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Exploring complete school effectiveness via quantile value added
Author(s) -
Page Garritt L.,
Martín Ernesto San,
Orellana Javiera,
González Jorge
Publication year - 2017
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/rssa.12195
Subject(s) - quantile , quantile regression , test (biology) , value (mathematics) , subsidy , econometrics , added value , distribution (mathematics) , control (management) , actuarial science , payment , economics , computer science , statistics , mathematics , finance , artificial intelligence , market economy , paleontology , mathematical analysis , biology
Summary In education studies value added is by and large defined in terms of a test score distribution mean. Therefore, all except a particular summary of the test score distribution is ignored. Developing a value‐added definition that incorporates the entire conditional distribution of students' scores given school effects and control variables would produce a more complete picture of a school's effectiveness and as a result provide more accurate information that could better guide policy decisions. Motivated in part by the current debate surrounding the recent proposal of eliminating co‐payment institutions as part of Chile's education reform, we provide a new definition of value added that is based on the quantiles of the conditional test score distribution. Further, we show that the quantile‐based value added can be estimated within a quantile mixed model regression framework. We apply the methodology to Chilean standardized test data and explore how information garnered facilitates school effectiveness comparisons between public schools and those that are subsidized with and without co‐payments.