Background
References
Appendix
On the Precision of MCAS SGPs
Damian W. Betebenner
National Center for the Improvement of Educational Assessment
Dover, NH
Massachusetts TAC
Cambridge, Massachusetts
April 9th, 2013
Damian W. Betebenner On the Precision of MCAS SGPs
Background
Quick Student Growth Percentiles
References
Model & Software for Student Growth Percentiles
Appendix
Background
Based upon feedback from stakeholders and to be consistent with
current reporting of precision of individual level data, over the past
year Massachusetts DESE has investigated methodologies for
reporting precision.
October 2012: Derived and reported precision results from
simulations based upon resampling at the state level.
Fall 2012: State to transition to anchored SGP analyses (i.e.,
baseline referenenced) from previous cohort based analyses.
April 2013: Derived and reported precision results based upon
resampling at the individual level for both cohort and anchored
analyses.
Damian W. Betebenner On the Precision of MCAS SGPs
Background
Quick Student Growth Percentiles
References
Model & Software for Student Growth Percentiles
Appendix
Student Growth Percentiles
Should we be surprised with a child’s current achievement given their prior
achievement?
Given a student’s prior scale scores and the associated conditional
density, their current scale score corresponds to a percentile of that
conditional distribution.
This percentile is the student’s growth percentile.
Growth percentiles are closely related to estimating the probability of
observing a student’s current achievement taking account of their
past achievement:
Pr(Current Achievement |Past Achievement ).
As such, growth percentiles describe the rarity of a student’s current
achievement conditional upon their prior achievement.
Damian W. Betebenner On the Precision of MCAS SGPs
Background
Quick Student Growth Percentiles
References
Model & Software for Student Growth Percentiles
Appendix
Student Growth Percentiles
Should we be surprised with a child’s current achievement given their
prior achievement?
Student growth percentiles answer this question.
Consider a low achieving student with 50th percentile growth and a
high achieving student with 50th percentile growth.
The low achieving student grew at a rate exceeding 50 percent of
similar students.
The high achieving student grew at a rate exceeding just 50 percent of
similar students.
The low achiever’s growth is probabilistically equivalent to the high
achiever’s.
Equally rare does not imply equally good!
Judgments about the adequacy of student growth require external
criteria. That is, criterion referencing.
Damian W. Betebenner On the Precision of MCAS SGPs
Background
Quick Student Growth Percentiles
References
Model & Software for Student Growth Percentiles
Appendix
Model for Student Growth Percentiles
Student growth percentiles are calculated using quantile regression
with B-spline smoothing, a generalized additive
model [Betebenner, 2008, Betebenner, 2009].
Quantile regression is used to model the complete distribution of the
response variable (current achievement)
B-spline smoothing is uses to accommodate non-linearity and
heteroscedasticity of the data.
The model/method is descriptive and all about data fit—it’s a data
mining procedure.
The model quantifies distance = rate · time probabilistically.
Damian W. Betebenner On the Precision of MCAS SGPs
Background
Quick Student Growth Percentiles
References
Model & Software for Student Growth Percentiles
Appendix
Model for Student Growth Percentiles
Conditional Decile Regression Curves Conditional Decile Regression Lines
2006−07 Math: Grade 5 versus 6 2006−07 Math: Grade 5 versus 6
1000 Unsatisfactory Partially Prof. 1000 Unsatisfactory Partially Prof.
Proficient Proficient
Partially Prof. Proficient Advanced Partially Prof. Proficient Advanced
Grade 6 Scale Score Grade 6 Scale Score
Grade 5 Scale Score Grade 5 Scale Score
Damian W. Betebenner On the Precision of MCAS SGPs
Background
Quick Student Growth Percentiles
References
Model & Software for Student Growth Percentiles
Appendix
Software for Student Growth Percentiles
Student Growth Percentiles and Percentile Growth Trajectories are
calculated using the open source R Software
Environment [R Development Core Team, 2013] and the SGP
package [Betebenner et al., 2013]
Creative Commons Share-and-Share Alike Licenses:
In working with state departments of education, the goal is to build
state capacity so states can perform and validate their own analyses
using software available and easily used by the state.
Software is open source and developed on the GitHub collaborative
version control system: https://github.com/dbetebenner/SGP
CSEMs (and other meta-data) for MCAS is maintained within the
package and developed/maintained using the GitHub collaborative
version control system:
https://github.com/dbetebenner/SGPstateData
Damian W. Betebenner On the Precision of MCAS SGPs
Background
Quick Student Growth Percentiles
References
Model & Software for Student Growth Percentiles
Appendix
Simulation Methodology
SGP precision is calculated by creating 100 SGPs for each student
by perturbing current and immediate prior growth theta scores.
Growth theta scores are linear transformations of theta scores.
