High-dimensional maximum marginal likelihood item factor analysis by adaptive quadrature

By S. Schilling, R. D. Bock. 2005

195 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/b881a2d39ebe7db9892b9a78efecb4e9c36de06e

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. S. Reise
2. Li Cai
3. R. Chalmers
4. Dennis A. Revicki
5. S. Haberman
6. Matthias von Davier
7. Sandip Sinharay
8. Xinming An

In Management Science and Operations Research

1. S. Reise
2. Li Cai
3. R. Chalmers
4. Sandip Sinharay
5. Matthias von Davier
6. Dennis A. Revicki
7. Yi-Hsuan Lee
8. Xinming An

In Statistics and Probability

1. Li Cai
2. Sandip Sinharay
3. S. Haberman
4. S. Reise
5. Matthias von Davier
6. Xinming An
7. R. Chalmers
8. Peter M. Bentler

Citations in the corpus

• A Modified MCEM Approach to Full Information Binary Factor Analysis (2008, methodology): “They found this to be a successful methodology, although it has been suggested more recently that adaptive quadrature would be more effective (Schilling and Bock 2005).”
• Metropolis-Hastings Robbins-Monro Algorithm for Confirmatory Item Factor Analysis (2010, methodology): “If the latent variables are believed to be continuous, a statistical model that is often applied in psychometric data analysis is item factor analysis (IFA; Bock, Gibbons, & Muraki, 1988; Meng & Schilling, 1996; Mislevy, 1986; Schilling & Bock, 2005; Wirth & Edwards, 2007).”
• Reporting of Subscores Using Multidimensional Item Response Theory (2010, methodology): “Required quadratures are performed by adaptive multivariate Gauss–Hermite integration ( Schilling & Bock, 2005 ).”
• A GENERAL PROGRAM FOR ITEM‐RESPONSE ANALYSIS THAT EMPLOYS THE STABILIZED NEWTON–RAPHSON ALGORITHM (2013, background): “In IRT, the number Qk of quadrature points needed for 1 ≤ k ≤ K for adequate integral approximation via adaptive quadrature is normally quite small, especially for relatively high dimension K (Schilling & Bock, 2005).”
• COMPARISON OF MULTIDIMENSIONAL ITEM RESPONSE MODELS: MULTIVARIATE NORMAL ABILITY DISTRIBUTIONS VERSUS MULTIVARIATE POLYTOMOUS ABILITY DISTRIBUTIONS (2008, result): “The relatively small number of points required is consistent with existing literature (Schilling & Bock, 2005).”
• Handbook of Item Response Theory Modeling : Applications to Typical Performance Assessment (2014, background): “Adaptive quadrature helps somewhat by requiring fewer points than fixed quadrature rules (see, e.g., Schilling & Bock, 2005), but does not solve the problem completely.”
• mirt: A Multidimensional Item Response Theory Package for the R Environment (2012, background): “A partial solution for a moderate number of dimensions was described by Schilling and Bock (2005), where the authors demonstrated that adaptive quadrature could be used for better accuracy when a smaller number of quadratures per dimension is used, but the problem of high-dimensional solutions…”