scholarly journals Detecting Multidimensional Differential Item Functioning with the Multiple Indicators Multiple Causes Model, the Item Response Theory Likelihood Ratio Test, and Logistic Regression

2017 ◽  
Vol 2 ◽  
Author(s):  
Okan Bulut ◽  
Youngsuk Suh
Keyword(s):  
Logistic Regression ◽  
Item Response Theory ◽  
Differential Item Functioning ◽  
Item Response ◽  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Ratio Test ◽  
Response Theory ◽  
Multiple Indicators ◽  
Item Functioning

scholarly journals МЕТОДИ ТА ПРОГРАМНІ ЗАСОБИ ДОСЛІДЖЕННЯ ВАЛІДНОСТІ ТЕСТОВИХ РЕЗУЛЬТАТІВ ДЛЯ ГРУП ТЕСТОВАНИХ З ПЕВНИМИ ІНДИВІДУАЛЬНИМИ ОСОБЛИВОСТЯМИ

2016 ◽  
Vol 50 (6) ◽  
pp. 165
Author(s):  
Tetiana V. Lisova
Keyword(s):  
Item Response Theory ◽  
Differential Item Functioning ◽  
Item Response ◽  
Real Data ◽  
Necessary Condition ◽  
Ratio Test ◽  
Response Theory ◽  
Item Functioning ◽  
Logistic Regression Method ◽  
The Given

The necessary condition for the presence of biased assessment by some test is differential item functioning in different groups of test takers. The ideas of some statistical methods for detecting Differential Item Functioning are described in the given article. They were developed in the framework of the main approaches to modeling test results: using contingency tables, regression models, multidimensional models and models of Item Response Theory. The Mantel-Haenszel procedure, logistic regression method, SIBTEST and Item Response Theory Likelihood Ratio Test are considered. The characteristics of each method and conditions of their application are specified. Overview of existing free software tools implementing these methods is carried out. Comparisons of these methods are conducted on the example of real data. Also notes that it is appropriate to use several methods simultaneously to reduce the risk of false conclusions.

A Bifactor Multidimensional Item Response Theory Model for Differential Item Functioning Analysis on Testlet-Based Items

2011 ◽  
Vol 35 (8) ◽  
pp. 604-622 ◽  
Author(s):  
Hirotaka Fukuhara ◽  
Akihito Kamata
Keyword(s):  
Item Response Theory ◽  
Differential Item Functioning ◽  
Item Response ◽  
Estimation Method ◽  
Multidimensional Item Response Theory ◽  
Multidimensional Item Response ◽  
Response Theory ◽  
Data Set ◽  
Detection Rates ◽  
Item Functioning

A differential item functioning (DIF) detection method for testlet-based data was proposed and evaluated in this study. The proposed DIF model is an extension of a bifactor multidimensional item response theory (MIRT) model for testlets. Unlike traditional item response theory (IRT) DIF models, the proposed model takes testlet effects into account, thus estimating DIF magnitude appropriately when a test is composed of testlets. A fully Bayesian estimation method was adopted for parameter estimation. The recovery of parameters was evaluated for the proposed DIF model. Simulation results revealed that the proposed bifactor MIRT DIF model produced better estimates of DIF magnitude and higher DIF detection rates than the traditional IRT DIF model for all simulation conditions. A real data analysis was also conducted by applying the proposed DIF model to a statewide reading assessment data set.

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