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2019/20 Undergraduate Module Catalogue

LUBS2095 Understanding Data in the Social Sciences

10 creditsClass Size: 30

Module manager: James Stone
Email: J.M.Stone@leeds.ac.uk

Taught: Semester 2 View Timetable

Year running 2019/20

Pre-requisite qualifications

GCSE Mathematics Grade C

This module is mutually exclusive with

LUBS1270Statistics for Economics and Business 1
LUBS1280Mathematical Economics
MATH0111Elementary Diff Calculus 1
MATH0212Elementary Integral Calculus (Version 1)
MATH0360Introduction to Applied Mathematics 1
MATH0365Foundation Probability and Statistics
MATH0370Introduction to Applied Mathematics 2
MATH0380Foundation Applied Mathematics for Business
MATH1050Calculus and Mathematical Analysis
MATH1400Modelling with Differential Equations

Module replaces

LUBS1180 Understanding Data in the Social Sciences.

This module is approved as a discovery module

Module summary

Numbers and data are everywhere in business, management and the social sciences. We see them in the news, in marketing and advertising, in politics, and increasingly in social media. As such, skills in interpreting quantitative data are highly sought after by employers in all sectors.This module will help you develop your data literacy skills and give you a critical eye for assessing statistical claims, and the analysis of quantitative data. Through a problem based approach, focusing on particular issues in business, management and the social sciences, you will develop your skills in understanding and interpreting data from simple descriptive representation of data, to the use of graphs, to bivariate analysis and confidence testing.The module aims to ensure that every student has the confidence to use quantitative methods should they want to; hence the emphasis is on understanding and interpretation with less focus on calculation than would be found in a traditional quantitative methods module.

Objectives

The objectives of this module are to develop students’ data literacy skills. In the social sciences, it essential to be able to assess statistical claims and recognise the merits and limitations of quantitative data. Through a focus on particular issues in business, management and the social sciences, this module will give students skills in understanding and interpreting data from simple descriptive representation of data, to the use of graphs, to bivariate analysis and confidence testing.

This module is being developed as part of the Nuffield Foundation’s Q-Step initiative, to increase the number of quantitatively-skilled social science graduates. The University of Leeds is one of only fifteen universities across the UK to establish a Q-Step Centre that is supporting the development and delivery of specialist undergraduate modules, pathways and placements to improve quantitative skills in social science undergraduate degrees. This module aims to ensure that every student has the confidence to use quantitative methods should they want to. It will also open up progression to other modules which will build on the quantitative skills acquired in this module.

Learning outcomes
Upon completion of this module, students will be able to:

- discuss the value of quantitative approaches to solving a wide range of business, management and social science issues;
- synthesise, critically evaluate and challenge how quantitative data is used within business, management and the social sciences;
- recognise the limitations of quantitative data;
- use quantitative data and be able to critique the analysis of others;
- identify and apply basic methods of data and statistical analysis to a range of business, management and social science issues;
- apply basic data literacy skills to practical analysis of datasets.

Skills outcomes;-

Transferable:
Upon completion of this module students will be able to:
- use the quantitative skills required to allow potential progression onto modules that require or benefit from possession of quantitative skills and knowledge.

Subject specific:
Upon completion of this module students will be able to:
- communicate their analysis of quantitative data to a lay audience.


Syllabus

Indicative content:

The use of quantitative data in business, management and the social sciences.

Big Data and its uses in business, management and the social sciences.

Working with numbers – describing and presenting data in business, management and the social sciences, to include descriptive data, averages.

What do your numbers mean – generalising from a sample to a wider population, to include probability hypothesis testing, confidence intervals.

Interpreting relationships between variables – correlation, regression and forecasting.

Looking at differences between groups.

Teaching methods

Delivery typeNumberLength hoursStudent hours
Lecture101.0010.00
Practical101.0010.00
Private study hours80.00
Total Contact hours20.00
Total hours (100hr per 10 credits)100.00

Private study

Students will be given academic readings on each of the topics, and will be expected to read these each week. There will also be preparation for the weekly computer based workshop tasks which will involve students preparing for worksheet based computer lab tasks where they will apply the core statistical knowledge to social science research and analysis.

Students will spend 20 hours (2 hours per week) preparing for the computer tasks, reading the necessary workshop material in advance. A further 20 hours will be spent on further development of data analysis skills through self-directed learning following the class. Worksheets provided by the course team will help students achieve this. Forty hours will be spent on preparing and completing the coursework.

Opportunities for Formative Feedback

Formative feedback provided verbally on progress on tutorial exercises. Opportunity to submit a plan of coursework with verbal or email feedback provided.

Methods of assessment


Coursework
Assessment typeNotes% of formal assessment
Project2,000 word Project Report100.00
Total percentage (Assessment Coursework)100.00

The resit for this module will be 100% by coursework.

Reading list

The reading list is available from the Library website

Last updated: 03/10/2018

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