Overview

This self-paced online course covers the basics of sample size estimation. It will be of use to those embarking on a research project and who are perhaps trying to complete ethics and grant application forms. Both precision and power estimation approaches are covered.

Content

The concepts of sample size estimation are introduced, including when and why this might be necessary. Formulae are presented and discussed. Excel sheets are provided and pre-programmed to facilitate use of the formulae.

In the first part of the course, the precision-based approach to sample size estimation is introduced, whereby the numbers are selected to produce a confidence interval for the relevant estimate with a specified width.

In the second part, we progress to the more traditional power calculations that aim to give the sample required to determine a specified difference with selected power and significance levels.

All terminology will be explained as it is introduced in the sessions.

Sample size estimation is discussed for the following measurements: Means, proportions/ percentages, rates, time to event data, measures of agreement and percentiles.

Learning outcomes

At the end of the course, delegates should understand the main principles of sample size calculations and be able to perform such as calculation for their own research. In particular, delegates will be able to:

• Understand why sample size calculations are important for research.
• Explain the main principles of statistical inference (e.g. standard errors, confidence intervals) and how this applies to sample size calculations.
• Recognise what type of sample size calculation is appropriate for given research proposal.
• Gather the necessary inputs / estimates that are required to perform the calculation and understand where these can typically be derived or found (e.g. from previous research papers).
• Understand the concept of ‘power’ and how changing this quantity will affect the resulting sample size.
• Have awareness of other more advanced topics such as how sample size calculations might be adapted in the context of cluster randomised controlled trials.

Course structure and teaching

This is an online, self-paced course that includes:

• Full electronic notes
• Short lecture videos (recorded outside of the classroom with screen recordings and annotation) that follow closely with the notes
• Interactive quizzes for each chapter
• Extended practical exercises for further comprehension.
• Spreadsheets to help perform sample size calculations.
• Extras to help with accessibility (e.g. video captions, transcripts, alternative image captions, slides).

Entry requirements

A basic level of statistical literacy is required as a prerequisite.

It is desirable for the course participants to have basic knowledge of statistics, i.e. notion of statistical inference, p-values and Confidence intervals.

Those who have completed the five-day Introduction to Statistics and Research Methods course run frequently by the Centre for Applied Statistics Courses (CASC) team will be equipped.

Cost and concessions

The standard price is £87.50 (including VAT).

A 50% discount is available for UCL staff, students, alumni. If you're eligible for a discount, email ich.statscou@ucl.ac.uk before booking to be sent the discount code.

The course is available for free to those associated with the Institute of Child Health or Great Ormond Street Hospital, and SLMS doctoral students. Please also email ich.statscou@ucl.ac.uk to gain a booking code.

Certificates

You can download a certificate of participation once you have completed all the session quizzes.

Find out about other statistics courses

CASC's stats courses are suitable for anyone requiring an understanding of research methodology and statistical analyses. The courses allow non-statisticians to interpret published research and/or undertake their own research studies. Find out more about CASC's full range of statistics courses.

Course team

Dr Dean Langan

Dean works as a lecturer, jointly based within the School of Life and Medical Sciences (SLMS) and the Centre for Applied Statistics Courses (CASC) at UCL. He has a Bachelor’s degree in Mathematics from University of Liverpool, a Master's degree in Medical Statistics from University of Leicester, and a PhD from University of York for his research in statistical methods for random-effects meta-analysis. He's worked as a statistician on a number of clinical trials related to stroke and myeloma at the Clinical Trials Research Unit in Leeds. His specialist areas include statistical methods for meta-analysis, R programming, clinical trial methodology and research design.

Course information last modified: 21 Mar 2024, 16:14