Come meet our faculty and learn more about UConn’s Research Methods, Measurement, & Evaluation (RMME) program! On Thursday, October 15th 2020, from 2:00 pm – 5:00 pm EDT, several RMME faculty members/graduates will participate in UConn’s Virtual Graduate School Fair. With one-on-one appointments and group information sessions (M.A. from 3:00pm – 3:30pm; Ph.D. from 3:30pm – 4:00pm), this event offers everything you need to decide that RMME is for you!

View our virtual profile at: https://app.joinhandshake.com/employers/572568 

Sarah D. Newton successfully defended her doctoral dissertation entitled, “Multilevel Model Selection and Effective Sample Size—In Information Criteria We Trust.” Congratulations, Dr. Newton!

Dakota Cintron successfully defended his doctoral dissertation entitled, “Statistical Estimation of Large Ordinal Factor Analysis Models.” Congratulations, Dr. Cintron!

Check out our fully online M.A. in Research Methods, Measurement, and Evaluation.

Dakota Cintron’s  oral defense of his dissertation, Statistical Estimation of Large Ordinal Factor Analysis Models, is Monday, August 31st at 3pm at https://uconn-cmr.webex.com/meet/dem98003

Dakota Cintron, a Ph.D. student in RMME, is an ETS Gulliksen Fellow for the 2019-2020 academic year.  Congratulations, Dakota!

NEW: Fully Online Master’s Program in RMME!

RMME Community members (now, both RMME PhD alumni), David Alexandro and Xiaowen Liu, discuss presented research at NERA 2017. Congratulations on this successful presentation, from the Research Methods, Measurement, & Evaluation Community!

 

 

Presenter: David Alexandro

Authors: Charles Martie, David Alexandro, William Estepar-Garcia, & Ajit Gopalakrishnan

Poster Presentation Title: Every Target and Milestone Matters: Developing Connecticut’s Evidence-Based Early Indication Tool (EIT)

Poster Abstract: Early warning systems typically focus on students’ dropout risk. The Connecticut State Department of Education extended this model to create the Early Indication Tool (EIT), a K-12 system that predicts student performance, identifies students who are at-risk of missing milestones and/or dropping out, and ultimately facilitates more timely interventions.