2024-25 Seminars and Recordings

September 30, 2024

December 13, 2024, 11 am CST

Friday the 13th & Single Case Experimental Designs: Allegedly Unlucky Encounters for Meta-Analysts

Speakers: Dr. John Ferron, University of South Florida, and Dr. Megan Kirby, Language Dynamics Group
Description: With increased use of single-case experimental designs to study intervention effects, there is a greater need to include single-case experimental design studies in meta-analyses. In this session, we will discuss alternative methods for estimating effects from single-case studies, illustrate the estimation of these effects from freely available software apps, and provide guidance about choosing among the effect size options.
Slides

Using GPT API Models as Second Screeners of Titles and Abstracts in High-Quality Systematic Reviews

Speaker: Dr. Mikkel Vembye, Danish Center for Social Science Research
Description: Independent human double screening of titles and abstracts is a critical step to ensure the quality of systematic reviews and meta-analyses. However, double screening is a resource-demanding procedure that slows down the review process. In this presentation, I will demonstrate how reviewers reliably can implement OpenAI’s GPT models as second screeners of title and abstracts to mitigate this resource demand. I will provide a practical demonstration of how to conduct title and abstract screening with GPT API models through the newly developed R package AIscreenR and highlight how the approach differs from simply using the ChatGPT interface. I will discuss implementation requirements for ensuring performance that is at least on par with typical human screening. To facilitate this, I will introduce a new screening benchmark scheme that is based on common human screening performance in 22 large-scale systematic reviews. As the initial proof of concept, I will present results of using GPT API screening in three large classification experiments. I will conclude by discussing limitations and future directions for this screening method and how it can be combined with traditional (semi-)automated screening tools. The background for this presentation can be found at https://osf.io/preprints/osf/yrhzm.
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Fitting Flexible Meta-Analytic Models with Structural Equation Modeling

Speaker: Dr. Mike Cheung, National University of Singapore
Description: Understanding the differences between various meta-analytic models, such as the fixed-effect, random-effects, and multiplicative error models, can be challenging for researchers. This presentation explains these models using a graphical representation within a structural equation modeling (SEM) framework. Users specify meta-analytic models using lavaan syntax. The metaSEM package in R can convert these models to graphical models, model-implied mean and variance, and fit models using the Full Information Maximum Likelihood (FIML) estimation method. Complex meta-analytic models, such as multivariate meta-analysis and mediation models, can also be fitted. Users without strong statistical and programming backgrounds can still develop and implement novel meta-analytic models that are not available in standard meta-analysis software.
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Getting Meta-Analyses Done: Practical Perspectives from Experienced Reviewers

Panelists:
- Dr. Carlton Fong, Texas State University
- Dr. Amanda Neitzel, Johns Hopkins University
- Dr. Peng Peng, University of Texas at Austin
Description: Our panel of prolific and experienced systematic reviewers will be discussing the very practical side of systematic reviews and meta-analyses, common challenges and roadblocks, and tips for getting meta-analyses done, with plenty of time for audience questions and discussion.
Video Recording
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