| Course | HR485 Strategic HRM: Analytics and Business Decision-Making |
|---|---|
| Unit | Unit 3 |
| Paper type | Workforce data audit |
| Length | About 880 words, 6 pages double-spaced |
| Format | APA 7 student paper |
| School | Purdue University Global |
| Program | BS in Business Administration (Human Resources) |
| Updated | October 2026 |
Free sample paper for HR485 Unit 3
Who Typed It, and When: A Unit 3 Workforce Data Audit of Seven HRIS Fields
[Student Name]
Purdue University Global
HR485: Strategic HRM: Analytics and Business Decision-Making
Unit 3 Workforce Data Audit
[Instructor Name]
[Date]
Gatehouse Memorial Group, its HRIS fields and every count are composite and illustrative; the sources cited are real.
What the Audit Protects
The next unit is a turnover study of location staff at Gatehouse's 21 funeral homes. It will segment departures by role, tenure, location and manager, and it will report reasons for leaving. Each of those cuts rests on a field in the HRIS. An analysis is only as sound as the way its inputs were recorded, so this audit examines the recording process rather than the records alone. Wang and Strong (1996) built their framework from the views of data users, for whom accuracy sat beside completeness, timeliness and relevance to the task, and that is the standard applied here: fit for a turnover study. The audit covers the twelve months of the cohort year, every location job code and the care center, and it changes no records; where a value is wrong, the audit reports how it came to be wrong and leaves correction to the repair step.
The Audit, Field by Field
Hire date. Entered by payroll from the signed offer, before the first paycheck, because pay depends on it. Test: compared with the first pay date for all 68 hires in the cohort year; none differed by more than one pay period. Grade: pass.
Job code. Chosen by the recruiter from the position being filled, and checked by payroll because pay rates attach to it. Test: compared with the position record for the same 68 hires; two mismatches, both corrected before the first pay date. Grade: pass.
Location cost center. Set by the position, and changed by a transfer transaction. Test: the eleven transfers between homes during the year all carried a matching cost center change. Grade: pass.
Termination reason. Selected by the location manager from a dropdown of fourteen choices while processing the separation. Test: of 85 voluntary exits, 33 carry the code personal reasons, 38.8 percent, more than any other code and more than twice the next. Grade: pass with caveat.
Regrettable-loss flag. A yes or no field the location manager may fill in on the same screen. Test: blank on 29 of the 85 voluntary exits, about a third. Grade: fail.
Manager ID. A lookup that shows the current manager of the employee's position, not the manager at the time. Test: for 61 location departures, 11 show a manager who arrived after the employee left. Grade: fail.
Exit survey response. Sent by email after the last day. Test: 26 of 85 voluntary leavers responded, 30.6 percent. Grade: pass with caveat.
Why the Caveats and Failures Happen
Personal reasons. The code is the fastest choice on the screen and requires no conversation with the departing employee. A manager processing a resignation on a Friday afternoon, with a viewing that evening, has every reason to choose it. The field is not false, but it is not informative. Hom et al. (2017) describe how turnover research separates departures an employer might have prevented from those it could not, and a code that lumps a move to a better-paying warehouse together with a spouse's job transfer erases that line. Repair: a required second field asking whether the employee named another employer, which takes one question and no judgment.
The regrettable flag. The field is optional, so it is filled in when a manager feels strongly and skipped otherwise. Blanks are therefore not random; they likely hide the departures nobody thought about. Repair: make the field required, with a third choice, unsure. This repair works only going forward; past records stay blank.
Manager ID. The field was built for routing approvals, so it always shows today's manager. Four of the 21 homes changed managers during the year, which is why eleven departures point to the wrong person. Repair: pull the manager at the separation date from the position history table, which keeps every change with its effective date. This repair reaches back through past records.
The manager ID is accurate for the job it was built to do, which is to say who manages the position now, and a turnover study asking who managed the person then would draw a false conclusion from it.
Exit survey. The leavers who answer are a self-selected group. Rousseau and Barends (2011) urge HR practitioners to judge how trustworthy each source of evidence is before acting on it, and a 31 percent response is evidence about the people who chose to reply. Repair: a two-question version handed out at the final meeting rather than emailed afterward.
What the Turnover Study May Claim
May claim. Counts and rates by role, tenure band, location and month, since those rest on fields that passed.
May claim with a stated caveat. Patterns in termination reasons, with a note that personal reasons covers nearly two in five exits, and exit survey themes, with a note that fewer than a third of leavers responded.
May not claim. Any finding by manager until manager IDs are rebuilt from position history, and any count of regrettable losses until the flag is required.
