方法对比

	Student Growth Percentiles	Educational Growth Curve Modeling
领域	Education	Education
方法族	Regression model	Regression model
起源年份	2009	1987
提出者	Damian W. Betebenner	Anthony Bryk & Stephen Raudenbush; Judith Singer & John Willett
类型	Normative growth description via conditional quantile regression	Longitudinal multilevel model of individual change
开创性文献	Betebenner, D. W. (2009). Norm- and criterion-referenced student growth. Educational Measurement: Issues and Practice, 28(4), 42–51.	Singer, J. D., & Willett, J. B. (2003). Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. Oxford University Press. ISBN: 9780195152968
别名	SGP, Conditional Status Percentiles, Betebenner Growth Percentiles, Quantile-Regression Growth Model	Latent Growth Curve Modeling in Education, Multilevel Growth Models for Achievement, Individual Growth Trajectory Analysis, Learning Trajectory Modeling
相关	4	4
摘要	Student growth percentiles (SGPs) describe how much a student grew academically relative to peers with similar score histories. Introduced by Damian Betebenner in 2009, the method fits a series of conditional quantile regressions of a current test score on prior scores, then reports each student's growth as the percentile rank they occupy within the distribution of students who had the same starting point. A student at the 70th growth percentile grew faster than 70 percent of academic peers, regardless of their absolute achievement level.	Educational growth curve modeling is a longitudinal multilevel technique for describing and explaining how individual students change over time on an outcome such as reading or mathematics achievement. Building on the hierarchical linear models framework formalized by Bryk and Raudenbush (1987) and the applied longitudinal treatment of Singer and Willett (2003), it fits each student a personal trajectory — an intercept and one or more slopes — and then models how those personal growth parameters vary across students and relate to learner characteristics, classrooms, and schools.