Using IBM SPSS and Excel Together to Analyse Employee Attrition Data
Excel and IBM SPSS were the tools that I relied on to get the job done while I was working on a recent assignment project for HR Analytics that involved analysing employee Attrition. Excel was wonderful for managing the smaller datasets and basic data visualisation, but the sophisticated statistical analysis tools of IBM SPSS were essential for analysing the bigger and more complicated datasets. Excel was great for handling the smaller datasets. In addition, the customizability of IBM SPSS and the capability to develop scripts in several programming languages enabled automation and customisation of analyses, while the intuitive interface of Excel made it simple to immediately visualise data. The disparity in price between the two products was another thing that needed to be taken into consideration. IBM SPSS was the more expensive option because using it required purchasing a licence. In conclusion, combining the advantages of both methods enabled a more extensive examination of employee turnover, which, in the end, led to a better knowledge of the fundamental trends and reasons that contribute to it.
Excel and IBM SPSS are both widely used programmes for doing data analysis; nevertheless, these two programmes are not identical in any respect, including their capabilities, advantages, or disadvantages. The following is a comparison of the two different kinds of tools:
Excel is a wonderful tool for managing datasets that are small to medium in size, but IBM SPSS is built to easily manage massive datasets. Both programmes are used for data processing. Excel can manage up to one million rows of data, whereas IBM SPSS can manage millions of rows of data in addition to thousands of variables.
Analysis features: IBM SPSS is a statistical analysis programme that contains a large range of statistical tests and analysis features that are not accessible in Excel. These statistical tests and analysis features may be used to examine and evaluate data. SPSS has a variety of sophisticated capabilities, including those for regression analysis, factor analysis, analysis of variance (ANOVA), and more. Excel is equipped with several fundamental statistical tools, but its capabilities pale in comparison to those of SPSS.
Excel is often easier to use and more user-friendly than IBM SPSS. Excel also has more capabilities than IBM SPSS. It features a user interface that is straightforward and easy to understand, and the vast majority of people are already familiar with it. On the other hand, IBM SPSS has a more challenging learning curve and need for some form of training to operate successfully.
Personalization: When compared to Excel, IBM SPSS offers a greater degree of personalization. Users have the ability to automate and personalise investigations by writing scripts and code in a variety of computer languages, including Python and R. Excel does have some scripting features, but they are somewhat restricted, and the programme is not nearly as customisable as IBM SPSS.
Price: Because IBM SPSS is a commercial product, in order to use it you will need to purchase a licence, which might be too expensive for sole proprietors and operators of small firms. Excel is available both as a component of the Microsoft Office suite, which a large number of people already have access to, as well as a stand-alone version, which can be purchased for a reduced price.
In a nutshell, IBM SPSS is a more potent piece of statistical analysis software than its competitors. It was developed specifically to manage complicated studies and massive datasets. On the other hand, the learning curve is more steep, and it may be more expensive. Excel is a more user-friendly tool for basic data analysis and visualisation, but it does not match favourably to SPSS in terms of its capabilities. To choose which tool is most appropriate for the user's particular analytical needs, it is necessary to take into account the user's preferences as well as their financial constraints.
Here is a quick synopsis of the work I completed for my project assignment and some thoughts I had while working on it.
When dealing with employee attrition, remember that several circumstances might cause an employee to depart. Qualitative data like employee feedback and exit interviews can complement data analysis. Since employee attrition may affect both individuals and the organisation, it's crucial to tackle this problem with respect and understanding.
Decision-making information and insights on employee turnover:
Find the most vulnerable clusters of workers: Look at the numbers to see if there are any obvious clusters of high-risk personnel, such as those in a particular division or who have been with the organisation for a long period. With this knowledge, companies may tailor their strategies to better retain certain demographics. A corporation may opt to enhance onboarding and training initiatives, for instance, after discovering that turnover is highest among workers with less than a year of service.
Raise morale in the workplace: Spend money on initiatives and programmes that boost employee participation. Employee turnover rates have been demonstrated to drop when engagement levels are high. If you want to impress your boss, wow your coworkers, or just impress yourself, try using some of these tips.
Discuss monetary and non-monetary incentives: Compare the company's pay and perks to those of other organisations in the same field, and adjust accordingly. To entice and keep the best and brightest, some businesses are introducing perks like telecommuting and adaptable work hours.
Talk to people as they leave: Gather information on why employees are leaving through exit interviews. Finding patterns and places for development becomes possible with this data. If, for instance, low job security is cited as a major factor in employees' decisions to leave, the organisation may look into ways to promote from within.
Check how happy your workers are: To keep tabs on how content your staff is with their work and the organisation as a whole, it's important to conduct frequent satisfaction surveys. Using this data, businesses may address issues before employees start considering leaving. For instance, if a survey finds that workers are unhappy with their work-life balance, the business can start providing more adaptable schedules.
Businesses may use this information to inform choices like whether or not to invest in employee engagement programmes, whether or not to make changes to remuneration and benefits, whether or not to launch new training and development initiatives, and whether or not to open up new lines of communication and solicit employee input. It's also possible that businesses may alter their hiring practises to appeal to and keep workers from high-risk demographics.
Reference:
Program Course (Analytics for HR) Under - Dr. Gunjan Mohan Sharma
