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Cathie Marsh Institute for Social Research (CMI)

Introduction to Data Analysis 2

Dates: 8 November 2019 and 24 April 2020
Time: 10am - 4.30pm
Instructor: Patricio Troncoso Ruiz and Jack Bailey 
Level: Introductory
Fee: £195 (£140 for those from educational, government and charitable institutions). 

CMI offers up to five subsidised places at a reduced rate of £60 per course day to research staff and students within Humanities at The University of Manchester. These places are awarded in order of application. 

Humanities PGR students at The University of Manchester can apply for a methods@manchester bursary to help cover their costs. All applications will be considered on a case-by-case basis and applicants will be required to provide a supporting statement from their supervisor. Applications for bursaries must be submitted at least two weeks in advance of the course date; applications submitted after this time will not be accepted. Retrospective applications cannot be made if courses have already taken place or payment has already been made.

Please click here to make a booking. If you are applying for a subsidised place, select the £60 University of Manchester option on the booking form. For queries about methods@manchester bursaries, contact methods@manchester.ac.uk (please note, you must have a confirmed place on the course before requesting a bursary application form). For any other queries about short courses, please contact cmi-shortcourses@manchester.ac.uk.

Please note: this is not guaranteed and is considered on a case by case basis. Please contact us for more information.

Outline

Introduction to Data Analysis 2 builds on the skills taught in Introduction to Data Analysis 1. Namely, data management, summary, and visualisation. In particular, the course introduces methods to analyse the relationship between variables using cross-tabulation and linear regression analysis. Like the IDA 1, it emphasises hands-on practical learning and uses real-world data and cutting-edge software.

Objectives

This course covers the following topics:

  • Understanding probability and statistical significance
  • Testing correlations between different types of variable
  • Analysing the relationship between variables using linear regression
  • When and how to control for confounding variables
  • Drawing inferences from your results

Prerequisites

Participants should have a basic familiarity with a statistical software package (e.g R or PSPP). Ideally, participants should also have taken Introduction to Data Analysis 1 or have equivalent experience.

Recommended reading

  • R for Data Science by Garrett Grolemund & Hadley Wickham

  • Discovering Statistics Using R by Andy Field, Jeremy Miles & Zoe Field
  • Social Research Methods by Alan Bryman

About the instructor

Jack Bailey is a doctoral researcher at the University of Manchester’s Department of Politics. His research focuses on how voters update their opinions in light of economic change and how this process affects their voting behaviour. Prior to starting his PhD, Jack completed the University’s MSc in Social Research Methods and Statistics. He also worked at Cardiff University and the London School of Economics and Political Science communicating research findings to the general public. This interest remains and motivates his approach to teaching statistics to non-specialist audiences.

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