BUS4036 develops the capacity to design, execute, and interpret marketing research — the systematic process of collecting, analyzing, and interpreting information to guide marketing decisions. Intuition and experience are valuable in marketing, but they are insufficient: systematic research reduces the cost of bad decisions by replacing assumption with evidence.
Research design types comparison
| Design | Purpose | Methods | Output |
|---|---|---|---|
| Exploratory | Understand a poorly defined problem; generate hypotheses | Focus groups, in-depth interviews, ethnography, secondary research, expert interviews | Insights, hypotheses, key themes — not statistically generalizable |
| Descriptive | Describe characteristics of a market, segment, or behavior accurately | Surveys, observational research, scanner data, social listening | Quantified descriptions — percentages, averages, frequencies, cross-tabulations |
| Causal | Establish cause-and-effect relationships | Experiments (A/B tests, controlled field experiments, conjoint analysis) | Causal conclusions — X causes Y (with confidence interval and significance level) |
What BUS4036 covers
Survey design is one of the most common and most poorly executed research methods in marketing practice. Effective survey design begins with translating the research question into measurable constructs, then designing questions that validly and reliably measure those constructs. Common survey design errors include: leading questions (framing questions in ways that push respondents toward a particular answer — "How satisfied were you with our outstanding customer service?" presupposes satisfaction); double-barreled questions (asking two questions in one — "How satisfied were you with the price and quality of the product?" cannot be answered clearly because the respondent may have different views on each); response scale issues (inconsistent scales, unlabeled midpoints, and acquiescence bias — the tendency of respondents to agree with statements regardless of content — all threaten validity); and order effects (the order in which questions are asked influences responses, particularly for attitude questions where earlier questions prime later ones). BUS4036 develops the skills to design valid, reliable survey instruments, pilot test them, and interpret the results with appropriate confidence.
Sampling is the process of selecting a subset of a population to represent the whole in the research. Probability sampling (every member of the population has a known, non-zero probability of being selected) allows statistical generalization from the sample to the population and enables the calculation of sampling error — the precision with which the sample estimates the population parameter. The main probability sampling methods are simple random sampling (each member has an equal chance of selection), systematic sampling (every nth member of a list is selected), stratified sampling (the population is divided into strata and samples are drawn from each), and cluster sampling (geographic or natural groupings are sampled, then all members within selected clusters are surveyed). Non-probability sampling (selection is based on convenience, judgment, or quota rather than random chance) is faster and cheaper but does not allow statistical generalization — a common critical distinction in evaluating the validity of marketing research findings. BUS4036 applies these sampling concepts to research design decisions, determining the appropriate sample size for a given precision requirement and selecting the sampling method that balances rigor, feasibility, and cost.
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Key topics you write about in BUS4036
- The marketing research process: problem definition, research design, data collection, analysis, reporting
- Primary vs secondary data: internal databases, syndicated data, government sources, primary data collection
- Qualitative methods: focus groups, in-depth interviews, ethnography, projective techniques
- Quantitative methods: surveys, observational research, experiments, scanner/behavioral data
- Measurement: scales (nominal, ordinal, interval, ratio), scale types (Likert, semantic differential, rating), reliability and validity
- Sampling: probability vs non-probability methods, sample size determination, sampling error
- Data analysis: descriptive statistics, cross-tabulations, hypothesis testing, regression basics
Qualitative vs quantitative research: when to use each
- Use qualitative when: the problem is exploratory and poorly defined; you need depth over breadth; you want to understand the "why" behind behaviors; you are generating hypotheses for later quantitative testing; sample sizes are small (10-30 participants in a focus group study can produce rich insights)
- Use quantitative when: you need statistically generalizable conclusions; you want to measure the prevalence of a behavior or attitude; you are testing specific hypotheses; you need to compare differences between groups with confidence intervals
- Use mixed methods when: you need both depth (qualitative exploration) and breadth (quantitative confirmation); sequential designs start with qualitative to generate hypotheses, then quantitative to test them; or concurrent designs collect both in parallel
- Neither is inherently superior — the research question determines which method is appropriate
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Frequently asked questions
Reliability refers to consistency — does the measurement produce the same result when applied to the same thing at different times (test-retest reliability), by different researchers (inter-rater reliability), or using different items measuring the same construct (internal consistency, measured by Cronbach's alpha)? Validity refers to accuracy — does the measurement actually measure what it claims to measure? A scale can be reliable without being valid (consistently measuring the wrong thing), but cannot be valid without being reliable (a valid measure must be consistent). The most critical form of validity in marketing research is construct validity — whether the questions or scales being used actually capture the theoretical construct of interest (brand loyalty, customer satisfaction, purchase intention). Content validity (whether the items cover all relevant aspects of the construct), criterion validity (whether the measure correlates as expected with other related measures), and convergent/discriminant validity are all relevant forms. Most marketing research validity failures are construct validity failures: researchers measure what is convenient to measure (attitudes) rather than what they need to predict (behavior).
