A sample size of 100 may be sufficient for quantitative research depending on various factors such as the study's aim, the expected effect size, and the desired statistical power.

Key Insights

• Importance of Sample Size Calculation:

  • Determining the required sample size is crucial for ensuring the quality and reliability of quantitative research. Insufficient sample sizes may not adequately answer research questions, while excessively large samples can waste resources and complicate the study
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  • Sample size calculations should consider the statistical analysis to be applied, acceptable precision levels, study power, confidence level, and the magnitude of practical significance differences (effect size)
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• Effect Size and Statistical Power:

  • Researchers typically use guidelines such as Cohen’s d and Pearson’s r to interpret effect sizes. These guidelines help in planning the sample size to achieve sufficient statistical power
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  • For accurate effect size estimation, researchers should aim for narrow confidence intervals, which may require larger sample sizes than those needed merely to achieve statistical significance
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• Context-Specific Considerations:

  • In health research, sample size calculations often fail to be reported, which can impede the reliability of study findings. Researchers need to engage in realistic dialogues about the appropriateness of the calculated sample size for their specific research questions and available resources
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  • Smaller sample sizes may suffice in certain contexts, such as when using specific classifiers in machine learning tasks, but larger samples generally provide better performance metrics
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Conclusion

A sample size of 100 can be adequate for quantitative research if it meets the study's requirements for statistical power, effect size, and precision. However, researchers must carefully consider these factors and engage in thorough planning to ensure the sample size is appropriate for their specific research context.