Apologies for cross -posting – PhD bursaries in ‘R’ this summer

R Summer School Courses

methods@manchester Summer School 1-12 July 2019

University of Manchester

Limited PhD Bursaries 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 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 CMI short-courses given throughout the year.

 

Bursaries

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 contact methods@manchester.ac.uk 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.

 

Available 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)
  • Introduction to longitudinal data analysis using R (8-12 July 2019)

Further information on the courses is set out below.

 

Bursary applications may be made to methods@manchester.ac.uk

 

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).

 

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.

 

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

 

Methods@Manchester

Tel: 0161 275 4269

https://www.methods.manchester.ac.uk/

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