Introductory description

Reproducibility is a corner-stone of any scientific enquiry. This module will introduce you to the idea of reproducibility and provide hands-on guidance on how to achieve reproducible results in quantitative political analysis through the software R.

The module is divided into four parts, the first of which covers the idea and principles of conducting reproducible research. From the second part onwards, we will follow the timeline of a real-word research project, and begin by exploring various techniques for data management. In the third part we will explore how to conduct and interpret one of the most frequently used methods in political science: logistic regression. The final part of the module is reserved for the professional presentation of results, both in the form of tables and beautiful-looking graphs.

The module is suitable as a sequel to PO11Q and PO12Q, but can also be taken as a stand-alone option with no pre- or post-requisites.

Module web page

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

i. REPRODUCIBILITY

1. The Replication Crisis

ii. DATA MANAGEMENT

1. Joining, Sub-setting and Ordering Data
2. Manipulating Variables
3. Missing Data

iii. DATA ANALYSIS

1. Logistic Regression
2. (READING WEEK)
3. Interpreting Logistic Regression

iv. PROFESSIONAL PRESENTATION

1. Graphs: The Magic of ggplot
2. Introduction to R Markdown
3. Presenting Results

Learning outcomes

By the end of the module, students should be able to:

• Proficiency in the software R
• Apply the concept of reproducibility
• Apply methods of data management in R
• Conduct and interpret logistic regression
• Create professional graphs in R
• Design professional tables
• Introductory proficiency in R Markdown

Indicative reading list

• Brians, Craig L., Lars Wilnat, Jarol B. Manheim and Richard C. Rich. 2010. Empirical Political Analysis. Pearson
• Bryman, A. 2008. Social Research Methods, 4th ed., Oxford: Oxford University Press
• Dalgaard, P. (2008) Introductory Statistics with R (Statistics and Computing). Springer
• King, G. (1995). Replication, replication. PS: Political Science and Politics, 28(3):541–559.
• Field, A. et al. (2012) Discovering Statistics with R. Sage
• Fogarty, B. (2018) Quantitative Social Science Data with R: An Introduction. Sage
• Gayle, V. J. and Lambert, P. S. (2017). The workflow: A practical guide to producing accurate, efficient, transparent and reproducible social survey data analysis. NCRM Working Paper.
• Grolemund, G. and Wickham, H. (2016) R for Data Science. O'Reilly Media
• Reiche, Florian (forthcoming) Introduction to Quantitative Methods in the Social Sciences. Oxford: Oxford University Press
• Scott Long, J. (1997) Regression models for categorical and limited dependent variables. Sage
• Stimson, J. A. (n.d.). Professional Writing in Political Science: A Highly opinionated Essay
• Stinerock, R. (2018) Statistics with R. Sage

Research element

Research project as assessment

Interdisciplinary

Research questions for assessment stem from the social sciences more generally.

International

International data sets, such as the World Development Indicators and the Polity IV index will be used.

Subject specific skills

• Data Management Skills
• Data Analysis Skills
• Identifying missing data
• Proficiency in R
• Adherence to the Replication Standard in Political Science
• Conducting and Interpreting Logistic regression
• Professional presentation of graphs and tables

Transferable skills

• Data management skills in R
• Communication skills
• Lateral thinking skills
• Skills in the distillation and application of complex information and ideas
• Critical thinking
• Analytical skills
• Independent research skills
• Problem solving
• Time management
• Skills in academic practice
• Decision making