References
Hom, P. W., Lee, T. W., Shaw, J. D., & Hausknecht, J. P. (2017). One hundred years of employee turnover theory and research. Journal of Applied Psychology, 102(3), 530-545. https://doi.org/10.1037/apl0000103
Rousseau, D. M., & Barends, E. G. R. (2011). Becoming an evidence-based HR practitioner. Human Resource Management Journal, 21(3), 221-235. https://doi.org/10.1111/j.1748-8583.2011.00173.x
Wang, R. Y., & Strong, D. M. (1996). Beyond accuracy: What data quality means to data consumers. Journal of Management Information Systems, 12(4), 5-33. https://doi.org/10.1080/07421222.1996.11518099
Reading the HR 485 Unit 3 instructions
For Unit 3 you audit the data a later analysis will depend on. Begin by naming that analysis and the fields it needs, so the audit has a purpose and a scope. For each field, describe how values are created: the person who enters them, the moment of entry, the choices available and anything that pushes the person toward one answer. Then test the field with a count, a comparison against another record or a check for impossible values, and report the result as a number. Give every field a grade and say what the grade allows. Fields that fail or carry caveats need an explanation of the mechanism behind the problem and a repair, with a note on whether the repair fixes past records or only future ones. The prompt closes by asking what the planned analysis may and may not claim, which turns the audit into a set of rules for the next unit.
Inside the HR485 Unit 3 example
An opening section names the location turnover study planned for Unit 4 and cites Wang and Strong on data quality as fitness for use. Seven labeled rows follow, each with its entry process, test and grade: hire dates match first pay dates for all 68 hires, job codes show two corrected mismatches, eleven transfers carry matching cost centers, personal reasons covers 38.8 percent of exits, the regrettable flag is blank on 29 and manager ID points to the wrong person on 11 of 61 location departures. The mechanism section explains each problem, with Hom and colleagues on avoidable departures and Rousseau and Barends on weighing evidence. The highlighted line clarifies that manager ID answers a different question. Repairs include a required question about the next employer, a required flag with an unsure option and a manager history pulled from the position table. A three-part list of permitted claims closes the audit.
Reading the HR485 Unit 3 grading rubric
The audit is marked on whether each grade rests on evidence and whether the explanations reach the cause. A field graded without a test earns little, so every row needs a count or comparison. Points go to describing the entry process precisely, because that is where most data problems start, and to repairs that match the mechanism. Distinguishing repairs that reach past records from those that only work going forward shows practical understanding. The final list of permitted claims carries weight, since it shows the student can translate data quality into limits on analysis. Grading fields individually rather than the data set as a whole is expected in this unit. A source on data quality and one on evidence-based HR, cited in APA 7, support the method. A grid or labeled rows are fine; the reasoning for failed fields should be in prose.
HR485 Unit 3 help from the desk
Audits often stop at saying some fields have missing values, without counting them or explaining why they are missing. Others treat data cleaning as the audit, changing values instead of explaining how they were created. A common oversight is a lookup field, such as manager or department, that shows current values rather than historical ones; test any field like that against dates. Some students grade every field as acceptable because the system is reliable, which misses that the people entering data have their own pressures. Repairs proposed without a mechanism, such as more training, rarely convince. Keep the final list of claims concrete: name what the next analysis can report, what needs a caveat and what must wait. If your class data set came with a codebook and you are not sure which fields to test, send it and we can help you plan the audit grid.
Get an HR485 Unit 3 example written to your instructions
Unit 3 works from whatever data your section issues, or from a description of the fields your own HR system keeps, with no employee records attached. Send that along with the rubric and instructions. The free first audit, returned inside 24-48h, grades each field by how its values get entered and states what later analysis may rely on. The paper above is an original model document written by our desk, not a submitted student paper and not an official Purdue University Global document.
More HR485 papers and BS in Business Administration (Human Resources) samples
- HR485 Unit 1: Discussion Board Post
- HR485 Unit 2: Metric Definition Brief
- HR485 Unit 4: Turnover Analysis
- HR485 Unit 5: Cost Model
- HR485 Unit 6: Workforce Planning Forecast
- HR485 Unit 7: HR Scorecard
- HR485 Unit 8: Seminar Reflection
- HR485 Unit 9: Executive Briefing
- HR485 Unit 10: Strategic HR Business Case
- HR435 Unit 3: Market Pricing Analysis
- HR400 Unit 3: Job Description Draft
- HR420 Unit 3: Discrimination Claim Analysis
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HR485 Unit 3 questions, answered
How is a data audit different from cleaning the data?
Cleaning changes values; an audit explains them. The sample fixes nothing in the records. It establishes which fields can carry an analysis, which need a warning beside every figure drawn from them, and which should not be used at all until the entry process changes. Cleaning may follow, but only after the audit has shown what is wrong and why.
Why grade fields rather than the whole data set?
Because one data set usually holds excellent fields and useless ones side by side. Hire dates entered by payroll for pay purposes are trustworthy; a reason picked from a dropdown in the last minutes of an exit is not. A single overall grade would either condemn the good fields or excuse the bad ones, and the analysis depends on knowing which is which.
What if my section's data set has no obvious problems?
Test it anyway and report the tests. Count blanks, check dates for impossible sequences, compare a lookup field against its history, and ask how the most common category got chosen. A clean result with its tests shown earns credit; a claim of cleanliness without them does not. Supplied data sets in many sections contain at least one deliberate flaw.
Where can I find a free HR485 Unit 3 sample paper?
This page holds a complete HR485 Unit 3 workforce data audit grading seven HRIS fields at a composite funeral group, with the test behind each grade, repairs and margin notes.
Why does the manager ID field fail?
It shows the current manager of each position, not the manager when the employee left. Four homes changed managers during the year, so 11 of 61 location departures point to someone who arrived after the employee had gone.