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A multilevel latent growth curve approach to predicting student proficiency

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Abstract

Value-added models and growth-based accountability aim to evaluate school’s performance based on student growth in learning. The current focus is on linking the results from value-added models to the ones from growth-based accountability systems including Adequate Yearly Progress decisions mandated by No Child Left Behind. We present a new statistical approach that extends the current value-added modeling possibilities and focuses on using latent longitudinal growth curves to estimate the probabilities of students reaching proficiency. The aim is to utilize time-series measures of student achievement scores to estimate latent growth curves and use them as predictors of a dichotomous outcome, such as proficiency or passing a high-stakes exam, within a single multilevel longitudinal model. We illustrated this method through analyzing a three-year data set of longitudinal achievement scores and California High School Exit Exam scores from a large urban school district. This latent variable growth logistic model is useful for (1) early identification of students at risk of failing or of those who are most in need; (2) a validation or/and adequacy of student growth over years with relation to distal outcome criteria; (3) evaluation of a longitudinal intervention study.

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Authors and Affiliations

  1. American Institutes for Research, 1000 Thomas Jefferson St, NW, Washington, DC, 20007, USA

    Kilchan Choi

  2. CRESST/University of California, Los Angeles, CA, USA

    Pete Goldschmidt

Authors
  1. Kilchan Choi
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  2. Pete Goldschmidt
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Correspondence to Kilchan Choi.

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Choi, K., Goldschmidt, P. A multilevel latent growth curve approach to predicting student proficiency. Asia Pacific Educ. Rev. 13, 199–208 (2012). https://doi.org/10.1007/s12564-011-9191-8

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  • DOI: https://doi.org/10.1007/s12564-011-9191-8

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