| Course | DBAX 9102 Business Research Project Stage 2 |
|---|---|
| Week | Week 8 |
| Paper type | Design commitment |
| 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 9102 Week 8
Choosing the Design and Naming What It Gives Up: A Commitment to a Multilevel Records Study of Coaching and Quota Attainment, With the Causal Claims It Cannot Make and the Controls That Narrow the Distance
Student Name
Walden University
DBAX 9102: Business Research Project Stage 2
Instructor Name
Month Day, Year
Choosing the Design and Naming What It Gives Up: A Commitment to a Multilevel Records Study of Coaching and Quota Attainment, With the Causal Claims It Cannot Make and the Controls That Narrow the Distance
The Commitment
The study will use a multilevel correlational design. Outside salespeople will be nested within their sales managers. For each salesperson, the study will relate the number of coaching sessions logged by their manager to recorded quota attainment over four quarters, alongside the salesperson's rating of the manager's coaching skill and controls for territory and tenure. The setting is my employer, an industrial supply distributor whose field sellers report to eleven managers, every one of them bound by a rule calling for two coaching sessions per salesperson each month.
Why This Design
Last week's comparison showed that this was the only design that met every requirement of the open remainder and could actually be run. A field experiment was ruled out because leadership would not suspend the coaching policy for some managers. A before-and-after branch comparison was ruled out because coaching was not logged before the policy and other changes happened the same year. A multilevel design is also suited to the data's structure: salespeople who share a manager are not independent, and treating them as if they were would overstate the precision of any result.
What the Choice Gives Up
Table 1
What the Design Can and Cannot Claim
| Claim | Can the design support it? | Why | |
|---|---|---|
| More logged coaching goes with higher quota attainment | Yes | Direct test of association | |
| Coaching skill goes with higher attainment, holding frequency constant | Yes | Both measured for each salesperson | |
| Frequency matters more where skill is high | Yes, as an interaction | Follows the pattern Dahling et al. (2016) reported | |
| More coaching causes higher attainment | No | No random assignment; reverse causation and omitted variables possible | |
| The policy improved results | No | No comparison period without the policy | |
Claims the design cannot support will not appear in the findings except as limitations.
The Threats That Remain
Three threats remain. The first is reverse causation: managers may coach struggling salespeople more often, which would make coaching look harmful. The second is omitted variables: a strong territory could lead both to more managerial attention and to higher attainment. The third is common source: if salespeople who are doing well rate their managers more favorably, the link between rated skill and attainment would be inflated. Antonakis et al. (2010) list these as the most frequent threats to causal claims in leadership research.
Controls That Narrow the Distance
None of the threats can be eliminated, but each can be narrowed. For reverse causation, the study will relate coaching in one quarter to attainment in the next, and will control for attainment in the prior quarter. For omitted variables, it will control for territory potential, using the company's estimate of each territory's sales opportunity, and for the salesperson's tenure. For common source, coaching frequency comes from logs and attainment from records, so only the skill rating shares a source with anything, and even that rating will be collected before the quarter's results are known. Podsakoff et al. (2003) recommend separating the timing and source of predictor and outcome measures for this reason.
A Small Qualitative Addition
To help interpret the results, I will interview four managers about how they meet the frequency target. If logged sessions turn out to be brief check-ins recorded to meet the policy, the frequency measure will mean something different from what it appears to mean, and the interviews will show it. The interview guide will ask managers to describe their last three logged sessions in detail, including length and topics.
Sample Size and Power
With sixty-four salespeople nested within eleven managers, the study is small for a multilevel design. Detecting a cross-level interaction between frequency and skill will be difficult unless the effect is fairly large. I will report the effect sizes and confidence intervals rather than relying only on significance tests, and I will treat the interaction as exploratory. Four quarters of data per salesperson add observations within each person, which helps estimate the main relationships more precisely than a single snapshot would.
