MKF2121 Marketing Research Methods
The Complete Study & Assessment Guide · S2 2026MKF2121 Overview
- Monash University
- Semester 2 2026
- Level 2
- Marketing research
Marketing Research Methods develops the complete reasoning chain from a management decision to defensible evidence and action. It begins by separating an action-oriented management decision problem from an information-oriented marketing research problem.
- Assessed by Written work, exercises and an in-tutorial test
- Core habit Match every conclusion to its research design
- Quantitative focus Interpret output through direction, magnitude and marketing meaning
- Submission check Recompute numbers and keep claims inside the evidence
What you get in the full MKF2121 Study & Assessment Guide
- 5 chapters, 70 pages — every assessed topic, week by week
- 47 original practice questions, each with a model answer
- 5 worked examples, solved the way MKF2121 marks
- 8 computed figures, redrawn from the course’s own numbers
- Before-you-submit checklist for the Written Assignment 2 (40%) — what markers reward, what costs marks
Free on this page: assessment table, chapter map, one worked example, key terms, FAQ. The rest is in the full study & assessment guide.
How MKF2121 is assessed
| Component | Weight | Format |
|---|---|---|
| Written Assignment 1: Marketing Research Proposal | 32% | Group research proposal |
| Written Assignment 2: Marketing Research Report | 40% | Individual research report |
| Exercise 1: Class Contribution | 14% | Individual tutorial contribution |
| Exercise 2: Student Subject Pool | 4% | Individual research participation |
| Quiz: In-tutorial Test | 10% | In-person multiple choice test |
The current detailed task page and final schedule state 32% and 40% for the two written assignments, reconciling to the handbook's 72% Written category. An overview slide instead shows 30% and 42%; this guide follows the detailed task page and final schedule. The handbook also publishes Quiz or Test at 10% and Exercise at 18%.
What other students are asking about MKF2121
- Where can I find lecture notes or study guides for MKF2121 at Monash?
- The MKF2121 study guide in AskSia Library covers Marketing Research Methods in 5 chapters, with the assessment table, a chapter-by-chapter topic map, key terms and a worked example, free to read on this page.
- Is there a final exam in MKF2121?
- No final exam is listed in the offering this AskSia Library guide is built from; MKF2121 is assessed by Written Assignment 1: Marketing Research Proposal 32%, Written Assignment 2: Marketing Research Report 40%, Exercise 1: Class Contribution 14%, Exercise 2: Student Subject Pool 4%, Quiz: In-tutorial Test 10%.
- What does MKF2121 cover on conclusive and descriptive research design?
- In AskSia Library's MKF2121 guide, the Conclusive and Descriptive Research Design chapter explains connect analytical models, variables, surveys, observation, measurement and pretesting to the inference required.
- What does MKF2121 cover on questionnaires, scaling, sampling, and experiments?
- In AskSia Library's MKF2121 guide, the Questionnaires, Scaling, Sampling, and Experiments chapter explains design response scales, define populations and frames, choose samples, and protect experimental validity.
- What does MKF2121 cover on data preparation and tests of differences?
- In AskSia Library's MKF2121 guide, the Data Preparation and Tests of Differences chapter explains prepare auditable variables and interpret one-sample, independent, paired and multi-group mean comparisons.
What MKF2121 covers
Follow the marketing research process from problem definition and exploratory evidence through research design, measurement, sampling, experimentation, statistical analysis, regression and segmentation.
Problem Definition and Exploratory Evidence
Translate management decisions into research problems, then use qualitative and secondary evidence to refine questions and hypotheses.Problem definition separates the action a manager is considering from the information needed to choose responsibly.
- Marketing research as a staged process with feedback
- Management decision problems and marketing research problems
- Context interviews, symptoms and rival explanations
- Descriptive, comparative, relational and causal questions
- Exploratory purpose and bounded inference
- Focus groups, depth interviews, laddering and projective routes
Conclusive and Descriptive Research Design
Connect analytical models, variables, surveys, observation, measurement and pretesting to the inference required.Conclusive research uses formal designs to answer specified questions or hypotheses. This chapter connects analytical models and variable roles to cross-sectional and longitudinal description, surveys, observation, measurement and pretesting.
