Searle_Sampling

Page 1: Overview of Sampling Techniques

• Clive Seale's Chapter Contents

  • Representative and purposive sampling

  • Probability sampling

    • History of probability sampling

  • Populations and sampling frames

  • Simple random sampling

  • Stratified sampling

  • Response rates

  • Non-probability and purposive sampling

    • Quota sampling

    • Snowball and volunteer sampling

    • Maximum variation sampling

    • Theoretical sampling

  • Sampling in case-study research

  • Sample size

  • Conclusion

Page 2: Importance of Sampling

• Purpose of Sampling

  • Researchers often lack resources for studying an entire population, necessitating the selection of a representative sample.

  • In qualitative research, sampling can focus on individuals or groups that enhance understanding of specific issues.

• Types of Sampling

  • Representative Sampling: Used to draw broader generalizations from surveys, aiming for statistical accuracy.

  • Purposive Sampling: Targets specific individuals to gain deeper insights, not necessarily aiming for generalization.

Page 3: The Non-Probability Sampling Approach

• Purposive sampling allows for flexibility; researchers can adapt their sampling based on insights gained during research.

• Examples of Purposive Sampling Decisions

  • Choosing families based on their potential to contribute to the study of family life rather than strict demographic representation.

Page 4: Probability Sampling History

• Historical Context: Early social surveys needed exhaustive resources, exemplified by William Booth's lengthy poverty survey.

• Key Contributions: Arthur Bowley pioneered sampling theory, allowing researchers to generalize findings without a full census.

Page 5: Enhancing Sampling Precision

• Standard Error: Measures accuracy in relation to the population. Larger sample sizes yield smaller standard errors.

• Principle of Confidence Intervals: As sample size increases, the confidence in generalizing findings improves.

Page 6: Populations and Sampling Frames

• Defining Populations: Crucial to accurately identify the target population before sampling. Potential populations can include various groups such as students, patients, etc.

• Sampling Frames: Must provide a comprehensive list of the population to be sampled.

Page 7: Types of Probability Sampling Methods

• Simple Random Sampling: Basic method where every member has an equal chance of selection.

• Stratified Sampling: Divides the population into strata and samples within each to ensure a representative sample.

Page 8: Stratified Sampling Explained

• Stratification: Ensures representation of important characteristics within the sample by selecting proportionally from subgroups.

• Disproportionate Sampling: Useful for including smaller populations to provide sufficient data for analysis.

Page 9: Cluster and Multi-Stage Sampling

• Cluster Sampling: Economical method for dispersed populations where random clusters are selected first.

• Multi-Stage Sampling: Draws samples through multiple stages, aiding efficiency without requiring a complete population list.

Page 10: Importance of Response Rates

• Maximizing Response Rates: Higher response rates increase sample representativeness.

• Methods for Improvement: Face-to-face interviews typically yield better rates compared to postal surveys.

Page 11: Addressing Non-Response Bias

• Impact of Non-Response: Non-response can introduce bias; hence, characteristics of responders vs. non-responders should be analyzed.

Page 12: Non-Probability Sampling Techniques

• Non-Probability Techniques: Often better suited for hidden groups or exploratory research.

• Quota Sampling: Fast and cost-effective but less reliable, as it may not represent the true diversity of the population.

Page 13: Snowball and Volunteer Sampling

• Snowball Sampling: Utilized for accessing hidden populations by referrals; however, it may limit diversity.

• Volunteer Sampling: Engages those who are particularly interested in a topic, but may be biased towards those with unique perspectives.

Page 14: Maximum Variation Sampling

• Purpose: Seeks to include a broad range of experiences rather than average cases to capture varied perspectives.

Page 15: Theoretical Sampling for Insights

• Theoretical Sampling: Chosen based on emerging findings to refine or develop theories during research.

Page 16: Case Study Sampling Considerations

• Sampling in Case Studies: Selection can depend on researcher biases, requiring careful consideration of how cases are sampled.

Page 17: Sampling within a Case Study

• Procedures: Researchers must determine how to sample participants once case studies are selected based on specific characteristics.

Page 18: Sample Size Determination

• Optimal Size: Depends on intended analyses; larger samples provide more reliability for subgroup analyses.

• Informational Redundancy: Identifies when collecting more data no longer yields new insights.

Page 19: Conclusions on Sample Size and Research Quality

• Sampling Drives Research Conclusions: Effective sampling shapes the validity of generalizations made in research studies.

• Importance of Thoughtful Sampling: Well-planned sampling strategies are essential for robust social research.