Academic Master

Human Resource And Management

Leadership Employment Dataset

The human resource department is one of the most important elements in any organization. The department is responsible for all the staffing activities, which include but are not limited to recruiting, firing, training, etc. For an organization to achieve its development and financial goals, it must have the best talents in the market. Therefore, the process of hiring is critical to organizational success. To acquire the best talent, an organization must have an effective recruitment system that scrutinizes job applicants and hires the most qualified or those with attributes that are important to organizational success (Huda & Ardi, 2021). For an organization to have an effective hiring process, it must use quantitative inferences. Ideally, using quantitative data enables an organization to prepare evidence-based hiring and decision-making models, increasing the process’s efficiency. To make an effective model, an organization would require data such as skills, qualifications, and personal attributes.

The non-profit organization should consider various attributes before hiring. To start with, the personality traits and skills of the applicants play an important role in defining a successful employee. Based on the data, the traits and skills of the most successful employees will be taken into consideration, and recommendations will be given based on the outcome of the analysis. Tests such as chi-square, analysis of variance (ANOVA), and independent sample tests will determine the relationship between performance and personal attributes and skills. Second, individual attributes and skills will be used to help the organization identify leaders and non-leaders from the applicants. Further, the qualitative and quantitative analysis findings will be very useful in helping an organization design a model that can be used as a contingency hiring plan in the future.


Huda, A., & Ardi, N. (2021). Predictive Analytic on Human Resource Department Data Based on Uncertain Numeric Features Classification. Int. J. Interact. Mob. Technol.15(8), 172 181.



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