Effect of Multiple Testing Adjustment in Differential Item Functioning Detection

By Jihye Kim, T. C. Oshima. 2013

64 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/ca92517ab120af31e1b90f70e91c71213fb1b60c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Francis Tuerlinckx
2. David Magis
3. P. De Boeck
4. Katherine E. Masyn

In Management Science and Operations Research

1. P. De Boeck
2. Francis Tuerlinckx
3. David Magis
4. Katherine E. Masyn
5. W. Van den Noortgate

In Statistics and Probability

1. P. De Boeck
2. Francis Tuerlinckx
3. David Magis
4. Katherine E. Masyn
5. Jiyun Zu
6. Patrick C. Kyllonen
7. Karen Nylund-Gibson
8. Alexandre J. S. Morin

Citations in the corpus

• Detection of Differential Item Functioning Using the Lasso Approach (2015, methodology): “First, by fitting Model 1 separately for each item, one enters into the previously mentioned multiple testing issue that was pointed out by Kim and Oshima (2013) among others.”
• Measurement Invariance and Differential Item Functioning in Latent Class Analysis With Stepwise Multiple Indicator Multiple Cause Modeling (2017, methodology): “The Type I error has been shown to be inflated with this approach (Finch, 2005) although there are effective adjustments to α levels or p values to control the Type I error inflation (J. Kim & Oshima, 2013).”