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2022/23 Undergraduate Module Catalogue

LUBS3185 People Analytics: Strategy and Practice

20 creditsClass Size: 31

Module manager: Xanthe Whittaker

Taught: Semester 2 (Jan to Jun) View Timetable

Year running 2022/23

This module is approved as a discovery module

Module summary

The world of business, management and human resources becomes increasingly dependent on data. Data comes from different sources, capturing a variety of performance outcomes and determinants of human behaviour. The contemporary HR profession increasingly depends on the ability to process and analyse data, and being able to translate quantitative outcomes into viable solutions to organisational problems.The module will introduce you to the fundamental principles of analytics in the context of human resource management, drawing on strategic human capital theory and other relevant perspectives. You will gain exposure to and understanding of quantitative techniques and software packages essential for developing analytical literacy. Using practical examples from the NHS and other large organisations, the module will help you to understand how HR analytics can be used to address organisational challenges.The module thus aims to ensure that every student is capable of analysing HR data and critically assessing outcomes of data analysis in light of practical HR problems. The module is supported by Q-Step at the University of Leeds. Q-Step is a £19.5 million programme designed to promote a step-change in quantitative social science training in the UK. Funded by the Nuffield Foundation, ESRC and HEFCE, Q-Step was developed as a strategic response to the shortage of quantitatively-skilled social science graduates.Q-Step is funding fifteen universities (including the University of Leeds) across the UK to establish Q-Step Centres that will support the development and delivery of specialist undergraduate programmes, including new courses, work placements and pathways to postgraduate study.


A prime objective of this module is to develop your capacity to use data and analytical tools to address strategic people management challenges in organisations. The module will develop understanding and conceptual knowledge of how different analytical methods can be used in an organisational context, and how results can be developed into actionable management insights.

Learning outcomes
Upon completion of this module, students will be able to:
- Demonstrate knowledge of the principles of data analytics in the context of human resource management;
- Analyse data using quantitative methods to model HR outcomes;
- Identify and give concise explanation of evidence-based solutions to practical HR problems.

Skills outcomes
Transferable skills:
- work with organisation-level and representative HR data;
- apply relevant quantitative techniques;
- IT and programming skills - gain proficiency in statistical software packages
- communication skills – effectively communicate practical solutions based on quantitative evidence.

Subject specific:
- use data literacy skills and statistical software packages to analyse HR data
- provide quantitative base for HR decision-making.


- Analytics in the context of HRM: what is analytics and how can analytics be applied to HR and human capital?
- Strategy and analytics involving strategic human capital theory, strategic management, evidence based management and critical evaluation of these concepts
- Understanding HR data: data generation and administration; classes of data involving attitudinal data, soft performance data and hard performance outcomes
- An introduction to the principles of causal inference, including the development of causal graphs and how these can be used to understand the people dimensions of business challenges
- Understanding how statistical modelling combined with causal inference can be used to gain insights into employee engagement, employee attrition and relationships between people and performance
- The ethical dimensions of people analytics
- The future of people analytics: analytics as craft; analytics as software and products; machine learning and automation

Teaching methods

Delivery typeNumberLength hoursStudent hours
Private study hours175.00
Total Contact hours25.00
Total hours (100hr per 10 credits)200.00

Private study

Students are expected to spend significant time outside of lectures checking their learning of techniques introduced in lectures, reading suggested materials and practicing software packages used to read, visualise HR data and conduct relevant analytical procedures.

Opportunities for Formative Feedback

Tutorial exercises.

Methods of assessment

Assessment typeNotes% of formal assessment
Report2,500 word Project Report100.00
Total percentage (Assessment Coursework)100.00

The resit for this module will be 100% by 2,500 word coursework.

Reading list

The reading list is available from the Library website

Last updated: 28/06/2022 15:38:32


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