Conjoint analysis is a quantitative research technique used to understand how consumers make tradeoffs between product attributes and how much they value each attribute — answering the question "what do consumers really want and what will they sacrifice to get it?" rather than "what do consumers say they want in the abstract?" (which often produces inflated importance ratings for every attribute). In a conjoint study, respondents are presented with hypothetical product profiles varying systematically across attribute levels (e.g., combinations of price, battery life, camera quality, and weight for a smartphone) and choose or rate their preferences. Statistical analysis of these choices reveals the "part-worth" utilities — how much value each attribute level adds — and the relative importance of each attribute in the tradeoff. Conjoint is particularly valuable for product development (which features justify cost?), pricing research (what price premium does an attribute command?), and segmentation (which customer segments weight attributes differently?). It produces more actionable insights than direct importance ratings because it forces tradeoffs rather than allowing respondents to rate everything as important.
A focus group is a qualitative research method in which a moderator guides a structured discussion among 6-10 participants on a specific topic — a product concept, advertising campaign, brand perception, or consumer experience. The interaction among participants is a defining feature: group dynamics can produce insights that individual interviews would not, as participants build on each other's ideas, reveal social norms, and stimulate recall. Focus groups are particularly useful for exploring consumer language and frameworks (how do people talk about this category?), generating hypotheses for quantitative testing, evaluating creative concepts early in development, and understanding the social dimensions of consumption. Their limitations are significant and often underestimated in practice: focus groups are not statistically representative (a typical study uses 2-3 groups of 6-10 participants, selected by convenience from willing participants — not random samples); group dynamics can suppress minority views (dominant participants shape the discussion, and social desirability suppresses honest dissent); and focus group participants tend to rationalize behavior and motivations post-hoc, producing articulate explanations that may not reflect actual purchase drivers. Malcolm Gladwell's "Blink" popularized the insight that people often cannot accurately explain what they like or why — which is why behavioral data often outperforms stated preference data from focus groups.
Sample size in survey research depends on the precision required (how narrow a confidence interval is acceptable?), the confidence level (how certain do you want to be that the interval contains the true population value — typically 95%), and the variability in the population (more heterogeneous populations require larger samples). The standard formula for a simple proportion (e.g., percentage of customers who would recommend the brand) is n = (z^2 * p * (1-p)) / e^2, where z is the z-score for the desired confidence level (1.96 for 95%), p is the estimated true proportion (0.5 maximizes required sample size when unknown), and e is the desired margin of error. For a 95% confidence level and ±5% margin of error, this yields approximately 385 respondents. Common sample sizes in practice: national consumer surveys use 1,000-2,000 for subgroup analyses; customer satisfaction tracking studies typically use 200-500 per segment; concept testing uses 150-300. These calculations apply to simple random samples — stratified and cluster samples require adjustments. Importantly, sample size determines precision, not accuracy — a large sample from a biased sampling frame is still biased.