This short course will introduce you to the major ideas behind structural equation modeling (SEM) and equip you with the knowledge necessary to get started on your own analysis using this method. SEM is a valuable tool beyond traditional regression models due to its flexibility, as it allows researchers to test more complex relationships among variables and to account for measurement error in analysis.

Participants should be familiar with regression modeling before taking this course. Experience using R will be helpful but is not required.

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They are Large, but should they be in Charge? Exploring the Possibility and Implausibility of Large Language Models in Social Science Research

Take a glance through recent news or social media and you would be hard-pressed not to see mentions of chatbots and artificial intelligence (AI) methods aimed at generating text, images and other content. Recent work by Eloundou and colleagues (2023) explored the potential impact of these types of generative AI on labor markets, and survey research, specifically was among the top two most impacted industries. In light of this finding, naturally, we wonder: how will this tech change the work in our field? How might chatbot technologies or related software be leveraged to support, enhance, or expand our work by assisting with a common research approach within the social sciences: surveys. And even still, how might survey and social science, more broadly, contribute to the improvement of large language models (LLMs)? In this workshop we will describe how LLMs work and showcase some of the current ways that these models are being used within the survey research process chronicling their applications in the design, collection, and analyses of survey data based on a large-scale systematic review of the social science, AI and computer science literature. We will also discuss some limitations of this technology as it relates to applications within the survey research process. We will also pose some relevant ideas around how survey research can also contribute to the development and refinement of large language models. And we note that no part of this abstract was generated using a chatbot.

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This one-day course will be offered via Zoom only. Attendance is required as it will not be recorded.

Course Summary:
Statistical machine learning is an interdisciplinary research area which is closely related to statistics, computer sciences, engineering, and bioinformatics. Many statistical machine learning techniques and algorithms have proven to be very useful for various scientific areas. This course will cover a number of unsupervised learning techniques for finding patterns and associations in Big Data. These include dimension reduction techniques such as principal components analysis and non-negative matrix factorization, clustering analysis and significance analysis, and network analysis with graphical models. The main emphasis will be on the analysis of real data sets from various scientific fields. The techniques discussed will be demonstrated in R.

This course is intended for researchers who have some knowledge of statistics and machine learning, and want to be introduced to relatively more advanced statistical machine learning topics.

Prerequisite:
Participants should be familiar with matrix linear algebra, linear regression and basic statistical and probability concepts, as well as some familiarity with R programming.

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The NC TraCS Institute's new cycle of Clinical and Translational Science (CTS) Pilot funding has been announced. Proposals are due July 8, 2025.

First released in 2022, CTS Pilots are focused on translational science. In preparation for this cycle of CTS Pilots, NC TraCS is holding 1-hour information sessions for investigators interested in learning more about this funding opportunity. Join the CTS Pilot Program leadership to hear about this pilot funding opportunity and have your questions answered.

Presenters
David Peden, MD, MS
CTS Pilot Program Module Lead, NC TraCS Institute
Senior Associate Dean for Translational Research, UNC School of Medicine

David Carroll, PhD
Director of Research Funding Development, NC TraCS Institute

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Automated Response Technology Integrated into EMR and Physician-Patient Communication

This NIH Collaboratory Rethinking Clinical Trials Grand Rounds features:

Ming Tai-Seale, PhD, MPH
Professor
Departments of Family Medicine and Medicine (Bioinformatics)
University of California San Diego School of Medicine
Director of UC San Diego Learning Health Systems Science Center

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