methods@manchester Summer School 1-12 July 2019
University of Manchester
Limited PhD Bursaries Available - Deadline for applications 30 April 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 - Please note, the deadline for PhD bursary applications is 30 April 2019.
We have a small number of subsidised places for PhD students, reducing the cost of a course to £300*. To apply or for further details please email [log in to unmask]<mailto:[log in to unmask]> for an application form confirming the course you are applying for.
*with the exception of Introduction to Longitudinal Data Analysis using R which will be reduced to £375.
* Creative approaches to qualitative research (1-5 July 2019)
* Introduction to Social Network Analysis using UCINET and Netdraw (1-5 July 2019)
* 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)
* Introduction to longitudinal data analysis using R (8-12 July 2019)
* Advanced social network analysis (8-12 July 2019)
* Data Visualisation (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 [log in to unmask]<mailto:[log in to unmask]>
Full details about the methods@manchester Summer School are available at the methods@manchester website<https://www.methods.manchester.ac.uk/connect/events/summer-school-2019/>.
• Creative approaches to qualitative research (1-5 July 2019)
This intermediate level course offers a hands-on introduction to creative approaches to doing qualitative research. The various stages of research will be covered, from data collection and analysis through to writing with qualitative data. We begin by introducing what we mean by doing qualitative research creatively, and course participants will also provide short introductions of their research projects. Participants will be given a practical and hands-on introduction to a range of creative qualitative methods of data collection, including various visual methods and mobile methods. The course will also cover creative ways of analysing qualitative data and some of the key practical and ethical issues in using creative methods. Finally, we discuss practical and intellectual strategies for writing with qualitative data, and consider how it is possible to theorise, or write conceptually, with such data. The course includes workshop exercises involving creating qualitative data, and ‘methods surgeries’ where participants will have the opportunity to work with their own data.
• Introduction to Social Network Analysis using UCINET and Netdraw (1-5 July 2019)
This is an introductory course, covering the concepts, methods and data analysis techniques of social network analysis. The course is based on the book "Analyzing Social Networks" by Borgatti et al. (Sage) and all participants will be issued with a copy of the book. The course begins with a general introduction to the distinct goals and perspectives of social network analysis, followed by a practical discussion of network data, covering issues of collection, validity, visualisation, and mathematical/computer representation. We then take up the methods of detection and description of structural properties, such as centrality, cohesion, subgroups and positional analysis techniques. This is a hands-on course largely based on the use of UCINET software and will give participants the experience of analysing real social network data using the techniques covered in the workshop. No prior knowledge of social network analysis is assumed for this course.
• 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.
Introduction to longitudinal data analysis using R (8-12 July 2019)
Longitudinal data (data collected multiple times from the same cases) is becoming increasingly popular due to the important insights it can bring us. For example, it can be used to track how individuals change in time and what the causes of change are. It can also be used to understand causal relationships or used as part of impact evaluation. Unfortunately, traditional models such as ordinary least squares regression are not appropriate as multiple individuals are nested in different time points. For this reason, specialised statistical models need to be learned.
In this course, you will learn the most important skills needed in order to prepare and analyse longitudinal data. We will cover statistical methods used in multiple research fields such as economics, sociology, psychology, developmental studies, marketing and biology. At the end of the course, you will be able to answer a number of different types of questions using longitudinal data: questions about causality and causal order, about changes in time and what explains it, and about the occurrence of events and their timing.
Throughout the week, we will use a combination of lecturing and applied sessions. For the applied sessions, we will use the statistical package R. R is becoming one of the leading statistical software due to its free and open source nature. In this course, you will learn how to effectively use it to answer longitudinal questions. We will cover both data management and cleaning, as well as different statistical methodologies such as regression analysis, multilevel analysis, structural equation modelling and survival analysis.
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.
Data Visualisation (8-12 July 2019)
This workshop will provide attendees with an accessible, practical and comprehensive understanding of the subject of data visualisation.
The focus of the training is to teach the craft of this discipline, helping delegates to know what to think, when to think and how to think about all the analytical and design decisions involved in any data-driven challenge.
The training is structured around a proven design process. Across the session delegates will build up, stage by stage, a detailed understanding of all the different aspects of decision-making that goes into any data visualisation project.
The teaching content will provide a mixture of practice, case-study and theoretical perspectives. The teaching methods will involve an energetic blend of teaching, discussion, and group practice. A large percentage of time will be allocated to practical exercises that vary in nature from evaluating work, conceiving ideas, sketching concepts, assessing data, and forensically assessing design choices.
The approach to teaching this subject is not framed around specific tools or applications. Across the session there will be references for some of the most common, contemporary technologies but the emphasis is on the underlying craft, regardless of your tools or technical skills.
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.
Tel: 0161 275 4269
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