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Applied Social Data Analysis in R


Module description

This module is intended for students with some prior exposure to quantitative research methods and will be delivered using R for statistical computing. Alternatively, it can be taken as the second component of the sequence of quantitative methods we offer: Introduction to Quantitative Social Research, Applied Social Data Analysis in R and Advanced Topics in Quantitative Social Research. 

The module teaches you research methods and prepares you to undertake further independent research in the social sciences. The core material covers complex data management in R, linear regression methods, including model diagnostics and binary logistic regression. By the end of the module, you will be able to apply these methods on a variety of data in an intelligent and thoughtful manner. You will get plenty of exposure to programming as a highly transferable skill in a range of domains outside statistics.

Indicative module syllabus

  • Refresher/Intro to: R programming, available resources, and online communities
  • Refresher: inferential statistics
  • Data management and visualising data
  • Linear regression:
    • Basic concepts
    • Estimation
    • Goodness of fit, R-squared
    • Categorical predictors and interaction terms
    • Residuals and outliers
    • Diagnostics
    • Multiple regression
  • Logistic regression

Learning objectives

By the end of this module, you will:

  • be aware of a range of research resources available to social scientists from data to specialised statistical packages
  • be able to perform intermedia quantitative analyses on a variety of data in an intelligent and thoughtful manner
  • have hands-on experience with R
  • be able to pick and apply research techniques that are suitable for the analysis of your own research problems
  • be able to interpret and critique more advanced published research
  • be able to write up and present the findings of intermediate quantitative analysis
  • be aware of debates around the applicability of quantitative and qualitative methods.