Published and source-checked on September 7, 2026.
Research question
This protocol asks whether documented delivery of job-related promotion feedback is associated with later internal applications. It is designed for an employer's internal operational study. It does not supply a universal effect size, prove causation, or predict whether a particular employee will leave.
What the sources can establish
BLS tenure and JOLTS materials provide national definitions and aggregate context for employment duration, hires, and separations. They do not set a company benchmark for this question. NIOSH materials support examining working conditions and worker well-being, but they do not turn an administrative process measure into a causal retention estimate.
Department of Labor recordkeeping and hours guidance helps define employment records that may enter the study. EEOC materials set boundaries for job-related, consistently administered employment practices. Census quality standards support written definitions, reproducible processing, and disclosure of error. The NIST Privacy Framework is useful for data governance. None of these sources validates an employee-level retention score.
Data sources and methodology
Build the study from posting, application, decision, and feedback-delivery records and later application, transfer, job, location, and employment records. Lock the population, observation window, outcome dates, exclusion rules, and missing-data treatment before comparing results. Retain the source-system identifier so a second analyst can reproduce each count without copying sensitive fields into a new uncontrolled file.
Create a cohort flow table. Show eligible records, exclusions by reason, incomplete follow-up, unknown outcomes, and the final denominator for every time window. A recent hire who has not yet reached a 90-day window cannot be counted as retained or separated at 90 days.
Compare employees inside similar jobs, locations, scheduled status, and start periods. Record material changes in staffing, policy, demand, systems, or leadership. Statistical adjustment can reduce measured imbalance, but it cannot make an observational exposure random.
Measures
| Measure | Definition | Main limitation |
|---|---|---|
| Exposure rate | Eligible events with the defined operating condition | Depends on record completeness |
| Response time | Time between locked start and response events | System time may differ from experienced time |
| Resolution rate | Events with documented outcome delivery | Delivery does not prove understanding |
| Retention window | Active status at a specified elapsed day | Transfers and leave require explicit rules |
| Separation rate | Defined separations among observable cohort members | Reason codes may be missing or inconsistent |
Report counts and absolute percentages before fitting a model. Show uncertainty intervals and the number of records behind every estimate. If the useful sample is small, descriptive results may be more honest than an adjusted model.
Bias and sensitivity checks
Test whether exposed and comparison groups differ before the operating event. Job mix, schedule, tenure, supervisor, location, and start month can create an apparent relationship. Display those differences instead of burying them inside one adjusted number.
Repeat the analysis under reasonable alternate definitions. Vary the exposure threshold, separate voluntary and involuntary exits, exclude periods with known system changes, and compare results by start cohort. Recalculate after auditing misclassified records. If the conclusion changes, report that instability.
Missing data deserves its own table. A missing timestamp may indicate poor system use, weak access, or an event that never occurred. Treating every blank as zero creates a cleaner spreadsheet and a weaker study.
Privacy and employment boundaries
Use aggregate findings to repair an operating process. Do not label employees as flight risks or feed the result into discipline, scheduling, selection, pay, or promotion decisions. Keep protected leave, accommodation, complaint, medical, and safety records inside their approved controls.
Limit the analytic extract to the fields needed for the research question. Restrict access by role, log corrections, set a deletion date, and review small cells before publication. A qualified owner should review legal, privacy, labor, safety, and equal-employment questions within their authority.
Interpretation plan
Predefine three outcomes: evidence consistent with an association, no material difference detected, and evidence too unstable to interpret. None is a causal finding. An association may justify testing a process correction. A null result may still reveal poor access or records. An unstable result should lead to better measurement, not a stronger claim.
The final report should state the population, dates, exact measures, missingness, operating changes, sensitivity results, and unresolved questions. A second analyst should reproduce headline counts. The accountable operating leader should record whether to test, revise, or stop an intervention.
Preregister the analysis and audit the result
Write the analysis plan before anyone sees retention outcomes. Name the primary comparison, the adjustment variables, the time windows, and the smallest difference that would prompt an operating review. Preserve the dated plan with the extraction code. If the team changes a definition later, keep both versions and explain why. This record separates planned analysis from an exploratory finding.
Reconcile the cohort against source totals before estimating a relationship. Trace a sample of exposed and comparison records through every join. Check duplicate employees, overlapping events, retroactive corrections, transfers, leave periods, and records created after the outcome. Each can change the denominator or assign an employee to the wrong group. Publish the reconciliation counts alongside the result.
Have another analyst reproduce the cohort and headline measures without relying on the first analysts working file. Differences should be resolved against authoritative records and logged. The review should also compare the analytic extract with the approved retention and access rules. Reproducibility is not only a code check. It tests whether two people can interpret the operating definitions in the same way.
After the main analysis, inspect influence and concentration. One site, supervisor, month, or high volume job may drive the result. Report the full cohort first, then the planned subgroup and leave one group out checks. Do not keep slicing until a favorable difference appears. Exploratory patterns belong in a clearly labeled follow-up question.
Archive the final specification, source dates, code version, aggregate output, reviewer decisions, and refresh date. Do not retain row-level extracts longer than the approved purpose requires. When the refresh runs, use the same definitions or show the bridge between versions. A result that cannot be reconstructed should not support an operating change.
Limitations
Administrative records were created to run the business, not to answer this research question. They may be late, incomplete, or defined differently across teams. Employees with observable records may differ from those without them. External labor conditions and internal changes can affect both the operating exposure and retention outcome. These limits prevent a universal or individual prediction.
Data Sources and Methodology
This guide reviews 10 public sources relevant to employee retention evidence. The sources were selected for definitional clarity, public availability, and direct relevance to the operating question. Figures retain the reporting period and definitions used by the cited publisher. The article separates national or sector context from company-specific inference, labels any planning example as a hypothesis, and does not present an association as proof of causation. Source titles and retrieval links appear at the end of the guide. Local policy, contracts, and qualified professional review still govern decisions.
Sources
- Employee Tenure in 2024, U.S. Bureau of Labor Statistics. Accessed September 7, 2026.
- Job Openings and Labor Turnover Survey Handbook of Methods, U.S. Bureau of Labor Statistics. Accessed September 7, 2026.
- NIOSH Worker Well-Being Questionnaire, NIOSH. Accessed September 7, 2026.
- Healthy Work Design and Well-Being Program, NIOSH. Accessed September 7, 2026.
- FLSA Recordkeeping Requirements, U.S. Department of Labor. Accessed September 7, 2026.
- Hours Worked Under the FLSA, U.S. Department of Labor. Accessed September 7, 2026.
- Employment Tests and Selection Procedures, EEOC. Accessed September 7, 2026.
- Prohibited Employment Policies and Practices, EEOC. Accessed September 7, 2026.
- Statistical Quality Standards, U.S. Census Bureau. Accessed September 7, 2026.
- Privacy Framework, National Institute of Standards and Technology. Accessed September 7, 2026.
For the operating companion, read Promotion Feedback Delivery Check.