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Physical Review Physics Education Research
written by Sarah Stephens and Michael Marder
[This paper is part of the Focused Collection on Quantitative Methods in PER: A Critical Examination.] The education of physicists requires annual progress in mathematics through grades K–12, continuing on to high school physics, undergraduate instruction, and often many years of postgraduate study. The physics and physics education research communities will need to study the complete educational system to increase performance and participation, especially for underrepresented groups. Analysis of long-term student outcomes and policy impacts requires predictive longitudinal techniques. We begin with concepts from fluid and statistical mechanics--trajectories and streamlines--to visualize longitudinal outcomes and make predictions. Drawing on coarse-graining procedures to model the movement of particles in a fluid, the longitudinal data are sorted into score bins to depict the flow of scores through time: trajectories depict the average scores over time for initial score bins and streamlines provide an approximate way to calculate the flow of student scores over many years based on only two consecutive years of data. However, due to the partially stochastic nature of observed scores, the coarse-graining procedure that sorts students into score bins amplifies a statistical phenomenon known as regression to the mean (RTM). As a result, streamlines do not provide an accurate prediction for the future performance of students. Here we discuss a new coarse-graining procedure, regression-corrected (RC) grouping, which reduces RTM in the streamlines. We explain the idea of RC streamlines through a toy model of freshman physics performance and then apply them to the realistic setting of the Texas State Longitudinal Data System, which contains standardized testing data for students throughout primary and secondary school since 2003. RC streamlines can identify effects of academic interventions on a time scale, which we illustrate through a particular policy intervention.
Physical Review Physics Education Research: Volume 15, Issue 2, Pages 020109
Subjects Levels Resource Types
Education Foundations
- Assessment
= Longitudinal
- Research Design & Methodology
= Data
= Statistics
Fluid Mechanics
- General
General Physics
- Physics Education Research
Other Sciences
- Mathematics
- Graduate/Professional
- Middle School
- High School
- Lower Undergraduate
- Upper Undergraduate
- Reference Material
= Article
= Nonfiction Reference
Intended Users Formats Ratings
- Researchers
- Professional/Practitioners
- application/pdf
- text/html
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Access Rights:
Free access
License:
This material is released under a Creative Commons Attribution 3.0 license.
Rights Holder:
American Physical Society
DOI:
10.1103/PhysRevPhysEducRes.15.020109
NSF Numbers:
DUE-1557273
DUE-1557295
DUE-1557294
DUE-155727
DUE-1557286
DUE-1557290
Keywords:
longitudinal outcomes, quantitative educational research, statistical mechanics, streamline plotting
Record Creator:
Metadata instance created August 23, 2019 by Sam McKagan
Record Updated:
March 8, 2023 by Caroline Hall
Last Update
when Cataloged:
July 3, 2019
Other Collections:

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Record Link
AIP Format
S. Stephens and M. Marder, , Phys. Rev. Phys. Educ. Res. 15 (2), 020109 (2019), WWW Document, (https://doi.org/10.1103/PhysRevPhysEducRes.15.020109).
AJP/PRST-PER
S. Stephens and M. Marder, Longitudinal predictions using regression-corrected grouping to reduce regression to the mean, Phys. Rev. Phys. Educ. Res. 15 (2), 020109 (2019), <https://doi.org/10.1103/PhysRevPhysEducRes.15.020109>.
APA Format
Stephens, S., & Marder, M. (2019, July 3). Longitudinal predictions using regression-corrected grouping to reduce regression to the mean. Phys. Rev. Phys. Educ. Res., 15(2), 020109. Retrieved May 23, 2024, from https://doi.org/10.1103/PhysRevPhysEducRes.15.020109
Chicago Format
Stephens, Sarah, and Michael Marder. "Longitudinal predictions using regression-corrected grouping to reduce regression to the mean." Phys. Rev. Phys. Educ. Res. 15, no. 2, (July 3, 2019): 020109, https://doi.org/10.1103/PhysRevPhysEducRes.15.020109 (accessed 23 May 2024).
MLA Format
Stephens, Sarah, and Michael Marder. "Longitudinal predictions using regression-corrected grouping to reduce regression to the mean." Phys. Rev. Phys. Educ. Res. 15.2 (2019): 020109. 23 May 2024 <https://doi.org/10.1103/PhysRevPhysEducRes.15.020109>.
BibTeX Export Format
@article{ Author = "Sarah Stephens and Michael Marder", Title = {Longitudinal predictions using regression-corrected grouping to reduce regression to the mean}, Journal = {Phys. Rev. Phys. Educ. Res.}, Volume = {15}, Number = {2}, Pages = {020109}, Month = {July}, Year = {2019} }
Refer Export Format

%A Sarah Stephens %A Michael Marder %T Longitudinal predictions using regression-corrected grouping to reduce regression to the mean %J Phys. Rev. Phys. Educ. Res. %V 15 %N 2 %D July 3, 2019 %P 020109 %U https://doi.org/10.1103/PhysRevPhysEducRes.15.020109 %O application/pdf

EndNote Export Format

%0 Journal Article %A Stephens, Sarah %A Marder, Michael %D July 3, 2019 %T Longitudinal predictions using regression-corrected grouping to reduce regression to the mean %J Phys. Rev. Phys. Educ. Res. %V 15 %N 2 %P 020109 %8 July 3, 2019 %U https://doi.org/10.1103/PhysRevPhysEducRes.15.020109


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Longitudinal predictions using regression-corrected grouping to reduce regression to the mean:

Is Part Of Focused Collection of Physical Review PER: Quantitative Methods in PER: A Critical Examination

A link to the full APS focused collection on quantitative methods in PER, published in 2019.

relation by Caroline Hall

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