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: 22 August 2022 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: please email socsci-qstep@glasgow.ac.uk.

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: 23 and 24 August 2022 2-day course (10.00:16.00)

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

Fee: £70

Registration: please email socsci-qstep@glasgow.ac.uk.

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-day;  25 August 2022 (10:00-16:00)

Delivery: Zoom Meeting with demonstrations and practical sessions, in addition to online material
Fee: £70

Registration: please email socsci-qstep@glasgow.ac.uk.

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.

Jo Ferrie is a methodologist and works with all forms of data. Based in the Sociology subject area within the School of Social & Political Sciences at the University of Glasgow, she is also Deputy Director for Training for the Scottish Graduate School of Social Sciences and founding Director of Glasgow Q-Step: a pan-UK programme designed to upskill social science undergraduates in how they use and work with numeric data. Jo’s scholarship work focuses on human rights implementation and the nature of the learning experience within knowledge exchange initiatives and the learning experience and methods. Jo is Principal Fellow of the Recognising Excellence in Teaching programme.

This course is structured in 6 parts to produce a flipped classroom. This is where you have time with the learning materials, and the actual ‘live’ part of the training is building a bridge from this learning to your own research. There will be a release of links to zoom-captured lectures during the week before summer school on:

• Qualitative epistemologies & coding
• Grounded theory
• Phenomenology
• Discourse analysis
• Participatory action research.

Flipped classroom event – live discussion.

The lectures will be accompanied by some excerpts from research transcripts, from published data and from a media article for you to practice skills introduced. The activities are carefully constructed to demonstrate the differences in knowledge produced, depending on the qualitative epistemology chosen. So, you are welcome to watch just the training session that you’re interested in but if you watch a few or all, you should grow in confidence in how you use, understand and make choices around qualitative approaches.

The sixth session is a ‘live’ session where I will be available to answer questions and hear about your research interests and challenges. I’m really excited to hear what you’re working on and the focus will be not on my teaching but on our practices.

The live discussion could go in a number of different directions: we can take time to think about defending your research choices or we could work at a more personal level to think through why qualitative methods can be so challenging and emotional. I’m keen that control of the narrative lies with you. 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.

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: 5x2h materials release 22 August 2022;

                2h Live session: 26 August 2022 (10:00-12:00)

Delivery: flipped classroom – videos, live-discussion and Q&A session

Fee: £70

Registration: please email socsci-qstep@glasgow.ac.uk.

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: 25-26 August 2022 2-day course (10:00-16:00)

Delivery: Zoom Meeting

Fee: £70

Registration: please email socsci-qstep@glasgow.ac.uk.

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: 22-26 August 2022 5-day course (9:00-12:00 & 14:00-17:00)

Delivery: Zoom Meeting

Fee: £200

Registration: please email socsci-qstep@glasgow.ac.uk.