Undergraduate Module Descriptor

SOC1041: Data Analysis in Social Science

This module descriptor refers to the 2018/9 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 Skills1. understand and apply a variety of statistical methods used in quantitative social science;
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 and R);
Discipline-Specific Skills4. 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 Skills8. 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.