Exploratory factor analysis or simply factor analysis is a technique used for the identification of the latent relational structure. Alexander beaujean and others published factor analysis using r find, read and cite all the research you need on researchgate. Bates provides the pdf documents for undergraduate students7. Factor analysis is one of the oldest structural models, having been developed by spearman in 1904. Factor analysis rachael smyth and andrew johnson introduction forthislab,wearegoingtoexplorethefactoranalysistechnique,lookingatbothprincipalaxisandprincipal. Models are entered via ram specification similar to proc calis in sas. Use the psych package for factor analysis and data. Introduction to factor analysis faculty naval postgraduate school. One of the most subtle tasks in factor analysis is determining the appropriate number of factors. Principal components and factor analysis in r functions. It is an assumption made for mathematical convenience. He tried to explain the relations correlations among a group of test scores, and suggested that these scores could be generated by a model with a single common factor, which. Using r for data analysis and graphics introduction, code and commentary j h maindonald centre for mathematics and its applications, australian national university.
Using r and the psych for factor analysis and principal components analysis. Understand the steps in conducting factor analysis and the r functionssyntax. Alexander beaujean, baylor university r r development core team, 2011. In addition to this standard function, some additional facilities are provided by. The princomp function produces an unrotated principal component analysis. Volume 18, number 4, february 20 issn 15317714 factor analysis using r a. Let us understand factor analysis through the following example. As for the factor means and variances, the assumption is that thefactors are standardized. Use principal components analysis pca to help decide. Confirmatory factor analysis cfa is a subset of the much wider structural equation modeling sem methodology. In the next few posts, we will explore the principal component method of factor analysis with the correlation matrix \ r \ as well as rotation of the loadings to help improve interpretation of the factors.
In addition to this standard function, some additional facilities are provided by the max function written by dirk enzmann, the psych library from william revelle, and the steiger r library functions. Similar to factor analysis, but conceptually quite different. Another goal of factor analysis is to reduce the number of variables. Using r for data analysis and graphics introduction, code. Factor analysis with the principal component method and r. Books giving further details are listed at the end. Steiger exploratory factor analysis with r can be performed using the factanal function. Use the psych package for factor analysis and data reduction william revelle department of psychology northwestern university june 1, 2019 contents 1 overview of this and related documents4 1. The analyst hopes to reduce the interpretation of a 200question test to the study of 4 or 5 factors. Exploratory factor analysis with r can be performed using the factanal function. Illustrate the application of factor analysis to survey data.