young people and adults

2020 ◽  
Author(s):  
Rulman Andrei Franco-Jimenez
Keyword(s):  
Factor Analysis ◽  
Young People ◽  
Confirmatory Factor Analysis ◽  
Invariant Measure ◽  
Psychometric Properties ◽  
Factorial Invariance ◽  
Anxiety Scale ◽  
Structural Evaluation ◽  
Confirmatory Factor ◽  
Fit Indexes

Background: The overall aim of the study was to translate into Spanish and evaluate the psychometric properties of the Coronavirus Anxiety Scale (CAS). Method: The sample was composed of 622 Peruvian young people and adults who answered CAS. The structural evaluation was conducted with confirmatory factor analysis. Factorial invariance was used to verify the equivalence by sex and age, Results: The unifactorial model revealed good fit indexes χ²(5) = 12.40,CFI = .998, TLI = .996, RMSEA = .049, SRMR = .033. Also, the internal consistency using McDonald´s Omega coefficient was high (ω > .80). The factorial invariance indicated that the unifactorial structure is stable according to age. Conclusion: The Peruvian version of the CAS has evidence to be considered an accurate, valid, and invariant measure and the results support the unifactorial model.

scholarly journals Modeling the construct validity of the Berlin Intelligence Structure Model

2015 ◽  
Vol 32 (1) ◽  
pp. 13-25 ◽  
Author(s):  
Heinz-Martin Süß ◽  
André Beauducel
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Construct Validity ◽  
Model Test ◽  
Representative Sample ◽  
Integrative Model ◽  
Structure Model ◽  
Confirmatory Factor ◽  
Analysis Models ◽  
High Degree

The Berlin Intelligence Structure Model is a hierarchical and faceted model which is originally based on an almost representative sample of tasks found in the literature. Therefore, the Berlin Intelligence Structure Model is an integrative model with a high degree of generality. The present paper investigates the construct validity of this model by using different confirmatory factor analysis models. The results show that the model assumptions are supported only in part by the data. Moreover, it is demonstrated that there are different possibilities to incorporate the Berlin Intelligence Structure Model assumptions into confirmatory factor analysis models. The results are discussed with regard to the validity of the Berlin Intelligence Structure Model test, and the validity of the model.

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