CSEMs associated with theta scores are transformed to growth theta
scale and used to simulated growth theta scores for each student.
QYt (τ|Yt −1 , . . . , Y1 ) = φij (Yj )βij (τ), (1)
Ytperturbed = Yt + where t ∼ N (0, CSEMYt )
Yt −1perturbed = Yt −1 + where t −1 ∼ N (0, CSEMYt −1 )
Precision estimates are derived for both cohort and anchored SGPs.
Results are provided in the following figures and in Table 2 of the
associated paper.
Damian W. Betebenner On the Precision of MCAS SGPs
SGP Standard Error by SGP and Prior Growth Theta SGP
ELA Grade 5 Std Error
Growth Theta (Prior)
SGP
SGP Standard Error by SGP and Current Growth Theta SGP
ELA Grade 5 Std Error
Growth Theta (Current)
SGP
SGP Standard Error by SGP and Prior Growth Theta SGP
ELA Grade 6 Std Error
Growth Theta (Prior)
SGP
SGP Standard Error by SGP and Current Growth Theta SGP
ELA Grade 6 Std Error
Growth Theta (Current)
SGP
SGP Standard Error by SGP and Prior Growth Theta SGP
ELA Grade 7 Std Error
Growth Theta (Prior)
SGP
SGP Standard Error by SGP and Current Growth Theta SGP
ELA Grade 7 Std Error
Growth Theta (Current)
SGP
SGP Standard Error by SGP and Prior Growth Theta SGP
ELA Grade 8 Std Error
Growth Theta (Prior)
SGP
SGP Standard Error by SGP and Current Growth Theta SGP
ELA Grade 8 Std Error
Growth Theta (Current)
SGP
SGP Standard Error by SGP and Prior Growth Theta SGP
ELA Grade 10 Std Error
Growth Theta (Prior)
SGP
SGP Standard Error by SGP and Current Growth Theta SGP
ELA Grade 10 Std Error
Growth Theta (Current)
SGP
SGP Standard Error by SGP and Prior Growth Theta
Growth Theta (Prior)
SGP
SGP Standard Error by SGP and Current Growth Theta
Growth Theta (Prior)
SGP
SGP Standard Error by SGP and Prior Growth Theta SGP
MATHEMATICS Grade 5 Std Error
Growth Theta (Prior)
SGP
SGP Standard Error by SGP and Current Growth Theta SGP
MATHEMATICS Grade 5 Std Error
Growth Theta (Current)
SGP
SGP Standard Error by SGP and Prior Growth Theta SGP
MATHEMATICS Grade 6 Std Error
Growth Theta (Prior)
SGP
SGP Standard Error by SGP and Current Growth Theta SGP
MATHEMATICS Grade 6 Std Error
Growth Theta (Current)
SGP
SGP Standard Error by SGP and Prior Growth Theta SGP
MATHEMATICS Grade 7 Std Error
Growth Theta (Prior)
SGP
SGP Standard Error by SGP and Current Growth Theta SGP
MATHEMATICS Grade 7 Std Error
Growth Theta (Current)
SGP
SGP Standard Error by SGP and Prior Growth Theta SGP
MATHEMATICS Grade 8 Std Error
Growth Theta (Prior)
SGP
SGP Standard Error by SGP and Current Growth Theta SGP
MATHEMATICS Grade 8 Std Error
Growth Theta (Current)
SGP
SGP Standard Error by SGP and Prior Growth Theta SGP
MATHEMATICS Grade 10 Std Error
Growth Theta (Prior)
SGP
SGP Standard Error by SGP and Current Growth Theta SGP
MATHEMATICS Grade 10 Std Error
Growth Theta (Current)
SGP
SGP Standard Error by SGP and Prior Growth Theta
Growth Theta (Prior)
SGP
SGP Standard Error by SGP and Current Growth Theta
Growth Theta (Prior)
SGP
Background
References
Appendix
References
Betebenner, D. W. (2008).
Toward a normative understanding of student growth.
In Ryan, K. E. and Shepard, L. A., editors, The Future of Test-Based
Educational Accountability, pages 155–170. Taylor & Francis, New
York.
Betebenner, D. W. (2009).
Norm- and criterion-referenced student growth.
Educational Measurement: Issues and Practice, 28(4):42–51.
Betebenner, D. W., Van Iwaarden, A., and Domingue, B. (2013).
SGP: An R Package for the Calculation and Visualization of Student
Growth Percentiles & Percentile Growth Trajectories.
R package version 1.0-4.0.
R Development Core Team (2013).
R: A language and environment for statistical computing.
R Foundation for Statistical Computing, Vienna, Austria.
Damian W. Betebenner On the Precision of MCAS SGPs
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About this copy
On the Precision of MCAS SGPs: Massachusetts TAC — Massachusetts Department of Education, April 9, 2013.
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