Undergraduate Module Descriptor

POL1041: Data Analysis in Social Science

This module descriptor refers to the 2019/0 academic year.

Module Aims

This module aims to provide you with an introductory knowledge of data analytical tools, including techniques for both descriptive and basic inferential statistics. It aims to teach you how to read and interpret quantitative information, to construct datasets from individual and aggregate level data, to summarize and present the important quantitative information in an effective and rigorous way, to look for and identify relevant trends and patterns in your data, and to conduct basic hypothesis tests. By the end of the module, you should be able to understand basic quantitative methods, to critically interpret quantitative information, and to conduct basic statistical analyses.

Intended Learning Outcomes (ILOs)
This module's assessment will evaluate your achievement of the ILOs listed here – you will see reference to these ILO numbers in the details of the assessment for this module.
On successfully completing the programme you will be able to:
Module-Specific Skills	1. understand and apply a variety of statistical methods used in quantitative political science research; 2. evaluate and contrast alternative quantitative methods based on an understanding of their advantages, drawbacks, and compatibility with the available data and the substantive questions of interest; 3. demonstrate acquired skills: confidence and competence in a computer package for statistical analysis (e.g. Excel, SPSS, Stata);
Discipline-Specific Skills	4. read, understand, interpret and evaluate basic statistical analyses in the professional literature; 5. use statistical evidence to empirically evaluate the (relative) validity of social science theories and hypotheses; 6. construct arguments based on (quantitative) empirical evidence for both written and oral presentation; 7. examine relationships between theoretical concepts in social science with real world data;
Personal and Key Skills	8. study independently; 9. communicate effectively in speech and writing; 10. use statistical software packages to summarize, analyze, and present statistical information; and 11. demonstrate the ability to work independently, within a limited time frame, and without access to external sources, to complete a specified task.

Module Content

Syllabus Plan

Whilst the module’s precise content may vary from year to year, it is envisaged that the syllabus will cover some or all of the following topics:

Introduction to data analysis in the social sciences
Creating data: conceptualization, operationalization, and measurement
Describing data I: tables and figures
Describing data II: descriptive “statistics”
Correlation and dependence
Randomness and probability
Sampling and “sampling distributions”
Statistical inference: confidence intervals
Hypothesis testing: introduction
Testing the difference between two means
Using quantitative methods in politics, sociology and criminology: illustration and examples

Learning and Teaching

This table provides an overview of how your hours of study for this module are allocated:

Scheduled Learning and Teaching Activities	Guided independent study	Placement / study abroad
26.5	123.5	0

...and this table provides a more detailed breakdown of the hours allocated to various study activities:

Category	Hours of study time	Description
Scheduled Learning and Teaching Activities	16.5	11 x 1.5 hour sessions
Scheduled Learning and Teaching Activities	10	10 x 1 hour computer lab sessions
Guided independent study	50	Writing up problem sets and completing lab assignments
Guided independent study	45.5	Reading and preparing for lectures and tutorials, and completing online quizzes
Guided independent study	28	Web-based activities to familiarise students with statistical software.

Online Resources

This module has online resources available via ELE (the Exeter Learning Environment).