Current RMME Master’s student (and Giftedness, Creativity, and Talent Development PhD student), Lihong Xie, gave an excellent guest presentation at Harvard University this March. Lihong visited Harvard’s Child/Adolescent Cognitive & Psychological Assessment class to speak and lead an engaging conversation about assessing youth intelligence and creativity.
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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RMME/STAT Joint Colloquium
Some Recent Developments in Educational and Psychological Measurement
Dr. Zhiliang Ying
Columbia University
Friday, March 29, at 11AM ET
In Person: AUST 202
Virtual: https://tinyurl.com/rmme-Ying
Measurement theory plays a foundational role in educational and psychological assessment. Classical item response theory (IRT) models are widely used in the design and analysis of educational tests and psychological surveys that involve multiple choice questions. In this talk, we will first discuss some recent progress related to variations and extensions of the classical IRT model-based methods. We will then turn to the modeling and analysis of process data arising from complex problem-solving items, which are increasingly being adopted in large scale educational assessment. New developments, including statistical models and machine learning algorithms, will be presented. Examples from educational testing and psychological assessment will be used for illustration.
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Are you traveling to Philadelphia, PA for the Eastern Psychological Association’s 2024 annual meeting? RMME is–and we’d love to meet you! Join us at booth #20 (Franklin Hall A, Downtown Marriott), for an opportunity to chat personally with Dr. Sarah D. Newton, the Associate Director of RMME Online Programs!
What are RMME faculty up to? Check out Dr. Bianca Montrosse-Moorhead’s Evaluation Café talk on February 28, 2024, at 12pm ET, to find out! Her featured presentation is entitled, “Modernizing Evaluation’s Cartography, Architectural Blueprint, and Definition”. Register here to reserve your spot in this excellent session: http://tinyurl.com/EvalCafe2024-BMM
ABSTRACT: In this Evaluation Café presentation, Dr. Montrosse-Moorhead will preview the Country of the Mind map, where the Evaluation Building is located on this map, and a close-up of the building itself. None of these have been visualized before in published scholarship. Dr. Montrosse-Moorhead will also share the proposed amended definition, why it is necessary, and the implications of adopting the amended definitions for evaluation practice; the implications for the instruments, methods, and techniques we use; and the implications for evaluation’s theoretical and metatheoretical scholarship.
On February 21, 2024, Dr. Bianca Montrosse-Moorhead (Associate Professor in RMME Programs) joined MERL Tech’s Natural Language Processing Community of Practice (NLP-CoP) to discuss the impact of Artificial Intelligence (AI) on future evaluation practice. This exciting panel also included other experts in the field, including Sarah Mason, Izzy Thornton, Tarek Azzam, Sahiti Bhaskara, and Blake Beckmann. The session covered a wide variety of topics, ranging from the birth of AI to consulting challenges and AI competencies. Click here for more information on the talk and to register for part two of this discussion series!
RMME is growing! We are currently seeking qualified instructors to teach graduate-level courses in Research Methods, Educational Measurement, Quantitative Methods, and Evaluation. Connecticut residents are especially encouraged to apply! Interested candidates should apply here.
UConn’s Research Methods, Measurement, & Evaluation (RMME) Programs are excited 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 the many ways you will make us proud, as a new RMME graduate! Congratulations, all, from the Research Methods, Measurement, & Evaluation Community!
The Research Methods, Measurement, & Evaluation (RMME) program is proud to recognize this year’s recipient of the 2023 RMME Book Award: Erbin Huang! Congratulations on this fantastic accomplishment, from the Research Methods, Measurement, & Evaluation Community!
The 2024 Modern Modeling Methods (M3) Conference is currently accepting proposals. Review the full call for proposals at: https://modeling.uconn.edu/call-for-proposals-2024/
Submit today! Proposals are due January 29!
For more information, visit: https://modeling.uconn.edu/
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