Multivariate data analysis technique that allows to reduce the dimensionality of a high set of interrelated variables through a set of variables called “factors”, from which the original variables can be extracted by means of linear combinations of them. Their construction is done by maximizing the common variance of the variables.
Published in Chapter:
Statistical Techniques for Research
Jose Carlos Casas-Rosal (Universidad de Córdoba, Spain), Carmen León-Mantero (Universidad de Córdoba, Spain), Noelia Jiménez-Fanjul (Universidad de Córdoba, Spain), and Alexander Maz-Machado (Universidad de Córdoba, Spain)
Copyright: © 2021
|Pages: 13
DOI: 10.4018/978-1-7998-3479-3.ch044
Abstract
Data analysis and statistics are tools that are involved in research and are essential to it. There is a wide variety of techniques that allow one to analyze any set of data depending of the desired goal. However, and due to the variety of techniques, its misuse is very common, and the results obtained from its application cannot be taken into consideration because of its non-validity. The objective of this chapter is to create a classification of the main statistical techniques used in different research fields, adding a brief definition of them and specifying their utility, data hypothesis needed for their use, and software required to use them. Special emphasis is placed in R, a free and open source software with multiple packages what allows one to apply these techniques in an effective and simple way. Among the statistical techniques that are desired to be included in this classification are descriptive analysis, graphic analysis, parametrical and non-parametrical hypothesis testing, principal component analysis, factor analysis, and structural equation modeling.