- Exploratory, conclusive, descriptive and causal design
- Verbal, graphical and mathematical models
- Predictors, outcomes, controls, mediators and moderators
- Cross-sectional and longitudinal evidence
- Survey modes, coverage and mode effects
- Structured and unstructured observation
Questionnaires, Scaling, Sampling, and Experiments
Design response scales, define populations and frames, choose samples, and protect experimental validity.Scaling, sampling and experimentation control three different claims: what a response value means, who the observed cases can represent and whether a contrast supports causality.
- Nominal, ordinal, interval and ratio properties
- Comparative ranking, paired and constant-sum tasks
- Continuous and itemised non-comparative scales
- Question order, routing and response options
- Target population, sampling frame and achieved sample
- Probability sampling and known selection chances
Data Preparation and Tests of Differences
Prepare auditable variables and interpret one-sample, independent, paired and multi-group mean comparisons.Difference testing begins with auditable preparation. This chapter preserves missing states, coding direction, case rules and derived variables before selecting one-sample, independent, paired or multi-group comparisons.
- Codebooks, missing states and preparation logs
- Hypotheses, thresholds and p-value meaning
- Independent-samples t tests and variance rows
- Paired-samples t tests and difference direction
- One-sample comparisons with benchmarks
- One-way ANOVA and the omnibus alternative
Association, Regression, and Segmentation
Read cross-tabs, chi-square, correlation and regression, then test whether segments support responsible action.Association analysis connects variable type to cross-tabulation, chi-square, correlation and regression.
- Variable types and association routes
- Cross-tab counts and conditional percentages
- Chi-square expected counts and effect size
- Pearson correlation direction and strength
- Regression intercepts, slopes and residuals
- F tests, coefficient tests and R-squared
Students learn to diagnose context, formulate problem components, research questions, variables and hypotheses, and use exploratory evidence to discover mechanisms without presenting a small qualitative sample as a population estimate.
The conclusive-design strand distinguishes descriptive research from causal research.
It connects verbal, graphical and mathematical models to survey and observation choices, measurement scales, questionnaire flow, pretesting, sampling frames and recruitment.
The central discipline is alignment: each method must provide information required by a named research question, each variable must represent a defined construct, and each sample must support the population language used in the conclusion.
The quantitative strand covers data preparation, cross-tabulation and chi-square, t tests, analysis of variance, correlation and regression.
Statistical significance is interpreted with group patterns, direction, magnitude, effect size, uncertainty and design boundaries.
The guide corrects common errors such as reading the variance-test p-value as the mean comparison, treating displayed .000 as a literal probability of zero, reversing the paired subtraction order, stating that every mean differs after an omnibus test, or calling a regression slope an intercept.
The final application is marketing interpretation.
Association can locate a promising mechanism or segment, but a causal recommendation needs a design that protects the counterfactual. Regression fit must be checked against residuals, range and new-data performance. Segments need distinctiveness, reachability, stability and actionability rather than catchy labels alone.
Across the unit, a complete answer states what the evidence shows, why the design supports that reading, who and when it represents, and which uncertainty should be tested next.
Worked example · free
Translate a retention decision into a research plan
- 2Separate the action request from the information problem and identify rival explanations.
- 2Define the population, variables and exploratory and descriptive evidence needed.
- 2State which design would be needed before claiming a discount causes renewal.
Key terms
- Management decision problem
- An action-oriented question about what the decision maker should do.
- Research problem
- An information-oriented statement of what must be learned to support a decision.
- Exploratory research
- A flexible design used to clarify problems, concepts and plausible explanations.
- Descriptive research
- A structured design used to estimate characteristics, differences and associations.
- Causal research
- A design that tests whether manipulating one variable changes another under controlled comparison.
- Sampling frame
- The operational list or procedure through which population members can be selected.
- Internal validity
- The credibility that an observed effect was caused by the treatment rather than an alternative.
