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Exploratory factorial analysis is one of the most frequently applied techniques in studies related to the development and validation of tests, because it is the technique par excellence that is used to explore the set of latent variables or common factors that explain the answers to the articles. If a query is made about what is published in relation to these types of analyses, two clearly separate trends will be found in scientific journals. One, the most numerous, in which the technique is applied to identify the underlying structure of the articles. (Lora, D. B., LaBrish, C., & Chalmers, R. P. 2014). This is an instrumental use of the analysis technique. The other, less numerous, in which the different criteria that are usually applied in the realization of an article are studied and compared. These studies investigate which decisions are more appropriate, according to the different conditions in which we apply this technique. In this case, the technique itself is the object of study, and it is so because the phases through which the factorial analysis passes require the application of decision criteria that, almost everything, have changed over time.
Exploratory analysis does not allow the researcher to define which articles are measured or which factors, nor the relationships that are assumed between the factors themselves, beyond whether they are related. It is called exploratory because we can only determine the number of factors we expect, but not their composition or the relationships that each of the factors maintains with the rest. In contrast, confirmatory analysis is characterized by allowing the researcher to define how many factors he expects, which factors are related to each other, and which articles are related to each factor (Marsh, H. W., Morin, A. J. S., Parker, P. D., & Kaur, G. 2014).
From this perspective, both methods are used to evaluate the factor structure underlying a correlation matrix, but while the exploratory analysis is used to “construct” the theory, the confirmatory analysis is used to “confirm” the theory. On the other hand, when one already has a clear idea about the variables being studied, the use of the confirmatory analysis makes it possible to test the hypothesized structure, testing whether the hypothesized model fits the data properly.
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