Primary Research (Field Research): This involves the collection of original data specifically for the research problem at hand. Common techniques include surveys, in-depth interviews, focus groups, and product trials.
Secondary Research (Desk Research): This involves the analysis of existing data that was previously collected for other purposes. Sources include internal sales reports, government statistics, trade journals, and internet articles.
Qualitative vs. Quantitative: Qualitative research explores the 'why' behind consumer behavior through open-ended discussions, while quantitative research focuses on the 'how many' using numerical data and statistical analysis.
| Feature | Qualitative Research | Quantitative Research |
|---|---|---|
| Goal | To understand motives and feelings | To measure and predict trends |
| Sample Size | Small, non-representative | Large, statistically significant |
| Data Type | Descriptive (words, images) | Numerical (stats, charts) |
| Flexibility | High; questions can evolve | Low; standardized instruments |
| Analysis | Subjective interpretation | Objective statistical analysis |
Method Selection: When asked to recommend a research method, always justify your choice based on the business's specific constraints, such as budget, time, and the type of information needed (e.g., 'why' vs. 'how many').
Evaluate Limitations: High-scoring answers often discuss the drawbacks of a method, such as the potential for interviewer bias in qualitative research or the lack of depth in quantitative surveys.
Contextual Application: Avoid generic definitions; instead, explain how a specific research tool (like a focus group) would help a specific type of business (like a niche clothing brand) understand its unique audience.
Sampling Bias: A common error is assuming that a small or non-representative sample reflects the entire population. If a survey only reaches existing customers, it fails to capture the opinions of potential new market segments.
Leading Questions: Researchers often accidentally design questions that nudge respondents toward a specific answer. This compromises the objectivity of the data and leads to inaccurate business conclusions.
Confusing Correlation with Causation: Just because two variables move together (e.g., higher temperatures and higher ice cream sales) doesn't mean one causes the other without further investigation into the underlying drivers.
