How to perform Principal component Analysis (PCA) on LIKERT SCALE ITEMS for QUESTIONNAIRE using SPSS

In this video, we'll guide you through the step-by-step process of conducting Principal Component Analysis (PCA) on Likert scale items in a questionnaire using SPSS. This tutorial is perfect for research scholars looking to simplify complex data into meaningful components.

Key Highlights:
Kaiser-Meyer-Olkin (KMO) Measure: Understand the importance of checking sampling adequacy to ensure PCA is appropriate for your data.
Bartlett's Test of Sphericity: Learn how to test whether your data's correlation matrix is suitable for PCA.
Eigenvalue and the K1 Rule: Discover how to determine the number of components to retain using the eigenvalue-greater-than-one rule.
Rotation (Varimax): See how to apply rotation to make your components more interpretable.
Interpreting PCA Results: Walk through how to interpret component loadings and label your components for further analysis.

By the end of this video, you'll be able to confidently run PCA on Likert scale items using SPSS, making your questionnaire analysis more robust and insightful. Don’t forget to like, share, and subscribe for more research tips!