JASP 0.14 Tutorial: Confirmatory Factor Analysis (CFA) (Episode 30)

EDIT/CORRECTION: There's an error in my description of the chi-square model fit outcome. I state that it is good that the p-value is very small and reflects a good model fit. As mentioned by a keen viewer, this chi-square application is the opposite for other NHST outcomes. Here, a significant chi-square actually represents a bad model fit. Essentially, you want a big p-value for this test. HOWEVER, chi-square tests are impacted poorly by large samples, so they tend to end up small with an N = 300+, which is general convention for Factor Analyses for sample size. In this JASP tutorial, I go through a Confirmatory Factor Analysis example, exploring the stats and their meaning/interpretation. UCLA Stats Dept lavaan R package tutorial & data: https://stats.idre.ucla.edu/r/seminars/rcfa/ JASP: https://jasp-stats.org NOTE: This tutorial uses the new preview/beta build of 0.14.1. This build contains slightly more functions/features than the previous builds used for tutorials on this channel, but it is functionally the same for the purposes of this tutorial. Find me on Twitter: https://twitter.com/ProfASwan Go to my website: https://swanpsych.com Twitch streams on psych & related topics: https://twitch.tv/cogpsychprof Discuss this video and others on my Discord channel: https://discord.gg/7T2jdg7Cb5