Abstract: Introduction. The unemployment index is one of the most important indicators of development in developed countries. The problem of unemployment is one of the most prominent problems that most societies in the world suffer from, in general, and in the Arab world, in particular.
Objective. The following discussion has three primary goals. These are to: (1) determine the demographic information, (2) investigate the causes of unemployment in Saudi Arabia and (3) compare the results between factor analysis (FA) and regression analysis (RA).
Methods and Materials. A cross-sectional questionnaire was distributed to the general population in the KSA to collect data, and data were collected using Google forms. Factor analysis and regression analysis were used to analyze the data.
Results. The findings revealed that H1, H2, H3, H4, and H5 were significant causes of unemployment in Saudi society, whereas H1 was a significant cause of unemployment in the RA examination.
Conclusion. As a result, H1 (expatriate foreign workers) was a significant source of unemployment.
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Keywords and phrases: unemployment, multivariate, factor analysis, regression analysis, SPSS.
Received: July 27, 2023; Accepted: September 25, 2023; Published: December 9, 2023
How to cite this article: Alanazi Talal Abdulrahman, A study of unemployment in Saudi society using multivariate techniques, Advances and Applications in Statistics 91(1) (2024), 47-58. http://dx.doi.org/10.17654/0972361724004
This Open Access Article is Licensed under Creative Commons Attribution 4.0 International License
References: [1] A. Alrasheedy, The cost of unemployment in Saudi Arabia, International Journal of Economics and Finance 11(11) (2019), 1-30. [2] N. Almutairi, The effects of oil price shocks on the macroeconomy: Economic growth and unemployment in Saudi Arabia, OPEC Energy Review 44(2) (2020), 181-204. [3] G. Pison, P. J. Rousseeuw, P. Filzmoser and C. Croux, Robust factor analysis, Journal of Multivariate Analysis 84(1) (2003), 145-172. [4] H. R. Keller and D. L. Massart, Evolving factor analysis, Chemometrics and intelligent laboratory systems, 12(3) (1991), 209-224. [5] G. K. Uyanık and N. Güler, A study on multiple linear regression analysis, Procedia-Social and Behavioral Sciences 106 (2013), 234-240. [6] Alanazi Talal Abdulrahman and Osama Alamri, Robust estimation methods used to study the reasons behind increasing divorce cases in Saudi society, Mathematical Problems in Engineering 2021 (2021), 6. Article ID 4027599. https://doi.org/10.1155/2021/4027599. [7] A. T. Abdulrahman, R. Alharbi, O. Alamri, D. Alnagar and B. Alruwaili, Application of supersaturated design to study the spread of electronic games, Mathematics and Statistics 9(3) (2021), 278-284. [8] Alanazi Talal Abdulrahman, Ishfaq Ahmad, Dalia Kamal Alnagar, R. A. Ramadan, T. A. Alraqad and Y. M. Jawarneh, Multivariate regression proposal to the gap between university education and labor market: the kingdom’s vision 2030, Mathematical Problems in Engineering 2022 (2022), 14. Article ID 7219523. https://doi.org/10.1155/2022/7219523. [9] A. T. Abdulrahman, Methods for designing experiments to study the actual causes of the housing crisis, International Journal of Analysis and Applications 19(4) (2021), 542-560. [10] Alanazi Talal Abdulrahman, Abdalwahab Omar Alshammari, Anas Alhur and Afrah Ali Alhur, Robustness of supersaturated design to study the causes of medical errors, Mathematical Problems in Engineering 2021 (2021), 7. Article ID 9682345. https://doi.org/10.1155/2021/9682345. [11] Alanazi Talal Abdulrahman, Njood Saud Alshammari, Dalia Kamal Alnagar and Ishfaq Ahmad, Analysis of the factors to search for the actual causes that led to the accumulation of debts on individuals, Mathematical Problems in Engineering 2022 (2022), 8. Article ID 5786941. https://doi.org/10.1155/2022/5786941.
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