TY - JOUR
AU - Markela Muca
AU - Llukan Puka
PY - 2014/01/31
Y2 - 2022/12/05
TI - R, MATLAB AND SPSS FOR FACTOR ANALYSIS PURPOSES: SOME PRACTICAL CONSIDERATIONS AND AN APPLICATION
JF - European Scientific Journal, ESJ
JA - ESJ
VL - 10
IS - 3
SE - Articles
DO - 10.19044/esj.2014.v10n3p%p
UR - https://eujournal.org/index.php/esj/article/view/2625
AB - Factor analysis and principal component analysis are two techniques which carry out in a set composed of p-variables as well as large number of data (in multivariable statistical analysis). Both techniques aim to reduce the number of primary variables by calculating a smaller number of them, called factors or principal components(Hair, et al., 2010).In most cases these techniques are applied together, even though they are two different methods. Both aproaches have similarities and differences. A view either oriented on similarities or the differences is reflected in software implementations. In this paper we express some practical considerations on three softwares: SPSS, MatLab and R, related to factor analysis. A real data set is used for this purpose. SPSS does not offer the PCA program as a separate menu item, as MatLab and R. The PCA program is integrated into the factor analysis program. Principal component and maximun likelihood are used to estimate weights, Varimax and Promax are used to estimate weight with rotation while regression technique is used to estimate scores. These are the only estimating procedure available in the base package of R. Finally, we will discuss their solutionswith the mentioned software packages
ER -