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Advanced modeling of item response data through the item response theory (IRT) or item factor analysis frameworks is becoming increasingly popular. In the social and behavioral sciences, the underlying structure of tests/assessments is often multidimensional (i.e., more than 1 latent variable/construct is represented in the items). This review provides an introduction and overview of flexMIRT 3.5, a commercially available statistical software program for the analysis of item response data that was developed specifically for fitting such complex models. Three models are used as motivating examples: (a) a bifactor model, (b) a multidimensional IRT (MIRT) model with correlated factors and (c) a diagnostic classification model with testlet structure. Discussion regarding flexMIRT input syntax and output files is provided and the topics of model fit evaluation, limited-information fit statistics in particular, and specifics of more recently introduced estimation methods and models are also covered.

Objectives: a) To evaluate the item and scale properties of the Oral Health Literacy Adults Questionnaire (OHL‐AQ) in an adult general population. b) To determine precision or accuracy of the respondents' estimated scores along the Oral Health Literacy (OHL) spectrum using item response theory (IRT) modeling. Methods: Survey data were collected from a convenience sample of 405 adult attendees of the 2014 Minnesota State Fair. We used the two‐parameter logistic (2PL) model for the item response theory (IRT) analyses of OHL‐AQ data and calibrated items to estimate model‐based item difficulty and discrimination parameters. Item and scale properties were also assessed by plotting and interpreting item characteristic curves (ICCs), test characteristic curve (TCC), and test information function (TIF). Results: Based on interpretation of model coefficients, statistical testing, and model fit criteria, we deemed the 2PL model superior and selected this model to examine item and scale properties. Scale reliability was shown to be good through the test information function (TIF). TIF from our analysis showed that higher levels of OHL were measured less precisely than lower levels of OHL. Conclusion: We demonstrated OHL‐AQ as a whole has promising psychometric properties. However, for equiprecise measurement across the scale range, the scale needs more items for measuring higher levels of OHL. [ABSTRACT FROM AUTHOR]

Objectives: To investigate differential item functioning across the Spanish and English versions of the Orofacial Esthetic Scale (OES). Methods: OES data were collected from a consecutive sample of a total of 622 adult dental patients (311 English‐ and 311 Spanish‐speaking adults) from HealthPartners dental clinics in Minnesota. We inspected boundary characteristic curves (BCCs) based on item response theory (IRT) in English speakers and Spanish speakers to examine differential item functioning (DIF). Using the Samejima's graded response model, we used the item response theory log‐likelihood ratio (IRTLR) approach to test whether DIF was statistically significant. Results: Inspection of the BCCs did not reveal substantial differences in item difficulty and discrimination between English speakers and Spanish speakers. When IRTLR tests were performed for the seven OES items, no item was flagged with significant DIF after P‐values were adjusted for multiple comparisons. Conclusions: Pooling of Orofacial Esthetic Scale (OES) data from English and Spanish‐speaking dental patients for analysis and interpretation is supported by the absence of differential item functioning (DIF) across the two language groups. [ABSTRACT FROM AUTHOR]