| Course | DBAX 9102 Business Research Project Stage 2 |
|---|---|
| Week | Week 4 |
| Paper type | Disagreement analysis |
| 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 4
Why the Coaching Studies Point Different Ways: A Disagreement Analysis Tracing the Gap Between Strong Meta-Analytic Effects and Weaker Field Results to Who Coached, How Performance Was Measured and What Was Counted
Student Name
Walden University
DBAX 9102: Business Research Project Stage 2
Instructor Name
Month Day, Year
Why the Coaching Studies Point Different Ways: A Disagreement Analysis Tracing the Gap Between Strong Meta-Analytic Effects and Weaker Field Results to Who Coached, How Performance Was Measured and What Was Counted
The Disagreement
Two bodies of research on coaching point in different directions. Meta-analyses report clear positive effects of coaching on performance and other outcomes (Theeboom et al., 2014; Jones et al., 2016). Field studies of coaching by direct managers report a consistent direction but weaker and less consistent relationships, especially when performance is taken from company records rather than ratings. For research on coaching and quota results in sales, the question is why, and which body of evidence better predicts what my study will find.
Candidate Explanations
Three explanations could account for the difference. The first is who coached: meta-analyses draw heavily on professional coaches, who are trained, chosen by the client and free of the evaluative role a manager holds. The second is how performance was measured: ratings by the coach, the manager or the employee are prone to common method bias, while records are not. The third is what was counted as coaching: some studies count any developmental conversation, others a structured session, and some measure frequency while others measure skill.
Testing Each Explanation
Table 1
Explanations Tested Against the Studies
| Explanation | If true, we would expect | What the studies show | Verdict | |
|---|---|---|---|
| Who coached | Larger effects for professional coaches than for managers | Jones et al. (2016) found larger effects for internal coaches than external ones in their sample, the opposite of the prediction | Not supported as stated; the internal and external comparison does not map neatly onto manager and professional | |
| How performance was measured | Larger effects with ratings than with records | In my 14 managerial studies, the 10 using ratings report larger associations than the 4 using records | Supported within my sample | |
| What was counted | Frequency measures showing weaker links than skill measures | Extra sessions helped mostly under skilled coaches (Dahling et al., 2016) | Supported in the one study that separates them | |
Comparisons within my sample are descriptive; the number of studies is too small for a formal moderator test.
Reading the Results
The first explanation fails in an instructive way. It seemed obvious that professional coaches would outperform managers, but Jones et al. (2016) found the opposite pattern for internal and external coaches. Internal coaches may know the context better and be available more often. That finding does not settle whether managers coach as well as internal specialists, but it removes the easiest explanation.
The second explanation holds within my sample. The studies that took performance from ratings reported stronger associations than those using records. This is the pattern Podsakoff et al. (2003) would predict if common method variance inflates ratings-based results. It suggests the field studies with records are the better guide to what a records-based study will find.
The third explanation is supported but thinly. Dahling et al. (2016) were alone in separating skill from frequency in a sales setting, and they found that frequency mattered mainly when skill was high. Session counts were not a reliable guide to effect size in the meta-analysis by Jones et al. (2016) either, which points the same way. If that pattern holds, a sales plan that prescribes frequency without attention to quality may produce little.
What the Analysis Implies
Two explanations survive, and they combine. Meta-analytic effects are larger partly because many studies use ratings, and partly because they measure coaching that is skilled by design. A study of managerial coaching using records, measuring both frequency and skill, should expect smaller effects than the meta-analyses and should not be surprised if frequency alone shows little relationship with quota attainment.
A Fourth Explanation Considered
Study length could also matter. Coaching programs studied in meta-analyses often ran for a few months and measured outcomes soon after, while some field studies measured performance across a full year. Short follow-up can capture an early burst of effort that fades, and long follow-up can dilute a real effect with other influences. My sample has too few studies reporting timing to test this, but the planned study will record quota attainment by quarter so that any change over time is visible rather than averaged away.
