Sampling in Qual

Module Overview

  • Module Leader: Dr. Fiona Coupar

  • Course: Methodology and Research for Effective Practice (MREP)

  • Week 5 Topic: Sampling

  • Lecture 3 Focus: Sampling in qualitative research

Sampling Methods

Types of Sampling

  • Probability (Random) Sampling

    • Ensures every individual has an equal chance of being selected.

  • Non-Probability (Non-Random) Sampling

    • Not everyone has a chance to be included; relies on subjective judgment.

Non-Probability Sampling Techniques

  1. Convenience (Incidental) Sampling

    • Participants are those most readily available.

    • Typically the easiest and cheapest form of sampling.

    • Access to a sampling frame may not always be possible.

  2. Purposive (Purposeful) Sampling

    • Participants chosen based on specific characteristics or criteria.

    • Often used for gaining deep understanding in phenomenological studies.

  3. Snowball Sampling

    • Starts with a small number of participants who help identify further participants.

    • Particularly useful for reaching marginalized or hidden populations.

    • It can be restrictive and is often combined with other sampling methods.

Key Considerations in Sampling

  • Quality of Data: The integrity and relevance of the data collected.

  • Characteristics of Individuals: The specific attributes that participants possess, which may influence study results.

  • Generalisability: Not a priority in qualitative research; focuses on detailed understanding rather than broad application.

Inclusion/Exclusion Criteria

  • Inclusion Criteria: Define the necessary characteristics for participants to be involved in a study.

  • Exclusion Criteria: Identify which characteristics disqualify individuals from participating.

  • Documentation: Clear criteria should be established and recorded for transparency and replication of studies.

Sample Size and Data Saturation

  • Sample Size: Typically smaller in qualitative research.

    • Focus on depth of understanding rather than breadth.

  • Data Saturation: Concept linked to grounded theory, indicating the point where no new information emerges, leading to the conclusion of data collection efforts.

References

  • Linsley, P. and Kane, R. (2022) Evidence-based Practice for Nurses and Allied Health Professionals. 5th ed. London: Sage Publications Ltd.

  • Moule, P. (2021) Making sense of research in nursing, health and social care. 7th ed. London: Sage Publications Ltd.

  • Polgar, S. and Thomas, S. (2019) Introduction to Research in the Health Sciences. 7th ed. Edinburgh: Elsevier.

  • Ross, T. (2012) A Survival Guide for Health Research Methods. Maidenhead: Open University Press.