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. 2014 Sep;41(9):644-52.
doi: 10.1111/joor.12191. Epub 2014 Jun 9.

Confirmatory Factor Analysis of the Oral Health Impact Profile

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Free PMC article

Confirmatory Factor Analysis of the Oral Health Impact Profile

M T John et al. J Oral Rehabil. .
Free PMC article

Abstract

Previous exploratory analyses suggest that the Oral Health Impact Profile (OHIP) consists of four correlated dimensions and that individual differences in OHIP total scores reflect an underlying higher-order factor. The aim of this report is to corroborate these findings in the Dimensions of Oral Health-Related Quality of Life (DOQ) Project, an international study of general population subjects and prosthodontic patients. Using the project's Validation Sample (n = 5022), we conducted confirmatory factor analyses in a sample of 4993 subjects with sufficiently complete data. In particular, we compared the psychometric performance of three models: a unidimensional model, a four-factor model and a bifactor model that included one general factor and four group factors. Using model-fit criteria and factor interpretability as guides, the four-factor model was deemed best in terms of strong item loadings, model fit (RMSEA = 0·05, CFI = 0·99) and interpretability. These results corroborate our previous findings that four highly correlated factors - which we have named Oral Function, Oro-facial Pain, Oro-facial Appearance and Psychosocial Impact - can be reliably extracted from the OHIP item pool. However, the good fit of the unidimensional model and the high interfactor correlations in the four-factor solution suggest that OHRQoL can also be sufficiently described with one score.

Keywords: Oral Health Impact Profile; confirmatory factor analysis; dimensions; factor structure; oral health-related quality of life.

Figures

Figure 1
Figure 1. Graphical display of the unidimensional (A), four-factor (B), and bifactor (C) models
Ovals represent latent factors, rectangles represent measured indicators for a latent factor. Lines connecting a latent factor to a measured indicator represent non-zero factor loadings.

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