LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

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

Photo by arifriyanto from unsplash

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… Click to show full abstract

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.

Keywords: bifactor models; depression inventory; beck depression; bifactor approach; bifactor; symptom oriented

Journal Title: Assessment
Year Published: 2018

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.