What a Reader Should Expect
The study will be able to say whether coaching frequency and skill go with quota attainment at this company, after reasonable controls. Whether the coaching policy itself works is beyond its reach. That honest limit is a cost of the only design available, and it will be stated in the proposal's first pages.
References
Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (2010). On making causal claims: A review and recommendations. Leadership Quarterly, 21(6), 1086-1120. https://doi.org/10.1016/j.leaqua.2010.10.010
Dahling, J. J., Taylor, S. R., Chau, S. L., & Dwight, S. A. (2016). Does coaching matter? A multilevel model linking managerial coaching skill and frequency to sales goal attainment. Personnel Psychology, 69(4), 863-894. https://doi.org/10.1111/peps.12123
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879-903. https://doi.org/10.1037/0021-9010.88.5.879
DBAX 9102 Week 8 instructions, in plain terms
Week 8 asks you to commit to a design and state what the choice gives up. State the commitment plainly, including structure, measures, sample and setting. Explain why this design follows from your comparison. In a table, list the claims a reader might hope the study supports and whether the design can support each, with reasons. Name the specific threats that remain, citing methods literature. Describe how controls, timing or measurement choices narrow each threat. Address sample size and statistical power honestly. Describe any small addition that helps interpretation. Close by telling the reader exactly what the finished study will and will not be able to say.
Inside the DBAX 9102 Week 8 example
The paper commits to a multilevel correlational design with salespeople nested within managers. A section explains why, following the comparison. A table lists five claims and the design's support for each. A section names reverse causation, omitted variables and common source as threats, citing Antonakis and colleagues. A section narrows each with lagged quarters, prior attainment, territory potential, tenure and separated timing, citing Podsakoff and colleagues. A section treats the frequency and skill interaction, following Dahling and colleagues, as exploratory given the sample. A section adds four manager interviews. A closing section states what readers should expect. Claims the design cannot support appear in the table so they cannot drift into the findings later.
DBAX 9102 Week 8 rubric: what earns full marks
The defining element is honesty about the design's limits, and a table of claims the design can and cannot support carries the most weight. Naming specific threats, instead of a stock warning about correlation, shows depth. Describing concrete steps that narrow each threat demonstrates methodological skill. Addressing power with a small sample shows candor. A small qualitative addition to aid interpretation shows thoughtfulness. Ending on exactly what the findings will be able to show keeps expectations fair. Commitments that overstate causal reach, ignore threats or promise results the sample cannot deliver will need revision. Treating an interaction as exploratory when the sample is small, and reporting intervals rather than only significance, shows statistical honesty. The table of supportable claims should appear early in the proposal.
DBAX 9102 Week 8 help from the desk
Write the design in two sentences, then list every claim you would like the finished study to make. Mark each one yes or no for what the design can actually support, and keep the no column visible. For each remaining threat, ask what you could measure or time differently to narrow it. Check whether your sample is large enough for the analyses you plan, and treat anything marginal as exploratory. Many students commit to a design and then write findings as though it were an experiment. Others list limitations so generally that a committee learns nothing. Be specific about what you cannot claim. Our writers can help you match each threat with a practical control.
Get a DBAX 9102 Week 8 example written to your instructions
Attach the Week 8 prompt, its rubric and the options compared the week before. A commitment document with its argued rival, validity trade-offs and foreclosure list is delivered within 24-48h; there is no fee for the first. It argues from design logic and never needs the organization's name.
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DBAX 9102 Week 8 questions, answered
Where can I find a free DBAX 9102 Week 8 sample paper?
Read the design commitment and its claims table below, free for all.
What design does the DBAX 9102 Week 8 paper commit to?
A multilevel correlational study with salespeople nested within managers.
What claims can it not support?
That coaching causes higher attainment or that the policy improved results.
How is reverse causation narrowed?
By relating coaching in one quarter to attainment in the next and controlling for prior attainment.
Why is the interaction exploratory?
Sixty-four salespeople and eleven managers make it hard to detect.