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ERIC Number: ED555706
Record Type: Non-Journal
Publication Date: 2014-Sep
Pages: 55
Abstractor: As Provided
Reference Count: 22
ISBN: N/A
ISSN: N/A
Student Growth Percentiles Based on MIRT: Implications of Calibrated Projection. CRESST Report 842
Monroe, Scott; Cai, Li; Choi, Kilchan
National Center for Research on Evaluation, Standards, and Student Testing (CRESST)
This research concerns a new proposal for calculating student growth percentiles (SGP, Betebenner, 2009). In Betebenner (2009), quantile regression (QR) is used to estimate the SGPs. However, measurement error in the score estimates, which always exists in practice, leads to bias in the QR-­based estimates (Shang, 2012). One way to address this issue is to estimate the SGPs using a modeling framework that can directly account for the measurement error. Multidimensional IRT (MIRT) is one such framework, and the one utilized here. To maximize the generality of the approach, the SNP-­MIRT model (Monroe, 2014), which estimates the shape of the latent variable density, is used to obtain model parameter estimates. These estimates are then used with the calibrated projection linking methodology (Thissen, Varni, et al., 2011, Thissen, Liu, Magnus, & Quinn, 2014, Cai, in press-­a, Cai, in-­press-­b) to produce SGP estimates. The methods are compared using simulated and empirical data.
National Center for Research on Evaluation, Standards, and Student Testing (CRESST). 300 Charles E Young Drive N, GSE&IS Building 3rd Floor, Mailbox 951522, Los Angeles, CA 90095-1522. Tel: 310-206-1532; Fax: 310-825-3883; Web site: http://www.cresst.org
Publication Type: Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: Institute of Education Sciences (ED); Bill and Melinda Gates Foundation
Authoring Institution: National Center for Research on Evaluation, Standards, and Student Testing
IES Funded: Yes
Grant or Contract Numbers: R305D140046