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Generating Multiple Imputations for Matrix Sampling Data Analyzed with Item Response Models.

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Bibliographic Details
Title: Generating Multiple Imputations for Matrix Sampling Data Analyzed with Item Response Models.
Language: English
Authors: Thomas, Neal, Gan, Nianci
Source: Journal of Educational and Behavioral Statistics. Win 1997 22(4):425-445.
Peer Reviewed: Y
Page Count: 21
Publication Date: 1997
Document Type: Journal Articles
Reports - Evaluative
Descriptors: Data Analysis, Item Response Theory, Matrices, Maximum Likelihood Statistics, Models, Research Design, Sampling
Assessment and Survey Identifiers: National Assessment of Educational Progress
ISSN: 1076-9986
Abstract: Describes and assesses missing data methods currently used to analyze data from matrix sampling designs implemented by the National Assessment of Educational Progress. Several improved methods are developed, and these models are evaluated using an EM algorithm to obtain maximum likelihood estimates followed by multiple imputation of complete data sets. (SLD)
Entry Date: 1998
Accession Number: EJ564705
Database: ERIC
FullText Text:
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  Data: Describes and assesses missing data methods currently used to analyze data from matrix sampling designs implemented by the National Assessment of Educational Progress. Several improved methods are developed, and these models are evaluated using an EM algorithm to obtain maximum likelihood estimates followed by multiple imputation of complete data sets. (SLD)
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      – Text: English
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        PageCount: 21
        StartPage: 425
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        Type: general
      – SubjectFull: Item Response Theory
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      – SubjectFull: Matrices
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      – SubjectFull: Maximum Likelihood Statistics
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      – SubjectFull: Research Design
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