methods@manchester Summer School – seven additional bursaries for PhD candidates available for selected courses

by | May 15, 2019 | Funding, Workshops/training | 0 comments

methods@manchester Summer School 1-12 July 2019
University of Manchester
Seven Additional Bursaries for PhD candidates available for selected courses – Deadline for applications midnight 20 May 2019

methods@manchester is delighted to be holding its annual Summer School from 1-12 July 2019.

The Summer School offers a range of specialised courses covering a variety of topics that are particularly relevant to postgraduate and ECR research in humanities. The selection includes software training as well as qualitative and quantitative analysis. The course content is based on approaches from across the various schools in the Faculty of Humanities at the University of Manchester.

Each Summer School course will run for one week, delivering four days of content to a five-day timetable (Monday afternoon to Friday lunch-time), building on successful methods@manchester and CMIST short-courses given throughout the year.

Bursaries – Seven additional bursaries for PhD candidates available for selected courses, application deadline midnight 20 May 2019.
We have a small number of subsidised places for PhD students, reducing the cost of a course by £300. To apply or for further details please email for an application form confirming the course you are applying for.

The additional bursaries are available to PhD candidates for the following courses:
• Getting started in R: an introduction to data analysis and visualisation (1-5 July 2019)
• Generalized linear models: a comprehensive system of analysis and graphics using R and the Rcommander (1-5 July 2019)
• Research Methods in Political Economy (1-5 July 2019)
• Advanced social network analysis (8-12 July 2019)
• Quantitative policy evaluation (8-12 July 2019)

Further information on the courses is set out below.

Bursary applications may be made to

Full details about the methods@manchester Summer School are available at the methods@manchester website.

• Getting started in R: an introduction to data analysis and visualisation (1-5 July 2019)
R is an open source programming language and software environment for performing statistical calculations and creating data visualisations. It is rapidly becoming the tool of choice for data analysts with a growing number of employers seeking candidates with R programming skills.
This course will provide you with all the tools you need to get started analysing data in R. We will introduce the tidyverse, a collection of R packages created by Hadley Wickham and others which provides an intuitive framework for using R for data analysis. Students will learn the basics of R programming and how to use R for effective data analysis. Practical examples of data analysis on social science topics will be provided.

• Generalized linear models: a comprehensive system of analysis and graphics using R and the Rcommander (1-5 July 2019)
This is a general course in data analysis using generalised linear models. It is designed to provide a relatively complete course in data analysis for post-graduate students. Analyses for many different types of data are included; OLS, logistic, Poisson, proportional-odds and multinomial logit models, enabling a wide range of data to be modelled. Graphical displays are extensively used, making the task of interpretation much simpler.
A general approach is used which deals with data (coding and manipulation), the formulation of research hypotheses, the analysis process and the interpretation of results. Participants will also learn about the use of contrast coding for categorical variables, interpreting and visualising interactions, regression diagnostics and data transformation and issues related to multicollinearity and variable selection.
The software package R is used in conjunction with the R-commander and the R-studio. These packages provide a simple yet powerful system for data analysis. No previous experience of using R is required for this course, nor is any previous experience of coding or using other statistical packages.
This course provides a number of practical sessions where participants are encouraged to analyse a variety of data and produce their own analyses. Analyses may be conducted on the networked computers provided, or participants may use their own computers; the initial sessions cover setting up the software on laptops (all operating systems are allowed).

Research Methods in Political Economy (1-5 July 2019)
This five-day workshop will equip scholars with the methodological tools for acquiring empirical knowledge in political economy and the theoretical tools for questioning the validity and limits of the knowledge produced.
It will begin with a brief exploration of the ontological and epistemological foundations of knowledge production, and then feature a series of intense workshop on different methodological approaches and techniques. This includes the quantitative approaches, such as Stata regression analyses and NVivo coding, as well as qualitative approaches, such as social network analysis and the conducting of semi-structured elite interviews. These sessions entail a combination of lectures, practical work and feedback. The course will then conclude with the utilisation of these techniques in your own specific research projects, presentations of your work, and a discussion of methodological strengths and weaknesses in each case.

Advanced social network analysis (8-12 July 2019)
An introduction to statistical analysis of networks and some advanced concepts building on the introductory course. To benefit fully from the course requires a basic knowledge of standard statistical methods, such regression analysis. The course aims to give a basic understanding of and working handle on drawing inference for structure and attributes for cross-sectional data. A fundamental notion of the course will be how the structure of observed graphs relate to various forms of random graphs. This will be developed in the context of non-parametric approaches and elaborated to the analysis of networks using exponential random graph models (ERGM) and permutation tests. The main focus will be on explaining structure but an outlook to explaining individual-level outcomes will be provided.
The participant will be provided with several hands-on exercises, applying the approaches to a suite of real-world data sets. We will use the stand-alone graphical user interface package [TS1] and R as well as other specialist sna software Eg Visone and UCINET. In R we will learn how to use the packages ‘sna’ and ‘statnet’. No familiarity with R is assumed but preparatory exercises will be provided ahead of the course.

Quantitative policy evaluation (8-12 July 2019)
This is an introductory course aimed at researchers, policy-makers, students and anyone interested in estimating the effect of a policy, intervention or experiment (using data). The course will benefit researchers, practitioners and students interested in supporting policy, research and theories with solid empirical evidence. This will include people working in areas as diverse as Medicine, Economics, Criminology, Politics, Psychology, Social Policy, or Sociology to mention but a few.
The course will emphasise concepts and implementation of methods, although pertinent theoretical results will be discussed. The applications discussed in the classes will come from all walks of Social Sciences, in order to reflect the wide-ranging reach of causal inference.

Click here to see highlights from our 2018 summer school event

Tel: 0161 275 4269
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