Getting Students Ready for HR Analytics

By David Caughlin and Talya Bauer, Portland State University

Recently, Caughlin and Bauer hosted a SAGE Talks webinar on HR Analytics. In this post, you can find slides from their webinar, a link to the webinar’s recording, and answers to questions they did not get to in the Q&A.

We ran out of time for the full Q&A during our recent webinar “Getting Students Ready for HR Analytics.” We were asked by SAGE to do a blog post summarizing our responses to the questions we covered in the webinar as well as the others that we didn’t have a chance to address. So, here goes…

Question:  What advice do you have for an instructor integrating analytics into their course for the first time?

Answer:  Focus on implementing a few simple, yet illustrative tools and techniques. We have found that many students may have some fear or a of lack confidence when it comes to data and analytics. Our suggestion is to begin by acknowledging and normalizing their fears. Let them know that it’s completely normal to make mistakes during data management, analysis, and interpretation, and that often some of the biggest learning opportunities arise from making mistakes. After that, we recommend starting with big picture ideas, such as examples of recognizable companies leveraging data analytics to inform decision making; cases can be great tools for conveying this information. In terms of hands-on data analytics, target quick “wins” initially. In our experience, we have found that many students do not necessarily have a clear idea of what HR data might entail; just showing them a sample dataset can be a powerful learning tool, especially if they are tasked with cleaning the data. As another example, show students just how easy it is to calculate and interpret an HR metric like turnover rate using a tool they are likely already familiar with (e.g., Excel). This can establish some quick “wins” that build students’ self-efficacy and diminish their fears. Bit by bit, more complex types of data analytics such as regression analysis can be added, and by the end of the term, you will likely be amazed by how quickly students can learn how to perform these analyses and make inferences and recommendations.

Question:   Can you talk about benefit of this approach for non-HR majors who might be taking an intro to HRM course?

Answer:   We can think of three separate reasons that we’ve seen that lead to this approach working really well for non-HR majors, as well as HR majors. First, non-HR majors are often used to data and analysis so seeing that HR focuses on these as well helps to lend credibility in the eyes of finance and operations management majors. Second, because of the first point, having non-HR majors in the room with HR majors can be helpful in creating ways that students from different interest areas can help one another learn. Third, non-HR majors might decide to switch to HR after learning that that contemporary HR incorporates psychological theory, information systems and technology, and statistics, mathematics, and data analysis.

Question:  Can you talk more about the H in HR Analytics and how data and analytics can benefit employees/people in organizations? 

Answer:   Sometimes it’s easy to feel like numbers are “cold” and ignore the human in “H”, and this is something that David Green tackled in a blog post last year. That can happen but when teaching HR analytics, we emphasize how important it is to consider the people behind the numbers. Taking a fairness lens, being consistent with all employees is a great way to treat employees well. As an example, a few years ago, Google employed analytics and found that everyone would benefit from an across-the-board 10% raise and that this would help the organization to retain top talent. While not all organizations have the resources of Google, it is a good case in point in where using data to help make people decisions was a win-win for employees and the employer. Plus, remembering the “H” in HR encourages us to engage in ethical decision making, as we are more likely to consider the potential for adverse consequences.

Question: How do you deal with the great range of readiness/aptitude among students for the key concepts in analytics?

Answer:  This is a great question. Students arrive at colleges and universities with quite a bit of variation in their quantitative skills and experience working with data. Further, college-level mathematics and statistics often vary in terms of how much they emphasize theory versus application. Plus, from a use it or lose perspective, we have found that students often forget foundational aspects of their quantitative coursework after the passage of a year or more. To support all learners, we recommend including supplementary information on relevant mathematics and statistics. In our textbook, we provide basic reviews of different analyses, and often this is all that is required to get students started on running their own analyses. We have also found that experience with different technology platforms can also be a barrier to entry. Thus, we recommend using a tool like Microsoft Excel that they likely already have some familiarity with. Even still, we often assume that students know little more than the basics of a tool like Excel, and this is reflected by the fact that we break down the Excel Extension tutorials in the textbook to very small steps with screenshots. For some students that might be overkill, and for those students, they can speed through the tutorial. For other students, it reduces ambiguity. Finally, we have found the “learn, do, teach” approach to be highly effect. Specifically, encourage students to focus on learning the underlying concepts and context initially (“learn”), and once they are comfortable, provide them with opportunities to practice (“do”). For those students who have high levels of readiness and aptitude, encourage them to coach other students, as peer-to-peer teaching can be a great way for more advanced students to practice articulating and explaining the concepts, thereby reinforcing and building upon their own knowledge and skills, and it provides other students with an opportunity to engage in social learning.

Question: How do you teach students to deal with outliers and missing data?

Answer:  For undergraduate students who are gaining their first exposure to HR analytics, we recommend talking about the concepts of outliers and missing data but provide them with “clean” datasets and examples in which they can practice applying the different analyses in a controlled environment. We have found that this helps students focus on the bigger picture as to why the analysis would be used in the first place (e.g., apply regression to validate selection tool/assessment). As they get more comfortable with the analyses, it is helpful to emphasize to students that real-world data will be messy and contain outliers or lack concerning amounts of data; similarly, we like to describe the concept of statistical power but leave in-depth coverage of that concept and related techniques to a more advanced course. We have found that sharing these messages can be a great way to communicate with students that they are just beginning their data-analytics journey, and for some students, this message might prompt them to seek out more advanced undergraduate coursework or to consider a graduate degree. For graduate students, we do place a lot of emphasis on outliers, missing data, statistical power, and statistical assumption testing. We like to simulate life-like datasets for our graduate students that have outliers and missing data, and then equip them with diagnostic tools and decision-making skills to navigate these complexities. In general, our approach has been to build fluency with our undergraduate students so that they become aware of concepts like outliers and missing data, and with our graduate students, our approach has been to progress past fluency to mastery, which to us means that they need hands-on experiences with more advanced concepts and techniques. At the very least, we try to help students understand the boundaries of their own expertise and how to recognize when they need to reach out to or seek consultation from an expert.

Question: What is your take on using platforms like “datacamp” to teach undergraduates principles of analytics (and even HR analytics specifically)?

Answer:   Admittedly, we do not have much experience with using platforms with datacamp. With that said, we are aware of people who have found success learning data analytics using such platforms. We have come across some platforms that do an excellent job of teaching the technical details but lack information about meaningful context and lack conceptual definitions and explanations. By the end of such tutorials, a student might know how to perform the technical operations, but they may not have a clear idea about why they performed those operations and what the results mean. Context is so very important when learning data analytics, and providing a rich backstory can (a) motivate students to learn by giving them a purpose or reason, and (b) build a framework within which students can construct a more robust understanding of the technical aspects, particularly around interpreting the results.

Question: Is there a particular HR Analytics text book that you would recommend? I am thinking that the text to which you’ve been referring is an introductory HR text, not an HR Analytics text.

Answer:   This really depends on what you are trying to teach. We have seen many books that work for different aspects of literacy, fluency, and mastery in HR analytics but not necessarily one book that would work across those levels easily.

We also got a few comments which we appreciate! 

Comment: No question, just a comment:  This was very well done.  Very informative.  Compliments to both presenters.

Response: Thank you for the compliment!

Comment: THANK YOU – such a helpful and interesting webinar.

Response: THANK YOU for joining us!

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