Several Research Methods, Measurement, & Evaluation (RMME) community members are presenting at the 2024 annual meeting of the Society for Research on Educational Effectiveness (SREE) this week. Check out their excellent contributions to the conference below:

Thursday, September 19

9.00 am to 10.30 pm
Location: Harborside Foyer (4, Baltimore Marriott Waterfront)
Session Title: 3G. – Exploring Predictive Validity and Disparities in Education: From Test Scores to Behavior Screeners
Presentation Title: Mapping the Research Base for Universal Behavior Screeners
Presenting Author: Kathleen Lane
Authors: Katie Pelton*

12.30 pm to 2.00 pm
Location: Kent AB (4, Baltimore Mariott Waterfront)
Session Title: 5F. – Artificial Intelligence and the Future of Educational Measurement and Evaluation
Presentation Title: Low-Cost Measurement with Large Language Models: An Application of Few-Shot Classification in Educational Evaluations
Author: Claudia Ventura*
Presenting Author: Kylie Anglin*

2.15 pm to 3.15 pm
Location: Kent AB (4, Baltimore Marriott Waterfront)
Session Title: Open Science Affinity Group Panel: How Do We Move Open Science Forward in Educational Research?
Panelists: Kara Finnigan, Elizabeth Tipton, Sean Grant
Moderator: D. Betsy McCoach*

Friday, September 20

9.00 am to 10.30 pm
Location: Harborside Foyer (4, Baltimore Marriott Waterfront)
Session Title: 7H. – Advancing Measurement and Assessment in Special Education
Presentation Title: Measurement Invariance and Predictive Validity of a Free-Access Universal Screening Tool: The SRSS-IE
Presenting Author: Kathleen Lane
Authors: Katie Pelton*

1.15 pm to 2.15 pm
Location: Harborside Foyer (4, Baltimore Marriott Waterfront)
Session Title: 8D. Organization of Schools and Systems
Presentation Title: Evaluation of a Brief Intervention: Achievement Gaps and Reliability
Authors: Joselyn Perez*, D. Betsy McCoach*

1.15 pm to 2.15 pm
Location: Kent AB (4, Baltimore Marriott Waterfront)
Session Title: 8F. Research Methods
Presentation Title: Deciphering Hyperparameter Choices in Machine Learning for Propensity Score Estimation: A Systematic Review of GBM and Random Forest Methods
Authors: Huibin Zhang, Walter Leite, Zachary Collier*, Kamal Chawla, Lingchen Kong, YongSeok Lee, Jia Quan
Presenting Author: Huibin Zhang

1.15 pm to 2.15 pm
Location: Harborside Foyer (4, Baltimore Marriott Waterfront)
Session Title: 8F. Research Methods
Presentation Title: Exploring Estimates of Multilevel Reliability for School Based Behavioral Measures
Authors: Katie Pelton*, D. Betsy McCoach*

2.15 p.m. – 3.45 p.m.
Location: Kent AB (4, Baltimore Mariott Waterfront)
Session Title: 9E. Advancing Educational Outcomes through Machine Learning and Predictive Analytics
Chair: Kylie Anglin*

*RMME Community member

Multiple RMME Community members participated in the 2024 Modern Modeling Methods (M3) Conference this June. Hosted by RMME faculty member, Dr. Betsy McCoach, the M3 Conference is a must-attend event for research methodologists, statisticians, statistical modelers, quantitative analysts, measurement experts, and more! Check out the photos below of RMME Community members in action at M3 2024!

Join UConn’s RMME Programs and all of the attendees of the 2024 Modern Modeling Methods (M3) Conference this June on UConn’s main campus in Storrs, CT, from June 24 – 26. Hosted by RMME faculty member, Dr. Betsy McCoach, the M3 Conference is a must-attend event for research methodologists, statisticians, statistical modelers, quantitative analysts, measurement experts, and more! Check out this sneak peek of presentations by RMME Community members this year! We hope to see you in Storrs this June!

