Factor analysis of Likert scale: Analysis and Interpretation using SPSS

This tutorial explains how to perform and interpret exploratory factor analysis (EFA) on Likert scale in SPSS. I discuss how to enter the data, select the various options, interpret the output (e.g., communalities, eigenvalues, factor loadings). I also discuss the difference among extraction techniques (Principal components analysis or PCA, Principal Axis Factoring or PAF and Maximum Likelihood or ML). I also discuss the difference between orthogonal and oblique rotation techniques like Varimax and Promax within SPSS. Likert scale is a commonly used rating scale in surveys, typically containing several statements, and respondents rate their level of agreement or disagreement on a scale ranging from strongly disagree coded as 1 to strongly agree coded as 5.
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Guidelines and dataset: drive.google.com/drive/folder...
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📌 Mohamed Benhima, PhD
WhatsApp: [phone] / wa.link/l6jvny
LinkedIn: / mohamed-benhima-phd-6a...
Mohammed V University, Rabat, Morocco ( [e-mail] )
Sorbonne University, Paris, France ( [e-mail] )

[@Prabhleen7]

Hi, How to rectify the cross loading in the factor analysis

[@s.kanagapushpa970]

Only one compenent was extracted the solutions cannot be rotated .. this statement was displayed sir..I can use factor analysis..(principal component analysis)

[@KazaikBenjamin-rp6os]

Your video is very educative and simple to understand but I have a question.

[@MohamedBenhimaPhD]

Thanks!
Welcome

[@ayeshaqadeer1964]

For which purpose we use factor analysis