How to factor-analyze your data right: do’s, don’ts, and how-to’s.
The current article provides a guideline for conducting factor analysis, a technique used to estimate the populationlevel factor structure underlying the given sample data. First, the distinction between exploratory and confirmatory factor analyses (EFA and CFA) is briefly discussed; along with this...
| Autor Principal: | Matsunaga, Masaki |
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| Formato: | info:eu-repo/semantics/article |
| Idioma: | spa |
| Publicado: |
Editorial Bonaventuriana
2018
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| Materias: | |
| Acceso en línea: |
Matsunaga, M. (2010). How to factor-analyze your data right: do’s, don’ts, and how-to’s. International Journal of Psychological Research, 3(1), 97–110. https://doi.org/10.21500/20112084.854 |
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| Sumario: |
The current article provides a guideline for conducting factor analysis, a technique used to estimate the populationlevel factor structure underlying the given sample data. First, the distinction between exploratory and confirmatory factor analyses (EFA and CFA) is briefly discussed; along with this discussion, the notion of principal component analysis and why it does not provide a valid substitute of factor analysis is noted. Second, a step-by-step walk-through of conducting factor analysis is illustrated; through these walk-through instructions, various decisions that need to be made in factor analysis are discussed and recommendations provided. Specifically, suggestions for how to carry out preliminary procedures, EFA, and CFA are provided with SPSS and LISREL syntax examples. Finally, some critical issues concerning the appropriate (and not-so-appropriate) use of factor analysis are discussed along with the discussion of recommended practices. |
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