Data Collection and Analysis

Module description

This module will train students to formulate clear and unambiguous research questions, and to answer them through systematic collection and analysis of data. Those who take it will learn to design questionnaires and content analysis codebooks, as well as learning the most fundamental forms of survey sampling and experimental design. They will learn about the basic types of data, and about how to visualise and analyse individual variables as well as the relationships between pairs of variables. They will receive an introduction to statistical inference, learning what concepts such as statistical significance and confidence intervals mean and how to interpret them as well as how to calculate them and (perhaps most importantly) the circumstances under which they should not be calculated. This will provide a thorough grounding in basic quantitative research methods, whether for use in the dissertation, for enhancing techniques learnt in other modules, or for employment purposes after graduation.

Assessment details

Coursework - Research design (60%)
Examination - MCQ Exam (40%)

Educational aims & objectives

This module will teach the skills and theoretical knowledge necessary to analyse small and large datasets using the programming language, R. It will go beyond what is taught on 7AAVBCS1 Social and Cultural Analytics by teaching students how to make sense of data using such techniques as contingency tables, correlation and linear regression, significance testing, and confidence intervals. It will also teach techniques for collecting / producing data through surveys and experiments, with an introduction to sampling, experiment design, and the use of psychometric questionnaires. By the time they complete this module, students will be ready to carry out meaningful data collection and analysis projects of their own, whether for their dissertations or in their future careers.

For students who have not studied 7AAVBCS1, there will be a 'bootcamp' session at the beginning to teach basic coding skills with R.

Learning outcomes

Students who have successfully completed the module will be able to:

• design a questionnaire
• design a codebook for content analysis
• articulate the practical differences between different approaches to sampling and experiment design
• carry out tabular and correlation analysis
• calculate and interpret margin of error, statistical significance, and confidence intervals, interpret them correctly, and recognise when they are not appropriate to use
• articulate the ethical considerations relevant to quantitative research
• work collaboratively
• code in R
• use the Git version control system

Module description disclaimer

King’s College London reviews the modules offered on a regular basis to provide up-to-date, innovative and relevant programmes of study. Therefore, modules offered may change. We suggest you keep an eye on the course finder on our website for updates.

Please note that modules with a practical component will be capped due to educational requirements, which may mean that we cannot guarantee a place to all students who elect to study this module.

Please note that the module descriptions above are related to the current academic year and are subject to change.