摘要：Factor analysis, one of those multivariate methods, is used for data reduction, determining the relationships between variables, and also as a classification method. The researchers frequently face with the observations that might have bad affects on the results while analysing data sets with many variables. Those kind of observations are called outliers. Outliers, usually evaluated as erroneous observations, may reflect important information about the data set, but on the other hand may cause misspecification of the model, biased parameter estimates, and may lead incorrect analysis results. The aim of this study is to use factor analysis method which will not make biased parameter estimations of the outliers. For this aim, among the robust local and scale parameter estimators which reduce the affects of outliers, fast minimum covariance determinant estimators are choosen and an application is performed. As a result, robust factor analysis estimation results are obtained which are mostly fitted to the data by reducing the affect of the outliers, with higher explained variability and with explained variables which are gathered on more significant factors.
其他摘要：Çok değişkenli analizlerden biri olan faktör analizi veri indirgeme, değişkenler arasındaki ilişkileri belirleme ve aynı zamanda sınıflandırma yöntemi olarak karşımıza çıkmaktadır. Çok sayıda değişken içeren veri setlerini analiz ederken araştırmacılar pr
