The Representative Subset
The research sample is a carefully selected subset of individuals from the population that is considered representative of the study universe.
To ensure the sample accurately represents the group from which you need information, you must apply:
- Quota Sampling: This involves dividing the sample into subgroups determined by socio-demographic variables such as gender, age, and region.
Factors to Consider for Sample Definition
Ensuring Accurate Results
For the sample to yield results close to reality, you should consider the following factors:
- Sample Size and Sampling Error: The larger the sample size, the smaller the sampling error, which means results are more reliable.
- Minimum Acceptable Sample: A minimum of 200 cases is typically acceptable for result analysis. This allows for segmentation by variables like gender. For more detailed analysis involving fragmented variables (e.g., age range, brands purchased, or place of purchase), consider expanding the sample to 400 or 500 cases.
- Research Type: The type of research also influences the sample size. Studies like U&A, exploratory phase studies, or Brand Health studies often require a larger sample size to generate robust insights.
Thank you for joining us in Episode 7 of the Zinklar Academy! We’ve explored the critical aspects of defining the research sample, including the importance of quota sampling, sample size, and the factors to consider for accurate results.
In our next session, we’ll delve into how to structure your study questionnaire to obtain the insights you need. Stay tuned for more valuable insights!