Sample size considerations

Definition

Sample size considerations refer to the process of determining the number of participants needed in a study to ensure that results are valid, reliable, and generalizable. This involves balancing statistical power, the potential for error, and practical constraints like time and resources. Adequate sample size is crucial for both descriptive research, where it helps in providing an accurate snapshot of a population, and explanatory sequential design, where it ensures robust findings across multiple stages of data collection.

5 Must Know Facts For Your Next Test

1. A larger sample size generally leads to more reliable results, reducing the margin of error and increasing statistical power.
2. In descriptive research, appropriate sample size helps in accurately capturing the characteristics of the population being studied.
3. For explanatory sequential design, adequate sample size ensures that findings from qualitative phases can be effectively validated in quantitative phases.
4. Sample size considerations are influenced by factors such as expected effect sizes, variability in the population, and desired confidence levels.
5. Failing to consider sample size can lead to underpowered studies that may yield misleading results or fail to detect true effects.

Review Questions

• How do sample size considerations impact the reliability of findings in descriptive research?
  • In descriptive research, sample size considerations are crucial because they directly affect the accuracy and reliability of the results. A well-determined sample size ensures that the data collected provides a true representation of the population being studied. If the sample is too small, it may not capture the diversity within the population, leading to skewed results that could misinform stakeholders or researchers.
• Discuss how sample size considerations influence the stages of data collection in explanatory sequential design.
  • In explanatory sequential design, sample size considerations play a key role at both qualitative and quantitative stages. Initially, a smaller qualitative sample is used to gather insights and identify key themes, but this must be followed by a sufficiently large quantitative sample to validate those themes statistically. If either stage lacks an adequate sample size, the overall findings may be compromised, leading to conclusions that are not fully supported by empirical evidence.
• Evaluate the consequences of neglecting proper sample size considerations in research design and its effects on generalizability.
  • Neglecting proper sample size considerations can lead to significant consequences in research design, including unreliable findings and low generalizability. When researchers use an inadequate sample, they risk producing results that do not accurately reflect the larger population, which undermines the validity of their conclusions. This lack of representativeness can mislead future research efforts and policy decisions based on those findings, ultimately reducing trust in research outcomes within the broader field.

Related terms

Statistical Power: The probability that a study will correctly reject a false null hypothesis, which is influenced by sample size, effect size, and significance level.

Sampling Error: The error that occurs when a sample does not accurately represent the population from which it was drawn, often affected by the sample size.

Generalizability:

The extent to which findings from a study can be applied to broader populations beyond the sample studied.