RMME/STAT Joint Colloquium

Shared Parameter Mixed-Effects Location Scale Models for Intensive Longitudinal Data

Dr. Donald Hedeker
University of Chicago

Friday, February 25, at 3:00PM ET

https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=m6944095dfb2736dba214a9c6f6397805

Intensive longitudinal data are increasingly encountered in many research areas. For example, ecological momentary assessment (EMA) and/or mobile health (mHealth) methods are often used to study subjective experiences within changing environmental contexts. In these studies, up to 30 or 40 observations are usually obtained for each subject over a period of a week or so, allowing one to characterize a subject’s mean and variance and specify models for both. In this presentation, we focus on an adolescent smoking study using EMA where interest is on characterizing changes in mood variation. We describe how covariates can influence the mood variances and also extend the statistical model by adding a subject-level random effect to the within-subject variance specification. This permits subjects to have influence on the mean, or location, and variability, or (square of the) scale, of their mood responses. The random effects are then shared in a modeling of future smoking levels. These mixed-effects location scale models have useful applications in many research areas where interest centers on the joint modeling of the mean and variance structure.

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RMME/STAT Joint Colloquium

Test Validation for a Crisis: Five Practical Heuristics for the Best and Worst of Times

Dr. Andrew Ho
Harvard University

Friday, January 28, at 3:00PM ET

https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=me0f80ec702d5508cf83ae6a23183fc3d

The COVID-19 pandemic has raised debate about the place of education and testing in a hierarchy of needs. What do tests tell us that other measures do not? Is testing worth the time? Do tests expose or exacerbate inequality? The academic consensus in the open-access AERA/APA/NCME Standards has not seemed to help proponents and critics of tests reach common ground. I propose five heuristics for test validation and demonstrate their usefulness for navigating test policy and test use in a time of crisis: 1) A “four quadrants” framework for purposes of educational tests. 2) The “Five Cs,” a mnemonic for the five types of validity evidence in the Standards. 3) “RTQ,” a mantra reminding test users to read items. 4) The “3 Ws,” a user-first perspective on testing. And 5) the “Two A’s Tradeoff” between Assets and Accountability. I illustrate application of these heuristics to the challenge of reporting aggregate-level test scores when populations and testing conditions change as they have over the pandemic (e.g., An, Ho, & Davis, in press; Ho, 2021). I define and discuss these heuristics in the hope that they increase consensus and improve test use in the best and worst of times.

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