2022/23 Taught Postgraduate Module Catalogue

LUBS5346M Data Analytics for Human Resources

30 creditsClass Size: 100

Module manager: Manhal Ali
Email: M.M.Ali@leeds.ac.uk

Taught: Semesters 1 & 2 (Sep to Jun) View Timetable

Year running 2022/23

Module replaces

LUBS5341M

This module is not approved as an Elective

Module summary

This module will give students grounding in the statistical concepts that underpin the practice of people analytics and the application of these concepts in practice using the types of data that organisations routinely collect about their people and operations. Central to the module is the concept of causal inference; how can we use statistical tools and concepts to understand the causes of human behaviour at work so that statistical analysis can guide management action? The focus of the module is statistical concepts and their application, not the underpinning statistical theory and mathematics. Teaching is through workshops that combine lecture material, computer exercises and discussion to interpret the resulting analysis.

Objectives

This module aims to give students grounding in the statistical concepts that underpin the practice of people analytics and the application of these concepts in practice using the types of data that organisations routinely collect about their people and operations. Central to the module is the concept of causal inference; how can we use statistical tools and concepts to understand the causes of human behaviour at work so that statistical analysis can guide management action? The focus of the module is statistical concepts and their application, not the underpinning statistical theory and mathematics.

Learning outcomes
1. To develop a conceptual understanding of inferential statistics and to be able to critically evaluate the strengths and limitations of classical inferential statistics for making causal inferences in the context of people analytics
2. To develop a conceptual understanding of how domain knowledge of people and organisations, and graphical analysis can be used to plan statistical analysis that provide a basis for causal inference.
3. Develop a conceptual understanding of common regression and machine learning methods and be able to critically evaluate the circumstances in which different types of model are appropriate and how results should be interpreted.
4. Develop a conceptual understanding of randomised experiments and their potential for use in people analytics
5. To be able to synthesise the knowledge from learning outcomes set out above in order to make critical judgements about how data and statistics can be used to support decision making in the field of people management.

Syllabus

Indicative list:
1. Populations, sampling and ecological validity
2. Descriptive statistics – what are they? What are they useful for? How to visualise them
3. Correlation and causality
4. Developing causal understanding through domain knowledge and graphical analysis
5. Distributions and their statistical properties
6. Multiple regression (different types for different distributions)
7. The problem of confounders, omitted variable bias etc.
8. Survival analysis
9. Regression forests (random forests, survival forests)
10. Randomised experiments – the power of randomisation for making inferences; analysing experimental data.

Teaching methods

Delivery type	Number	Length hours	Student hours
Workshop	11	3.00	33.00
Private study hours			267.00
Total Contact hours			33.00
Total hours (100hr per 10 credits)			300.00

Opportunities for Formative Feedback

Students will receive two forms of formative feedback:
Weekly self-marked quizzes to test knowledge and understanding of key concepts covered in the previous week. In workshop feedback on how well they have answered questions in seminar discussion; such discussions will be developing and testing the knowledge and understanding required for the coursework

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
Report	2,000 word report that critically evaluates the implications for people management practice of statistical analysis from a case study	50.00
Total percentage (Assessment Coursework)		50.00

Resit for this module will be a 3 hour exam for 100% of the module mark.

Exams

Exam type	Exam duration	% of formal assessment
Standard exam (closed essays, MCQs etc) (S1)	2 hr	50.00
Total percentage (Assessment Exams)		50.00

Resit for this module will be a 3 hour exam for 100% of the module mark.

Reading list

The reading list is available from the Library website

Last updated: 28/07/2022 13:50:37

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