Dr Francesca Manzi

This course is compulsory on the MSc in Human Resources and Organisations (Human Resource Management/CIPD). This course is available on the MSc in Human Resources and Organisations (International Employment Relations/CIPD) and MSc in Human Resources and Organisations (Organisational Behaviour). This course is not available as an outside option.

This course explores the role of data and analytics in human resource management (HRM). The current world of work contains a wide array of information that can be used to make work more engaging to employees and organizations more efficient. Rather than making human resource decisions based on traditions or gut instinct, we can bring science into the way people are managed by leveraging data and empirical evidence. This course combines substantive people management issues such as performance management, recruitment and selection, DEI, engagement, and employee turnover with data-driven decision-making skills. Students will work on multiple sample datasets and real-world cases to identify HRM problems, learn the basics of data analysis, interpret statistical outcomes, and make relevant people decisions. In addition, the course will discuss emerging technologies such as AI and machine learning in HRM and discuss the ethical issues that arise with their use.  By the end of the course, students will be prepared to evaluate analytical evidence to make ethical people decisions and become better managers. 

The intended learning outcomes are: 

• To understand what HR managers need to know about people analytics
• To identify the advantages and limitations of using analytics to solve HR problems
• To recognize, understand, and interpret basic statistical tests (e.g., correlation, regression, t-tests, ANOVA, mediation, etc.)
• To critically evaluate ethical issues with emerging technologies in HRM

15 hours of lectures and 15 hours of seminars in the WT.

This course consists of interactive and practical discussions in lectures and hands-on case analysis and data interpretation during seminars. Although directly analysing data is not required, students are encouraged to follow weekly step-by-step tutorials on statistical tests and to attend the relevant Digital Skills Lab workshops.

In its Ethics Code, LSE upholds a commitment to intellectual freedom. This means we will protect the freedom of expression of our students and staff and the right to engage in healthy debate in the classroom.

A formative case report allows students to practice their analytics skills by identifying key questions and variables, selecting the appropriate statistical test, interpreting statistical output, and providing data driven recommendations. This report is designed to prepare students for their final analytics project report.

• Text book: Edwards, Martin and Kirsten Edwards. 2019. Predictive HR Analytics: Mastering the HR Metric. Publisher: Kogan Page. ISBN: 9780749484446.
• Davenport, Thomas H., Jeanne Harris, and Jeremy Shapiro. 2010. Competing on talent analytics. Harvard Business Review, 88(10): 52-58.
• Tambe, Prasanna, Peter Cappelli, and Valery Yakubovich. 2019. Artificial intelligence in human resources management: challenges and a path forward." California Management Review 61(4): 15-42.
• Case study:  Polzer, J.T. & Huall, O. (2020). People Analytics at McKinsey. Harvard Business School Case: 9-418-023.

Report (60%) in the ST.
Quiz (30%) and class participation (10%) in the WT.

The course will be assessed via the following methods:

Class Participation assessed on identifying HRM issues and the role of data and analytics in people decisions during seminar discussion (10%)

An individual analytics case report (1500 words) to identify problem(s), select tests, interpret findings, and provide data driven recommendations (60%)

Three quizzes of 10% each testing student skills on identifying variables, appropriate statistical tests, and sound reporting of findings.