MKTG3001 Marketing Research

How MKTG3001 is assessed

All four weights come from the Semester 2, 2026 class summary on the ANU Programs and Courses website and total 100%. Tasks 2 and 3 each combine a group part with an individual part, so 65% of the grade is earned individually. The examination date is set by the Examinations Office and published on Canvas once confirmed.

Current MKTG3001 dates

Dates are as published in the Semester 2, 2026 class summary. Confirm exact deadlines and submission settings in the live LMS.

What MKTG3001 covers

Eleven chapters follow the lecture weeks, from framing a marketing problem and reviewing what is already known, through qualitative and quantitative designs, sampling and scales, to analysing survey data and reporting findings to a client.

The course description frames it as gathering, acquiring, appraising and applying evidence for decision making, and the seven learning outcomes follow a research project from start to finish: identify the marketing problem, choose an ethical methodology, retrieve primary and secondary data, establish its quality, reliability and validity, integrate the evidence, find solutions and produce an evidence-based report.

The semester runs in the same order.

The early weeks ask when research is worth doing at all, how a vague business concern becomes a research problem with aligned questions and objectives, and how secondary data and a literature review sharpen that problem by exposing a research gap.

The middle weeks are about design: qualitative methodology and exploratory research, then quantitative methodology with descriptive and causal designs, experiments, survey modes and questionnaire wording, followed by sampling procedures, levels of measurement, scale formats, reliability and validity.

The final weeks turn to analysis: thematic coding and triangulation for interviews, then validating, coding and tabulating survey data, testing differences with t-tests and one-way ANOVA, measuring association with Pearson correlation, predicting outcomes with regression, and finally communicating findings to a client who needs a decision rather than a statistics lesson.

Much of the grade comes from a group research project for a real industry client, but each project task also carries an individual part, and the course closes with a closed-book, handwritten final examination made up of essay questions.

This guide follows the lecture weeks chapter by chapter, explains each idea with an original figure and scenario, and ends every chapter with essay-style practice in the shape the exam uses.

Worked example · free

Experiments or surveys: choosing a data collection method for a price test

Key terms

Research design

The overall plan that guides how a study will be conducted, chosen after the methodology and before the specific data collection and analysis methods.

Focus group

A qualitative questioning method in which a moderator guides a small group discussion so that participants react to one another's views.

Sampling procedure

The rule a study uses to decide which members of the target population become participants, either through randomisation with a known chance of selection or without it.

Convenience sampling

A non-probability procedure that recruits whoever is easiest to reach, which is fast and cheap but can leave the sample unlike the population it is meant to describe.

Likert scale

A measurement scale format that captures the degree or intensity of agreement, satisfaction, importance, frequency or likelihood through ordered response options.

Reliability

The consistency of a measure, judged by whether it produces consistent results across related items.

Validity

The accuracy of a measure, judged by whether it measures what it intends to measure and covers the construct adequately.

Thematic analysis

A qualitative analysis method that identifies recurring patterns or themes across interview or open-ended data.

Triangulation

A way of strengthening qualitative findings by examining the issue from different perspectives, such as different data sources, methods, researchers, theories or settings.

P-value

The statistic used to decide a hypothesis test, where a value below 0.05 is treated as statistically significant and leads the researcher to reject the null hypothesis.

Multiple regression

A regression model in which two or more independent variables are used together to predict one dependent variable.

Non-academic audience

Readers such as industry clients who care about business relevance and practical implications more than theoretical or methodological detail.

MKTG3001 FAQ

Questions students ask in each MKTG3001 chapter

Is marketing research the same as market research?

Not quite. Market research usually describes studying a particular market, such as its size or competitors. Marketing research is broader: it covers any information gathered systematically to guide marketing decisions, including testing a product concept, measuring advertising response, setting prices or choosing distribution channels, as well as understanding the market itself. Chapter 01 · Marketing Research for Managerial Decisions

When should a company skip marketing research?

Skipping a study is sensible when existing data already answer the question credibly, or when research is impractical. A decision due within days, a budget that cannot cover fieldwork, or a concept so sensitive that testing it could tip off competitors are all grounds. Results that would arrive too late to act on are another reason to proceed without new data. Chapter 02 · Research Problems, Questions and Objectives

What is the difference between primary and secondary data in marketing research?