- External validity
- The extent to which a result transfers across populations, settings, periods and implementations.
- Chi-square test
- A test comparing observed categorical counts with counts expected under independence.
- Paired t test
- A mean comparison conducted on differences between linked observations.
- Eta-squared
- The share of observed outcome variation allocated to group differences in an ANOVA decomposition.
- Regression slope
- The expected outcome change associated with one predictor unit under the fitted model.
MKF2121 FAQ
How is Marketing Research Methods assessed?
The current structure combines two written assignments worth 32% and 40%, class contribution worth 14%, research participation worth 4%, and an in-tutorial test worth 10%. The detailed task page and final schedule reconcile with the handbook's aggregate categories.
How do exploratory and conclusive research differ?
Exploratory research discovers concepts, language and plausible explanations when the problem remains ambiguous. Conclusive research uses a more formal design to answer specified questions or hypotheses through descriptive measurement or causal comparison.
What makes a questionnaire defensible?
Every item maps to an information requirement, represents a defined construct, uses response options respondents can interpret, follows tested routing and is pretested with intended users. The collection mode must also reach the population relevant to the decision.
Can a large convenience sample represent the market?
Large size reduces random fluctuation within the achieved respondent set but does not remove selection or coverage bias. Population claims require a defensible frame and selection route, or the conclusion must remain bounded to respondents.
How should a significant test be interpreted?
State the decision about the null, then report the observed direction, magnitude and uncertainty. Translate the result into marketing meaning and keep causal language inside the design that produced the comparison.
Does correlation prove a marketing variable causes an outcome?
No. Correlation describes linear association and can reflect reverse direction, omitted causes, restricted range or mixed segments. A causal effect needs a design or identification argument that supplies a credible counterfactual.
How should regression output guide a decision?
Interpret each slope in its units and conditional context, inspect fit and residual behaviour, respect the observed predictor range and test predictive performance on suitable new data. Then distinguish a useful predictor from a proven intervention lever.
Questions students ask in each MKF2121 chapter
- Why is the manager's action request not yet a research problem?
- An action request already points toward what the manager might do, while research must identify what information would distinguish competing explanations and reduce uncertainty. Translation prevents a preferred action from deciding the questions and evidence in advance. Chapter 01 · Problem Definition and Exploratory Evidence
- How does a descriptive design differ from a causal design?
- A descriptive design estimates characteristics, differences or associations as observed. A causal design manipulates a treatment and protects a counterfactual comparison so that an outcome difference can be attributed more credibly to the treatment. Chapter 02 · Conclusive and Descriptive Research Design
- Do numeric codes make a variable metric?
- No. Digits can label unordered categories, ordered levels or quantities. The represented property determines the measurement scale and permissible comparisons, so customer IDs or region codes remain nominal even when software stores them as numbers. Chapter 03 · Questionnaires, Scaling, Sampling, and Experiments
- What does a displayed p-value of .000 mean?
- It is rounded output rather than a probability literally equal to zero. Report it as p below .001 and keep the result tied to the test assumptions, estimate and research design. Chapter 04 · Data Preparation and Tests of Differences
- Which percentage direction should a cross-tab use?
- Condition on the groups named in the research comparison. If the question asks channel preference within segments, each segment is the denominator. State within before the percentage so the interpretation cannot silently reverse. Chapter 05 · Association, Regression, and Segmentation
How to prepare for the assessments
Study the unit as a chain of decisions rather than isolated definitions. First practise translating management actions into information problems and mapping each component to a question, variable, design, population and analysis.
Next build comparison tables for exploratory, descriptive and causal designs; qualitative methods; measurement levels; probability and non-probability sampling; and independent, paired and multi-group tests.
For statistical output, use the same sequence every time: identify the research question and observation structure, select the correct row, state the hypothesis decision, read direction and magnitude, then write the marketing interpretation and design boundary. Recompute paired differences, eta-squared and regression meanings rather than copying labels.
Finish practice answers with the current decision and the next evidence needed, not a generic request for more research.
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