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MSAEM Estimation for Confirmatory Multidimensional Four-Parameter Normal Ogive Models
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Jia Liu; Xiangbin Meng; Gongjun Xu; Wei Gao; Ningzhong Shi – Grantee Submission, 2024
In this paper, we develop a mixed stochastic approximation expectation-maximization (MSAEM) algorithm coupled with a Gibbs sampler to compute the marginalized maximum a posteriori estimate (MMAPE) of a confirmatory multidimensional four-parameter normal ogive (M4PNO) model. The proposed MSAEM algorithm not only has the computational advantages of…
Descriptors: Algorithms, Achievement Tests, International Assessment, Foreign Countries
MSAEM Estimation for Confirmatory Multidimensional Four-Parameter Normal Ogive Models
Peer reviewed Peer reviewed
Direct linkDirect link
Jia Liu; Xiangbin Meng; Gongjun Xu; Wei Gao; Ningzhong Shi – Journal of Educational Measurement, 2024
In this paper, we develop a mixed stochastic approximation expectation-maximization (MSAEM) algorithm coupled with a Gibbs sampler to compute the marginalized maximum a posteriori estimate (MMAPE) of a confirmatory multidimensional four-parameter normal ogive (M4PNO) model. The proposed MSAEM algorithm not only has the computational advantages of…
Descriptors: Algorithms, Achievement Tests, Foreign Countries, International Assessment
The Effects Of Random Error And Subsampling Of Dimensions On Recovery Of Configurations By Non-Metric Multidimensional Scaling
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Cohen, Harvey S.; Jones, Lawrence E. – Psychometrika, 1974
Descriptors: Algorithms, Correlation, Models, Multidimensional Scaling
Component Analysis with Different Sets of Constraints on Different Dimensions.
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Takane, Yoshio; And Others – Psychometrika, 1995
A model is proposed in which different sets of linear constraints are imposed on different dimensions in component analysis and classical multidimensional scaling frameworks. An algorithm is presented for fitting the model to the data by least squares. Examples demonstrate the method. (SLD)
Descriptors: Algorithms, Equations (Mathematics), Factor Analysis, Least Squares Statistics
Linear Programming Techniques For Multidimensional Analysis Of Preferences
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Srinivasan, V.; Shocker, Allan D. – Psychometrika, 1973
This paper offers a new methodology for analyzing individual differences in preference judgments with regard to a set of stimuli. (Author)
Descriptors: Algorithms, Goodness of Fit, Models, Multidimensional Scaling