Primary data is collected by the researcher specifically for the current question, through surveys, interviews or observation. Secondary data already exists, having been compiled for another purpose by the firm itself or by outside bodies. Researchers normally exhaust secondary sources first because they are cheaper and faster, and turn to primary collection only when those sources cannot answer the question reliably. Chapter 03 · Secondary Data, Literature Reviews and Research Gaps

What is the difference between research methodology and research design?

Methodology is the broad approach, qualitative or quantitative, to collecting and analysing primary data. Research design is one of three decisions made inside that approach: the overall plan for running the study. The other two decisions are the data collection technique and the analysis technique, so a design such as exploratory still needs a separate method choice. Chapter 04 · Exploratory and Observational Research Designs

What is the difference between descriptive and causal research in marketing?

Descriptive studies record patterns as they occur, for example which age group buys most or whether satisfaction tracks loyalty. Causal studies intervene: the researcher alters something such as price or an advertisement and holds other factors steady to see whether the outcome shifts. Only the second kind can support a claim that one variable drives another, which is why experiments are linked to causal work. Chapter 05 · Quantitative Research Designs and Questionnaires

What is the difference between stratified and quota sampling?

Both divide the population into subgroups and make sure each subgroup is represented. Stratified sampling then draws members at random within every stratum, so it belongs to the probability family and needs a full list. Quota sampling simply keeps recruiting available people until each subgroup target is met, so the chance of selection is unknown and bias can creep in. Chapter 06 · Sampling Methods and Measurement Scales

What is the difference between thematic analysis and content analysis?

Thematic analysis searches interview or focus group material for recurring patterns and relies on the researcher's judgement to name them. Content analysis sorts material into predefined or emerging categories and is well suited to large volumes such as online reviews or documents. Its weakness is that categorising short pieces of text can strip away the context that gives them meaning. Chapter 07 · Qualitative Data Analysis and Triangulation

Why don't the percentages add up to 100% on a select-all-that-apply question?

Each respondent can tick several options, so the same person is counted under more than one category. Every percentage still uses the number of respondents as its base, which means the column can total well over 100%. Compare options by how many people selected each one rather than reading the shares as slices of a single whole. Chapter 08 · Preparing Survey Data and Basic Analysis

How do I decide between a t-test and ANOVA for survey data?

Count the groups your coded data actually contains for the grouping question. Two groups point to an independent-samples t-test; three or more point to one-way ANOVA. A question with five printed options that you later merge into two categories therefore needs a t-test, so always check the recoding before naming the method. Chapter 09 · Testing Group Differences and Correlation

What is the difference between correlation and regression in marketing research?

Correlation measures whether two variables move together and sits with the intermediate techniques. Regression is treated as an advanced technique because it designates one variable as the outcome and estimates how far that outcome is expected to shift when a predictor changes, which lets a manager forecast results such as purchase or loyalty scores. Chapter 10 · Regression Analysis and Prediction

How should I report a survey result that is under half of respondents?

State the exact percentage alongside the number of respondents, for example 41% of 250. Words such as most, majority or nearly everyone are only defensible above 50%, and even then a precise figure reads as more credible to a manager than a vague quantifier. If the share is a large minority, say so plainly rather than inflating it. Chapter 11 · Communicating Marketing Research Findings

How to study for the exam

Revise this course as one research project told in eleven steps, because the exam questions drop you into the middle of a project and ask what should happen next.

Start with the concept comparison tables for each week and turn every table into a pair of contrasting sentences you can say without notes: methodology versus design, descriptive versus causal, probability versus non-probability, reliability versus validity, t-test versus one-way ANOVA, correlation versus regression.

Next, take the reflective questions week by week and write timed answers of about 200 to 250 words by hand, since the exam is handwritten and closed book. For each answer, check four things: the exact course term, a link to a specific fact in the scenario, one sentence rejecting the obvious alternative, and a closing decision or consequence.

Leave statistics for focused practice: read invented outputs aloud, deciding reject or fail to reject at 0.05, stating the direction of a coefficient and translating R squared into plain words. Finally, connect each week to your own group project, because explaining why your team chose its design, sample, scales and analyses is the same reasoning the essay questions reward.

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