HLTH2024 Research Methods in Health - Module 6.2: Sample Bias

Overview

• Lecturer: John Bidewell
• Speaker: Miriam Kannedy
• Focus: Understanding sampling bias and its impact on research validity.

Key Concepts

Sample Bias

• Definition: A major threat to validity in quantitative research.
• Impact: Affects the ability to generalize results beyond the study.

Target Population

• Definition: Every possible case of interest such as an illness or community group.

Representativeness

• Definition: How well a research sample resembles the target population.
• Importance: Enables results to be generalizable to the relevant population.

Bias

• Occurs through systematic or random errors.
• A biased sample is not representative and results may not generalize.

Sampling Error

• Definition: Random, accidental differences between sample and population.
• Larger sample sizes reduce sampling error but do not correct for bias.

Relationship Between Sample Size and Representativeness

• Unbiased Sampling Method: Increasing the sample size can reduce sampling error.
• Biased Sampling Method: Increasing sample size does not reduce bias.

Types of Research

Quantitative Research

• Representativeness: Crucial for generalizing results to the population.
• Bias: Should be minimized to improve representativeness.

Qualitative Research

• Less focus on generalizing to the population.
• Sample-specific results can be sufficient if relevant to the study's aims.

Importance in Healthcare

• Biased evidence may not apply to the practitioner’s patients or clients.
• Ensuring the research sample represents the target population is critical.

Demonstration of Sample Size Effects

• Using samples of size 10, 100, and 1,000 to show probabilities of representativeness.
• Larger samples reduce sampling error but not bias when methods are biased.

Summary

• Target Population: Cases of research interest, can be demographic or illness groups.
• Representativeness: Matching sample characteristics to target population.
• Sampling Error: Random effects, reduced with larger samples.
• Bias: Results from poor sampling methods, not corrected by larger samples.
• Relevance: Biased samples lead to irrelevant evidence for healthcare practice.

Conclusion

Understanding and addressing sample bias is critical for the validity and applicability of research results, especially in healthcare, to ensure evidence-based practice is grounded in relevant and generalizable data.