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Identification and estimation of bounds on school performance measures: a nonparametric analysis of a mixture model with verification
Author(s) -
Dominitz Jeff,
Sherman Robert P.
Publication year - 2006
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.912
Subject(s) - nonparametric statistics , monotonic function , range (aeronautics) , set (abstract data type) , identification (biology) , computer science , econometrics , test (biology) , data set , mathematics , statistics , artificial intelligence , mathematical analysis , paleontology , botany , biology , programming language , materials science , composite material
This paper identifies and nonparametrically estimates sharp bounds on school performance measures based on test scores that may not be valid for all students. A mixture model with verification is developed to handle this problem. This is a mixture model for data that can be partitioned into two sets, one of which (the so‐called verified set) is more likely to be from the distribution of interest than the other. An administrative classification of each student as English proficient or limited English proficient determines these sets. An analysis of performance measures for some California public schools reveals how verification information and plausible monotonicity restrictions can bound the range of disagreement about school performance based on observed scores. Copyright © 2006 John Wiley & Sons, Ltd.