Judith Herb College of Arts, Social Sciences and Education

Overview

Faculty and students, and invited speakers will introduce a research method, potentially demonstrating its application, in a relaxed and informal atmosphere. This series is designed to raise awareness of research methodologies within the college and across the broader academic community. The event will welcome Judith Herb College of Arts, Social Sciences and Arts faculty and students, along with faculty from other departments and all those interested from outside the college.

Spring 2026 Seminar

Modern Approaches to Evaluating Psychological Measures: Moving Beyond Factor Analysis

Thursday, Jan. 29, Noon-1 p.m.
Carver Center and Online Via Microsoft Teams

Presenters: David Dueber, Michael Toland

Overview: Psychological items often reflect multiple sources of variance, and modern psychometric models can help us understand that complexity. This talk introduces contemporary approaches to validity evidence using exploratory structural equation modeling (ESEM) and bifactor models, and contrasts them with traditional exploratory factor analysis (EFA) and confirmatory factor analysis (CFA).

We show how ESEM incorporates theoretical subdomain structures while allowing realistic cross-loadings, and how bifactor models extend naturally to multitrait–multimethod contexts to separate general, specific, and method variance. Attendees will learn how these models provide clearer insights into dimensionality, item functioning, and score meaning, ultimately strengthening the validity arguments behind psychological measures. 

Past SMART Seminars

Introduction to R for Data Analysis

Presenters: Angie Keene, Dr. David Dueber

Overview: R is a widely used programming language in statistics, data science, and academic research, and a free, open-source tool for complex statistical analysis, data visualization, and reproducible reporting of data. This talk showcases advanced techniques using R in R Studio from an experienced user, and highlights suggestions on how to begin learning R, with a shared experience of learning R in R Studio from a beginner level.

Introduction to Multilevel Modeling

Presenter: Drs. Michael Toland and David Dueber

Overview: This talk offers a beginner-friendly introduction to multilevel modeling, a powerful statistical technique used to analyze data with a nested or hierarchical structure (like students within schools or teachers in schools). We will explore when and why multilevel models are useful, focusing on real-world examples to make the concepts clear.

 video of seminar

Introduction to Multilevel Modeling with Applications

Presenter: Drs. Michael Toland and David Dueber

Overview: This talk offers a beginner-friendly introduction to multilevel modeling, a powerful statistical technique used to analyze data with a nested or hierarchical structure (like students within schools or teachers in schools). We will explore when and why multilevel models are useful, focusing on real-world examples to make the concepts clear. The talk will cover the basics of building and interpreting these models, including how they handle data at multiple levels to provide more accurate insights. No advanced statistics knowledge is required—just a basic understanding of concepts like averages and variability.

video of seminar

Fundamentals of Item Response Theory

Presenter: Drs. Michael Toland and David Dueber

Overview: This session introduces measurement fundamentals, contrasting Classical Test Theory (CTT) and Item Response Theory (IRT), and discusses essential concepts such as true score reliability, item parameters, and the adaptability of IRT models. Key topics include model specification, evaluation, and applications of IRT, such as computer adaptive testing and cross-cultural measurement.

video of seminar

Contact Us

Email herbinnovationcenter@utoledo.edu if you have questions or suggestions.