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Measuring Achievement with Latent Structure Models

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Alternative Approaches to the Assessment of Achievement

Part of the book series: Evaluation in Education and Human Services ((EEHS,volume 16))

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Abstract

The basic assumption in latent class models designed to measure achievement is that a student can be described as knowing or not knowing the answer to a test item, and that inferences about the student’s ability level should take this notion into account. The goals of a test might be to determine how many of the items an examinee knows, which items are known or which are not known, or what proportion of items among a domain of items are known. The problem is that examinees might give the correct response when they do not know, or they might carelessly give the wrong response when they actually do know. Latent class models can be used in an attempt to measure and correct the effects of these errors when addressing a particular measurement problem. Even if some other model is ultimately preferred, such as a latent trait model, latent class models are potentially useful.

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Authors and Affiliations

  1. Center for Student Testing, Evaluation and Standards, Graduate School of Education, University of California Los Angeles, Los Angeles, CA, 90024, USA

    David L. McArthur PhD

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  1. David L. McArthur PhD
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Editors and Affiliations

  1. Center for Student Testing, Evaluation and Standards, Graduate School of Education, University of California Los Angeles, Los Angeles, CA, 90024, USA

    David L. McArthur PhD

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© 1987 Kluwer Academic Publishers

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McArthur, D.L. (1987). Measuring Achievement with Latent Structure Models. In: McArthur, D.L. (eds) Alternative Approaches to the Assessment of Achievement. Evaluation in Education and Human Services, vol 16. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-3257-9_6

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  • DOI: https://doi.org/10.1007/978-94-009-3257-9_6

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-010-7961-7

  • Online ISBN: 978-94-009-3257-9

  • eBook Packages: Springer Book Archive

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