| Course | DBAX 9103 Business Research Project Stage 3 |
|---|---|
| Week | Week 3 |
| Paper type | Deviation record |
| Length | About 750 words, 5 pages |
| Format | APA 7 student paper |
| School | Walden University |
| Program | Doctor of Business Administration (DBA) |
| Updated | October 2026 |
Free sample paper for DBAX 9103 Week 3
Every Departure From the Proposal, on the Record: A Deviation Record for a Caregiver Retention Study, Including the Change From a Thirty-Day to a Fourteen-Day Check-In Window and Why the Original Plan Would Have Misled
Student Name
Walden University
DBAX 9103: Business Research Project Stage 3
Instructor Name
Month Day, Year
Every Departure From the Proposal, on the Record: A Deviation Record for a Caregiver Retention Study, Including the Change From a Thirty-Day to a Fourteen-Day Check-In Window and Why the Original Plan Would Have Misled
Why a Deviation Record
No study runs as proposed, and this record is where that stops being private. Simmons et al. (2011) showed that undisclosed flexibility in how data are collected and analyzed can make almost any result look significant. The remedy they proposed is disclosure: report every decision that departed from the plan, and why. This record lists every departure from my approved proposal in the order each occurred, for the project on early supervisor contact, onboarding and three-month staying power among home care aides.
The Deviations
Table 1
Departures From the Approved Proposal
| Number | Proposal said | What was done | When decided | Reason | Effect on results | |
|---|---|---|---|---|---|
| 1 | Check-ins counted over the first 30 days | Check-ins counted over the first 14 days, among caregivers still employed at day 14 | Before the main analysis, after inspecting separation dates | Caregivers who left early had fewer days in which to receive check-ins, biasing the 30-day count | Main estimate smaller; see below | |
| 2 | Sample of all hires | Rehires excluded | During preparation | Rehires had prior tenure and onboarding exemptions | 22 caregivers excluded | |
| 3 | Missing data handled by multiple imputation | Caregivers without check-in logs excluded | During preparation | Logs missing for a system reason unrelated to caregivers; 2.3 percent of hires | 14 caregivers excluded | |
| 4 | Travel distance as a control | Travel distance retained, log-transformed | During preparation | Strongly skewed distribution | Negligible | |
| 5 | Interaction tested at the 0.05 level | Interaction reported with interval; no threshold applied | After consulting the chair | Sample too small for a reliable interaction test | Reported as inconclusive | |
Deviations 1, 3 and 5 were approved by the chair in writing before the final analysis was run.
The Deviation That Mattered Most
The proposal planned to count check-ins over each caregiver's first 30 days and relate that count to retention at day 90. When I examined separation dates, I found that 45 caregivers left within their first 14 days and others left between days 14 and 30. A caregiver who leaves on day 9 cannot receive check-ins on days 10 through 30, so the 30-day count is partly a measure of how long the caregiver stayed, which is the outcome. This builds a positive relationship into the data whether or not check-ins matter.
The fix was to fix the window at the first 14 days and include only caregivers still employed on day 14, so every caregiver in the analysis had the same opportunity to receive check-ins. This reduced the sample to 531 and the estimated odds ratio from 1.41 to 1.33. The original analysis is reported as a sensitivity check, labeled as biased, so readers can judge how far the correction moved the estimate.
Smaller Deviations
Excluding rehires removed caregivers whose onboarding was waived, which would otherwise have been miscoded as noncompletion. Excluding 14 caregivers without logs, rather than imputing their check-ins, followed the reasoning Newman (2014) gives for small amounts of data missing for reasons unrelated to the outcome: with 2.3 percent missing, deletion was unlikely to bias estimates, and imputing a count from no information would add noise. Transforming travel distance changed nothing of substance.
Deviations Considered and Rejected
After seeing the results, I considered redefining onboarding completion as completing four of six modules rather than all six, which might have produced a significant association. I did not, because the decision would have been made after seeing the data. Gelman and Loken (2014) describe how such data-dependent choices, even made in good faith, inflate false positives.
