RMME is seeking qualified instructors to teach graduate-level courses in Research Methods, Educational Measurement, Quantitative Methods, and Evaluation. Connecticut residents are especially encouraged to apply! Interested candidates should apply here.
RMME/STAT Joint Colloquium
Bayesian Longitudinal (Non)Linear Mediation Models
Dr. Nidhi Kohli
University of Minnesota, Twin Cities
Friday, May 2, at 11 AM ET
https://tinyurl.com/rmme-Kohli
This study develops Bayesian (non)linear random effects mediation models (B(N)REMM) to directly estimate both linear and nonlinear longitudinal mediation effects, overcoming limitations in existing structural equation modeling (SEM) approaches. We propose two models: a linear trend model (L-BREMM) and a segmented trend model using linear-linear piecewise functions with random changepoints (P-BREMM). We also examine the impact of omitting confounders in (non)linear mediation models using data from the Early Childhood Longitudinal Study-Kindergarten Cohort (ECLS-K). Furthermore, we extend this framework to develop Bayesian (nonlinear) growth mixture mediation models (B(N)GMMM), which assess heterogeneous treatment effects (HTE) of the intervention variable X on the longitudinal dependent variable Y, mediated by longitudinal variable M. To evaluate the robustness of these methods, we conducted a comprehensive Monte Carlo simulation study for all the models.
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Members of the RMME Community present at the 2025 annual meetings of the American Educational Research Association (AERA) and the National Council on Measurement in Education (NCME)! Be sure to check out all of this outstanding RMME Community work in Denver, Colorado, from Tuesday, April 22, through Sunday, April 27–we look forward to seeing you all there!
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Will you be in New York, NY for this year’s annual meeting of the Eastern Psychological Association (EPA)? If so, be sure to check out the RMME Programs Exhibits booth (#10) in the New York Marriott Marquis’s 5th floor West Side Ballroom! The exhibits are open from 8am – 5pm on Friday (3/7) and Saturday (3/8).
This is an excellent opportunity to meet with RMME’s Associate Director of Online Programs, Dr. Sarah D. Newton, and ask any questions you have about RMME Programs! So, feel free to stop by and chat–we would love to meet you!
RMME/STAT Joint Colloquium
Adventitious Error Everywhere
Dr. Paul De Boeck
The Ohio State University
Friday, February 7, at 11 AM ET
https://tinyurl.com/rmme-PDeBoeck
Adventitious error is a concept introduced by Wu and Browne (Psychometrika, 2015) to explain imperfect goodness of fit of covariance structure models (CSMs, i.e., factor models, SEM). The paper was published together with critical remarks from the reviewers. In my presentation I will discuss, illustrate, and speculate about the potential of adventitious error beyond CSM, (a) as a unitary framework to understand and deal with underestimated inferential uncertainty regarding relations between variables, heterogeneity in meta-analysis, violations of measurement invariance, individual differences in the validity of tests, and (b) as a joint framework for reliability and validity. The presentation is partly based on De Boeck, DeKay, and Pek (Psychometrika, 2024, 89, 1055-1073).
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RMME is seeking qualified instructors to teach graduate-level courses in Research Methods, Educational Measurement, Quantitative Methods, and Evaluation. Connecticut residents are especially encouraged to apply! Interested candidates should apply here.
RMME/STAT Joint Colloquium
Challenges in Time-varying Causal Effect Moderation Analysis in Mobile Health
Dr. Walter Dempsey
University of Michigan
Friday, January 24, at 11 AM ET
https://tinyurl.com/rmme-Dempsey
Twin revolutions in wearable technologies and smartphone-delivered digital health interventions have significantly expanded the accessibility and uptake of mobile health (mHealth) interventions in multiple domains of health sciences. Sequentially randomized experiments called micro-randomized trials (MRTs) have grown in popularity as a means to empirically evaluate the effectiveness of mHealth intervention components. MRTs have motivated a new class of causal estimands, termed “causal excursion effects”, that allow health scientists to answer important scientific questions about how intervention effectiveness may change over time or be moderated by individual characteristics, time-varying context, or past responses. In this talk, we present two new tools for causal effect moderation analysis. First, we consider a meta-learner perspective, where any supervised learning algorithm can be used to assist in the estimation of the causal excursion effect. We will present theoretical results and accompanying simulation experiments to demonstrate relative efficiency gains. Practical utility of the proposed methods is demonstrated by analyzing data from a multi-institution cohort of first year medical residents in the United States. Second, we will consider effect moderation with tens or hundreds of potential moderators. In this setting, it becomes necessary to use the observed data to select a simpler model for effect moderation and then make valid statistical inference. We propose a two-stage procedure to solve this problem that leverages recent advances in post-selective inference using randomization. We will discuss asymptotic validity of the conditional selective inference procedure and the importance of randomization. Simulation studies verify the asymptotic results. We end with an analysis of an MRT for promoting physical activity in cardiac rehabilitation to demonstrate the utility of the method.
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UConn’s Research Methods, Measurement, & Evaluation (RMME) Programs Community is so proud of our Fall 2024 RMME Master’s degree program and Graduate Certificate in Program Evaluation program graduates! We are thrilled to celebrate this educational milestone with you all and look forward to watching you grow as outstanding RMME alumni and professionals! Congratulations, from the entire Research Methods, Measurement, & Evaluation Community!
The Rhode Island Society for Technology in Education (RISTE), jointly with the Rhode Island Science Teachers Association (RISTA) and the Rhode Island Math Teachers Association (RIMTA), are hosting STEM Super Saturday! This annual event for educators will take place at the Rhode Island Nursing Education Center, in Providence, RI, on January 11, 2025.
UConn’s RMME Programs are thrilled to participate in, and support the event, by hosting an exhibits booth for attendees! So, if you will be there, be sure to stop by for an opportunity to chat personally with Dr. Sarah D. Newton, the Associate Director of RMME Online Programs! We cannot wait to see you there!
RMME/STAT Joint Colloquium
Recent Lessons on Mixing Mail and Web Data Collection Modes in Household Surveys
Dr. Kristen Olson
University of Nebraska-Lincoln
Friday, December 6, at 11 AM ET
https://tinyurl.com/rmme-Olson
Survey designs are increasingly incorporating self-administered modes – namely mail and web – to counter decreasing response rates and increasing costs in interviewer-administered surveys. In this talk, I will cover recent research related to mixing modes of self-administered data collection in household surveys, with a focus on designing for survey participation. I will explore theory for why sample members may select one self-administered mode over another, how modes affect response rates, representation and costs, and design decisions related to recruitment materials for mixed-mode surveys.
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