scholarly journals Differential Item Functioning (DIF) in composite health measurement scale: Recommendations for characterizing DIF with meaningful consequences within the Rasch model framework

PLoS ONE ◽  
2019 ◽  
Vol 14 (4) ◽  
pp. e0215073 ◽  
Author(s):  
Alexandra Rouquette ◽  
Jean-Benoit Hardouin ◽  
Alexis Vanhaesebrouck ◽  
Véronique Sébille ◽  
Joël Coste
Keyword(s):  
Differential Item Functioning ◽  
Rasch Model ◽  
Measurement Scale ◽  
Health Measurement ◽  
Model Framework ◽  
Item Functioning ◽  
The Rasch Model ◽  
Health Measurement Scale

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.

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.

Determination of Gender Differential Item Functioning in Tegal Students' Scientific Literacy Skills with Integrated Science (SLiSIS) Test Using Rasch Model

2021 ◽  
Vol 10 (2) ◽  
pp. 270-281
Author(s):  
P. Susongko ◽  
Y. Arfiani ◽  
M. Kusuma
Keyword(s):  
High School Students ◽  
Construct Validity ◽  
Differential Item Functioning ◽  
Rasch Model ◽  
Scientific Literacy ◽  
Wald Test ◽  
Literacy Skills ◽  
School Students ◽  
Integrated Science ◽  
Item Functioning

The emergence of Differential Item Functioning (DIF) indicates an external bias in an item. This study aims to identify items at scientific literacy skills with integrated science (SLiSIS) test that experience DIF based on gender. Moreover, it is analyzed the emergence of DIF, especially related to the test construct measured, and concluded on how far the validity of the SLiSIS test from the construct validity of consequential type. The study was conducted with a quantitative approach by using a survey or non-experimental methods. The samples of this study were the responses of the SLiSIS test taken from 310 eleventh-grade high school students in the science program from SMA 2 and SMA 3 Tegal. The DIF analysis technique used Wald Test with the Rasch model. From the findings, eight items contained DIF in a 95 % level of trust. In 99 % level of trust, three items contained DIF, items 1, 6, and 38 or 7%. The DIF is caused by differences in test-takers ability following the measured construct, so it is not a test bias. Thus, the emergence of DIF on SLiSIS test items does not threaten the construct validity of the consequential type.

Sign in / Sign up

Export Citation Format

Share Document