Processing and analysing AMR data

Introduction

This course looks at how antimicrobial resistance (AMR) data is transformed into information, locally, nationally and globally. It provides an overview of the stages from data collection, to data management and data analysis. It introduces the core concepts, approaches and methods for analysing data, including descriptive and inferential statistics, and how they can be used to answer important questions about AMR. Sources of error and bias will also be reviewed.

This course is part of a series of courses related to AMR data. You should complete Fundamentals of data for AMR and Sampling (either the animal health or human health version) in order to understand the basics of AMR data before starting this course. By the end of the course, you should be able to:

• describe components of the information cycle
• list and explain principles of best practice for data collection
• list and explain principles of best practice for data management
• explain the difference between descriptive and inferential statistics
• calculate measures of central tendency
• understand concepts related to hypothesis testing
• interpret reported findings from a hypothesis test, including strength of statistical evidence, and potential sources of error and bias.

In order to achieve your digital badge and Statement of Participation for this course, you must:

• click on every page of the course
• pass the end-of-course quiz
• complete the course satisfaction survey.

The quiz allows up to three attempts at each question. A passing grade is 50% or more.

When you have successfully achieved the completion criteria listed above you will receive an email notification that your badge and Statement of Participation have been awarded. (Please note that it can take up to 24 hours for these to be issued.)