RMME News & Updates
UConn’s RMME Programs are thrilled to celebrate our newest graduates from RMME Master’s degree program and RMME’s Graduate Certificate in Program Evaluation program! We cannot wait to see all of your outstanding accomplishments as a new RMME grad! Congratulations, Anthony!
RMME student, Alexandra Perez, successfully defended her doctoral dissertation this month. Her work is entitled, “Validity Threats When Comparing Results from Randomized Control Trials and Regression Discontinuity Designs.” Congratulations, Dr. Perez!
Register today for the 2023 Modern Modeling Methods Conference. Conference registration ends June 19!
2023 Modern Modeling Methods (M3) Conference
Dates: June 26 – June 28, 2023
Location: University of Connecticut’s Main Campus in Storrs, CT
Description: The Modern Modeling Methods (M3) Conference is an interdisciplinary conference designed to showcase the latest modeling methods and to present research related to these methodologies. Planned events include:
See the M3 Preliminary Program for a full list of talks.
Visit our website: modeling.uconn.edu.
All of the faculty and staff at UConn’s RMME Programs are so proud to recognize our newest graduates from
We are thrilled for you all, and we cannot wait to see all of the amazing things you will accomplish with your new credentials! Congratulations, Daniel, Katlin, Katie, Karen, Amanda, and Claudia!!!!
Drs. Bengt Muthén, Tihomir Asparouhov, & Ellen Hamaker will present a full-day pre-conference workshop at the 2023 Modern Modeling Methods Conference (see details below). Register soon because early-bird conference registration ends May 31, and all conference registration ends June 19!
2023 Modern Modeling Methods (M3) Pre-Conference Workshop
Date: Monday, June 26, 2023
Time: 8:30am – 5:00pm ET
Location: University of Connecticut’s Main Campus in Storrs, CT
Speakers: Bengt Muthén (UCLA, Mplus), Tihomir Asparouhov (Mplus), & Ellen Hamaker (University of Utrecht)
Workshop Title: New Features in Mplus Version 8.9 and Forthcoming 8.10
RMME/STAT Joint Colloquium
Applications of Artificial Intelligence and Natural Language Processing in Educational Measurement
Dr. Matthias von Davier
Boston College
Friday, April 21, at 11AM ET
https://tinyurl.com/rmme-vonDavier
This talk will provide an overview of the applications of Artificial Intelligence (AI) and Natural Language Processing (NLP) in educational measurement, focusing on automated item generation, automated scoring, and test assembly in multilingual assessments. We will discuss the potential benefits of AI and NLP for educational measurement, including increased efficiency, improved accuracy and reliability of assessment, and increased access to assessment technology for low-resource languages. We will examine the current state of the technology, including challenges associated with developing and deploying AI and NLP-based educational assessment systems. We will also discuss future directions for research and development in this area, including the development of methods for assessing and validating AI- and NLP-based systems and the potential for AI and NLP to improve assessment fairness and reduce assessment bias.
Loading...
RMME PhD Alumnus, Dr. Graham Rifenbark (far right), and PE Certificate Student & Neag PhD Student, Christopher Esposito (far left), attend the CEC 2023 Convention with colleagues.
Register today for the 2023 Modern Modeling Methods Conference. Early-bird conference registration ends May 31!
2023 Modern Modeling Methods (M3) Conference
Dates: June 26 – June 28, 2023
Location: University of Connecticut’s Main Campus in Storrs, CT
Description: The Modern Modeling Methods (M3) Conference is an interdisciplinary conference designed to showcase the latest modeling methods and to present research related to these methodologies. Planned events include:
See the M3 Preliminary Program for a full list of talks.
Visit our website: modeling.uconn.edu.
RMME/STAT Joint Colloquium
A Bayesian Nonparametric Approach to Geographic and two-Dimensional Regression Discontinuity Designs
Dr. Luke Miratrix
Harvard University
Friday, April 7, at 11AM ET
https://tinyurl.com/rmme-Miratrix
Geographical and two-dimensional regression discontinuity designs (RDDs) extend the classic, univariate RDD to multivariate, spatial contexts. We propose a framework for analyzing such designs with Gaussian process regression. This yields a Bayesian posterior distribution of the treatment effect at every point along the border, allowing for impact heterogeneity. We can then aggregate along the border to obtain an overall local average treatment effect (LATE) estimate. We address nuances of having a functional estimand defined on a border with potentially intricate topology, particularly with respect to defining the target estimand of interest. The Bayesian estimate of the LATE can also be used as a test statistic in a hypothesis test with good frequentist properties, which we validate using simulations and placebo tests. We demonstrate our methodology with a dataset of property sales in New York City, to assess whether there is a discontinuity in housing prices at the border between school districts. We also discuss application of this method to the context of treatment as a function of two forcing variables, such as falling below a threshold for either a reading or math test.
Loading...
RMME/STAT Joint Colloquium
Modeling Coarsened Categorical Variables: Techniques and Software
Dr. Joseph L. Schafer
U.S. Census Bureau
Friday, March 24, at 11AM ET
https://tinyurl.com/rmme-Schafer
Coarsened data can express intermediate states of knowledge between fully observed and fully missing. For example, when classifying survey respondents by cigarette smoking behavior as 1=never smoked, 2=former smoker, or 3=current smoker, we may encounter some who reported having smoked in the past but whose current activity is unknown (either 2 or 3, but not 1). Software for categorical data modeling typically provides codes for missing values but lacks convenient ways to convey states of partial knowledge. A new R package cvam: Coarsened Variable Modeling, extends R’s implementation of categorical variables (factors) and fits log-linear and latent-class models to incomplete datasets containing coarsened and missing values. Methods include maximum likelihood estimation using an expectation-maximization algorithm, approximate Bayesian and Bayesian inference via Markov chain Monte Carlo. Functions are also provided for comparing models, predicting missing values, creating multiple imputations, and generating partially or fully synthetic data. In the first major application of this software, data from the U.S. Decennial Census and administrative records were combined to predict citizenship status for 309 million residents of the United States.
Loading...
Reload document 
Open in new tab