Module Code: H8HRAQM
Long Title HR Analytics and Quantitative Methods
Title HR Analytics and Quantitative Methods
Module Level: LEVEL 8
EQF Level: 6
EHEA Level: First Cycle
Credits: 10
Module Coordinator: Pauline Kelly Phelan
Module Author: Isabela Da Silva
Departments: School of Business
Specifications of the qualifications and experience required of staff  
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Demonstrate a comprehensive understanding of statistical principles, theories and methods and appreciate how they apply in a range of business decision making situations.
LO2 Recognise and evaluate different types of data, quantitative and qualitive, and associated statistical measures and their appropriateness in a range of scenarios.
LO3 Understand the role of data in demonstrating return on investment (ROI) of HRM strategies and initiatives such as L&D, recruitment, reward etc. Communicate and interpret statistical findings/output in a technical and non-technical manner.
LO4 Ability to critique the concepts & theories underpinning data and analytics, design & development, evidence-based practice and critical decision-making.
LO5 Demonstrate how to translate data analysis and results into tangible predictive business applications i.e.: demonstrate the ability to use analytics to build the case for L&D and other HR initiatives.
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is required before enrolment on this module. While the prior learning is expressed as named NCI module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Entry requirements

As per programme requirements. 
 

Module Content & Assessment

Indicative Content
Overview and purpose of HR analytics and data.
Types of HR metrics and data  Balanced Scorecards & KPIs  Strategic Workforce Planning  Strategy & data driven decision-making  Measuring performance & potential  Human Capital reporting  Linking Human Resources to ROI - financial HR, cost of absenteeism, L&D, turnover etc.
Understanding Quantitative and Qualitive data principles and concepts
Probability: The concepts and language of probability  The role of probability in statistics   Approaches to assigning probabilities   Rules of addition and multiplication for computing probability  Conditional probability
Measures of Central Tendency
Mean: Arithmetic versus Geometric  Mode  Median
Measures of Dispersion
Range & Mean Absolute Deviation  Variance & Standard Deviation (Population and Sample)  Symmetric Distributions and Skewness
Qualitive Methods
How to collect, analyse, and interpreting non-numerical data, such as language, opinions etc. Methods include; Coding Grounded theory in collecting data Narrative research how to interpret stories to understand how employees understand the organisation through their experiences and perceptions Action research that links theory and practice that can drive organisational changes
Defining Metrics
Evaluate and appraise different types of data, graphics and statistical measures and their appropriateness in a range of scenarios. Key areas include;  Descriptive analytics and use of multidimensional data  Predictive analytics  Prescriptive analytics  Understanding qualitative and qualitive performance metrics i.e., L&D, performance, workforce planning, staff surveys etc.
Data Overview
Understanding the importance of data integrity and quality, difference of correlation and causation. Understand the concepts of various data sources - qualitative and quantitative and the importance of consistency and reliability of data inputs for reporting Understanding the importance of data integrity and quality and how to use practical techniques to assess the integrity of data and avoid common pitfalls  How to analyse data  Understand the theoretical concepts of big data, data mining etc.  Understanding of the General Data Protection Regulation (GDPR) and ethical issues concerning analytics
Role of analytics in HRM strategy
Building the business case for HR metrics  How to build support amongst stakeholders  Application of data analysis for business strategic goals
Examination of key HR analytics and data
How to examine, evaluate and provide insights from HR data (quantitative and qualitive) and in areas such as absenteeism, turnover, pay, legislation - gender pay gap, performance management, talent management, L&D, culture (staff surveys), employee demographics etc.  How to design a data system through case studies and practical examples
Assessment Breakdown%
Coursework100.00%

Assessments

Full Time

Coursework
Assessment Type: Continuous Assessment % of total: 100
Assessment Date: n/a Outcome addressed: 1,2,3,4,5
Non-Marked: No
Assessment Description:
As a HR consultant (internal or external to the organisation) you have been requested to design a new HR metric system or review and improve an existing HR analytics system in the organisation. You are to advise management on the various statistical theories and methods. Additionally, you are to advise management of the process of HR data management, systems and the benefits and weakness of HR analytics. As a consultant you should consider the organisation’s requirements, budgets and strategy and make recommendations that will best suit the organisation. You can take a choose specific and or a combination of HR metrics such as leadership, morale, absence management, performance management, equality, training etc. or provide a general HR analytical framework for the organisation to consider.  The assessment can be based on your organisation, one familiar to you or a case study.  Assessment Criteria   While the focus will be on the quality rather than the quantity of content,  the assignment will be 2,000 – 2,500 words maximum.  Harvard protocol must be used (style and referencing).   The assignment should be written in (1.5 spacing), in Arial 12 or Times New Roman 12 in justified format and submitted to via ‘Turnitin’.   Note that pages should be numbered.   The assignment requires an application of appropriate module concepts.
No End of Module Assessment
No Workplace Assessment
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.
Reassessment Description
Candidates will attempt the repeat assessment for the module, if they do not successfully pass the module. Learners are required to attempt all assessments attaching to a module. For those modules where all learning outcomes are assessable with a final examination, the learner does not have to re-sit failed individual CA components.

NCIRL reserves the right to alter the nature and timings of assessment

 

Module Workload

Module Target Workload Hours 0 Hours
Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Classroom and demonstrations 30 Per Semester 2.50
Tutorial Mentoring and small-group tutoring 12 Per Semester 1.00
Directed Learning Directed e-learning 6 Per Semester 0.50
Independent Learning Independent learning 202 Per Semester 16.83
Total Weekly Contact Hours 4.00
 

Module Resources

Recommended Book Resources
  • Khan, N., Milliner, D. (2020), Introduction to People Analytics, A practical guide to data-driven HR, Kogan Page.
  • Lind D.A., Marchal W.G., and Wathen S.A. (2020), Statistical Techniques in Business and Economics, 18th. McGraw Hill.
Supplementary Book Resources
  • Barends, E. and Rousseau, D. (2018), Evidence-based management: how to use evidence to make better organizational decisions, Kogan Page.
  • Berenson, M., Levine, Szabat, K.A. (2015), Basic Business Statistics,Global Edition, 13th. Pearson Education.
  • Ferrar, J. and Green, D. (2021), Excellence in People Analytics, How to Use Workforce Data to Create Business Value, Kogan Page.
  • Field, A. (2020), Discovering Statistics Using R, 2nd. Sage Publications.
  • Marler, J.H. and Boudreau, J.W. (2017), An evidence-based review of HR analytics, International Journal of Human Resource Management.
  • Marr, B. (2018), Data-driven HR: how to use analytics and metrics to drive performance, Kogan Page.
  • Mattox, J.R., Parsky, P. and Hall, C. (2020), Learning analytics: using talent data to improve business outcomes, 2nd ed. Kogan Page.
  • Sclater, N. (2017), Learning analytics explained, Routledge.
This module does not have any article/paper resources
Other Resources
Discussion Note: