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[Commlist] methods@manchester Summer School / University of Manchester
Wed May 08 13:30:43 GMT 2019
*methods@manchester Summer School 1-12 July 2019*
*University of Manchester*
*Limited Course Places available**
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
early career research in humanities. The selection includes software
training as well as qualitative and quantitative methods analysis. The
course content is based on approaches from across the various schools in
the Faculty of Humanities at the University of Manchester, as well as
external experts. The award winning Andy Kirk is delivering a new data
visualisation course for us this year.
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).
* *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.
Full details about the methods@manchester Summer School are available at
the methods@manchester website
·*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
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
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
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
*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
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
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.
Click here to see highlights from our 2018 summer school event
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