00:00 Introduction 05:16 Why do social scientists care about SEM? 07:47 What are SEM and what is their relationships with causal inference? 09:48 Why SEMs are not necessarily causal 14:00 Simplicity beats complexity: always 17:57 Why would a researcher use a SEM? 21:32 Why do SEMs help with measurement error? 24:55 What is measurement error in social sciences? 28:40 Examples where SEMs work well 30:58 Examples where SEMs DON'T work well 32:49 Beginner resources for learning SEM 35:12 Dr. Christian Geiser's books 36:42 Causal Inference books that discuss SEMs 43:02 How can SEM handle causal mediation models 46:40 Good or bad: many indicators for a latent variable? 49:07 Why do we need to use multi-level CFA? 51:25 Are pre-registration, a thorough theoretical framework, and a strong empirical background enough? 📚 Resources:👉My Causal Inference in Statistics book, first Chapter Free:https://justinbelair.ca/causal-inference-in-statistics-book/👉My Introduction to Biostatistics course: https://learning.causalpython.io/courses/intro-to-biostatistics-with-justin-belair👉 Guide with many resources: https://www.biostatistics.ca/causal-inference-guide-books-courses-and-more/👉 https://www.goquantfish.com/Community:👉My monthly newsletter, where I share material, code, community updates, and more: https://causal-inference-in-statistics.beehiiv.com/subscribe👉A community Discord with Q&A, office hours, a meme channel, and more: https://discord.com/invite/2xKvHMhYDg👉A causal inference Linkedin group with +3000 members: https://www.linkedin.com/groups/13065272/👉Follow me on Linkedin: https://www.linkedin.com/in/justinbelair/Recommended books:Bollen (1989). Structural equations with latent variables. New York: Wiley.Kenny, D. A. (1979). Correlation and causality. New York: Wiley.Raykov & Marcoulides, A First Course in Structural Equation Modellin✅ Don’t forget to subscribe for more content on causal inference, biostatistics, and methodology!