Welcome to the module 3 of my lecture series on covariance-based structural equation modeling (CB-SEM).
The introduction to the module can be found here.
In this module we cover the basics, and the theory relating to exploratory factor analysis (EFA). The first three lectures cover the basics of EFA and the last three lectures demonstrate how to conduct EFA using SPSS.
EFA is an extremely common data reduction method used in social sciences but not many of us (researchers as well as practitioners) understand the theory behind it and the different analytic options it offers. In this module, I aim to explain the important concepts relating to EFA and its application in the analysis of data for social sciences.
In the first lecture, we cover the different extraction options: especially the Principal Components Analysis (PCA) and Principal Axis Factoring (PAF) methods.
In the second lecture, I explain the concepts of eigenvalues and communality that are often used in EFA.
In the third lecture, I explain the concept of rotation and the difference between oblique rotation and orthogonal rotation.
Lectures 4 to 6 demonstrate the analysis of data using SPSS.
With this module, we will finish the basics that are needed to understand SEM and from the next module onwards, we will start covering the principles and workings of SEM.
I hope you will find these videos useful. Material for the series can be downloaded from: https://drive.google.com/drive/folders/1vvMb8QfB7-hPKuyo9Rdk9sGEOUqYYOND
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Prof. Vishal Gupta