Efficient and precise Ultra-QuickDASH scale measuring lymphedema impact developed using computerized adaptive testing

Abstract Purpose This study aimed to evaluate and improve the accuracy and efficiency of the QuickDASH for use in assessment of limb function in patients with upper extremity lymphedema using modern psychometric techniques. Method We conducted confirmative factor analysis (CFA) and Mokken analysis to examine the assumption of unidimensionality for IRT model on data from 285 patients who completed the QuickDASH, and then fit the data to Samejima’s graded response model (GRM) and assessed the assumption of local independence of items and calibrated the item responses for CAT simulation. Results Initial CFA and Mokken analyses demonstrated good scalability of items and unidimensionality. However, the local independence of items assumption was violated between items 9 (severity of pain) and 11 (sleeping difficulty due to pain) (Yen’s Q3 = 0.46) and disordered thresholds were evident for item 5 (cutting food). After addressing these breaches of assumptions, the re-analyzed GRM with the remaining 10 items achieved an improved fit. Simulation of CAT administration demonstrated a high correlation between scores on the CAT and the QuickDash (r = 0.98). Items 2 (doing heavy chores) and 8 (limiting work or daily activities) were the most frequently used. The correlation among factor scores derived from the QuickDASH version with 11 items and the Ultra-QuickDASH version with items 2 and 8 was as high as 0.91. Conclusion By administering just these two best performing QuickDash items we can obtain estimates that are very similar to those obtained from the full-length QuickDash without the need for CAT technology.

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Dynamic and Comprehensive Item Selection Strategies for Computerized Adaptive Testing Based on Graded Response Model

Acta Psychologica Sinica ◽

10.3724/sp.j.1041.2012.00400 ◽

2013 ◽

Vol 44 (3) ◽

pp. 400-412 ◽

Cited By ~ 1

Author(s):

Fen LUO ◽

Shu-Liang DING ◽

Xiao-Qing WANG

Keyword(s):

Computerized Adaptive Testing ◽

Adaptive Testing ◽

Item Selection ◽

Response Model ◽

Graded Response Model ◽

Selection Strategies ◽

Graded Response

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Application of the Graded Response Model to a Scale of Empathic Behavior

International journal of psychological research ◽

10.21500/20112084.3753 ◽

2019 ◽

Vol 12 (1) ◽

pp. 49-56

Author(s):

Sofía E. Auné ◽

Facundo J. P. Abal ◽

Horacio F. Attorresi

Keyword(s):

Differential Item Functioning ◽

Response Model ◽

Graded Response Model ◽

Local Independence ◽

Information Function ◽

Item Functioning ◽

Graded Response ◽

Low Levels ◽

Gender Differential ◽

Empathic Behavior

The results obtained from the application of the Graded Response Model (GRM) to the items of the Scale of Empathic Behavior whose authors are Auné, Abal and Attorresi (2017) are presented. The sample was obtained by accessibility and consisted of Argentine university students (80% Women). None of the items presented uniform or non-uniform gender differential item functioning. The GRM assumptions of local independence and unidimensionality were successfully corroborated. Threshold parameters tended to be located at low levels of the trait scale, whereas discrimination parameters were high. The analysis of the information function showed acceptable precision in low and middle empathic behavior level.

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Using the Stan Program for Bayesian Item Response Theory

Educational and Psychological Measurement ◽

10.1177/0013164417693666 ◽

2017 ◽

Vol 78 (3) ◽

pp. 384-408 ◽

Cited By ~ 19

Author(s):

Yong Luo ◽

Hong Jiao

Keyword(s):

Item Response Theory ◽

Item Response ◽

Model Comparison ◽

Response Model ◽

Graded Response Model ◽

Response Theory ◽

Irt Model ◽

Irt Models ◽

Nominal Response Model ◽

Graded Response

Stan is a new Bayesian statistical software program that implements the powerful and efficient Hamiltonian Monte Carlo (HMC) algorithm. To date there is not a source that systematically provides Stan code for various item response theory (IRT) models. This article provides Stan code for three representative IRT models, including the three-parameter logistic IRT model, the graded response model, and the nominal response model. We demonstrate how IRT model comparison can be conducted with Stan and how the provided Stan code for simple IRT models can be easily extended to their multidimensional and multilevel cases.

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Matching IRT Models to Patient-Reported Outcomes Constructs: The Graded Response and Log-Logistic Models for Scaling Depression

Psychometrika ◽

10.1007/s11336-021-09802-0 ◽

2021 ◽

Author(s):

Steven P. Reise ◽

Han Du ◽

Emily F. Wong ◽

Anne S. Hubbard ◽

Mark G. Haviland

Keyword(s):

Cognitive Ability ◽

Logistic Model ◽

Patient Reported Outcomes ◽

Logistic Models ◽

Response Model ◽

Graded Response Model ◽

Irt Model ◽

Irt Models ◽

Graded Response ◽

Patient Reported

AbstractItem response theory (IRT) model applications extend well beyond cognitive ability testing, and various patient-reported outcomes (PRO) measures are among the more prominent examples. PRO (and like) constructs differ from cognitive ability constructs in many ways, and these differences have model fitting implications. With a few notable exceptions, however, most IRT applications to PRO constructs rely on traditional IRT models, such as the graded response model. We review some notable differences between cognitive and PRO constructs and how these differences can present challenges for traditional IRT model applications. We then apply two models (the traditional graded response model and an alternative log-logistic model) to depression measure data drawn from the Patient-Reported Outcomes Measurement Information System project. We do not claim that one model is “a better fit” or more “valid” than the other; rather, we show that the log-logistic model may be more consistent with the construct of depression as a unipolar phenomenon. Clearly, the graded response and log-logistic models can lead to different conclusions about the psychometrics of an instrument and the scaling of individual differences. We underscore, too, that, in general, explorations of which model may be more appropriate cannot be decided only by fit index comparisons; these decisions may require the integration of psychometrics with theory and research findings on the construct of interest.

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