scholarly journals Combining EQ-5D-5L items into a level summary score: demonstrating feasibility using non-parametric item response theory using an international dataset

Author(s):  
You-Shan Feng ◽  
Ruixuan Jiang ◽  
A. Simon Pickard ◽  
Thomas Kohlmann

Abstract Background The EQ-5D-5L is a well-established health questionnaire that estimates health utilities by applying preference-based weights. Limited work has been done to examine alternative scoring approaches when utility weights are unavailable or inapplicable. We examined whether the Mokken scaling approach can elucidate 1) if the level summary score is appropriate for the EQ-5D-5L and 2) an interpretation of such a score. Methods The R package “mokken” was used to assess monotonicity (scaling coefficients H, automated item selection procedure) and manifest invariant item ordering (MIIO: paired item response functions [IRF], HT). We used a rich dataset (the Multiple Instrument Comparison, MIC) which includes EQ-5D-5L data from six Western countries. Results While all EQ-5D-5L items demonstrated monotonicity, the anxiety/depression (AD) item had weak scalability (Hi = 0.377). Without AD, scalability improved from Hs = 0.559 to Hs = 0.714. MIIO revealed that the 5 items can be ordered, and the ordering is moderately accurate in the MIC data (HT = 0.463). Excluding AD, HT improves to 0.743. Results were largely consistent across disease and country subgroups. Discussion The 5 items of the EQ-5D-5L form a moderate to strong Mokken scale, enabling persons to be ordered using the level summary score. Item ordering suggests that the lower range of the score represents mainly problems with pain and anxiety/depression, the mid-range indicates additional problems with mobility and usual activities, and middle to higher range of scores reveals additional limitations with self-care. Scalability and item ordering are even stronger when the anxiety/depression item is not included in the scale.

2005 ◽  
Vol 32 (2) ◽  
pp. 127-139
Author(s):  
Matthijs J. Warrens ◽  
Willem J. Heiser ◽  
Dato N. M. de Gruijter

2020 ◽  
Vol 44 (7-8) ◽  
pp. 566-567
Author(s):  
Shaoyang Guo ◽  
Chanjin Zheng ◽  
Justin L. Kern

A recently released R package IRTBEMM is presented in this article. This package puts together several new estimation algorithms (Bayesian EMM, Bayesian E3M, and their maximum likelihood versions) for the Item Response Theory (IRT) models with guessing and slipping parameters (e.g., 3PL, 4PL, 1PL-G, and 1PL-AG models). IRTBEMM should be of interest to the researchers in IRT estimation and applying IRT models with the guessing and slipping effects to real datasets.


2008 ◽  
Vol 24 (1) ◽  
pp. 49-56 ◽  
Author(s):  
Wolfgang A. Rauch ◽  
Karl Schweizer ◽  
Helfried Moosbrugger

Abstract. In this study the psychometric properties of the Personal Optimism scale of the POSO-E questionnaire ( Schweizer & Koch, 2001 ) for the assessment of dispositional optimism are evaluated by applying Samejima's (1969) graded response model, a parametric item response theory (IRT) model for polytomous data. Model fit is extensively evaluated via fit checks on the lower-order margins of the contingency table of observed and expected responses and visual checks of fit plots comparing observed and expected category response functions. The model proves appropriate for the data; a small amount of misfit is interpreted in terms of previous research using other measures for optimism. Item parameters and information functions show that optimism can be measured accurately, especially at moderately low to middle levels of the latent trait scale, and particularly by the negatively worded items.


2018 ◽  
Vol 79 (3) ◽  
pp. 545-557 ◽  
Author(s):  
Dimiter M. Dimitrov ◽  
Yong Luo

An approach to scoring tests with binary items, referred to as D-scoring method, was previously developed as a classical analog to basic models in item response theory (IRT) for binary items. As some tests include polytomous items, this study offers an approach to D-scoring of such items and parallels the results with those obtained under the graded response model (GRM) for ordered polytomous items in the framework of IRT. The proposed design of using D-scoring with “virtual” binary items generated from polytomous items provides (a) ability scores that are consistent with their GRM counterparts and (b) item category response functions analogous to those obtained under the GRM. This approach provides a unified framework for D-scoring and psychometric analysis of tests with binary and/or polytomous items that can be efficient in different scenarios of educational and psychological assessment.


2019 ◽  
Vol 27 (4) ◽  
pp. 481-502
Author(s):  
Xiang Zhou

Opinion surveys often employ multiple items to measure the respondent’s underlying value, belief, or attitude. To analyze such types of data, researchers have often followed a two-step approach by first constructing a composite measure and then using it in subsequent analysis. This paper presents a class of hierarchical item response models that help integrate measurement and analysis. In this approach, individual responses to multiple items stem from a latent preference, of which both the mean and variance may depend on observed covariates. Compared with the two-step approach, the hierarchical approach reduces bias, increases efficiency, and facilitates direct comparison across surveys covering different sets of items. Moreover, it enables us to investigate not only how preferences differ among groups, vary across regions, and evolve over time, but also levels, patterns, and trends of attitude polarization and ideological constraint. An open-source R package, hIRT, is available for fitting the proposed models.


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