Part 1 – Descriptive Stats & EDA
You are hired as a HR Analytics consultant to assist the HR Manager of this company
1. The HR manager proposed that the firm should hire more employees below the age of 35. The managing partner notes that this would be a trade-off because this could create a scarcity of experienced professionals.
Find out if employees below the age of 35 are more innovative than ones above 35 (Use CLAGE for AGE, MEANINNOVATION for innovation.
2. The company where this data has come from has been struggling to innovate. To enable more innovation, the HR manager has proposed to organize a number of networking events in the firm to stimulate interdepartmental connections.
The managing partner of this firm, however, is skeptical. Test whether there is a significant association between networking behavior and innovation. Use the appropriate test you learned in this module. What is the outcome of the test?
The HR manager is interested in other predictors for innovation. She asks you to analyze all the key concept in the engagement survey in an exploratory analysis. This kind of analysis is used to discover significant relationships without a pre-defined hypothesis.
3. What is the risk of such an analysis?
4. Conduct an exploratory analysis using the following variables. Which variables could potentially predict innovation?
• Normative commitment
• Tertius iungens
• Employee engagement
• Personal initiative
• Networking behavior
• Career self-management
• Organizational commitment
• Professional commitment
• Time pressure
• Work experience
Part 2 – Predictive Analytics
In the quest for innovation, the HR manager is looking into factors that the company should select on when they bring in new recruits. She asks you to look at the engagement survey, and specifically to Tertius Iungens (MEANTI). Tertius iungens is a networking behavior in which a person will connect
unconnected people in their network that will have use for each other.
This means that instead of channeling all the information between two connections, the person connects them with each other so
there is a free flow of information and new ideas.
1. Create the strongest regression model to predict innovative behavior. Evaluate the model.
2. Create the strongest regression model to predict performance. Evaluate the model.
3. Create the strongest regression model to predict engagement. Evaluate the model.
4. Based on this data set, which variables do you think could be predictors of mobility behavior (CSMmobility, an indicator of turnover intention)?
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