Learning Objectives
After reading this chapter, you should understand:
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The principles of exploratory and confirmatory factor analysis.
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The difference between principal components analysis and principal axis factoring.
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Key terms such as EigenvaluesEigenvalues, communality, factor loadings, and factor scores.
How to determine whether data are suitable for carrying out an exploratory factor analysis.
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How to interpret SPSS factor analysis output.
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The principles of reliability analysisreliability analysis and how to carry it out in SPSS.
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The basic idea behind structural equation modeling. structural equation modeling.
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- 1.
This number is calculated as kċ(k−1)/2, with k being the number of items to compare.
- 2.
Note that this changes when oblique rotation is used. We will discuss factor rotation later in this chapter.
- 3.
When variables are perfectly correlated (the correlation is −1 or 1), factor analysis is not needed.
- 4.
Note that in Fig. 8.3, we consider a special case as the five variables are scaled down into a two-dimensional space. Actually, in this set-up, it would be possible to explain all five items by means of the two factors. However, in real-life, the five items span a five-dimensional vector space.
- 5.
Researchers often argue along the lines of measurement error when distinguishing between principal components analysis and principal axis factoring (e.g., Hair et al. 2010). However, as this distinction does not really have implications for market research studies, we omitted this argument.
- 6.
Alternative procedures include the Bartlett method and the Anderson–Rubin method, which are designed to overcome potential problems associated with the regression technique. However, these problems are of rather theoretical nature and of little importance to market research practice.
- 7.
Note that in extreme cases, Alpha can also take on negative values.
- 8.
Check the Web Appendix (→ Chapter 8) for an application of more advanced methods for determining the number of factors.
- 9.
In the Web Appendix (→ Chapter 8), we illustrate the use of the parallel analysis, the broken stick method, and the minimum average partial test for determining the number of factors using this dataset.
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Sarstedt, M., Mooi, E. (2014). Factor Analysis. In: A Concise Guide to Market Research. Springer Texts in Business and Economics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53965-7_8
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