Summer School

Students outside in the quadrangle

Hello All!

I’d like to announce the 5th Annual University of Glasgow’s Q-Step Centre’s Summer School!

This year we are expanding our course offerings to include:

Courses:

Download Program: Summer School 2021

Format: Each course in our Research Methods Summer Program will be run using an online model this year. Course offerings range from 1/2-day to week-long sessions.

Dates: Monday 23 August – Friday 27 August 2021.

Question: please contact us at with the subject heading “Summer School Questions”.

To register: please follow the links to the appropriate Eventbrite page,

Registration closes 31 July 2021!

We look forward to seeing you this summer!

Best wishes,

Dr. Niccole M. Pamphilis and the Q-Step Team at Glasgow

 


Course Descriptions


Course options are detailed below with descriptions of each course, dates for each session and registration links. After registering for a course, the School of Social and Political Sciences at the University of Glasgow will be in touch regarding fee payments.

We hope to you find something of interest and join us in August!

Introduction to R/RStudio for Social Scientist

Summary: As quantitative data becomes more accessible, researchers are turning to software that can do more and is free to access. The first part of the course will walk you through the basics of R/RStudio, from how the interface should look on your computer, the language of R, installing packages, and basic command structures. The second part provides a brief introduction to data management and reporting in R.

Prerequisites: Access to R/RStudio

Duration: 23 August 2021 1-Day course (10:00-16:00)

Instructor: Dr. Thees Spreckelsen

Delivery: Zoom Meeting with demonstrations and practical sessions, in addition to online material

Fee: £50

Registration: Eventbrite


Survey Analysis and Missing data in R

Summary: Representative sample surveys are key to social science whether for employment numbers or studying attitudes towards the environment. To achieve representativeness we need to consider the sampling process and missing data. Moreover, survey response are usually yes/no, agree to strongly disagree, or categorical (What is your occupation) that require generalized linear models.

The first part of the course discusses survey sampling, and how accounting for sampling weights and strata may lead to more appropriate population estimates. Using the European Social Survey we analyze survey-weighted responses in R.

The second part introduces students to techniques for dealing with missing data - a key thread to representativeness. The focus will be on multiple imputation a flexible technique beyond the survey context.

The third part of course provides a brief introduction to regression models for standard survey responses: binary (yes/no), ordered (Disagree a lot…agree…Agree a lot), and categorical (Occupation/Ethnicity). Followed the application of these generalized linear models accounting for survey sampling and missing data using R.

Prerequisites: Access and introductory experience using R/RStudio;* a good understanding of ordinary least squares regression (OLS). Self-check: What is an OLS residual and how would you calculate it.

Instructor: Dr. Thees Spreckelsen

Duration: 24 and 25 August 2021 2-day course (10.00:16.00)

Delivery: Zoom Meeting with demonstrations and practical sessions, in addition to online material

Fee: £70

Registration: Eventbrite


Text mining and qualitative data visualisation using R

Summary: We will explore basic test visualisation using tidytext package:

  • Learn how to clean qualitative data, calculate and visualise word frequencies
  • Create a workload
  • Perform sentiment analysis
  • Visualise themes from qualitative data analysis

Prerequisites: Access to R/RStudio

Instructor: Dr. Joanna Wincenciak

Duration: 1/2-day; 3 hours 26 August 2021 (10:00-13:00)

Delivery: Zoom Meeting with demonstrations and practical sessions, in addition to online material
Fee: £40

Registration: Eventbrite


Qualitative Epistemologies: Understanding the Threads in your Tapestry of Methods

Summary: This is an introductory course that covers fundamental questions about qualitative approaches and competing ontologies and epistemologies. The course begins with an examination of coding drawing a distinction between descriptive and theoretical coding.

Part 2 will look at Grounded Theory as an early qualitative framework. Part 3 will look at Phenomenology and Part 4 will look at Discourse Analysis. Students should only sign up if they intend to cover both days of training.

We will draw on different kinds of qualitative material, from interviews, documents, letters to newspaper articles and field diaries and there will be time for students to work with data as they apply their learning.

Prerequisite: The training is available to anyone interested in how qualitative research works. It will work at an introductory level and as a refresher course for those more advanced.

Instructor: Dr. Jo Ferrie

Duration: 26-27 August 2021 2-day course (10:00-17:00)

Delivery: Zoom Meeting

Fee: £70

Registration: Eventbrite


Introduction to Machine Learning

Summary: Machine learning methods involve methods that deal with multivariate data, learning hidden structures and prediction. In particular within prediction methods, classification is a special case where explanatory variables are used to predict which one of a number of classes an object belongs to, e.g. is an email spam or not spam, is a person likely to vote for a particular party over the others, etc. For multivariate data you may wish to reduce the number of variables for either simpler modelling or to try to discover hidden concepts within the data.

This course will look at a subset of these types of methods including: principal component and a brief discussion of factor analysis, classification using k-nearest neighbours, classification and regression trees and discriminant analysis classification. In addition to lectures giving background on the methods and the intuition behind them to aid understanding, there will be computing sessions in R showing how to implement these methods on real data examples.

Prerequisites: Familiarity with R is essential and basics of statistics (linear regression, normal distributions and probability) are needed.

Instructor: Dr. Nema Dean

Duration: 26-27 August 2021 2-day course (10:00-16:00)

Delivery: Zoom Meeting

Fee: £70

Registration: Eventbrite


Social Network Analysis

Summary: This workshop examines the theoretical and statistical analysis of social networks from an interdisciplinary perspective. Participants learn about the nature, structures, and dynamics of social networks that are relevant to fields such as management, public health, sociology, politics, psychology, economics, and anthropology. The workshop covers the ontology of networks, major theoretical approaches, common research designs, descriptive statistics, and a variety of techniques for statistical inference. The workshop, conducted virtually over Zoom, includes didactic sessions, hands-on computer exercises, and individualized projects by participants. The principal goal of workshop is for participants to learn to conduct their own empirical research on social networks on a variety of topics.

Prerequisites: Admission to post-graduate study at the level of MRes or PhD. Strictly enforced.

Instructor: Dr. Michael Heaney

Duration: 23-27 August 2021 5-day course (9:00-12:00 & 14:00-17:00)

Delivery: Zoom Meeting

Fee: £200

Registration: Eventbrite