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.

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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