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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