Course Details in 2026/27 Session

Module Title	LH Quantitative Data Analysis
School	School of Social Policy
Department	Soc Policy, Sociology & Crimin
Module Code	08 35229
Module Lead	Matt Bennett
Level	Honours Level
Credits	20
Semester	Semester 2
Pre-requisites
Co-requisites
Restrictions	None
Contact Hours	Lecture-24 hours Tutorial-10 hours Supervised time in studio/workshop-6 hours Guided independent study-160 hours Total: 200 hours
Exclusions
Description	This applied course covers the fundamental elements and approaches to handling and analysing quantitative survey data. The emphasis is on developing an adequate understanding of basic theoretical statistical principles, descriptive and exploratory methods of analysis, graphical representation, operational procedures and interpretation of statistical results using STATA. Quantitative Data Analysis focuses on identifying secondary data sources, operationalizing key theoretical concepts, cleaning and recoding variables, presenting descriptive statistics, inferential statistics and advanced regression techniques such as OLS regression and logistic regression. Through this applied module, students will also be introduced to a number of important topics, including theory testing and development; philosophy of science and research judgement; and replication in quantitative research. This module builds on the quantitative methods curriculum covered in Social Research II (year 2) by developing advanced quantitative methods skills as part of an independent research project.
Learning Outcomes	By the end of the module students should be able to: Utilise a range of complex databases to undertake secondary data analysis. Operationalise concepts accurately in actual research. Demonstrate data management skills, including preparation, variable coding and recoding in STATA. Demonstrate knowledge of the basic principles and assumptions of descriptive and inferential statistical methods to complex datasets. Interpret descriptive and inferential statistical methods as presented in published work. Recognise the strengths and limitations of a range of data analysis methods and identify which are best suited to address specific research questions or hypotheses. Report on and present quantitative research findings. Understand the role and limitations of tests of statistical significance and appreciate the difference between theoretical and statistical significance. Apply statistical methods to research questions drawing on both descriptive and multivariate analyses including regression techniques.
Assessment	35229-01 : 4000 word summative essay : Coursework (100%)
Assessment Methods & Exceptions	Assessment: A 5000-word data analysis report that describes the data and methods, and presents and discusses appropriate descriptive and inferential statistical techniques to answer a substantive topic of your choice. Reassessment: N/A
Other
Reading List