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2019 | OriginalPaper | Buchkapitel

8. Principal Component and Factor Analysis

verfasst von : Marko Sarstedt, Erik Mooi

Erschienen in: A Concise Guide to Market Research

Verlag: Springer Berlin Heidelberg

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Abstract

We first provide comprehensive and advanced access to principal component analysis, factor analysis, and reliability analysis. Based on a discussion of the different types of factor analytic procedures (exploratory factor analysis, confirmatory factor analysis, and structural equation modeling), we introduce the steps involved in a principal component analysis and a reliability analysis, offering guidelines for executing them in SPSS. Specifically, we cover the requirements for running an analysis, modern options for extracting the factors and deciding on their number, as well as for interpreting and judging the quality of the results. Based on a step-by-step description of SPSS’s menu options, we present an in-depth discussion of each element of the SPSS output. Interpretation of output can be difficult, which we make much easier by means of various illustrations and applications, using a detailed case study to quickly make sense of the results. We conclude with suggestions for further readings on the use, application, and interpretation of factor analytic procedures.

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Vorheriges Kapitel Regression Analysis

Nächstes Kapitel Cluster Analysis

Other methods for carrying out factor analyses include, for example, unweighted least squares, generalized least squares, or maximum likelihood but these are statistically complex.

Related discussions have been raised in structural equation modeling, where researchers have heatedly discussed the strengths and limitations of factor-based and component-based approaches (e.g. Sarstedt et al. 2016a; Hair et al. 2017b).

Note that Fig. 8.3 describes a special case, as the five variables are scaled down into a two-dimensional space. In this set-up, it would be possible for the two factors to explain all five items. However, in real-life, the five items span a five-dimensional vector space.

Note that this changes when oblique rotation is used. We will discuss factor rotation later in this chapter.

Note that this is only the case in PCA. When using factor analysis, the standard deviations are different from one (DiStefano et al. 2009).

Note that we omitted the error terms for clarity.

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Titel: Principal Component and Factor Analysis
verfasst von: Marko Sarstedt
Erik Mooi
Verlag: Springer Berlin Heidelberg
Buch: A Concise Guide to Market Research
Print ISBN: 978-3-662-56706-7

Electronic ISBN: 978-3-662-56707-4

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-662-56707-4_8