2018/19 Undergraduate Module Catalogue

LUBS1185 Understanding Statistics in the Social Sciences

10 creditsClass Size: 50

Module manager: Danat Valizade
Email: D.Valizade@leeds.ac.uk

Taught: Semester 1 (Sep to Jan) View Timetable

Year running 2018/19

Pre-requisite qualifications

GCSE Maths - Grade B or above

This module is mutually exclusive with

LUBS1270	Statistics for Economics and Business 1
LUBS1525	Analytical Methods
LUBS1630	Introductory Statistics for Business
LUBS2920	Advanced Analytical Methods
MATH0360	Introduction to Applied Mathematics 1
MATH0365	Foundation Probability and Statistics
MATH0380	Foundation Applied Mathematics for Business
MATH1050	Calculus and Mathematical Analysis
MATH1725	Introduction to Statistics

This module is approved as a discovery module

Module summary

Recent decades have brought data analytics to the forefront of transferrable skills, actively sought by employers. Yet, social sciences are still laggards in terms of the use of analytical skills and a range training opportunities offered. This module will provide a succinct introduction to a burgeoning field of data science, with an explicit focus on the fundamental pillars of statistical modelling and the rationale of emerging analytical disciplines involving machine learning and predictive analytics.This module is essential for developing analytical literacy and will provide you with a critical outlook for interpreting statistical outcomes. Not only will the module set out key statistical approaches, but it will also focus on the application of quantitative formulae in solving practical problems. You will be introduced to a range data sources across the social sciences and gain competence in the R statistical software environment. While the module overlaps with other analytical courses it is orientated towards various disciplines across the social sciences and is deemed prerequisite to research methods courses and advanced quantitative modules. The module aims to ensure that every student is capable of implementing appropriate statistical formulae, focusing on understanding and application of statistical techniques rather than on calculation and mathematical deduction.

Objectives

A key objective of this module is to develop analytical literacy and statistical skills. In the social sciences, it is imperative to understand the rationale for statistical modelling, with its merits and limitations. Starting from basic concepts of frequency and probability distribution through to the notions of inference and hypothesis testing, the module will demonstrate how the use of statistical formulae can facilitate the solution of relevant problems across the social sciences. One of the key features of the module is the use of statistical software packages and visualisation techniques to ensure a better understanding of statistical principles. Mechanisms that underlie statistical laws and methods will be explained on the basis of embedded computer codes implemented in the R statistical software environment, one of the dominant tools in contemporary data science. This innovative teaching approach will allow students to comprehend basic statistics, overcome anxiety relating to the intricacy of quantitative methods and learn how to solve actual statistical problems with the help of the bespoke software package.

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 Centres 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 in level 2 and 3 which will build on the quantitative skills acquired in this module.

Learning outcomes
Upon completion of this module, students will be able to:
- understand and describe statistical techniques and implement them in practice;
- work with statistical software packages and use programming codes to execute statistical analysis;
- accurately apply parametric and non-parametric statistical techniques to datasets across the social sciences;
- critically assess upshots of statistical analysis, place findings in a wider social, economic and organisational context.

Skills outcomes
Transferable skills:
- analytical skills; understanding and interpreting statistical and mathematical concepts;
- critical thinking; assessing and contextualising statistical analysis;
- IT skills; R statistical software, data visualisation, statistical programming;
- research skills; devising statistical and predictive models based on scientific theories.

Subject specific skills:
- communicate statistical techniques and upshots of statistical analysis to a wider audience;
- apply statistical techniques to analyse data in the social sciences

Syllabus

Indicative content:

- The use of statistical formulae in the social sciences, with application in economic studies, management and sociology
- Statistical inference - rationale and application, generalising from a sample to a wider population, confidence intervals
- Probability distribution function: classification and impact on statistical inference
- Hypothesis testing: differences between groups, frequentist approach and Bayesian hypothesis testing; parametric and non-parametric statistics
- Working with variables - describing, visualising and presenting data
- Interpreting relationships between variables - correlation, regression and forecasting
- Constructing analytical models - deterministic and stochastic modelling, regression analysis

Teaching methods

Delivery type	Number	Length hours	Student hours
Lecture	10	1.00	10.00
Seminar	10	1.00	10.00
Private study hours			80.00
Total Contact hours			20.00
Total hours (100hr per 10 credits)			100.00

Private study

Students are expected to spend significant time outside of lectures checking their learning of techniques introduced in lectures, practising questions which require them to interpret statistical concepts and execute statistical analysis in the suggested software package.

Opportunities for Formative Feedback

Tutorial exercises.

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
Report	2,000 word Project Report	100.00
Total percentage (Assessment Coursework)		100.00

The resit for this module will be 100% by 2,000 word coursework.

Reading list

The reading list is available from the Library website

Last updated: 10/09/2019

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