2016
DOI: 10.1177/1073191116678925
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Resolving Dimensionality Problems With WHOQOL-BREF Item Responses

Abstract: The World Health Organization Quality of Life Scale (WHOQOL-BREF) is predicated on a multidimensional perspective on quality of life (QOL); yet studies are unclear about the latent structure underlying responses. This article reports on a study conducted to investigate the structure of WHOQOL-BREF scores. Competing latent structures of the data were examined in a general population sample. In addition, the complete factorial invariance of the retained model was investigated across gender. We also investigated … Show more

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Cited by 31 publications
(46 citation statements)
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“…In general population samples, the WHOQOL-BREF can be fit by a bifactor model, which has demonstrated complete factorial invariance across genders (Perera et al, 2018). In the current study, we employed a 5-item global Perera et al, 2018). In our SPARK sample (n = 872), these items exhibited adequate fit to a unidimensional factor model (WLSMV estimation; CFI = 0.995, TLI = 0.989, SRMR = 0.035), and reliability for this five-item composite was good ( = 0.897).…”
Section: Quality Of Life Compositementioning
confidence: 99%
“…In general population samples, the WHOQOL-BREF can be fit by a bifactor model, which has demonstrated complete factorial invariance across genders (Perera et al, 2018). In the current study, we employed a 5-item global Perera et al, 2018). In our SPARK sample (n = 872), these items exhibited adequate fit to a unidimensional factor model (WLSMV estimation; CFI = 0.995, TLI = 0.989, SRMR = 0.035), and reliability for this five-item composite was good ( = 0.897).…”
Section: Quality Of Life Compositementioning
confidence: 99%
“…Residualised facets is the most extreme in that it assigns all common variance to the broad traits. In contrast, the bifactor model (Chen, Hayes, Carver, Laurenceau, & Zhang, ; McAbee, Oswald, & Connelly, ; Perera, Izadikhah, O’Connor, & McIlveen, ) distributes common variance between factors and facets. The bifactor model also provides a way of separating evaluative variance from more descriptive trait variance (Anglim, Morse, et al, ).…”
Section: Recommendations For Researchersmentioning
confidence: 99%
“…Residualized facets is the most extreme in that it assigns all common variance to the broad traits. In contrast, the bifactor model (Chen, Hayes, Carver, Laurenceau, & Zhang, 2012;McAbee, Oswald, & Connelly, 2014;Perera, Izadikhah, O'Connor, & McIlveen, 2016) distributes common variance between factors and facets. The bifactor model also provides a way of separating evaluative variance from more descriptive trait variance (Anglim, Morse, et al, 2017).…”
Section: Recommendations For Researchersmentioning
confidence: 99%