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MGT 4380: Human Resource Analytics Study Guide for the First Exam…
MGT 4380: Human Resource Analytics
Study Guide for the First Exam
Be familiar with the following concepts and how to apply them. The exam will include roughly 40 multiple-choice questions. Some questions will be more conceptual in nature (e.g., “What is the purpose of HR analytics?”), while others will present you with fictional organizational scenarios in which you have to apply your understanding of concepts to the situation. If you have been sufficiently engaged with the course material, you should not have trouble coming to the right answer for the multiple-choice questions. Again, this is a closed book exam, so you will have to put all your class notes and your textbook away while completing the exam. When studying of the exam, it’s important to reference the Powerpoint slides and required videos, as well as brush up on concepts we’ve discussed by re-reading relevant sections of the textbooks. It is also helpful to reference the “key takeaways” at the end of each set of Powerpoint slides.
Content Areas:
Module 1: The Value of HR Analytics and Fundamental Concepts
How is HR analytics defined? What is the purpose of using HR analytics in organizations?
What is the history of HR analytics? How was HR viewed in the past within organizations?
What does analytics allow HR and decision-makers to do in terms of understanding the impact of people at work?
What are some key HR-related questions, or areas, that analytics can help to address?
Are all organizations using analytics to the same degree or do they differ in terms of their analytics “maturity”?
What are the diverse types of roles that can be found on HR analytics teams?
What’s the difference between a sample and a population? Is a sample mean for a given variable (e.g., employee engagement) the same as the population for the same variable?
What’s the basic distinction between descriptive and inferential statistics?
To make inferences in inferential statistics and HR analytics, we need data, which involves measuring variables. What is a variable?
What is the distinction between an independent variable (IV) and a dependent variable (DV)?
What are the four types of scales of measurement for variables? Employee survey measures, consisting of certain statements rated on, say, a 1-7 scale are an example of which scale type? Survey measures of employee demographic factors (e.g., gender, race, managerial status) are examples of which type of scale of measurement?
Module 2: Research Design, Measurement, Methods of Data Collection, Descriptive Statistics, and Purposeful HR Analytics
In terms of research design, what are the characteristics of and distinctions between non-experimental, survey designs vs. experimental designs vs. quasi-experimental designs?
What is the distinction between quantitative and qualitative research?
What are the three types of measures of central tendency in descriptive statistics?
Range, variance, and standard deviation are all measures of what in descriptive statistics?
What does the standard deviation for a variable specifically capture?
What is the role of asking relevant business questions in purposeful HR analytics? What are the qualities of a god business question in any HR analytics project?
Module 3: Inferential Statistics, Hypothesis Testing, and Correlation Analysis
When analyzing employee data, why do we have to use inferential statistics?
In inferential statistics, we engage in hypothesis testing. What is a hypothesis?
What are the five different elements of a significant test in hypothesis testing?
What does a correlation coefficient tell us about the relationship between two variables measured quantitatively (e.g., store-level employee engagement and profitability)?
Does a correlation between two variables mean that one variable causes the other?
Module 4: HR Analytics and Return on Investment (ROI), Data Sources, Measurement Reliability and Validity, and Two-Group Comparison Tests
Why do we need multiple types of data in HR analytics projects to assess the return on investment (ROI) of HR initiatives? What types of data might be used in projects?
Why does measurement reliability and validity matter so much in HR analytics? What is the distinction between reliability and validity in measurement?
How do we quantitatively measure “intangible” constructs in HR analytics (e.g., subjective self-report measures, behavioral measures, performance, measures, and physiological measures)?
What are the different forms of reliability in measurement of variables?
What are the different forms of validity in measurement of variables?
What is the purpose of a two-group comparison test, such an independent samples t-test? How do two-group comparison tests compare to correlation analysis? What is the distinction between independent and dependent samples in two-group comparisons?
Module 5: Setting your HR Analytics Direction, Engaging with Stakeholders, and Getting a Quick Win
When getting started building an HR analytics function and setting an analytics direction, why is it vital to identify project sponsors, understand the drivers of demand for analytics in the organization, and generating a memorable vision and mission?
What types of HR analytics stakeholders are there in and outside of an organization?
With respect to “stakeholders served,” why is it important to engage HR (e.g., CHRO) and business (e.g., CEO) leaders as key stakeholders in HR analytics projects? When is it easier to engage these stakeholders in analytics projects? What should be a primary goal of conversations with such individuals?
Regarding “stakeholders depended upon,” why is it important to engage data owners, technology owners, subject matters experts, etc. in analytics projects?
With respect to “stakeholders impacted,” why is it important to engage employees and managers in HR analytics projects? Why is it important to communicate the expected benefits of such projects to such individuals and keep in mind their interests?
