[Commlist] methods@manchester Summer School / University of Manchester

*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).
*Available courses:*

  * *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 
<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.

Click here to see highlights from our 2018 summer school event 
https://www.youtube.com/watch?v=7NMRbnvW5Q8
methods@manchester
https://www.methods.manchester.ac.uk/

	
	
	
	
	
	
	

	

	


	
	
	

	
	
	

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