Applied Quantitative Data Analysis

Module titleApplied Quantitative Data Analysis
Module codePOLM809
Academic year2018/9
Module staff

Professor Oliver James (Convenor)

Duration: Term123
Duration: Weeks


Number students taking module (anticipated)


Description - summary of the module content

Module description

The purpose of the course is to improve your quantitative skills and to stimulate interest in quantitative methods across humanities and social sciences. A basic understanding of data collection, analysis and interpretation is essential for contemporary research in many disciplines, both to enable researchers to make direct use of these techniques in their own research and for meaningfully engaging with work that uses these approaches. The course prepares you to conduct research on topics that involve quantitative evidence. However, we note that the line between quantitative and qualitative data is often blurred (e.g. nominal categories). This module complements the closely linked modules on research methods training (POLM140 and POLM141) to deliver detailed methodological and technical knowledge of a wide range of quantitative analytical methods used in social science research.  

Module aims - intentions of the module

POLM809 intends to provide an advanced introduction into quantitative methods in the social sciences. You will acquire skills to analyse data in various forms and using a variety of quantitative tools, techniques and software packages.  You will learn the strengths and weaknesses of various techniques and be taught how to deal with issues such as missing data and data bias. By the end of a course of practical demonstrations, associated lectures, and practical assignments, this module aims to have enhanced   your skills in the analysis and presentation of quantitative data appropriate to a wide range of research problems. Throughout the module, emphasis will be placed on applying the techniques learned and the practical experience of analysing quantitative data sets. You will learn how to construct data sets from individual and aggregate level data, how to describe and visualize relevant data patterns using graphical tools, how to analyse the data using the appropriate statistical tools, and how to interpret the results of this analysis. You will focus on the analysis of questionnaires, historical data, content analysis and other data sources. Examples will be drawn from the humanities and social sciences

Intended Learning Outcomes (ILOs)

ILO: Module-specific skills

On successfully completing the module you will be able to...

  • 1. recognize and evaluate in writing the diversity of specialized techniques and approaches involved in analysing research information, both quantitative and qualitative;
  • 2. critically evaluate in writing the issues involved in application of research design in the context of the social sciences;
  • 3. Demonstrate acquired skills in data analysis
  • 4. demonstrate acquired skills in a computer package for statistical analysis (e.g. SPSS, Stata);
  • 5. Show ability to present analysed data in a coherent and effective manner.

ILO: Discipline-specific skills

On successfully completing the module you will be able to...

  • 6. demonstrate understanding in the use of advanced tools and techniques of quantitative research;
  • 7. construct well thought out and rigorous data analysis, tables and reports for both written and oral presentation;
  • 8. examine relationships between complex theoretical concepts with real world, empirical data;

ILO: Personal and key skills

On successfully completing the module you will be able to...

  • 9. demonstrate an advanced ability to study independently and effectively;
  • 10. deliver accurate and nuanced presentations to your peers, and communicate effectively in speech and writing; and
  • 11. use IT for the retrieval and the presentation of a wide variety of information.

Syllabus plan

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:


Topic 1: Introduction: why use quantitative data and

Topic 2: Inferential statistics, a primer

Topic 3: Collecting data, sampling, data management and data integrity

Topic 4: Describing data and dealing with missing data

Topic 5: Writing up the results

Topic 6: Testing relationships between variables

Topic 7: Visual displays of data

Topic 8: Multivariate statistics

Topic 9: Ordinal and binomial data Topic 10: Advanced techniques Topic 11: Student Presentations

The module will be taught through 7 weekly two-hour sessions (including introductory session). There will be a mix of formal lecture led by the co-ordinator, practical experience, student presentations and student discussion. The emphasis is on active seminar participation, practical experience and the development of techniques and tools with regard to assessed work. The techniques will be explored through appropriate practical work and independent study.

Learning and teaching

Learning activities and teaching methods (given in hours of study time)

Scheduled Learning and Teaching ActivitiesGuided independent studyPlacement / study abroad
14 136 0

Details of learning activities and teaching methods

CategoryHours of study timeDescription
Scheduled Learning and Teaching Activities14 hours7 weekly two-hour sessions (including introductory session).
Guided independent study66Reading, thinking and preparing for weekly sessions
Guided independent study10Web-based learning
Web-based learning60Preparation and completion of assessments


Formative assessment

Form of assessmentSize of the assessment (eg length / duration)ILOs assessedFeedback method
Performance in sessionsWeekly1-11Verbal Feedback

Summative assessment (% of credit)

CourseworkWritten examsPractical exams

Details of summative assessment

Form of assessment% of creditSize of the assessment (eg length / duration)ILOs assessedFeedback method
3 practical assignments (written), with exercises focusing on data analysis, visualization and interpretation 75500 words each (25% each)1-11Written feedback
Final assignment (written): Essay discussing how to use the tools and techniques covered during the module to address a relevant research question251,500 words1-11Written feedback


Details of re-assessment (where required by referral or deferral)

Original form of assessmentForm of re-assessmentILOs re-assessedTimescale for re-assessment
3 practical assignments (written), with exercises focusing on data analysis, visualization and interpretation3 practical assignments (written), with exercises focusing on data analysis, visualization and interpretation1-11August/September reassessment period
Written assignment discussing how to use the tools and techniques covered during the module to address a relevant research questionFinal assignment (1,500 words)1-11August/September reassessment period


Indicative learning resources - Basic reading

Basic reading:

Making History Count: A Primer in Quantitative Methods for Historians (2002, Cambridge) by Charles H. Feinstein and Mark Thomas

History by Numbers: An Introduction to Quantitative Approaches by Pat Hudson

Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (2003, Sage) by John W. Creswell

Discovering Statistics Using SPSS (Introducing Statistical Methods S.) (2005, Sage) by Andy Field

Research Methods in the Social Sciences w/Data Bank CD (2007, Worth Publishers)

by Chava Frankfort-Nachmias and David Nachmias

The Elements of Social Scientific Thinking (9th edition, Thomson Learning) by Kenneth Hoover and Todd Donovan

Additional resources available on ELE –

Module has an active ELE page

Key words search

Applied Quantitative Data Analysis

Credit value15
Module ECTS


Module pre-requisites


Module co-requisites


NQF level (module)


Available as distance learning?


Origin date


Last revision date