Performance of the Generalized <i>S‐X</i><sup>2</sup> Item Fit Index for Polytomous IRT Models

Kang, Taehoon; Chen, Troy T.

doi:10.1111/j.1745-3984.2008.00071.x

Cited by 123 publications

(63 citation statements)

References 33 publications

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“…As a summed score-based method, S – X 2 was found to perform better than the traditional Chi-square-type item-fit statistics as well as to yield acceptable power and maintain a nominal Type I error rate for detecting misfit with normally distributed data [31, 32]. However, the performance of S – X 2 in detecting misfit can be influenced by the data distribution and test length [32, 33]. While statistical tests to detect misfit can be sensitive to the aforementioned factors, another direction for assessing item fit is the use of graphical approaches, which is strongly advocated by Hambleton and Han [5].…”

Section: Discussionmentioning

confidence: 99%

Impact of IRT item misfit on score estimates and severity classifications: an examination of PROMIS depression and pain interference item banks

Zhao

2016

Qual Life Res

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Purpose In patient-reported outcome research that utilizes item response theory (IRT), using statistical significance tests to detect misfit is usually the focus of IRT model-data fit evaluations. However, such evaluations rarely address the impact/consequence of using misfitting items on the intended clinical applications. This study was designed to evaluate the impact of IRT item misfit on score estimates and severity classifications and to demonstrate a recommended process of model-fit evaluation. Methods Using secondary data sources collected from the Patient-Reported Outcome Measurement Information System (PROMIS) wave 1 testing phase, analyses were con ducted based on PROMIS depression (28 items; 782 cases) and pain interference (41 items; 845 cases) item banks. The identification of misfitting items was assessed using Orlando and Thissen’s summed-score item-fit statistics and graphical displays. The impact of misfit was evaluated according to the agreement of both IRT-derived T-scores and severity classifications between inclusion and exclusion of misfitting items. Results The examination of the presence and impact of misfit suggested that item misfit had a negligible impact on the T-score estimates and severity classifications with the general population sample in the PROMIS depression and pain interference item banks, implying that the impact of item misfit was insignificant. Conclusions Findings support the T-score estimates in the two item banks as robust against item misfit at both the group and individual levels and add confidence to the use of T-scores for severity diagnosis in the studied sample. Recommendations on approaches for identifying item misfit (statistical significance) and assessing the misfit impact (practical significance) are given.

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Section: Discussionmentioning

confidence: 99%

Impact of IRT item misfit on score estimates and severity classifications: an examination of PROMIS depression and pain interference item banks

Zhao

2016

Qual Life Res

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“…For instance, the 3PL estimates three item parameters resulting in df = H − 3. Yen's Q 1 and Bock's χ 2 are not standard output in any commercial IRT software packages, despite being presented in a range of widely cited sources (see Kang & Chen, ; Orlando & Thissen, ; Reise, ; Sinharay, ; Stone & Zhang, ).…”

Section: Traditional Methods For Evaluating Model‐data Fitmentioning

confidence: 99%

“…IRFs that are a good fit to the data can yield appropriate inferences and predictions. IRFs that do not demonstrate good fit to the data run the risk of several undesirable outcomes, including biased ability and item parameter estimates (Wainer & Thissen, ; Yen, ) that jeopardize the appropriate application of IRT models in such areas as test development, equating, and computer adaptive testing (Kang & Chen, ). The consideration of model‐data fit is an important step in test development (see Standard 3.9 of the Standards for Educational and Psychological Testing ; AERA/APA/NCME, ), with misfitting items often being discarded from the potential item pool (Sinharay, ; Wilson, ).…”

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confidence: 99%

An NCME Instructional Module on Item‐Fit Statistics for Item Response Theory Models

Ames

Penfield

2015

Educational Measurement

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Drawing valid inferences from item response theory (IRT) models is contingent upon a good fit of the data to the model. Violations of model‐data fit have numerous consequences, limiting the usefulness and applicability of the model. This instructional module provides an overview of methods used for evaluating the fit of IRT models. Upon completing this module, the reader will have an understanding of traditional and Bayesian approaches for evaluating model‐data fit of IRT models, the relative advantages of each approach, and the software available to implement each method.

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“…Information content of items were calculated using Fisher information which is formulated as minus the expectation of the second derivative of the log-likelihood of the model [28]. To evaluate the item fit, the generalized Orlando and Thissen's S-X 2 index for polytomous data was used [35], comparing the observed and expected response frequencies under the estimated MIRT model. Eventually items with S-X 2 p-value<0.001 were considered poorly fitted [36].…”

Section: Discussionmentioning

confidence: 99%

Multidimensional Item Response Theory to Assess Psychological Properties of GHQ-12 in School Children Parents

Haem

Doostfatemeh

2020

Preprint

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Background: Multidimensional item response theory (MIRT) model provides an ideal foundation to assess psychological properties of a questionnaire designed with multidimensional structure. This study aims to present the first use of MIRT models to investigate psychological properties of GHQ-12. Methods: A total of 1104 school children parents completed the Persian version of GHQ-12 questionnaire. A MIRT model was applied to model observed scores for each GHQ-12 item as a function of the subject’s latent traits while taking into account the correlation between dimensions of the questionnaire. The goodness of fit indices were reported and items fit were assessed. Individual items were described in detail through item characteristic curves and the amount of information carried by different items were presented using information curves. Results: The MIRT analysis of the 12 item GHQ successfully demonstrated two factor structure corresponding to psychological distress and social dysfunction. The model showed all items were fitted adequately. Items varied in their discrimination ranged from 0.86 to 2.35 and 1.18 to 2.41 for psychological distress and social dysfunction, respectively. Moreover, item 8 and item 2 provided the least information in psychological distress and social dysfunction dimensions, respectively. Conclusions: The developed framework to evaluate psychological properties of GHQ-12 can be a suitable alternative to traditional approaches and also unidimensional IRT models which its use have been restricted due to multidimensional structure of the questionnaire.

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Performance of the Generalized S‐X² Item Fit Index for Polytomous IRT Models

Cited by 123 publications

References 33 publications

Impact of IRT item misfit on score estimates and severity classifications: an examination of PROMIS depression and pain interference item banks

Impact of IRT item misfit on score estimates and severity classifications: an examination of PROMIS depression and pain interference item banks

An NCME Instructional Module on Item‐Fit Statistics for Item Response Theory Models

Multidimensional Item Response Theory to Assess Psychological Properties of GHQ-12 in School Children Parents

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Performance of the Generalized S‐X2 Item Fit Index for Polytomous IRT Models

Cited by 123 publications

References 33 publications

Impact of IRT item misfit on score estimates and severity classifications: an examination of PROMIS depression and pain interference item banks

Impact of IRT item misfit on score estimates and severity classifications: an examination of PROMIS depression and pain interference item banks

An NCME Instructional Module on Item‐Fit Statistics for Item Response Theory Models

Multidimensional Item Response Theory to Assess Psychological Properties of GHQ-12 in School Children Parents

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Performance of the Generalized S‐X² Item Fit Index for Polytomous IRT Models