An Optimal Unbiased Classification Rule for Mastery Testing Based on Longitudinal Data.

Tan, E. S.; And Others

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Peer reviewed

ERIC Number: EJ511087

Record Type: CIJE

Publication Date: 1995

Pages: N/A

Abstractor: N/A

ISBN: N/A

ISSN: ISSN-0013-1644

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An Optimal Unbiased Classification Rule for Mastery Testing Based on Longitudinal Data.

Tan, E. S.; And Others

Educational and Psychological Measurement, v55 n4 p595-612 Aug 1995

An optimal unbiased classification rule is proposed based on a longitudinal model for the measurement of change in ability. In general, the rule predicts future level of knowledge by using information about level of knowledge at entrance, its rate of growth, and the amount of within-individual variation. (SLD)

Descriptors: Ability, Change, Classification, Individual Differences, Knowledge Level, Longitudinal Studies, Mastery Tests, Measurement Techniques, Models, Prediction, Statistical Bias

Publication Type: Reports - Evaluative; Journal Articles

Education Level: N/A

Audience: N/A

Language: English

Sponsor: N/A

Authoring Institution: N/A

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Author Affiliations: N/A