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

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.

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.

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.

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.

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.

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.

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.

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.

Maximizing Response Rates: Higher response rates increase sample representativeness.
Methods for Improvement: Face-to-face interviews typically yield better rates compared to postal surveys.

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

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.

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.

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

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

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

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

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.

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.