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RMME PhD alumna, Dr. Jessica Flake, presents her “Measurement, Not Schmeasurement” talk at the 2024 annual meeting of the Association for Psychological Science, in San Francisco, California. Join Dr. Flake on Thursday, May 23, at 9am, for this outstanding presentation!

Join RMME Community members for multiple outstanding research presentations at the 2024 Wallace Research Symposium on Talent Development. This year, the Wallace Symposium will be hosted on the University of Connecticut’s main campus, in Storrs, CT (and the home of RMME Programs). So, remember to join us for these excellent presentations with RMME Community members this month!

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RMME/CEPARE Colloquium

Lessons from the Field: Working with Practitioners to Create Opportunities for Educational Research

Dr. Robert Schoen

Florida State University

Friday, April 19, at 11AM ET

Gentry 144

Dr. Robert Schoen is an associate professor of mathematics education in the School of Teacher Education and the associate director of the Florida Center for Research in Science, Technology, Engineering, and Mathematics in the Learning Systems Institute at Florida State University. Dr. Rob Schoen has directed more than one-dozen randomized controlled trials of educational interventions in applied settings. He will share stories and examples about how he decides what research opportunities to pursue and some of the strategies he has used to support successful implementation of those studies over a long period.

*Please contact Dr. Sarah D. Newton at sarah.newton@uconn.edu for access information to remotely attend this talk*

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RMME/CEPARE Colloquium

Designing a Measure of Implementation for a Non-Prescriptive Mathematics Intervention

Dr. Robert Schoen

Florida State University

Thursday, April 18, at 3PM ET

Gentry 142

Dr. Robert Schoen is an associate professor of mathematics education in the School of Teacher Education and the associate director of the Florida Center for Research in Science, Technology, Engineering, and Mathematics in the Learning Systems Institute at Florida State University. This talk will address the various phases in the development, use, and validation of an instrument designed to measure implementation of Cognitively Guided Instruction (CGI) during mathematics instruction. Several experimental trials of CGI-based teacher professional development programs indicate that the CGI programs increased student achievement. But the CGI programs did not offer clear guidance about how to teach mathematics, complicating the process of measure development and validation.

*Please contact Dr. Sarah D. Newton at sarah.newton@uconn.edu for access information to remotely attend this talk*

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Members of the RMME Community will share their work in a variety of different research presentations at the 2024 annual meetings of the American Educational Research Association (AERA) and the National Council on Measurement in Education (NCME). Be sure to check out these awesome RMME Community sessions in Philadelphia, PA this month!

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RMME/STAT Joint Colloquium

In Defense of Unrestricted Spatial Regression

Dr. Dale Zimmerman

University of Iowa

Friday, April 12, at 11AM ET

AUST 202

http://tinyurl.com/rmme-Zimmerman

Spatial regression is commonly used in the environmental, social, and other sciences to study relationships between spatially referenced data and other variables, and to predict variables at locations where they are not observed. Spatial confounding, i.e., collinearity between fixed effects and random effects in a spatial regression model, can adversely affect estimates of the fixed effects, and it has been argued that something ought to be done to “fix” it. Restricted spatial regression methods have been proposed as a remedy for spatial confounding. Such methods replace inference for the fixed effects of the original spatial regression model with inference for those effects under a model in which the random effects are restricted to a subspace orthogonal to the column space of the fixed effects model matrix; thus, they “deconfound” the two types of effects. We prove, however, using classical linear model theory, that frequentist inference for the fixed effects of a deconfounded linear model is generally inferior to that for the fixed effects of the original spatial linear model; in fact, it is even inferior to inference for the corresponding nonspatial model (i.e., inference based on ordinary least squares). We show further that deconfounding also leads to inferior predictive inferences. Based on these results, we argue against the use of restricted spatial regression, in favor of plain old (unrestricted) spatial regression. This is joint work with Jay Ver Hoef of NOAA National Marine Mammal Laboratory and was published in 2022 in The American Statistician.

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