Factor Analysis: Combining Related Question-Items into Latent Variables

Saiyidi Mat Roni () and Hadrian Geri Djajadikerta ()
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Saiyidi Mat Roni: Edith Cowan University, School of Business and Law
Hadrian Geri Djajadikerta: Edith Cowan University, School of Business and Law

Chapter Chapter 4 in Data Analysis with SPSS for Survey-based Research, 2021, pp 55-67 from Springer

Abstract: Abstract This is the fun part. This is the first time you get to see if your survey questions actually work the way you wish they do. In this chapter, we explain how to ‘compress’ multiple questions in your survey into a set of groups. These groups become your latent (unobserved) variables such as attitude, intention, and motivation each measured by many questions. You can see how the questions cluster together giving you the structure you expect or sometimes speaking to you that there is another pattern hidden in the dataset which breaks your anticipation (and your heart). It’s like driving – even if you have Google map guiding you to your destination, the actual journey can differ from your plan.

Keywords: Communality; Factor analysis; Kaiser-Meyer-Olkin; Oblique rotation; Orthogonal rotation; Principal component analysis; Rotation method (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-16-0193-4_4

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DOI: 10.1007/978-981-16-0193-4_4

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