Unit 2: Research design and observation
Marketing Research notes · PTU syllabus (MBA 302-18)
On this page
- Unit summary
- Causality and experimentation
- Extraneous variables and sources of error
- After-only and before–after designs
- Time series designs
- Statistical designs: randomised block, Latin square and factorial
- Ex post facto research
- Test marketing
- Direct and indirect observation research
- Developing a research proposal
- Key terms
- Quick revision
- Important questions
Unit summary
Causal designs test whether a marketing action actually changes outcomes, while observation records what people really do. This unit covers causal research designs — after-only, before–after, time series, Latin square, factorial, ex post facto and randomised block — direct and indirect observation research, and developing a research proposal.
After this unit you can
- Explain validity and errors in experiments
- Explain pre-experimental, quasi-experimental and statistical designs
- Explain direct and indirect observation methods
- Develop a research proposal
PTU syllabus topics
- Causal research designs (after-only, before-after, time series, Latin square, factorial, ex-post facto, randomized block)
- direct and indirect observation research
- developing a research proposal
After-only
Treat, then measure
Simple; no baseline
Before-after
Measure, treat, measure
Shows change; testing effect possible
Before-after with control
Adds an untreated control group
Stronger causal evidence
Factorial
Two or more factors at once
Shows interactions
Topic 1
Causality and experimentation
- Conditions for causality: concomitant variation (X and Y vary together), time order (X occurs before Y), elimination of other possible causes.
- Experiment terms: independent variable (treatment, e.g., price), dependent variable (sales), test units, control group, extraneous variables.
- Internal validity: was the change really caused by the treatment? External validity: can results be generalised?
- Symbols: X = treatment, O = observation, R = random assignment, EG = experimental group, CG = control group.
Topic 2
Extraneous variables and sources of error
- History: outside events during the experiment.
- Maturation: changes in subjects over time (fatigue, learning).
- Testing effect: the first test influences the second.
- Instrumentation: changes in measuring tools or observers.
- Selection bias: groups that differ from the start.
- Mortality (attrition): subjects dropping out.
- Statistical regression: extreme scores moving toward the average.
Using a control group and random assignment reduces these errors.
Topic 3
After-only and before–after designs
After-only (one-shot case study)
EG: X O1
No pre-measure or control — cannot rule out other causes
One-group before–after
EG: O1 X O2
History, maturation and testing effects possible
Before–after with control group
EG: R O1 X O2; CG: R O3 O4
Treatment effect = (O2 − O1) − (O4 − O3); controls most threats
After-only with control group
EG: R X O1; CG: R O2
Effect = O1 − O2; avoids testing effect
Example
Sales in test stores rise from 500 to 620 units after a display (O2 − O1 = 120) while control stores rise from 480 to 520 (40). Display effect = 120 − 40 = 80 units.
Topic 4
Time series designs
- Time series (quasi-experiment): O1 O2 O3 X O4 O5 O6 — repeated measures before and after the treatment on the same group (e.g., weekly sales before and after an ad campaign).
- Multiple time series: adds a control group measured at the same points — stronger evidence.
- Use: when random assignment is not possible; consumer panels make it practical.
Topic 5
Statistical designs: randomised block, Latin square and factorial
Completely randomised design
Treatments assigned randomly to test units
Randomised block design
Units grouped into blocks on one extraneous variable (store size), treatments randomised within blocks
Latin square design
Controls two extraneous variables (store and week) with a square arrangement — each treatment once in each row and column
Factorial design
Tests two or more independent variables and their interaction (price × advertising)
- Latin square example: three price levels tested across three stores over three weeks so that each price appears once in each store and each week.
- Factorial example: 2 × 3 design — two package designs × three price levels = six treatment combinations; reveals whether the best price depends on the package.
- Analysed with ANOVA.
Topic 6
Ex post facto research
- Ex post facto ("after the fact") research studies an effect that has already occurred and looks back for causes; the researcher cannot manipulate the independent variable.
- Example: comparing heavy and light users of a brand to see whether exposure to a past campaign differed.
- Limitation: cannot establish causality firmly — self-selection and other variables may explain differences.
Topic 7
Test marketing
- Standard test market: launch in selected cities through normal channels. Controlled test market: research firm handles distribution in selected stores. Simulated test market: lab store and purchase-intention models.
- Uses: forecast sales, test the marketing mix; risks: cost, time, competitor interference, revealing plans.
Topic 8
Direct and indirect observation research
Direct
Observing behaviour as it happens — shoppers in a store, mystery shopping
Indirect
Observing records or traces of past behaviour — pantry audits, garbology, website logs
Structured or unstructured
Pre-specified checklist vs open recording
Disguised or undisguised
Respondents unaware or aware of being observed
Human or mechanical
Observers vs cameras, eye-tracking, scanners, people meters, web analytics
- Merits: records actual behaviour, no respondent bias, useful with children or non-verbal behaviour. Limits: cannot observe motives or attitudes, costly, ethical concerns about privacy.
Example
A retailer's eye-tracking study shows shoppers rarely look below knee level, so high-margin products are moved to eye-level shelves.
Topic 9
Developing a research proposal
- 1
Executive summary
- 2
Background and problem definition
- 3
Research objectives and hypotheses
- 4
Research design and methodology
Design, sampling, data collection, instruments
- 5
Data analysis plan
- 6
Time schedule
Gantt chart
- 7
Budget
- 8
Report format and deliverables
- 9
Appendices
- Purpose: agreement between client and researcher on scope, method, cost and time; a basis for evaluating the project.
Key terms
- Internal validity
- Confidence that the treatment caused the observed effect
- Control group
- Group not exposed to the treatment
- Latin square design
- Design controlling two extraneous variables
- Factorial design
- Design testing two or more variables and their interaction
- Ex post facto research
- Study of causes after the effect has occurred
Quick revision
- Causality: concomitant variation, time order, eliminating other causes.
- Errors: history, maturation, testing, instrumentation, selection, mortality, regression.
- After-only, before–after, with control groups; time series designs.
- Randomised block, Latin square, factorial; ex post facto; test marketing.
- Observation: direct, indirect, structured, disguised, mechanical; research proposal contents.
Important exam questions
Practice questions written to the PTU exam pattern for this unit's syllabus: short answers (Section A style) and long answers (Sections B and C style).
Short-answer questions
- Q1.State the conditions for causality.
- Q2.Distinguish internal and external validity.
- Q3.Write the notation for a before–after design with control group.
- Q4.What is a Latin square design?
- Q5.What is ex post facto research?
- Q6.Distinguish direct and indirect observation.
Long-answer questions
- Q1.Explain experimental designs used in causal research with notation.
- Q2.Explain randomised block, Latin square and factorial designs with marketing examples.
- Q3.Discuss observation methods in marketing research.
- Q4.Explain the contents of a marketing research proposal.
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