Please note: This course will be taught in hybrid mode. Hybrid delivery of courses will include synchronous live sessions during which on campus and online students will be taught simultaneously.
Rabia Malik is a Lecturer/Assistant Professor in the Department of Government at the University of Essex, which she joined in July 2020. Before this, she received her Ph.D. in Political Science from the University of Rochester in 2016, was a Post-Doctoral Associate at New York University Abu Dhabi (2016-2019) and spent a year at the Lahore University of Management Sciences (LUMS). Her research uses both observational and experimental data to study questions related to distributive politics and development, political accountability, clientelism, and gender, particularly in South Asia. She has also taught classes on quantitative methods, authoritarianism, and South Asian politics. Rabia’s research has appeared in The Journal of Politics, The British Journal of Political Science, Comparative Political Studies and Legislative Studies Quarterly.
Course description and goals:
This course introduces participants to the analysis of quantitative data both theoretically and empirically. Introductory quantitative methods will be covered along with an introduction to the free, open-source software R. R is a highly versatile software environment suitable for introductory and advanced quantitative social science and data analysis. The course offers participants a near-complete foundation for introductory quantitative analysis and to use R for all commonly encountered tasks in social science data analytics. The primary goal of the course is to equip participants with the tools necessary to conduct their own data analysis independently. To do so, the course will cover relevant statistical and descriptive techniques with a strong focus on applied skills and data visualization that will be introduced using R.
The course will cover various topics, both theoretically and in R, including the following:
– Introduction to R and programming in R
– Descriptive statistics
– Data visualization with advanced R packages
– Data import and management, including working with “messy” datasets
– Variable types and creating new variables in R
– Data uncertainty
– Correlations and causal relationships
– Linear regressions, assumptions, interpretation
– Non-linear relationships in OLS
– Binary dependent variables
Course Objectives:
Upon successful completion of the course, participants will be able to consume quantitative texts for social science, download and manage messy datasets, describe and visualize various sorts of data, conduct their own statistical analyses, and use R for most commonly encountered tasks in social science data analysis. Participants will leave with the knowledge of a range of introductory statistical techniques and the ability to implement these in their own research. They will also be comfortable using R for descriptive and statistical analyses, and for managing complex datasets. The course is most suitable for researchers at the beginning of their quantitative training though those with existing background in quantitative social sciences wishing to acquire a new, free, open- source, and highly versatile set of tools (in R) will also benefit. Participants will also learn to incorporate data analysis and document creation (via R Markdown).
Course Prerequisites:
Participants are advised to have a basic background in introductory statistics or concurrently be enrolled in an introductory statistics course. However, initial exposure to statistical techniques up to linear regression (at a fundamental level) is not required as the course will go through necessary background concepts as well. No background in R or computer programming is required or expected. The course introduces R from a beginner’s perspective.
Representative Background Reading:
Since this is an introductory course, participants are not required to do any prior reading.
Required text (will be provided by ESS):
Agresti, Alan. (2018). Statistical Methods for the Social Sciences (Fifth Edition). Pearson.
Background knowledge required:
Maths:
Calculus – Elementary
Linear Regression – Elementary*
Statistics:
OLS – Elementary*
* It is helpful if students have some background on these topics but not a requirement.
For participation in this course, students are required to bring with them their own laptops.
