Data Analysis and Decision Making MGMT5504
Assessment #2 Individual Project- Data Descriptors
(Individual assessment)
(30% total)
The purpose of this project is to identify the ‘best’ combination of independent variables (drivers of the key performance indicator) to predict the dependent variable (key performance indicator). The dependent variable in this case is job performance however you will need decide which one variable (from those in the table below- see Table 1) is the best measure of job performance. The statistical technique you need to use is regression analysis (multiple linear regression OR logistic regression). In the MS Excel workbook you will notice a column labelled data_type (0= training data, 1= test data). You need to refine your model using the training data then test your final model with the test data. There are many metrics for model validation, we’ll use one known as Mean Absolute Error (also called MAE). With the MAE metric, we take the absolute value of each error (error=actual−predicted). This converts each error to a positive number. We then take the average of those absolute errors. Before doing this however, there are many steps- see diagram below (repeated from Assessment 2 Information sheet).
| Variable Label | Variable Description |
| ID | Employee ID |
| Data_type | Use the training data to build and refine your model. Use the test data to test your final/chosen model. (0=training, 1=test) |
| Department | The functional department that the employee belongs to. |
| Role | The employee’s position or title. |
| Will_Relocate | Is the employee willing to relocate? (1=yes, 0=no) |
| Percent_Remote | The percentage of the employee’s work that is done remotely. |
| EMP_Sat_OnPrem_1- EMP_Sat_OnPrem_5 | Five items from a survey that was sent to employees by a third party. On prem (On premise) means work on the corporation’s physical work locations. The items relate to satisfaction with on premise work. The higher the number the higher the employee satisfaction |
| EMP_Sat_Remote_1- EMP_Sat_Remote_5 | Five items from a survey that was sent to employees by a third party. Remote (distance employee) means work away from the corporation’s physical work locations. The items relate to satisfaction with remote work. The higher the number the higher the employee satisfaction |
| EMP_Engagement_1- EMP_Engagement_5 | Five items from a survey that was sent to employees by a third party. Engagement represents the employee’s feeling about how they feel about being engaged in company activities.The higher the number the higher the employee engagement |
| last_evaluation | The score on the last employee evaluation.The higher the number the more positive the evaluation |
| number_project | The number of projects the employee works on throughout the year. |
| average_monthly_hours | The average number of hours the employee works. |
| time_spend_company | Years of service. |
| Work_accident | Has the employee been involved in a work accident? (0=no, 1=yes) |
| Function | What is your function within the organisation? |
| Salary | Relative pay grade (low, medium, high) by role. |
| Gender | Gender or how the person identifies. (M=male, F=female) |
| LinkedIn_Hits | The number of times employee visits LinkedIn networking sites. |
| Emp_Work_Status_2- Emp_Work_Status_5 | Four items from a survey that was sent to employees by a third party. Status represents how strongly employee feels about their status level in the organization. The higher the number the more positive the employee feels about their status level |
| Emp_Identity | How the employee identifies themselves with the company. The higher the number the more positively the employee identifies with the company |
| Emp_Role | How the employee identifies themselves with the importance of their role in the company. The higher the number the more regard the employee has towards the importance of their role with the company |
| Emp_Position | How the employee identifies themselves with the importance of their position in the company. The higher the number the more regard the employee has towards the importance of their position with the company |
| Emp_Title | How the employee feels about their title. The higher the number the more positive the employee feels about their title |
| Emp_Competitive_1- Emp_Competitive_5 | Five items from a survey that was sent to employees by a third party. How employee feels about the competitive nature of work in the organization. The higher the number the more positive the employee feels about the competitive nature of the work |
| Emp_Collaborative_1- Emp_Collaborative_5 | Five items from a survey that was sent to employees by a third party. How employee feels about the collaborative nature of work in the organization. The higher the number the more positive the employee feels about the collaborative nature of the work |
| Sensor_StepCount | Sentient devices are used to capture certain employee activities. In this case number of steps. |
| Sensor_Heartbeat (Average/Min) | Sentient devices are used to capture certain employee activities. In this case heartbeat. |
| Sensor_Proximity (1-highest/10-lowest) | Sentient devices are used to capture certain employee activities. In this case how close they are to their company laptop. |
Steps:
- Refer to the Assessment information handout and marking rubric handout.
- Understand the dataset—refer to the Excel data (HR_data.xlsx) to get a feel for the data and survey items.
- Select the training data only.
- Screen and clean the data- check for errors.
- Code String variables (ie Department, Role, Function, Salary, Gender) using 0 and 1.
- Reverse-score negatively-worded items if necessary.
- Create composite variables (ie Satisfaction on Premise, Satisfaction Remote, Engagement, Work Status, Competitive, Collaborative).
- Run descriptive statistics to understand the data and test assumptions (ie normality) – charts (bar, pie, histogram), tables (crosstabs), and numerical summaries (mean, median, mode, standard deviation) for each variable.
- Analyse the training data- (ie run correlations and multiple regression analysis) to answer the research question (identify the ‘best’ combination of independent variables (drivers of the key performance indicator) to predict the dependent variable (key performance indicator)). Make sure you check the assumptions are met. Correlations give you an idea of which variables are likely to be good predictors.
- Run the final regression model on the test data.
- Fully interpret/explain the results.
- Communicate the results/findings in non-statistical terms with FIVE Powerpoint slides. Please note: you do not need to display all MS Excel output; include the output that best ‘tells the story’ of the data.
- Upload the Powerpoint slides and MS Excel Workbook file to LMS before 6th May 2pm.
- SMILE!
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