Improving School Accountability Measures
A 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.
|Date of creation:||Mar 2001|
|Date of revision:|
|Publication status:||published as Kane, Thomas J. and Douglas O. Staiger. "The Promise And Pitfalls Of Using Imprecise School Accountability Measures," Journal of Economic Perspectives, 2002, v16(4,Fall), 91-114.|
|Contact details of provider:|| Postal: National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.|
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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.:
- 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.
- 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.
- 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.
- Mark McClellan & Douglas Staiger, 1999. "The Quality of Health Care Providers," NBER Working Papers 7327, National Bureau of Economic Research, Inc.
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