Use ChatGPT for HR Analytics

ChatGPT can save time when carrying out HR analytics in many ways. Below is a simple example that anyone with some fundamental knowledge of Excel can carry out.

You are part of my HR analytics team. Turnover in the company has been high, and I want you to investigate the causes. We have employee data from our HRIS, including level in the organization age, gender, pay levels against our salary range midpoints, tenure with the company, time since the last promotion, number and date of training courses attended which were paid by the company, their performance appraisal rating history, home address which can help determine distance from their home to the office. We also have our recent engagement survey results, including engagement score by department, including the employee's assessment of their supervisor as well as their desire to stay in the company. Create a list of 10 possible hypotheses we can explore with this data, and include the kind of statistical analysis we should carry out to see if the data supports or not these hypotheses. Finally, show examples of graphs or charts we can use to visualise the results of these statistical analyses.

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Sure, I'd be happy to help you with that. Here are 10 possible hypotheses we can explore with the data you provided, along with the statistical analysis and visualisation methods that can be used:

1. Pay level: Employees may be leaving the company due to being underpaid. We can perform a regression analysis to determine the relationship between pay level and turnover rate. A scatterplot can be used to visualise the relationship between these two variables.

2. Age: Older employees may be more likely to leave the company due to retirement. We can create a histogram to determine the distribution of age among employees who have left the company.

3. Gender: Women may be leaving the company at a higher rate than men due to gender discrimination or a lack of promotion opportunities. We can perform a chi-squared test to determine if there is a significant difference in the turnover rate between men and women.

4. Tenure: Employees with shorter tenure may be leaving the company at a higher rate than those with longer tenure. We can perform a t-test to determine if there is a significant difference in the turnover rate between employees with short and long tenure.

5. Performance appraisal rating: Employees who consistently receive poor performance appraisal ratings may be more likely to leave the company. We can perform a correlation analysis to determine the relationship between performance appraisal ratings and turnover rate.

6. Training courses: Employees who have not received training courses or who have not attended training courses in a long time may be more likely to leave the company. We can perform a regression analysis to determine the relationship between training courses and turnover rate. A bar chart can be used to visualise the average number of training courses attended by employees who left the company.

7. Distance from home to the office: Employees who live far away from the office may be more likely to leave the company. We can perform a t-test to determine if there is a significant difference in the turnover rate between employees who live close to the office and those who live far away.

8. Performance appraisal rating history: Employees who have a history of receiving poor performance appraisal ratings may be more likely to leave the company. We can perform a regression analysis to determine the relationship between performance appraisal rating history and turnover rate.

9. Engagement score: Departments with lower engagement scores may have higher turnover rates. We can perform a correlation analysis to determine the relationship between departmental engagement scores and turnover rate. A scatterplot can be used to visualise the relationship between these two variables.

10. Supervisor assessment: Employees who rate their supervisor poorly may be more likely to leave the company. We can perform a t-test to determine if there is a significant difference in the turnover rate between employees who rate their supervisor poorly and those who rate them well.

For each of these hypotheses, we can use different types of charts or graphs to visualise the results, such as histograms, scatterplots, bar charts, and line graphs. For example, we can use a scatterplot to show the relationship between pay level and turnover rate, a bar chart to show the average number of training courses attended by employees who left the company, and a line graph to show the trend in turnover rate over time.

These are all very useful suggestions for testing. Any or all of these can point at possible areas for improvement in HR policies or practices with existing data that all HR departments should have.

However, you may feel that analyzing one variable at a time may not be right, but you are not sure if it may be better to create a multiple regression model where the variables interact. Let's ask ChatGPT for the pros and cons of one approach over the other.

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Do you recommend that we test each of these hypotheses separately, or should these be combined in a multiple regression model? What would be the advantages and disadvantages of each of these two approaches? Format your answer as a table with bullet points.

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Certainly, here's the table with the advantages and disadvantages of each approach, formatted as requested:

	​Advantage	Disadvantages
Single Hypothesis Testing	Easier to interpret results for each hypothesis separately Less complex analysis and modeling Less data required for each hypothesis Can by more efficient if testing a small number of hypotheses	Can lead to type I errors (false positivesI if multiple hypotheses are tested without correction Can miss interactions and complex relationships among variables Can be time-consuming if many hypotheses are being tested Cannot account for multiple explanatory variable simultaneously
Multiple Regression Analysis	Can account for multiple explanatory variables simultaneously Can detect interactions and complex relationships among variables Can provide more accurate predictions and modelin Less likely to lead to type I errors when multiple hypotheses are tested Can be more efficient if many hypotheses are being tested

I hope this format is useful!

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In this case, ease of presentation may supersede increased accuracy. What do you think?