Overview

This self-paced online course offers an introduction to the principles, methods of assessment, and the appropriateness of statistical analyses for different types of measurement validity and reliability. Particular focus is given to statistical assessment of reliability over time, context and rater.

Content

Understanding and assessing reliability and validity are important when developing and using new tests and measurement tools.

This course gives an introduction to the concepts of reliability and validity, how the two are associated, and the methods commonly used for assessing these factors in different data types and study designs. Knowledge of basic statistical concepts (e.g., p-values, confidence intervals) is beneficial for this course.

The following topics will be covered during this one-day course:

• Types of validity
• Assessing validity of numeric and categorical outcomes
• Types of reliability
• Assessing reliability for numeric outcomes
• Bland-Altman Limits of Agreement
• Intraclass Correlation
• Assessing reliability for categorical outcomes
• Kappa
• Internal reliability of assessment measures
• Correlation

Learning outcomes

At the end of the course, delegates will be in a much better position to develop, test and refine their own measurement tool relating to their own research.

Furthermore, delegates will understand the implications of using imperfect measurements and thus be more qualified to perform critical appraisal of journal articles documenting quantitative and qualitative research.

In particular, delegates will be able to:

• Understand the difference between reliability and validity of a measurement.
• Distinguish between different types of reliability and validity assessments and understand what the purpose of each is.
• Set up a reliability or validity study, with appreciation of the type of data required and other aspects of the study design.
• Perform reliability and/or validity assessments for both numeric and categoric measurements.

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
• Revision summaries
• Support will also be available through a forum, where you can ask questions related to the course materials.

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 £75 (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: 23 Oct 2023, 16:03