الفهرس 
Measuring job quality of Egypt
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Abstract
The aim of this work is to introduce a robust measure for the job quality concept in Egypt. To achieve this goal a proposed composite index for the job quality is constructed. In order to investigate the robustness of that composite index a non-parametric rank based approach for the principal component analysis (PCA) is introduced. PCA is a multivariate statistical analysis technique that used for data reduction. The data reduction multivariate technique is a general term applied to datasets with highly correlated indicators. The PCA multivariate statistical analysis technique is suffering from extreme values and large amount of noise or outliers in the data. This is primarily caused by bias interference from other factors that are present in the data during the process of data collection or while data processing phase. In this regard, a novel non-parametric robust rank-based technique for conducting PCA using the Weighted Wilcoxon Norm (WWL1-norm) instead of L2-Norm or L1-Norm is introduced. This dissertation is concerned also with the improvements and processes that are applied to real collected data in order to enhance targeting groups based on their characteristics, particularly with multivariate PCA proposed technique to construct the job quality composite index. Those primarily include Global Sensitivity Analysis (GSA) and Local Sensitivity Analysis (LSA) techniques. Both techniques have been investigated, implemented and compared regarding their abilities to check the factors of influence and the factor of less impact on the constructed composite job quality index using the real data of survey of young people in Egypt (SYPE 2009)