Quick Answer
How many respondents do you need for a dissertation survey?
There is no universal number. Your target sample should reflect the population, research design, planned statistical analysis, desired precision or power, expected response rate and practical access. A formula or power analysis is more defensible than choosing a convenient round number.
Start With the Research Design
Students often ask whether 100, 200 or 300 respondents are enough. The better question is what sample is needed for the specific research design. A descriptive survey, a regression model and a study comparing several groups have different information needs.
Write down the target population, main research question, key variables and planned analysis before choosing a number. This prevents the common mistake of collecting a convenient sample first and trying to justify it afterwards.
Define the Population Clearly
The population is the group your conclusions aim to describe, such as all final-year business students at one university or all customers using a specified service. If the population is finite and known, its size can affect a precision-based sample calculation.
State inclusion and exclusion criteria. A clear population definition also helps identify a sampling frame and prevents claims about groups that were never actually eligible for the survey.
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Choose a Sample-Size Method That Matches the Study
Precision-based formulas are useful when the main goal is estimating proportions or percentages. Power analysis is usually more informative when the study tests hypotheses, compares groups or uses regression. Published sample-size tables can be acceptable when your programme explicitly teaches them and their assumptions fit the project.
Do not mix formulas simply because they produce convenient numbers. The method should match the purpose of the analysis.
Adjust for Non-Response
The number of completed questionnaires you need is not the same as the number of people you should invite. If you need 200 usable responses and expect about half of invited participants to respond, you may need to approach roughly 400 eligible people.
Use a realistic estimate based on your recruitment method and access. Online links shared through weak networks can produce much lower completion than surveys distributed through a controlled class or organisation.
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Match Sample Size to the Planned Analysis
More complex models generally need more information than simple descriptive tables. Regression with many predictors, subgroup comparisons and interaction effects can require a larger sample to estimate effects with reasonable precision.
For regression or group comparison, a formal power analysis based on expected effect size, alpha and desired power is usually stronger than a simple rule such as a fixed number of respondents per variable.
How to Justify Sample Size in the Dissertation
State the population, the method used to estimate the target, the assumptions behind it and the final recruitment goal. Then report how many people were approached, how many responded and how many cases remained after screening.
If the achieved sample is lower than planned, report that honestly and discuss the effect on precision, power or generalisability instead of claiming that the sample is sufficient without evidence.
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Common Sample-Size Mistakes
Choosing a round number with no methodological reason, confusing invitations with valid responses, using a formula that does not match the analysis, ignoring incomplete questionnaires, and treating a rule of thumb as a universal law are common problems. Sample quality and recruitment coverage matter as much as the final number.
Before finalising the number, compare the sample-size calculation with the actual recruitment route. A statistically attractive target is not useful if the sampling frame cannot reach the intended population. Record the assumptions you used so a supervisor can understand how the target was produced and why the achieved sample may differ.
Practical Final Check
If your main goal is a population percentage, margin of error and confidence level may be central. If your main goal is multiple regression, effect size, predictor count and desired power may be more relevant. If access is severely limited, you may need to narrow the population or simplify the analysis instead of pretending that a much smaller sample meets the original design.
A precision-based calculation asks how closely a sample estimate should approximate a population value. Power analysis asks how likely a statistical test is to detect an effect of a specified size. Feasibility asks whether the planned recruitment can realistically deliver the required number of valid cases. These questions overlap, but they are not interchangeable. A dissertation should normally identify which one drives the target sample and explain why.
Precision, Power and Feasibility Are Different Questions
Key Takeaways
- Define the population before estimating sample size.
- Match the calculation method to the design and analysis.
- Adjust recruitment for expected non-response.
- Use power analysis when hypothesis testing makes it appropriate.
- Report assumptions and achieved responses transparently.
- Treat feasibility limits as part of the design.
