Item difficulty parameter estimation using the idea of the graded response model and computerized adaptive testing

2009 ◽  
Vol 51 (1) ◽  
pp. 1-12 ◽  
Author(s):  
KOKEN OZAKI ◽  
HIDEKI TOYODA
Author(s):  
Cai Xu ◽  
Mark V. Schaverien ◽  
Joani M. Christensen ◽  
Chris J. Sidey-Gibbons

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.


2009 ◽  
Vol 40 (11) ◽  
pp. 1212-1220 ◽  
Author(s):  
Zhao-Sheng LUO ◽  
Xue-Lian OUYANG ◽  
Shu-Qing QI ◽  
Hai-Qi DAI ◽  
Shu-Liang DING

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