Unit
- Research question: A well-defined research question guides the entire study. It should be specific, measurable, and achievable within available resources. For example, instead of asking "Does advertising work?" ask "How does online display advertising affect brand recall among 25- to 45-year-old consumers in the US?"
- Hypothesis types: The null hypothesis (H0) states no effect or relationship; the alternative hypothesis (H1) states the expected effect. Researchers test H0 and either reject or fail to reject it based on statistical evidence.
- Directional vs non-directional: Directional hypotheses predict the direction of the effect (e.g., Group A has higher scores than Group B). Non-directional hypotheses predict a difference without specifying direction, requiring two-tailed tests.
- Falsifiability: A hypothesis must be able to be proven false through observation. This principle, from Karl Popper, ensures that research is testable and not merely confirming biases.
- Conceptual vs operational definitions: Conceptual definitions describe the abstract meaning of a construct (e.g., customer satisfaction). Operational definitions specify how it will be measured (e.g., a 7-point Likert scale score).
- Literature review role: Reviewing existing studies helps refine questions, avoid duplication, and identify gaps. It also informs hypothesis formulation by showing what has already been supported or refuted.
- PICO framework: For intervention studies, use PICO: Population, Intervention, Comparison, Outcome. This structure helps build focused questions for experimental designs.
- Feasibility check: Before finalizing, confirm that data can be collected within budget, time, and ethical constraints. Unanswerable questions waste resources.
- Independent and dependent variables: The independent variable is manipulated or categorized by the researcher to observe its effect. The dependent variable is the outcome measured. Clearly identifying both prevents ambiguity and strengthens causal inference in quantitative studies.
- Confounding variables: These are extraneous factors that influence both the independent and dependent variables, potentially biasing results. Researchers control confounds through randomization, stratification, or statistical adjustment to isolate the true effect of interest.
- Sampling strategy: The research question must align with the target population and sampling method. Probability sampling allows generalization, while non probability sampling is quicker but may introduce bias. Sample size directly affects statistical power and precision of estimates.
- Ethical considerations: Research questions should respect participant autonomy, privacy, and informed consent. Institutional review boards (IRBs) require that potential harms are minimized. Ethical alignment also affects data collection methods and the scope of acceptable inquiry.
- Pilot testing: Before full deployment, a pilot study tests the research question, instruments, and procedures on a small sample. This step reveals unclear wording, technical issues, or unrealistic response burdens, allowing refinements that improve data quality and reduce wasted effort.
Campaign Case Study
Campaign: Northbridge Consumer Insights Project (fictional)
Objective: Determine the impact of in-store signage redesign on purchase intent among grocery shoppers aged 18-35.
Target Audience: Primary grocery shoppers in metropolitan areas, evenly split across age groups and income levels.
Channels: In-store surveys, online follow-up questionnaires, and POS data collection over a 4-week period.
Results: The redesigned signage increased purchase intent by 12 percent (p
Quick Revision
- Research questions must be specific, measurable, and feasible to guide the study.
- Hypotheses are testable predictions, with null and alternative forms.
- Operational definitions connect abstract concepts to measurable variables.
- Use frameworks like PICO to structure focused research questions.