Improving School Accountability Measures
AbstractA growing number of states are using annual school-level test scores as part of their school accountability systems. We highlight an under-appreciated weakness of that approach the imprecision of school-level test score means -- and propose a method for better discerning signal from noise in annual school report cards. For an elementary school of average size in North Carolina, we estimate that 28 percent of the variance in 5th grade reading scores is due to sampling variation and about 10 percent is due to other non-persistent sources. More troubling, we estimate that less than half of the variance in the mean gain in reading performance between 4th and 5th grade is due to persistent differences between schools. We use these estimates of the variance components in an empirical Bayes framework to generate filtered' predictions of school performance, which have much greater predictive value than the mean for a single year. We also identify evidence of within-school heterogeneity in classroom level gains, which suggests the importance of teacher effects.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 8156.
Date of creation: Mar 2001
Date of revision:
Note: CH PE
Contact details of provider:
Postal: National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.
Web page: http://www.nber.org
More information through EDIRC
Find related papers by JEL classification:
- I2 - Health, Education, and Welfare - - Education
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Dean R. Hyslop & Guido W. Imbens, 2000.
"Bias from Classical and Other Forms of Measurement Error,"
NBER Technical Working Papers
0257, National Bureau of Economic Research, Inc.
- Hyslop, Dean R & Imbens, Guido W, 2001. "Bias from Classical and Other Forms of Measurement Error," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 475-81, October.
- Mark McClellan & Douglas Staiger, 1999. "The Quality of Health Care Providers," NBER Working Papers 7327, National Bureau of Economic Research, Inc.
- Chamberlain, Gary, 1984. "Panel data," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 22, pages 1247-1318 Elsevier.
This item has more than 25 citations. To prevent cluttering this page, these citations are listed on a separate page. reading list or among the top items on IDEAS.Access and download statistics
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ().
If references are entirely missing, you can add them using this form.