Page 95 - REC :: MBA CURRICULUM AND SYLLABUS :: R2019

Subject Code Subject Name (Theory course) Category L T P C
BA19P57 Human Resource Metrics And Analytics NFE 3 0 0 3

Objectives: To help students understand the concept of HR Analytics and its application in several HR processes and
strategies. The course is designed broadly to:
 To familiarize the students with the basic concepts, techniques, and tools of HR metrics and analytics
 To promote understanding of HRIS in organising, analysing and presenting data for HR analytics.
 To impart understanding of predictive analytics in HR based on statistical tools and techniques like regression
analysis, graphs, tables and spread sheets (Excel)
 To comprehend and analyse the application of HR analytics in data-driven decision-making with respect to
staffing, supply and demand forecasting and employee turnover.
 To discuss and impart skills in HR analytics for other applications like diversity management, employee
engagement, compensation management and employee performance.

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UNIT-I Understanding HR Metrics and Analytics: Defining HR analytics-Understanding HR 9
indicators, metrics and data-Human capital data storage-HR analytics and HR people strategy-
Current state of HR analytics.

UNIT-II HRIS: Planning and implementing a new HRIS – Security and privacy considerations – 9
Information sources-Using SPSS-Preparing the data.

UNIT-III Strategies for Analysis: Statistical analysis for HR – Types of data-Individual and group-level 9
data for analysis--Graphs, tables, spread sheets, data manipulation using Excel -Descriptive
statistics-Measures of Central tendency-Regression analysis.

UNIT-IV Data-driven Decision-Making in HR: Supply and demand-forecasting techniques-Staffing 9
analysis –measuring reliability and validity in selection methods, Case examples in recruitment
and selection analysis using SPSS - Turnover analysis- Why predicting turnover is important? ;
Case examples in Turnover analysis using SPSS.

UNIT-V UsingAnalytics in other HR Applications: Measuring and managing diversity and inclusion 9
using descriptive statistics – Measuring employee engagement and drivers of engagement using
Factor analysis in SPSS –Total Compensation analysis-predicting employee performance using
regression in SPSS (Case examples for all the analyses to be discussed).

Total Contact Hours : 45

Course Outcomes: Upon successful completion of the course, the students should be able to:
Understand various tools and techniques adopted in HR analytics and metrics for various HR processes and

applications.
 Apply their knowledge related to HRIS and SPSS in preparing data for analysis
 Critically analyse various strategies for individual and group level data analysis using statistical tools.
 Evaluate techniques in data-driven decision-making in staffing and turnover analysis.
 Develop creative thinking and decision-making in using analytics for other applications in HR.

Text Book(s)

1 Predictive Analysis for Human Resources by Jac Fitz-Enz and John R. Mattox II, Wiley
Winning on HR Analytics: Leveraging Data for Competitive Advantage by Ramesh Soundararajan and Kuldeep
2
Singh, Sage
The New HR Analytics: Predicting theEconomic Value of Your Company’s Human Capital Investments byJac
3
Fitz-enz, AMA.

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