How the Chair Was Consulted
Each deviation decided before the final analysis was sent to my chair in a short memo stating the original plan, the proposed change, the reason and whether I had seen any results that the change could affect. For the window change, I had seen separation dates but no model output. The chair approved it and asked that the biased estimate be reported alongside the corrected one, which the sensitivity table now does. Keeping these memos makes the sequence of decisions checkable, which matters more than any single decision.
Where Each Deviation Appears in the Report
Deviation 1 is described in the method chapter and its effect shown in a sensitivity table. Deviations 2 and 3 appear in the participant flow. Deviation 5 shapes how the interaction is reported in the results chapter and its discussion.
References
Gelman, A., & Loken, E. (2014). The statistical crisis in science. American Scientist, 102(6), 460-465. https://doi.org/10.1511/2014.111.460
Newman, D. A. (2014). Missing data: Five practical guidelines. Organizational Research Methods, 17(4), 372-411. https://doi.org/10.1177/1094428114548590
Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological Science, 22(11), 1359-1366. https://doi.org/10.1177/0956797611417632
What the DBAX 9103 Week 3 instructions ask for
Week 3 asks you to record every departure from your approved proposal. Explain why a deviation record belongs in the study. Set out each deviation in a table: the planned step, the step taken, the date of the decision, the reason and the effect on results. Explain the most consequential deviation in detail, including how it changed the main estimate. Describe smaller deviations briefly with their justification. Report any deviation you considered after seeing results and rejected, and why. Describe how your chair was consulted and whether you had seen results that a change could affect. Close by saying where each deviation appears in the final report.
Inside the DBAX 9103 Week 3 example
The record opens with Simmons and colleagues on undisclosed flexibility. A table lists five deviations: the check-in window, rehires, missing logs, travel transformation and the interaction threshold. A section explains the window change: 45 early leavers, a tenure bias built into the 30-day count and an estimate falling from 1.41 to 1.33. A section justifies excluding missing logs, citing Newman. A section reports a rejected post hoc redefinition of onboarding, citing Gelman and Loken. A section describes memos to the chair and the request to report the biased estimate. A closing section maps each deviation to the report. Each row in the table records whether results had been seen when the decision was made, the column that matters most to a skeptical reader.
Where the marks sit in the DBAX 9103 Week 3 rubric
The core expectation is completeness: every departure, with timing and reason. A table recording when each decision was made, relative to seeing results, carries the most weight. A detailed account of the most consequential deviation, including its effect on the estimate, shows transparency. Reporting rejected post hoc changes demonstrates integrity. Documented chair consultation shows accountability. Mapping deviations to the report shows they will not be lost. Records that list only minor changes, omit timing or hide the effect of a deviation on results fall below the standard for Stage 3. Reporting the main estimate under both the original and the revised plan lets readers see the size of each change for themselves. Mapping every deviation to a section of the final report shows it will stay visible.
Common DBAX 9103 Week 3 mistakes, and how to avoid them
Put your approved proposal beside your final analysis and go line by line. Every difference is a deviation, however small. For each, write down when you decided it and whether you had seen any results that the decision could affect; that timing is what readers most need. Calculate the main result both ways for any deviation that changes it, and report both. Note changes you considered and rejected. Many students treat deviations as embarrassing and leave them out, which makes honest work look suspicious. Others report them without saying when they were made. Send each change to your chair as you go. Our writers can help you build a deviation table from your proposal and output.
Get a DBAX 9103 Week 3 example written to your instructions
Paste in the Week 3 prompt and rubric and list the departures your study has met, a line for each. The deviation table and its consequence paragraphs arrive within 24-48h, and your first request costs nothing. Rows in the draft come from a parallel study, which keeps the account of what actually went wrong in yours with you.
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DBAX 9103 Week 3 questions, answered
Where can I find a free DBAX 9103 Week 3 sample paper?
Read the full deviation record below, free of charge.
What was the most consequential deviation in DBAX 9103 Week 3?
Changing the check-in window from 30 days to 14 days.
Why was the window changed?
Early leavers had fewer days to receive check-ins, which biased the 30-day count.
How did the change affect the estimate?
The odds ratio fell from 1.41 to 1.33.
What post hoc change was rejected?
Redefining onboarding completion as four of six modules.