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 prosthodontics patients. Using the project's Validation Sample (N=5,022), we conducted confirmatory factor analyses in a sample of 4,993 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, Orofacial Pain, Orofacial Appearance, and Psychosocial Impact —can be reliably extracted from the OHIP item pool. However, the good fit of the unidimensional model and the high inter-factor correlations in the four-factor solution suggest that OHRQoL can also be sufficiently described with one score.

Although oral health-related quality of life (OHRQoL) as measured by the Oral Health Impact Profile (OHIP) is thought to be multidimensional, the nature of these dimensions is not known. The aim of this report was to explore the dimensionality of the OHIP using the Dimensions of OHRQoL (DOQ) Project, an international study of general population subjects and prosthodontics patients. Using the project's Learning Sample (N=5,173), we conducted an exploratory factor analysis on the 46 OHIP items not specifically referring to dentures for 5,146 subjects with sufficiently complete data. The first eigenvalue (27.0) of the polychoric correlation matrix was more than ten times larger than the second eigenvalue (2.6), suggesting the presence of a dominant, higher-order general factor. Follow-up analyses with Horn's parallel analysis revealed a viable second-order, four-factor solution. An oblique rotation of this solution revealed four highly correlated factors that we named Oral Function, Orofacial Pain, Orofacial Appearance, and Psychosocial Impact. These four dimensions and the strong general factor are two viable hypotheses for the factor structure of the OHIP.

Purpose A desideratum of oral health-related quality of life (OHRQoL) instruments - such as the Oral Health Impact Profile (OHIP) - is that they accurately reflect the structure of the measured construct(s). With this goal in mind, the Dimensions of Oral Health-Related Quality of Life (DOQ) Project was proposed to investigate the number and nature of OHRQoL dimensions measured by OHIP. In this report, we describe our aggregate data set for the factor analyses in the project, which consists of responses to the 49-item OHIP from general population subjects and prosthodontics patients from 6 countries, including a large age range of adult subjects and both genders. Material and methods The DOQ Project's aggregate data set combines data from 35 individual studies conducted in Croatia, Germany, Hungary, Japan, Slovenia, and Sweden. Results The combined data set includes 10,778 OHIPs from 9,348 individuals (N=6,349 general population subjects, N=2,999 prosthodontic patients). To elucidate the OHIP latent structure, the aggregated data were split into a Learning Sample (N=5,173) for exploratory analyses and a Validation Sample (N=5,022) for confirmatory analyses. Additional data (N=583) were assigned to a third data set. Conclusion The Dimensions of Oral Health-Related Quality of Life Project contains a large amount of international data and is representative of populations where OHIP is intended to be used. It is well-suited to assess the dimensionality of the questionnaire.

The patient groups generally performed in a similarly impaired fashion across tasks, suggesting that WM impairment and attention lapsing are general features of psychotic disorders. Capacity estimates from the Change Localization and Detection tasks were related to functional capacity and outcome, suggesting that these methods may be useful in a clinical context.

In this study, we explored item and person parameter recovery of the four-parameter model (4PM) in over 24,000 real, realistic, and idealized data sets. In the first analyses, we fit the 4PM and three alternative models to data from three Minnesota Multiphasic Personality Inventory-Adolescent form factor scales using Bayesian modal estimation (BME). Our results indicated that the 4PM fits these scales better than simpler item Response Theory (IRT) models. Next, using the parameter estimates from these real data analyses, we estimated 4PM item parameters in 6,000 realistic data sets to establish minimum sample size requirements for accurate item and person parameter recovery. Using a factorial design that crossed discrete levels of item parameters, sample size, and test length, we also fit the 4PM to an additional 18,000 idealized data sets to extend our parameter recovery findings. Our combined results demonstrated that 4PM item parameters and parameter functions (e.g., item response functions) can be accurately estimated using BME in moderate to large samples (N ⩾ 5, 000) and person parameters can be accurately estimated in smaller samples (N ⩾ 1, 000). In the supplemental files, we report annotated [Formula: see text] code that shows how to estimate 4PM item and person parameters in [Formula: see text] (Chalmers, 2012 ).