Abstract
It seems that the application of value added modeling (VAM) in educational settings has been gaining momentum in the past decade or so due to the interest in using test scores to evaluate teachers or schools, and currently myriads of VAM models are available for VAM researchers and practitioners. Despite the large number of VAM models, McCaffrey et al. (2004) summarized the relations among them and concluded that many can be viewed as special cases of persistence models. In persistence models, student scores are calculated based on the sum of teacher effects across years. Since different students may change teachers every year and have different membership in multiple group units, such models are also referred to as “multiple membership” models (Browne et al. 2001 Rasbash and Browne 2001). Persistence models differ from each other in the value of the persistence parameter, which, ranging from 0 to 1, denotes how teacher effects at the current year persist into the subsequent years, may it be vanished, undiminished, or diminished. The Variable Persistence (VP) model (Lockwood et al. 2007 McCaffrey et al. 2004) had been considered more flexible due to its free estimation of the persistence parameter, while other persistence models constrain its value to be either 0 or 1.
Keywords
- Teacher Effect
- Persistence Model
- Informative Prior Distribution
- Unstructured Covariance Matrix
- Multiple Membership
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Luo, Y., Jiao, H., Lissitz, R. (2015). An Empirical Study of the Impact of the Choice of Persistence Models in Value Added Modeling upon Teacher Effect Estimates. In: van der Ark, L., Bolt, D., Wang, WC., Douglas, J., Chow, SM. (eds) Quantitative Psychology Research. Springer Proceedings in Mathematics & Statistics, vol 140. Springer, Cham. https://doi.org/10.1007/978-3-319-19977-1_11
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DOI: https://doi.org/10.1007/978-3-319-19977-1_11
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