The Rasch Testlet Model

By Wen-Chung Wang, Mark R. Wilson. 2005

197 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/8bf5009d467d9d653cad9c26793d9291f39558a5

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. Sophia Rabe-Hesketh
2. Minjeong Jeon
3. R. D. Bock
4. Donald Hedeker
5. Frank Rijmen
6. Christine E. DeMars
7. Li Cai

In Management Science and Operations Research

1. Sophia Rabe-Hesketh
2. Minjeong Jeon
3. Donald Hedeker
4. Frank Rijmen
5. Angela Stover
6. A. Skrondal
7. Christine E. DeMars
8. Y. Copur-Gencturk

In Statistics and Probability

1. Sophia Rabe-Hesketh
2. Donald Hedeker
3. R. D. Bock
4. Minjeong Jeon
5. David J. Kupfer
6. Robert D. Gibbons
7. Frank Rijmen
8. A. Skrondal

Citations in the corpus

• Modeling Differential Item Functioning Using a Generalization of the Multiple-Group Bifactor Model (2013, methodology): “For the bifactor model, Fukuhara and Kamata (2011) allowed only the mean of the general dimension to differ between groups, whereas Wang and Wilson (2005) and Wang, Bradlow, Wainer, and Muller (2008) did not allow for any group differences in the latent variable distributions.”
• Full-Information Item Bifactor Analysis of Graded Response Data (2007, background): “More recently, Wang and Wilson (2005) extended the ‘‘Rasch testlet model’’ to the graded response case.”
• Critical Values for Yen’s Q3: Identification of Local Dependence in the Rasch Model Using Residual Correlations (2017, unknown): “Other approaches include using testlet models (Wang & Wilson, 2005; Wilson & Adams, 1995) or a bifactor model (Reise, 2012).”