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Cathie Marsh Institute for Social Research

methods@manchester adds new courses to Summer School 2017

3 April 2017

methods@manchester have announced three new courses for Summer School 2017. The annual Summer School takes place at The University of Manchester from 26-30 June and 3-7 July 2017.

The additional courses which have been revealed include software training using R and Mplus:

Generalized Linear Models: a comprehensive system of analysis and graphics using R and the R Commander

This is a general course in data analysis using generalized linear models.  It is designed to provide a relatively complete course in data analysis for post-graduate students.  Analyses for many different types 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 RStudio.  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 lap-tops (all operating systems are allowed).

The main objective of this course is to provide a general method for modelling a wide range of data using regression-based techniques.  Participants will be able to select, run and interpret models for continuous, ordered and unordered data using modern graphical techniques.

Getting Started in R: an introduction to data analysis and visualisation

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.

1. R and the 'tidyverse'

This session will introduce R and RStudio and cover the basics of R programming and good coding practice. We will also discuss R packages and how to use them, with a particular focus on those that make up the 'tidyverse'. We also introduce R Markdown which will be used to report our analyses throughout the course.

2. Import and Tidy

Data scientists spend about 60% of their time cleaning and organizing data (CrowdFlower Data Science Report 2016: 6). This session will show you how to 'tidy' your data ready for analysis in R. In particular, we'll show you how to take data stored in a flat file, database, or web API, and load it into a dataframe in R. We will also talk about consistent data structures, and how to achieve them.

3. Transform

Together with importing and tidying, transforming data is one of the key element of data analysis. We will cover subsetting your data (to narrow your focus), creating new variables from existing ones, and calculating summary statistics.

4. Visualise

Data visualisation is what brings your data to life. This session will provide you with the skills and tools to create the perfect (static and interactive) visualisation for your data.

5. Bringing it all together

In this last session we review all we have learned on this course, and think about how we can bring it all together in dynamic outputs, such as interactive documents, plots, and Shiny applications.

Course objectives

After this course, users should be able to:

  • implement the basic operations of R;
  • read data in multiple forms;
  • clean, manipulate, explor and visualise data in R

Structural Equation Modelling with Mplus

This course gives a hands on introduction to what is possible in a latent variable analysis framework using Mplus. Building up the different sides of latent variable modelling and structural equation modeling step by step, eight different types of analysis are tackled.

Regression, Path Analysis, Confirmatory Factor Analysis, Item Response Theory, Measurement modelling, Latent Class Analysis, Longitudinal Analysis and lastly, hybrids of these are all topics of the course covered in lectures and practical analysis in Mplus.

Bringing your own data and research questions is highly recommended!

Course objectives

  • Distinguish and understand different types of latent variable analysis;
  • Learn how to do basic and advanced structural equation modelling;
  • Understand how to combine different techniques in one model; and
  • Learn how to use Mplus

The courses are open for booking alongside the previously announced courses:

Further information about Summer School 2017 is available on the methods@manchester website.

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