Limits
The analysis compares groups of studies, not a formal meta-analysis. Other differences, such as industry, sample size or the year a study was run, could account for some of the pattern, and a larger set of records-based studies would be needed to rule them out.
References
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
Jones, R. J., Woods, S. A., & Guillaume, Y. R. F. (2016). The effectiveness of workplace coaching: A meta-analysis of learning and performance outcomes from coaching. Journal of Occupational and Organizational Psychology, 89(2), 249-277. https://doi.org/10.1111/joop.12119
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
Theeboom, T., Beersma, B., & van Vianen, A. E. M. (2014). Does coaching work? A meta-analysis on the effects of coaching on individual level outcomes in an organizational context. Journal of Positive Psychology, 9(1), 1-18. https://doi.org/10.1080/17439760.2013.837499
DBAX 9102 Week 4 instructions, in plain terms
Week 4 asks you to analyze a disagreement in your literature. State the disagreement precisely, naming the bodies of evidence on each side. Propose at least three explanations for it. For each, state what we would expect to see if it were true, then check that expectation against the studies in a table, giving a verdict. Read each verdict for what it implies about your planned study. Consider any further explanation your sample cannot test and how your design might address it. State what the surviving explanations imply about the results your study should expect, and note the limits of comparing groups of studies without a formal meta-analysis.
How the DBAX 9102 Week 4 example is put together
The paper states the gap between meta-analytic and managerial field results. A section proposes three explanations. A table tests each against the studies. A section explains why the first fails, citing Jones and colleagues on internal coaches. A section shows ratings-based studies reporting larger associations, citing Podsakoff and colleagues. A section reports Dahling and colleagues on frequency depending on skill and Jones and colleagues on session counts. A section concludes that a records-based study should expect smaller effects, with Theeboom and colleagues for contrast. A section raises study length, with quarterly reporting as a response. A closing section names the limits of descriptive comparison. Each verdict is a single word followed by the evidence, so readers can see at once which explanations survived and which did not.
DBAX 9102 Week 4 rubric: what earns full marks
Analyses earn credit by turning a vague disagreement into testable explanations. Stating what each explanation predicts, before checking, carries the most weight. A table with explicit verdicts shows rigor. Reporting an explanation that failed, and what that teaches, shows honesty. Linking surviving explanations to expectations for the planned study shows purpose. Acknowledging untested explanations and the limits of descriptive comparison shows maturity. Analyses that simply note that studies disagree, pick the explanation that favors the author's hypothesis or ignore outcome measurement receive weaker evaluations at this stage. Linking the surviving explanations to concrete choices in the planned study, such as using records and measuring skill, is what turns the analysis from commentary into design reasoning. A note on what the sample is too small to test shows proportion.
DBAX 9102 Week 4 help: mistakes that cost marks
Write the disagreement as two sentences, one for each side, with the typical size or direction of findings. Then brainstorm reasons the sides could differ: who was studied, who delivered the intervention, how outcomes were measured, how long studies ran. For each reason, ask what pattern you would see in the studies if it were the cause, and look. Expect at least one plausible reason to fail. Many students explain disagreements with whatever reason supports their own study. Others treat disagreement as a sign the literature is weak and move on. A disagreement explained is a design decision made. Our writers can help you set out explanations and check them against your extraction sheet.
Get a DBAX 9102 Week 4 example written to your instructions
For this analysis the useful inputs are the Week 4 prompt with its rubric, plus the conflicting studies, cited or named by side. Candidate explanations are tested and a verdict reached in a draft delivered within 24-48h, with the first sample free. Paywalled studies are fine; their citations are enough to build from.
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DBAX 9102 Week 4 questions, answered
Where can I find a free DBAX 9102 Week 4 sample paper?
The disagreement analysis and its verdict table are free to read below.
What disagreement does the DBAX 9102 Week 4 paper analyze?
Larger coaching effects in meta-analyses than in managerial field studies using records.
Which explanation failed?
That professional coaches produce larger effects than managers.
Which explanations survived?
Ratings-based outcomes inflate results, and frequency captures less than skill.
What should the planned study expect?
Smaller effects than the meta-analyses report.