Analytics in Understanding Employee Attrition

Do you need this or any other assignment done for you from scratch?

We assure you a quality paper that is 100% free from plagiarism and AI.

You can choose either format of your choice ( Apa, Mla, Havard, Chicago, or any other)

NB: We do not resell your papers. Upon ordering, we do an original paper exclusively for you.

NB: All your data is kept safe from the public.

Click Here To Order Now!

Analytics in Understanding Employee Attrition

Table of Contents

Research Question
Basic Background
Methodology
Possible Data Sources
References

The concept of employee attrition is one of the pillars of human resources research and business in general. The problem of attrition when employees leave faster than they are hired can signify many undiscovered issues at a company (Frye et al., 2018). It requires deep analysis to ensure that the rate of attrition is not connected to employee concerns (Frye et al., 2018). For this reason, it is essential to consider different ways of measuring attrition, using data in a useful way, and predicting the rate of attrition based on some fundamental business characteristics. To answer the related questions, the following research proposal considers the topic of employee attrition.

Research Question

The research question focuses on statistics as a tool for analyzing attrition rates. The question may contain several segments, the foremost being:

How can Analytics help a company to understand the reasons behind employee attrition?

Additionally, it is possible to pose several secondary research questions, such as:

Can attrition rates be predicted using analytics?
Can companies use statistics to understand the specific reasons that increase or decrease attrition to manipulate attrition rates?

Basic Background

The issue of attrition can be viewed from several angles, as some degree of attrition is necessary for the company to grow. For example, attrition may be related to saved costs for the employer if the latter finds that a smaller number of workers can sufficiently complete all tasks (Repaso et al., 2022). At the same time, high turnover has many significant risks for any business, as it requires the company to spend money on hiring and training new recruits more often than necessary (Repaso et al., 2022). The negative consequences greatly outweigh the potential benefits and put businesses in a difficult position.

Moreover, attrition can be measured on a company-wide as well as a department level and problems can be separated into general and localized. A business can suffer from high attrition rates in one department and fail to see the problem in time to manage the issue (Frye et al., 2018). These reasons highlight the need for statistics on employee attrition. Several research studies have explored the use of predictive technology, machine learning, artificial intelligence, and various statistical methods (Kamath et al., 2019; Pratt et al., 2021; Raza et al., 2022). Nevertheless, one has to add to the existing scholarship to assess the possibility of prediction and analysis for companies to decrease their expenses and improve employee retention.

Methodology

The proposed research will employ a quantitative methodology to show the specific impact of statistical analysis on employee attrition. As an example, one may use a study by Repaso et al. (2022), who analyze company data collected from Kaggle and employ statistical analysis based on machine learning. While it is not necessary to use machine learning in the present project, one may take the idea of a descriptive study. The research will collect data from Kaggle, including the same set of major characteristics as well as companies attrition rates in a set period of time. These data will be analyzed using regression analysis to investigate potential correlations between the two variables.

Possible Data Sources

To collect the necessary data, the researcher has to obtain information about companies from a selected industry or segment. Such data as attrition rates may not be available to the public. However, for the purposes of this research, all data is available on Kaggle (2022). Therefore, one can use a single source of information for the investigation. Using data from one available source eliminates the problem of data inconsistency and incompleteness, creating a strong foundation for reliable conclusions.

References

Frye, A., Boomhower, C., Smith, M., Vitovsky, L., & Fabricant, S. (2018). Employee attrition: What makes an employee quit? SMU Data Science Review, 1(1), 9.

Kaggle. (2022). Employee attrition.

Kamath, D. R., Jamsandekar, D. S., & Naik, D. P. (2019). Machine learning approach for employee attrition analysis. International Journal of Trend in Scientific Research and Development IJTSRD, no. Special Issue-FIIIIPM2019, 62-67.

Pratt, M., Boudhane, M., & Cakula, S. (2021). Employee attrition estimation using random forest algorithm. Baltic Journal of Modern Computing, 9(1), 49-66.

Raza, A., Munir, K., Almutairi, M., Younas, F., & Fareed, M. M. S. (2022). Predicting employee attrition using machine learning approaches. Applied Sciences, 12(13), 6424.

Repaso, J. A. A., Capariño, E. T., Hermogenes, M. G. G., & Perez, J. G. (2022). Determining factors resulting to employee attrition using data mining techniques. IJ Education and Management Engineering, 12(3), 22-29.

Click Here To Order Now!