scholarly journals The Rasch Model Cannot Reveal Systematic Differential Item Functioning in Single Tests: Subset DIF Analysis as an Alternative Methodology

2021 ◽  
Vol 6 ◽  
Author(s):  
Stephen Humphry ◽  
Paul Montuoro
Keyword(s):  
Differential Item Functioning ◽  
Rasch Model ◽  
Latent Trait ◽  
Parameter Estimate ◽  
Parameter Estimates ◽  
Test Items ◽  
Person Parameter ◽  
Item Functioning ◽  
Alternative Methodology ◽  
The Rasch Model

This article demonstrates that the Rasch model cannot reveal systematic differential item functioning (DIF) in single tests. The person total score is the sufficient statistic for the person parameter estimate, eliminating the possibility for residuals at the test level. An alternative approach is to use subset DIF analysis to search for DIF in item subsets that form the components of the broader latent trait. In this methodology, person parameter estimates are initially calculated using all test items. Then, in separate analyses, these person estimates are compared to the observed means in each subset, and the residuals assessed. As such, this methodology tests the assumption that the person locations in each factor group are invariant across subsets. The first objective is to demonstrate that in single tests differences in factor groups will appear as differences in the mean person estimates and the distributions of these estimates. The second objective is to demonstrate how subset DIF analysis reveals differences between person estimates and the observed means in subsets. Implications for practitioners are discussed.

Methodological aspects of differential item functioning in the Rasch model

2007 ◽  
Vol 10 (3) ◽  
pp. 309-324 ◽  
Author(s):  
John Brodersen ◽  
David Meads ◽  
Svend Kreiner ◽  
Hanne Thorsen ◽  
Lynda Doward ◽  
...  
Keyword(s):  
Differential Item Functioning ◽  
Rasch Model ◽  
Item Functioning ◽  
Methodological Aspects ◽  
The Rasch Model

scholarly journals A Rasch-Validated Version of the Upper Extremity Functional Index for Interval-Level Measurement of Upper Extremity Function

2013 ◽  
Vol 93 (11) ◽  
pp. 1507-1519 ◽  
Author(s):  
Clayon B. Hamilton ◽  
Bert M. Chesworth
Keyword(s):  
Differential Item Functioning ◽  
Upper Extremity ◽  
Rasch Model ◽  
Rasch Analysis ◽  
Measurement Properties ◽  
Functional Index ◽  
Extremity Function ◽  
Item Functioning ◽  
Upper Extremity Function ◽  
The Rasch Model

Background The original 20-item Upper Extremity Functional Index (UEFI) has not undergone Rasch validation. Objective The purpose of this study was to determine whether Rasch analysis supports the UEFI as a measure of a single construct (ie, upper extremity function) and whether a Rasch-validated UEFI has adequate reproducibility for individual-level patient evaluation. Design This was a secondary analysis of data from a repeated-measures study designed to evaluate the measurement properties of the UEFI over a 3-week period. Methods Patients (n=239) with musculoskeletal upper extremity disorders were recruited from 17 physical therapy clinics across 4 Canadian provinces. Rasch analysis of the UEFI measurement properties was performed. If the UEFI did not fit the Rasch model, misfitting patients were deleted, items with poor response structure were corrected, and misfitting items and redundant items were deleted. The impact of differential item functioning on the ability estimate of patients was investigated. Results A 15-item modified UEFI was derived to achieve fit to the Rasch model where the total score was supported as a measure of upper extremity function only. The resultant UEFI-15 interval-level scale (0–100, worst to best state) demonstrated excellent internal consistency (person separation index=0.94) and test-retest reliability (intraclass correlation coefficient [2,1]=.95). The minimal detectable change at the 90% confidence interval was 8.1. Limitations Patients who were ambidextrous or bilaterally affected were excluded to allow for the analysis of differential item functioning due to limb involvement and arm dominance. Conclusion Rasch analysis did not support the validity of the 20-item UEFI. However, the UEFI-15 was a valid and reliable interval-level measure of a single dimension: upper extremity function. Rasch analysis supports using the UEFI-15 in physical therapist practice to quantify upper extremity function in patients with musculoskeletal disorders of the upper extremity.

The MIMIC Model as a Tool for Differential Bundle Functioning Detection

2012 ◽  
Vol 36 (1) ◽  
pp. 40-59 ◽  
Author(s):  
W. Holmes Finch
Keyword(s):  
Group Performance ◽  
Latent Trait ◽  
Monte Carlo Simulation Study ◽  
Mimic Model ◽  
Test Items ◽  
Multiple Indicators ◽  
Item Functioning ◽  
Exploratory Approach ◽  
Alternative Methodology ◽  
Differential Bundle Functioning

Increasingly, researchers interested in identifying potentially biased test items are encouraged to use a confirmatory, rather than exploratory, approach. One such method for confirmatory testing is rooted in differential bundle functioning (DBF), where hypotheses regarding potential differential item functioning (DIF) for sets of items (bundles) are developed based on the substantive nature of the items and expected differences in group performance. Most often, analyses of these bundles for DBF have been conducted using simultaneous item bias test (SIBTEST). The goal of the current study was to introduce an alternative methodology for DBF detection based on the multiple indicators multiple cause (MIMIC) model and to compare this alternative with the traditional SIBTEST-based approach using a Monte Carlo simulation study. The results of this study showed that the MIMIC model performed as well as SIBTEST in ideal conditions, and better when the reference and focal groups had different means on the primary latent trait being measured. In addition, the MIMIC model was more accurate at detecting the presence of DBF for two bundles simultaneously than was SIBTEST. The discussion focuses on recommendations for practitioners.

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