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ERIC Number: EJ1004579
Record Type: Journal
Publication Date: 2013-Apr
Pages: 26
Abstractor: As Provided
Reference Count: 37
ISSN: ISSN-1076-9986
Contrasting OLS and Quantile Regression Approaches to Student "Growth" Percentiles
Castellano, Katherine Elizabeth; Ho, Andrew Dean
Journal of Educational and Behavioral Statistics, v38 n2 p190-215 Apr 2013
Regression methods can locate student test scores in a conditional distribution, given past scores. This article contrasts and clarifies two approaches to describing these locations in terms of readily interpretable percentile ranks or "conditional status percentile ranks." The first is Betebenner's quantile regression approach that results in "Student Growth Percentiles." The second is an ordinary least squares (OLS) regression approach that involves expressing OLS regression residuals as percentile ranks. The study describes the empirical and conceptual similarity of the two metrics in simulated and real-data scenarios. The metrics contrast in their scale-transformation invariance and sample size requirements but are comparable in their dependence on the number of prior years used as conditioning variables. These results support guidelines for selecting the model that best fits the data and have implications for the interpretations of these percentiles ranks as "growth" measures. (Contains 5 tables and 4 figures.)
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Publication Type: Journal Articles; Reports - Evaluative
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A