Principal Component Analysis Technique for Finding the Best Applicant for a Job

Abstract

This paper focuses on the use of principal component analysis technique (PCA) in choosing the best applicant for a job in Cihan University-Erbil. Cihan University has a panel of judges (University staff) to help in choosing the applicants for a job by evaluating or rating each one on different scale of preference and different type of characteristics. This process usually creates complicated multivariate data structure, which consists of 25 applicants for a job rated by a panel of judges on 17 characteristics [25 rows, applicants, and 17 columns, characteristics]. PCA plays a crucial role in conducting impactful research as it offers a potent technique for analyzing multivariate data. Researchers can utilize this method to extract valuable information that aids decision-makers in problem-solving. To ensure the appropriateness of data for PCA, certain testing procedures are necessary. In this study, two tests, namely the Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy and Bartlett's Test of Sphericity, were performed, and their significance is vital. The findings indicate that the data employed in this research are suitable for PCA. Scoring and ranking procedures as extra tools were used to see that applicant No. (1) is the first accepted for a job, applicant No. (17) is the second, applicant No. (12) is the third, and so on.

Keywords

• Allocating Scores and Ranks
• Eigen Values and Eigen Vectors
• Matrices
• Multivariate analysis
• Principal component analysis