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.

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/

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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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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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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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:

• Monday, June 26: Full-day preconference workshop by Bengt Muthén, Tihomir Asparouhov, and Ellen Hamaker–“New Features in Mplus Version 8.9 and Forthcoming 8.10”
• Tuesday (June 27) & Wednesday (June 28): Keynote presentations by Bengt Muthén and Ellen Hamaker; Talks by Tihomir Asparouhov, Jay Magidson, Daniel McNeish, David A. Kenny, and many others.

See the M3 Preliminary Program for a full list of talks.

Visit our website: modeling.uconn.edu.

Register Here!

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

Register Here

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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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:

• Monday, June 26: Full-day preconference workshop by Bengt Muthén, Tihomir Asparouhov, and Ellen Hamaker–“New Features in Mplus Version 8.9 and Forthcoming 8.10”
• Tuesday (June 27) & Wednesday (June 28): Keynote presentations by Bengt Muthén and Ellen Hamaker; Talks by Tihomir Asparouhov, Jay Magidson, Daniel McNeish, David A. Kenny, and many others.

See the M3 Preliminary Program for a full list of talks.

Visit our website: modeling.uconn.edu.

Register Here!

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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