On the identifiability of factor analysis models

2006 ◽  
pp. 297-317 ◽  
Author(s):  
Giorgio Picci
Keyword(s):  
Factor Analysis ◽  
Analysis Models

Skew-normal factor analysis models with incomplete data

2014 ◽  
Vol 42 (4) ◽  
pp. 789-805 ◽  
Author(s):  
M. Liu ◽  
T.I. Lin
Keyword(s):  
Factor Analysis ◽  
Incomplete Data ◽  
Analysis Models ◽  
Skew Normal

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.

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