Sampling Techniques

Overview

The collation of a robust data set is of paramount importance. The data required must be collected with an understanding of the time, cost, precision and accuracy possible from a chosen method and population.

Is this right for me?

This course is suitable for all staff regardless of grade.

Aims and Objectives

Attendees on the course will gain an appreciation of the underpinning principles associated with defining and collecting a sample for analysis, including:

• Defining the required population, sampling frame and sample size
• Sampling techniques such as simple random sampling, stratified random sampling, systematic sampling, cluster sampling and quota sampling. The use of random tables in sampling will also be covered
• The use of sampling techniques for both qualitative and quantitative data using different collection methodologies such as surveys and experimental data