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A Conventional Estimator of AYP Status A student’s score on a test can be assumed to be a random variable (recall the ideas of true score and measurement error) Further, an estimate of the proportion of proficient students depends on all the individual students’ scores, each having measurement error. It follows then that pˆi also has measurement error and is a random quantity. 14
A Conventional Estimator of AYP Status Now consider k = 1,2, …,K subgroups within an individual school i, each having m ik students, and each having proportion proficient p ik Again, p ik is unobserved and estimated by pˆik , which is 0 compared to the target p k to determine AYP status (here 0 pk0is p k specified as a group-specific target, but this is not necessary). Defining the AYP score as a comparative quantity, for school i, K m ik 0 AYP score 100 pˆ ik pk , m which is compared to the threshold value of 0. School i is declared to have met AYP if the score is equal or greater than 0. k 1 i 15
Page 1 and 2: An Alternate Estimate of AYP A Fora
Page 3 and 4: NCLB and Adequate Yearly Progress
Page 5 and 6: The Demands of NCLB NCLB pushed ch
Page 7 and 8: To Facilitate the Present Discussio
Page 9 and 10: State Solutions for Classification
Page 11 and 12: The Challenge of Heterogeneity The
Page 13: A Conventional Estimator of AYP Sta
Page 17 and 18: A Conventional Estimator of AYP Sta
Page 19 and 20: Conventional Estimation of AYP Unce
Page 21 and 22: Conventional Estimation of AYP Unce
Page 23 and 24: A Bayes Estimator of AYP Status Fi
Page 25 and 26: A Bayes Estimator of AYP Status Th
Page 27 and 28: Performance of the Estimators 27
Page 29 and 30: Flexibility of the Bayes Estimator
Page 31 and 32: References Brennan, R. L. and Kane,
Page 33: Software for Bayesian Approach App

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