Colloquia

Upcoming RMME/STAT Colloquium (10/11): Sandip Sinharay, “Assessment of Fit of Item Response Theory Models: Full-information and Limited-information Methods, Item and Person Fit Analysis, and Beyond”

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RMME/STAT Joint Colloquium

Assessment of Fit of Item Response Theory Models: Full-information and Limited-information Methods, Item and Person Fit Analysis, and Beyond

Dr. Sandip Sinharay

Educational Testing Service (ETS) Research Institute

Friday, October 11, at 11:15 AM ET

AUST 110

https://tinyurl.com/rmme-Sinharay

Item response theory (IRT) is one of the central methodological pillars supporting many large and high-profile assessment programs globally. IRT analysis is essentially a type of discrete multivariate analysis and is performed using IRT models that are latent variable models for discrete data. However, IRT models involve multiple assumptions like conditional independence, monotonicity etc. and the results obtained from IRT models may not be accurate if one or more of the assumptions are not met, that is, if there is IRT model misfit. This presentation will include a comprehensive review of the literature on the assessment of fit of IRT models. The presenter will discuss various approaches and concepts regarding IRT model fit including full-information and limited-information methods, residual analysis, item and person-fit analysis, Bayesian methods, analysis for differential item functioning, and assessment of practical significance of misfit. A real data example will be used to illustrate some of the approaches. One goal of the presentation is to stimulate discussions involving the audience members regarding IRT model-fit assessment.

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Upcoming RMME/CEPARE Colloquium (4/19): Robert Schoen, “Lessons from the Field: Working with Practitioners to Create Opportunities for Educational Research”

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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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Upcoming RMME/CEPARE Colloquium (4/18): Robert Schoen, “Designing a Measure of Implementation for a Non-Prescriptive Mathematics Intervention”

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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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Upcoming RMME/STAT Colloquium (4/12): Dale Zimmerman, “In Defense of Unrestricted Spatial Regression”

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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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Upcoming RMME/STAT Colloquium (3/29): Zhiliang Ying, “Some Recent Developments in Educational and Psychological Measurement”

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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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Upcoming RMME/STAT Colloquium (12/1): Irini Moustaki, “Some New Developments on Pairwise Likelihood Estimation & Testing in Latent Variable Models”

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RMME/STAT Joint Colloquium

Some New Developments on Pairwise Likelihood Estimation & Testing in Latent Variable Models

Dr. Irini Moustaki
London School of Economics

Friday, December 1, at 11AM ET

https://tinyurl.com/rmme-Moustaki

Pairwise likelihood is a limited-information method used to estimate latent variable models, including factor analyses of categorical data. It avoids evaluating high-dimensional integrals and, thus, is computationally more efficient than full information maximum likelihood. This talk will discuss two new developments in the estimation and testing of latent variable models for binary data under the pairwise likelihood framework. The first development is about estimation and limited information goodness-of-fit test statistics under complex sampling. The performance of the estimation and the proposed test statistics under simple random sampling and unequal probability sampling is evaluated using simulated data. The second development focuses on computational aspects of pairwise likelihood. Despite its computational advantages it can still be demanding for large-scale problems that involve many observed variables. We propose an approximation of the pairwise likelihood estimator, derived from an optimization procedure relying on stochastic gradients. The stochastic gradients are constructed by subsampling the pairwise log-likelihood contributions, for which the subsampling scheme controls the per-iteration computational complexity. The stochastic estimator is shown to be asymptotically equivalent to the pairwise likelihood one. However, finite sample performances can be improved by compounding the sampling variability of the data with the uncertainty introduced by the subsampling scheme. We demonstrate the performance of the proposed method using simulation studies and two real data applications.

 

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Upcoming RMME/STAT Colloquium (11/3): Xinyuan Song, “Hidden Markov Models with an Unknown Number of Hidden States”

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RMME/STAT Joint Colloquium

Hidden Markov Models with an Unknown Number of Hidden States

Dr. Xinyuan Song
The Chinese University of Hong Kong

Friday, November 3, at 10AM ET

https://tinyurl.com/rmme-Song

Hidden Markov models (HMMs) are valuable tools for analyzing longitudinal data due to their capability to describe dynamic heterogeneity. Conventional HMMs typically assume that the number of hidden states (i.e., the order of HMMs) is known or predetermined through criterion-based methods. This talk discusses double-penalized procedures for simultaneous order selection and parameter estimation for homogeneous and heterogeneous HMMs. We develop novel computing algorithms to address the challenges of updating the order. Furthermore, we establish the consistency of order and parameter estimators. Simulation studies show that the proposed procedures considerably outperform the commonly used criterion-based methods. An application to the Alzheimer’s Disease Neuroimaging Initiative study further confirms the utility of the proposed method.

 

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Upcoming RMME/STAT Colloquium (10/13): Wes Bonifay, “Uncovering the Hidden Complexity of Statistical Models”

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RMME/STAT Joint Colloquium

Uncovering the Hidden Complexity of Statistical Models

Dr. Wes Bonifay
University of Missouri

Friday, October 13, at 11AM ET

https://tinyurl.com/rmme-Bonifay

Model complexity is the ability of a statistical model to fit a wide range of data patterns. Complexity is routinely assessed by simply counting the number of freely estimated parameters in a given model. However, complexity is also affected by configural form, that is, by the particular arrangement of the variables in the model. Recent considerations of configural complexity have found that certain models have an inherent tendency to fit well to any possible data (sometimes achieving superior goodness-of-fit when compared to alternative models that contain a greater number of free parameters!). In this talk, Dr. Bonifay will present a method for evaluating configural complexity and demonstrate how more sophisticated considerations of complexity can improve applied research in the social sciences.

 

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Upcoming RMME/STAT Colloquium (4/21): Matthias von Davier, “Applications of Artificial Intelligence and Natural Language Processing in Educational Measurement”

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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.

 

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Upcoming RMME/STAT Colloquium (4/7): Luke Miratrix, “A Bayesian Nonparametric Approach to Geographic and two-Dimensional Regression Discontinuity Designs”

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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.

 

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