| Course | HRM-7640 Technology, Data, and Analytics as Change Agents |
|---|---|
| Module | Module 1 |
| Paper type | Discussion forum: initial response and two comments |
| Length | About 445 words |
| Format | Discussion post with APA 7 citations |
| School | Thomas Edison State University |
| Program | MBA in Human Resource Management |
| Updated | October 2026 |
Free sample paper for HRM 7640 Module 1
Module 1 Discussion: What Should HR Try to Predict?
From Counting Departures to Predicting Them: A First Analytics Question for a Home Health Agency
Every month at Shoreline Home Health, I produce a turnover report: how many aides left, from which territories and with what tenure. Leaders read it, sigh and move on. Last year 38 percent of new aides left within ninety days. The report tells us what happened, but it does not tell us which new aides are at risk or what we could do differently.
Fitz-enz and Mattox (2014) describe a progression from descriptive analytics, which summarizes what happened, through predictive analytics, which estimates what is likely to happen, to prescriptive analytics, which recommends actions. Most HR departments, including ours, stay at the descriptive level. Reviewing the HR analytics literature, Marler and Boudreau (2017) found plenty of enthusiasm but little published proof of impact, and concluded that value comes from tying people data to business results and from HR teams able to analyze and explain.
The question I think we should try to predict first is: which newly hired aides are likely to leave within ninety days, and why? It matters because early turnover is our largest HR cost and because patients notice when their aide keeps changing. We already hold the data we would need: commute distance from each aide's home to their patients, schedule type, pay rate, prior experience, whether they were assigned a peer mentor and their recruiting source.
Building this would need three roles. A business sponsor, our director of operations, would define the question and commit to acting on the results. An analyst, me with support from our finance team, would prepare the data and build a model. And field supervisors would test whether the predictions make sense and use them. Without the sponsor and supervisors, a model would become another report that people read and ignore.
Comment on Rosa's Post
Rosa, you suggested predicting which employees will become high performers. That is valuable, but performance ratings in many organizations are inconsistent, which makes them a weak outcome to predict. Turnover has the advantage of being measured precisely. Would your organization need to improve its performance ratings before using them in a predictive model?
Comment on Kevin's Post
Kevin, your concern about employees feeling watched by analytics is important. Being open about what data are used and why, and using predictions to offer support, such as a mentor or a schedule change, rather than to screen people out, might help build trust. Rasmussen and Ulrich (2015) argue that analytics should start from business problems rather than from data, which also makes the purpose easier to explain. Has your company told employees how it uses HR data?
References
Fitz-enz, J., & Mattox, J. R., II. (2014). Predictive analytics for human resources. John Wiley & Sons.
Marler, J. H., & Boudreau, J. W. (2017). An evidence-based review of HR analytics. The International Journal of Human Resource Management, 28(1), 3-26. https://doi.org/10.1080/09585192.2016.1244699
Rasmussen, T., & Ulrich, D. (2015). Learning from practice: How HR analytics avoids being a management fad. Organizational Dynamics, 44(3), 236-242. https://doi.org/10.1016/j.orgdyn.2015.05.008
What the HRM 7640 Module 1 instructions ask for
Seven graded forums account for 35 percent of the HRM-7640 grade, with an ungraded welcome thread beside them. The Module 1 forum covers predictive analytics: its roles, models and methods. Expect a prompt asking you to distinguish types of analytics, identify a question your organization could answer with prediction and explain what data, roles and methods would be needed. Ground the post in the course text and analytics research, set it in an organization you know and reply meaningfully to two or more peers. Choose a question that matters to the business, since later modules build toward an analytics plan. A question with a measurable outcome and data you already hold makes the best starting point.
How this HRM 7640 Module 1 example is built
The initial response opens with the organization's current descriptive report and the outcome it misses. It defines descriptive, predictive and prescriptive analytics from the text and uses a research review to explain what makes analytics valuable. It then poses a specific prediction question, explains why it matters and lists the data already available. It closes by naming three roles needed to act on the prediction. The first comment questions a classmate's outcome measure; the second suggests ways to build trust, supported by research, and asks a question. Naming the data already available shows the question is realistic, not hypothetical. The roles section explains who would act on the prediction.
Reading the HRM 7640 Module 1 grading rubric
The forum mark weighs the first post, its use of the readings and studies, its link to a workplace and the value of the replies. Mentors look for a clear distinction among types of analytics, a prediction question tied to a business outcome and realistic thinking about data and roles. Replies that test a classmate's question for data quality or ethical concerns show critical engagement. Clear writing and citations in APA style are expected. Mentors also value posts that anticipate how results would be used and by whom. Linking the prediction question to a cost or customer outcome shows strategic thinking. Replies that raise data quality or ethical issues demonstrate critical engagement with analytics.
HRM 7640 Module 1 help: mistakes that cost marks
Posts lose points when they define analytics terms without proposing a question, when the question has no business value, or when they ignore who will act on results. Others choose outcomes that are hard to measure. Start from a business problem. Check that you have data for the outcome and predictors. Name the sponsor who will act. In replies, help classmates sharpen their question. A free first sample of your own HRM-7640 Module 1 post is available on request. Avoid questions whose answers would not change any decision. Check that your outcome is recorded consistently. Keep replies focused on improving the classmate's question.
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HRM 7640 Module 1 questions, answered
Where can I find a free HRM 7640 Module 1 sample paper?
All of it is printed above: a post that turns a monthly turnover report into a prediction question, followed by two classmate replies.
What textbook does HRM-7640 use?
Fitz-enz and Mattox's Predictive Analytics for Human Resources, from Wiley.
What is the difference between descriptive and predictive analytics?
The first looks back and reports what occurred; the second looks ahead and estimates what will probably occur, such as who may resign.
What is prescriptive analytics?
Analytics that recommends actions, such as which intervention would most reduce an employee's risk of leaving.
Why do HR analytics projects fail?
Often because they start from data instead of a business problem, or because no leader commits to acting on the results.