D374 is MKTG 5020 Market Research, a graduate School of Business course worth 3 competency units. The catalog describes it as the role of marketing research in strategic decision making and the systematic collection of data. Both halves of that sentence get assessed, and the first half is the one students skip. Research that is not attached to a decision has nothing to justify its cost, and graduate aspects in this subject are usually written to catch exactly that gap.
Every research design starts from a decision, not a topic
The discipline that makes this course straightforward is a chain you can write on one line: decision, then the information needed to make it, then the question that produces that information, then the method that answers the question, then the analysis that turns answers into a recommendation.
Run the chain backwards on any draft and its weaknesses appear immediately. If the analysis produces a number nobody would act on, the question was wrong. If the question cannot be answered by the method proposed, the design is broken. And if there is no decision at the top of the chain, the whole project is an exercise in curiosity, which is the most common thing wrong with student research proposals.
Write the decision explicitly in your opening. Whether to launch this product in this segment. Whether to raise this price. Which of two positioning claims to build a campaign around. A named decision makes every later choice defensible, and it gives an evaluator a standard against which to score your design.
WGU marks the outcome Competent or Not Competent, with no letter grades and no ordinary grade point average, and performance assessment work can be revised and resubmitted with no penalty attached to the result. Design the study you would actually field, state its limits, and submit rather than hedging into a proposal that commits to nothing.
Turning scored aspects into a research proposal
If your course is assessed by a performance assessment, the aspects your evaluator scores are the proposal's sections, and each needs a score of 2 by itself. Research deliverables often include an instrument, a sampling plan or an analysis table, so decide early which aspect each artefact serves.
Here is the arithmetic where an instrument is involved. Suppose the rubric shows eight scored aspects, and the prompt asks for a proposal of roughly 2,600 words with a draft questionnaire in an appendix. Appendices usually sit outside the word count, but the questionnaire still needs about 200 words of justification in the body explaining why those items and that order. Reserve that 200, plus 250 for the business situation and 100 for a close, leaving 2,050 across eight aspects, or roughly 256 words each.
Then reallocate toward the design aspects. Sections that ask you to justify a method, defend a sampling approach or address validity need more than 256 words, while sections that ask you to state an objective or list a limitation need less. Research rubrics concentrate their weight on justification, which is a useful thing to know before you spend an evening describing the company.
A structure for a market research deliverable
The dominant genre here is a research proposal or a research report. The proposal structure below is the one that maps most cleanly onto how these tasks are scored.
| Section | What it establishes | The test it must pass | Frequent weakness |
|---|---|---|---|
| Management decision | The choice the business faces | Could a manager act on the outcome | A topic rather than a decision |
| Research objectives | What the study must find out | Each objective maps to the decision | Objectives broader than the decision requires |
| Research questions | Specific answerable questions | Each is answerable by a stated method | Questions that no instrument could resolve |
| Design choice | Exploratory, descriptive or causal, and why | Design matches the question type | A survey proposed for a causal question |
| Sampling plan | Population, frame, method and size | The frame actually reaches the population | Convenience sampling presented as representative |
| Instrument | The questions or protocol themselves | Items measure the construct without leading | Double barrelled and leading items |
| Analysis plan | What will be done with the data | Analysis is possible given the measurement level | Means calculated on categorical data |
| Limitations and ethics | What the study cannot show and how participants are protected | Honest and specific | Boilerplate that could belong to any study |
Method craft, which is this subject's version of citation craft
In most courses the evidence layer is about sources. Here it is about sources and method together, because a proposal is judged on whether its design could produce trustworthy information.
Cite methodology to research methods literature rather than to a marketing textbook chapter, particularly for sampling, scale construction and validity. Cite secondary data properly and use more of it than you expect to: government statistical agencies, industry associations and published academic studies frequently answer part of a research question at no cost, and a proposal that spends money collecting what is already public is a weak proposal. Saying explicitly what secondary sources establish and what remains unknown is often its own scored aspect, and it strengthens the case for the primary study you are proposing.
Three technical habits protect the design. Match measurement level to analysis, so nominal categories get counts and proportions while interval scales get means. Address validity in plain language, saying what each item is supposed to measure and why it does. And write the ethics section specifically, naming consent, anonymity, data storage and the right to withdraw, rather than promising to follow ethical guidelines in general.
What earns Competent, and what comes back
Passing proposals are fieldable. Someone could take the document and run the study. The decision is named, the objectives serve it, the design matches the question, the sample can be reached, the instrument would produce usable data, and the analysis plan is possible given what the instrument measures.
Returns follow four patterns. Objectives that do not map to any decision. A method chosen before the question, usually a survey because surveys feel default. A sampling plan that describes a population and a recruiting method that could never reach it. And an analysis plan that promises statistics the measurement level does not support, which is the failure most likely to be caught by an evaluator with a research background.
The instrument itself repays a careful second pass, because a questionnaire can look perfectly reasonable and still be unusable. Read every item aloud and ask three questions of it. Does it contain two questions joined by the word and, in which case a respondent who feels differently about each half cannot answer honestly. Does it suggest a preferred answer through its wording, which biases the whole distribution. And does it assume knowledge or behaviour the respondent may not have, so that people who have never used the category are forced into a meaningless response. Ten minutes of that reading catches most instrument level failures, and instrument quality is very often its own scored aspect.
Response scales deserve the same attention. Decide deliberately whether to offer a midpoint, because including one lets people avoid committing while excluding one forces a direction that may not exist. Keep scale labels consistent across the questionnaire so respondents are not switching mental rulers between sections. And record in the proposal what measurement level each item produces, since that decision governs everything the analysis plan can legitimately promise later.
Six mistakes that cost time in D374
Starting with the method. The question determines the design, and any other order is visible from the first page.
Ignoring secondary data. Some of what you propose to discover is already published, and saying so improves the proposal rather than weakening it.
Leading questions. Items that suggest their own answer invalidate the finding, and instruments are read closely.
Sample size with no basis. Say what drives the number, whether precision, subgroup analysis or practical constraint.
Vague ethics. Name consent, anonymity, storage and withdrawal. Generic assurance scores nothing.
No limitations section. Saying what your design cannot show is a mark of research literacy, and it is frequently an aspect of its own.
How we help on this course, and where we stop
We work on the written proposal or report: mapping the rubric, budgeting aspects toward the justification sections, drafting a model proposal with its instrument so you can study the structure and rewrite it in your own voice, checking that your analysis plan matches your measurement, and reading a returned evaluation to name the edits that will clear it. We do not recruit participants, field surveys or collect data from real people on your behalf. If D374 carries an objective assessment on your plan, that exam is proctored and our role is preparation only. We are never present during an assessment, never take one for a student, and never ask for or handle WGU portal credentials.
Designing the study for D374?
Send the scenario, the task instructions and the rubric. We come back with the decision to question chain, the design that fits it and a straight read on whether your instrument would produce analysable data.
Three questions students ask about D374
Do I have to actually collect data, or just design the study?
How do I choose between qualitative and quantitative methods?
What sample size should I propose?
Where D374 sits in WGU's programs
The July 2026 catalog places this code in 2 current WGU programs. Open a program page for the complete standard path and term positions. The live Degree Plan remains authoritative after transfer credit, substitutions, and mentor planning.
The assessments, one by one
The public catalog does not publish this course's PA/OA identity or task count. WGU Tutors publishes at most one PA manual per course and only from a WGU-controlled public rubric. Until that source exists, PA help begins from the student's real Course of Study and OA support remains preparation only.