Introduction to methods for computational social science
SOCI3001
CPD-LG.07
10:30 - 12:20
Wednesday
2nd semester
Lecture venue
Lecture time
Offer semester
The volume of data produced by society continues to grow at an exponential pace. Tools that harness the increasing amounts of data are providing new insights into ongoing changes in our societies. This course provides an introduction to data analysis using R; a popular open-source statistical programming language used for data mining, modelling, and visualisation. The course begins with an introduction to the R and RStudio working environments, providing an outline of common core functions for data analysis and getting help using R. Next, common data structures, variables, and data types will be demonstrated. Students will then learn how to write code scripts to utilize the popular tidyverse set of R packages for data manipulation and visualization. Finally, we will learn how to carry out a range of statistical regression analyses in R, in order to summarise relationships, account for hierarchies in data sets and effectively assess and communicate our model results. It is strongly recommended that students complete the course SOCI2030 Quantitative Research Methods or SOCI2095 Quantitative Social Sciences: from correlation to causality (or equivalent) prior to enrolling in this course.
Use the basic functionalities of R, including using scripts to write code scripts for carrying out data analyses.
Import, wrangle, combine and summarise data using R.
Visualise variables and relationships in R.
Fit a range of regression models and assess their plausibility in summarizing relationships in the data
Combine and summarise data collected at multiple levels (such as individual characteristics and the characteristics of their surroundings) in a single multi-level regression model.
Develop digital literacy skills to find, evaluate, create, and communicate information from raw data.
Gain a fundamental understanding of data visualization methods.
Tasks
Weighting
In Class Test
40%
Data Analysis Report
30%
Examination
30%
We will use chapters from the following books. All key readings and recommended materials will be uploaded on Moodle.
Gelman, A., Hill, J., & Vehtari, A. (2020). Regression and Other Stories (Analytical Methods for Social Research). Cambridge: Cambridge University Press. doi:10.1017/9781139161879
Hadley Wickham, Mine Çetinkaya-Rundel, Garrett Grolemund. R for Data Science, 2nd Edition Released June 2023. Publisher(s): O'Reilly Media, Inc.
Max Kuhn, Julia Silge. Tidy Modeling with R: A Framework for Modeling in the Tidyverse. 1st Edition Released July 2022. Publisher(s): O'Reilly Media, Inc.
