Module Details for the Academic Year 2018/2019

POL50070 Quantitative Methods I (CORE) (TCD)

Introduction to the use of data for statistical analysis in political science. The module will introduce concepts such as measurement, variables, statistical data, and provide an introduction to basic descriptive statistics summarizing numerical data, both graphically and numerically. It will introduce the basics of statistical inference, drawing conclusions about populations on the basis of sample data, using the basic operations of hypothesis tests and regression analysis. Foundational knowledge of frequentist and Bayesian statistical inference will be provided and the end result will be basic ability to perform multiple regression analysis, both with continuous and with dichotomous dependent variables.

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- basic understanding of working with R and RStudio
- being able to summarize and describe statistical data
- solid understanding of frequentist statistical inference
- basic understanding of Bayesian statistical inference
- basic understanding of executing and interpreting multiple regression
- preliminary understanding of logistic regression
Item Workload






Description % of Final Grade Timing
Assignment: Assignment


Week 3
Assignment: Assignment


Week 6
Assignment: Assignment


Week 12
Assignment: Assignment


Week 9
Essay: Essay


Coursework (End of Trimester)


This module is not passable by compensation

Resit Opportunities

In-semester assessment


If you fail this module and the module is on offer the following semester, you must repeat the module. You should register for repeating the module at the start of the following semester. If the module is NOT running in the following semester then there will be a resit available in the form of an ‘in semester’ assessment. You should register for this ‘in semester assessment’ at the start of the following semester. Note that it is YOUR responsibility to contact the Module Coordinator to find out what the ‘in semester assessment’ will be and when it will take place.

Module Requisites and Incompatibles

Equivalent Modules

Prior Learning

Curricular information is subject to change