2025-03-262025-03-2630-09-2016https://pyxida.aueb.gr/handle/123456789/7341Principal Component Analysis is the oldest and most famous technique of Multivariate Analysis and can be used as a tool for researchers to deal with missingness in datasets. The aim of this thesis is the description, the analysis and the comparison of the techniques that belong in the category of Principal Component Analysis. All these available techniques are presented with respect to their theoretical framework and then a comparison of these methods in different percentages of missingness and for different types of datasets (simulated and real) follows in order to see which method responds better depending on the case and which is totally the most reliable.80 p.CC BY: Attribution alone 4.0https://creativecommons.org/licenses/by/4.0/Principal Components AnalysisMultivariate analysisFixed effect modelRandom effect modelText