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An item response theory approach to longitudinal analysis with application to summer setback in preschool language/literacy

Kim, Sunhee; Camilli, Gregory

Background: As the popularity of classroom observations has increased, they have been implemented in many longitudinal studies with large probability samples. Given the complexity of longitudinal measurements, there is a need for tools to investigate both growth and the properties of the measurement scale. Methods: A practical IRT model with an embedded growth model is illustrated to examine the psychometric characteristics of classroom assessments for preschool children, and also to show how nonlinear learning over time can be investigated. This approach is applied to data collected for the Academic Rating Scale (ARS) in the literacy domain, which was administered on four occasions over two years. Results: The model enabled an effective illustration of overall and individual gains over two academic years. In particular, a significant de-acceleration in latent literacy skills during summer was observed. The results also provided psychometric support for the argument that ARS literacy can be used to assess developmental skill levels consistent with theories of early literacy acquisition. Conclusions: The proposed IRT approach provided growth parameters that are estimated directly, rather than obtaining these coefficients from estimated growth scores—which may result in biased and inconsistent estimates of growth parameters. The model is also capable of simultaneously representing parameters of items and persons.

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Title
Large-scale Assessments in Education
DOI
https://doi.org/10.1186/2196-0739-2-1

More About This Work

Academic Units
Medicine
Published Here
September 23, 2014
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