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R. Adams, Mark Wilson, Margaret Wu (1997)
Multilevel Item Response Models: An Approach to Errors in Variables RegressionJournal of Educational Statistics, 22
R. Mislevy (1983)
Item Response Models for Grouped DataJournal of Educational Statistics, 8
R. Bock, M. Lieberman (1970)
Fitting a response model forn dichotomously scored itemsPsychometrika, 35
A. Zwinderman (1991)
A generalized rasch model for manifest predictorsPsychometrika, 56
E. Muraki (1992)
A GENERALIZED PARTIAL CREDIT MODEL: APPLICATION OF AN EM ALGORITHMETS Research Report Series, 1992
H. Hoijtink, A. Boomsma (1996)
Statistical inference based on latent ability estimatesPsychometrika, 61
S. Raudenbush, A. Bryk (1992)
Hierarchical Linear Models: Applications and Data Analysis Methods
G. Fischer (1983)
Logistic latent trait models with linear constraintsPsychometrika, 48
G. Fischer (1995)
The Linear Logistic Test Model
W. Sanders, Sandra Horn (1994)
The tennessee value-added assessment system (TVAAS): Mixed-model methodology in educational assessmentJournal of Personnel Evaluation in Education, 8
G. Fischer (1973)
The linear logistic test model as an instrument in educational researchActa Psychologica, 37
J. Neyman, E. Scott (1948)
Consistent Estimates Based on Partially Consistent ObservationsEconometrica, 16
R. Adams, Mark Wilson, Wen-Chung Wang (1997)
The Multidimensional Random Coefficients Multinomial Logit ModelApplied Psychological Measurement, 21
R. Bock, R. Mislevy (1989)
A HIERARCHICAL ITEM RESPONSE MODEL FOR EDUCATIONAL TESTING
Muraki Muraki (1992)
A generalized partial credit model: Application of an EM algorithmApplied Psychological Measurement, 17
D. Thissen, L. Steinberg, H. Wainer (1993)
Detection of differential item functioning using the parameters of item response models.
P. Cheek, P. McCullagh, J. Nelder (1990)
Generalized Linear Models, 2nd Edn.Applied statistics, 39
R. Bock, M. Zimowski (1997)
Multiple Group IRT
J. Singer (1998)
Using SAS PROC MIXED to Fit Multilevel Models, Hierarchical Models, and Individual Growth ModelsJournal of Educational Statistics, 23
R. Bock, Eiji Murakl, Will Pfeiffenberger (1988)
Item Pool Maintenance in the Presence of Item Parameter Drift.Journal of Educational Measurement, 25
R. Bock, M. Aitkin (1981)
Marginal maximum likelihood estimation of item parameters: Application of an EM algorithmPsychometrika, 46
The hierarchical generalized linear model (HGLM) is presented as an explicit, two‐level formulation of a multilevel item response model. In this paper, it is shown that the HGLM is equivalent to the Rasch model and that, characteristic of the HGLM, person ability can be expressed in the form of random effects rather than parameters. The two‐level item analysis model is presented as a latent regression model with person‐characteristic variables. Furthermore, it is shown that the two‐level HGLM model can be extended to a three‐level latent regression model that permits investigation of the variation of students' performance across groups, such as is found in classrooms and schools, and of the interactive effect of person‐and group‐characteristic variables.
Journal of Educational Measurement – Wiley
Published: Mar 1, 2001
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