Factor analysis: predicted variance and covariance of indicators - part 1

This video provides an example as to how we can use the model estimated variance-covariance matrices for the factors and errors in order to derive the variance and covariance of indicators. Check out ben-lambert.com/econometrics-... for course materials, and information regarding updates on each of the courses. Quite excitingly (for me at least), I am about to publish a whole series of new videos on Bayesian statistics on youtube. See here for information: ben-lambert.com/bayesian/ Accompanying this series, there will be a book: www.amazon.co.uk/gp/product/1...

[@robsellers8416]

Where in the world are we going to extract these weightings for UNOBSERVED factors on our OBSERVED variables?

[@mscherer1586]

My exact same question

[@PoppingPro98]

The weightings are also known as loadings (often represented as lambda). You extract these "weightings" using a fitting procedure, such as MLE or PAF. Below is an excerpt from Wikipedia if you're interested:
"Fitting procedures are used to estimate the factor loadings and unique variances of the model (Factor loadings are the regression coefficients between items and factors and measure the influence of a common factor on a measured variable)."
Please see: en.wikipedia.org/wiki/Exploratory_factor_analysis
Alternatively, Ben has part 1 and part 2 on the topic. Part 1 is here: ua-cam.com/video/SfhJPUw1bCk/v-deo.html

[@Dupamine]

Why are we multiplying them in this very specific manner tho ?

[@hongsheny67]

is all purple number here correlation ?