The calculated sample size for my social sciences study is 341 with 5% margin error at a 95% Confidence level. However, from preliminary studies, an average of 30 participants are available monthly (without factoring in sampling). Data collection period is strictly a month or two, and I might not get the required numbers on time. In this case, what can I do to reduce this sample size? Any way to justify this reduction?
If your calculated sample size is 341 at a 5% margin of error and 95% confidence level, but you can only collect data from 30–60 participants due to time limits, you can reduce the required sample size by adjusting a few things. First, increasing the margin of error to 7% or 10% and reducing the confidence level to 90% are acceptable in many social science studies and will significantly lower the sample size. If your total population is small, using that figure in your calculation will also reduce the requirement. You can also switch to a non-probability sampling method, like purposive or convenience sampling, and justify this based on practical constraints. Adding qualitative data such as interviews or open-ended survey questions can help strengthen your findings even with a smaller group. In your report, you can explain that due to limited time and participant availability, the sample size was reduced with adjusted statistical parameters, and supported through mixed-methods or focused analysis.
Reducing your sample size is methodologically sound if:
- You transparently explain the rationale
- You adjust your research goals accordingly
- You acknowledge the limitations in your conclusions
Here are a few quick ways to cut down your target from 341 to something like 30–60 (although this is not as simple as we've made it out to be):
- Increase your margin of error (e.g. from 5% to 10%)
- Lower your confidence level (e.g. from 95% to 90%)
- Apply a finite population correction if your population is small
- Reframe the project as a pilot/exploratory study
- Combine a small quantitative sample with qualitative interviews
- Base your power analysis on an expected large effect size
In your methods section, simply note the 1–2-month recruitment cap, state your adjusted error/confidence parameters, frame it as exploratory, and acknowledge the limits of a smaller N.
Yes, you can reduce the sample size due to data collection constraints. Though it is not ideal.
If you will be working with the reduced sample size you have gathered, it is important to mention the constraints in your methodology and as part of the limitations of the study. You should also include in the discussion section, how the reduction could have affected the results of the study.
In addition, a bootstrap sample as well as simulated data can be obtained and statistical analysis is conducted on the datasets. Then, the differences between the estimates of the reduced sample and the bootstrap/simulated data can be assessed.
Also, a sensitivity analysis should be carried out to determine if the obtained estimates are robust.
I hope these suggestions help. Cheers!!!
In addition to the previous recommendations, you may also want to consider: 1) planning a follow-up/phase 2 study with additional participants, if your sponsor will support this plan; and/or 2) create an experiment with replication (e.g., each participant performs the experiment twice), which will strengthen your findings without necessarily doubling the amount of time required for each participant.
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