Building A General Cross-Lagged Panel Model (GCLM)

[We’re pleased to welcome Michael J. Zyphur of the University of Melbourne and his co-authors. They recently published an article in Organizational Research Methods entitled, “From Data to Causes I: Building A General Cross-Lagged Panel Model (GCLM),” which is currently free to read for a limited time. Below, they feature a presentation on their research:]

How can research infer causality when they haven’t run experiments? With purely observational data, our two-paper series describes a novel modeling approach we call the General Cross-Lagged Panel Model or GCLM. This method is designed to facilitate specific types of causal inference. Our online materials include code to automate the GCLM in the program Mplus, and we have a YouTube presentation introducing the model and its logic: https://youtu.be/tHnnaRNPbXs

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